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1.  Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts 
PLoS Computational Biology  2009;5(7):e1000450.
The recently proposed concept of molecular connectivity maps enables researchers to integrate experimental measurements of genes, proteins, metabolites, and drug compounds under similar biological conditions. The study of these maps provides opportunities for future toxicogenomics and drug discovery applications. We developed a computational framework to build disease-specific drug-protein connectivity maps. We integrated gene/protein and drug connectivity information based on protein interaction networks and literature mining, without requiring gene expression profile information derived from drug perturbation experiments on disease samples. We described the development and application of this computational framework using Alzheimer's Disease (AD) as a primary example in three steps. First, molecular interaction networks were incorporated to reduce bias and improve relevance of AD seed proteins. Second, PubMed abstracts were used to retrieve enriched drug terms that are indirectly associated with AD through molecular mechanistic studies. Third and lastly, a comprehensive AD connectivity map was created by relating enriched drugs and related proteins in literature. We showed that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems. Our initial explorations of the AD connectivity map yielded a new hypothesis that diltiazem and quinidine may be investigated as candidate drugs for AD treatment. Molecular connectivity maps derived computationally can help study molecular signature differences between different classes of drugs in specific disease contexts. To achieve overall good data coverage and quality, a series of statistical methods have been developed to overcome high levels of data noise in biological networks and literature mining results. Further development of computational molecular connectivity maps to cover major disease areas will likely set up a new model for drug development, in which therapeutic/toxicological profiles of candidate drugs can be checked computationally before costly clinical trials begin.
Author Summary
Molecular connectivity maps between drugs and a wide range of bio-molecular entities can help researchers to study and compare the molecular therapeutic/toxicological profiles of many candidate drugs. Recent studies in this area have focused on linking drug molecules and genes in specific disease contexts using drug-perturbed gene expression experiments, which can be costly and time-consuming to derive. In this paper, we developed a computational framework to build disease-specific drug-protein connectivity maps, by mining molecular interaction networks and PubMed abstracts. Using Alzheimer's Disease (AD) as a case study, we described how drug-protein molecular connectivity maps can be constructed to overcome data coverage and noise issues inherent in automatically extracted results. We showed that this new approach outperformed both curated drug target databases and conventional text mining systems in retrieving disease-related drugs, with an overall balanced performance of sensitivity, specificity, and positive predictive values. The AD molecular connectivity map contained novel information on AD-related genes/proteins, AD candidate drugs, and protein therapeutic/toxicological profiles of all the AD candidate drugs. Bi-clustering of the molecular connectivity map revealed interesting patterns of functionally similar proteins and drugs, therefore creating new opportunities for future drug development applications.
doi:10.1371/journal.pcbi.1000450
PMCID: PMC2709445  PMID: 19649302
2.  Abstracts from the 3rd International Genomic Medicine Conference (3rd IGMC 2015) 
Shay, Jerry W. | Homma, Noriko | Zhou, Ruyun | Naseer, Muhammad Imran | Chaudhary, Adeel G. | Al-Qahtani, Mohammed | Hirokawa, Nobutaka | Goudarzi, Maryam | Fornace, Albert J. | Baeesa, Saleh | Hussain, Deema | Bangash, Mohammed | Alghamdi, Fahad | Schulten, Hans-Juergen | Carracedo, Angel | Khan, Ishaq | Qashqari, Hanadi | Madkhali, Nawal | Saka, Mohamad | Saini, Kulvinder S. | Jamal, Awatif | Al-Maghrabi, Jaudah | Abuzenadah, Adel | Chaudhary, Adeel | Al Qahtani, Mohammed | Damanhouri, Ghazi | Alkhatabi, Heba | Goodeve, Anne | Crookes, Laura | Niksic, Nikolas | Beauchamp, Nicholas | Abuzenadah, Adel M. | Vaught, Jim | Budowle, Bruce | Assidi, Mourad | Buhmeida, Abdelbaset | Al-Maghrabi, Jaudah | Buhmeida, Abdelbaset | Assidi, Mourad | Merdad, Leena | Kumar, Sudhir | Miura, Sayaka | Gomez, Karen | Carracedo, Angel | Rasool, Mahmood | Rebai, Ahmed | Karim, Sajjad | Eldin, Hend F. Nour | Abusamra, Heba | Alhathli, Elham M. | Salem, Nada | Al-Qahtani, Mohammed H. | Kumar, Sudhir | Faheem, Hossam | Agarwa, Ashok | Nieschlag, Eberhard | Wistuba, Joachim | Damm, Oliver S. | Beg, Mohd A. | Abdel-Meguid, Taha A. | Mosli, Hisham A. | Bajouh, Osama S. | Abuzenadah, Adel M. | Al-Qahtani, Mohammed H. | Coskun, Serdar | Abu-Elmagd, Muhammad | Buhmeida, Abdelbaset | Dallol, Ashraf | Al-Maghrabi, Jaudah | Hakamy, Sahar | Al-Qahtani, Wejdan | Al-Harbi, Asia | Hussain, Shireen | Assidi, Mourad | Al-Qahtani, Mohammed | Abuzenadah, Adel | Ozkosem, Burak | DuBois, Rick | Messaoudi, Safia S. | Dandana, Maryam T. | Mahjoub, Touhami | Almawi, Wassim Y. | Abdalla, S. | Al-Aama, M. Nabil | Elzawahry, Asmaa | Takahashi, Tsuyoshi | Mimaki, Sachiyo | Furukawa, Eisaku | Nakatsuka, Rie | Kurosaka, Isao | Nishigaki, Takahiko | Nakamura, Hiromi | Serada, Satoshi | Naka, Tetsuji | Hirota, Seiichi | Shibata, Tatsuhiro | Tsuchihara, Katsuya | Nishida, Toshirou | Kato, Mamoru | Mehmood, Sajid | Ashraf, Naeem Mahmood | Asif, Awais | Bilal, Muhammad | Mehmood, Malik Siddique | Hussain, Aadil | Jamal, Qazi Mohammad Sajid | Siddiqui, Mughees Uddin | Alzohairy, Mohammad A. | Al Karaawi, Mohammad A. | Nedjadi, Taoufik | Al-Maghrabi, Jaudah | Assidi, Mourad | Al-Khattabi, Heba | Al-Ammari, Adel | Al-Sayyad, Ahmed | Buhmeida, Abdelbaset | Al-Qahtani, Mohammed | Zitouni, Hédia | Raguema, Nozha | Ali, Marwa Ben | Malah, Wided | Lfalah, Raja | Almawi, Wassim | Mahjoub, Touhami | Elanbari, Mohammed | Ptitsyn, Andrey | Mahjoub, Sana | El Ghali, Rabeb | Achour, Bechir | Amor, Nidhal Ben | Assidi, Mourad | N’siri, Brahim | Morjani, Hamid | Nedjadi, Taoufik | Al-Ammari, Adel | Al-Sayyad, Ahmed | Salem, Nada | Azhar, Esam | Al-Maghrabi, Jaudah | Chayeb, Vera | Dendena, Maryam | Zitouni, Hedia | Zouari-Limayem, Khedija | Mahjoub, Touhami | Refaat, Bassem | Ashshi, Ahmed M. | Batwa, Sarah A. | Ramadan, Hazem | Awad, Amal | Ateya, Ahmed | El-Shemi, Adel Galal Ahmed | Ashshi, Ahmad | Basalamah, Mohammed | Na, Youjin | Yun, Chae-Ok | El-Shemi, Adel Galal Ahmed | Ashshi, Ahmad | Basalamah, Mohammed | Na, Youjin | Yun, Chae-Ok | El-Shemi, Adel Galal | Refaat, Bassem | Kensara, Osama | Abdelfattah, Amr | Dheeb, Batol Imran | Al-Halbosiy, Mohammed M. F. | Al lihabi, Rghad Kadhim | Khashman, Basim Mohammed | Laiche, Djouhri | Adeel, Chaudhary | Taoufik, Nedjadi | Al-Afghani, Hani | Łastowska, Maria | Al-Balool, Haya H. | Sheth, Harsh | Mercer, Emma | Coxhead, Jonathan M. | Redfern, Chris P. F. | Peters, Heiko | Burt, Alastair D. | Santibanez-Koref, Mauro | Bacon, Chris M. | Chesler, Louis | Rust, Alistair G. | Adams, David J. | Williamson, Daniel | Clifford, Steven C. | Jackson, Michael S. | Singh, Mala | Mansuri, Mohmmad Shoab | Jadeja, Shahnawaz D. | Patel, Hima | Marfatia, Yogesh S. | Begum, Rasheedunnisa | Mohamed, Amal M. | Kamel, Alaa K. | Helmy, Nivin A. | Hammad, Sayda A. | Kayed, Hesham F. | Shehab, Marwa I. | El Gerzawy, Assad | Ead, Maha M. | Ead, Ola M. | Mekkawy, Mona | Mazen, Innas | El-Ruby, Mona | Shahid, S. M. A. | Jamal, Qazi Mohammad Sajid | Arif, J. M. | Lohani, Mohtashim | Imen, Moumni | Leila, Chaouch | Houyem, Ouragini | Kais, Douzi | Fethi, Chaouachi Dorra Mellouli | Mohamed, Bejaoui | Salem, Abbes | Faggad, Areeg | Gebreslasie, Amanuel T. | Zaki, Hani Y. | Abdalla, Badreldin E. | AlShammari, Maha S. | Al-Ali, Rhaya | Al-Balawi, Nader | Al-Enazi, Mansour | Al-Muraikhi, Ali | Busaleh, Fadi | Al-Sahwan, Ali | Borgio, Francis | Sayyed, Abdulazeez | Al-Ali, Amein | Acharya, Sadananda | Zaki, Maha S. | El-Bassyouni, Hala T. | Shehab, Marwa I. | Elshal, Mohammed F. | M., Kaleemuddin | Aldahlawi, Alia M. | Saadah, Omar | McCoy, J. Philip | El-Tarras, Adel E. | Awad, Nabil S. | Alharthi, Abdulla A. | Ibrahim, Mohamed M. M. | Alsehli, Haneen S. | Dallol, Ashraf | Gari, Abdullah M. | Abbas, Mohammed M. | Kadam, Roaa A. | Gari, Mazen M. | Alkaff, Mohmmed H. | Abuzenadah, Adel M. | Gari, Mamdooh A. | Abusamra, Heba | Karim, Sajjad | eldin, Hend F. Nour | Alhathli, Elham M. | Salem, Nada | Kumar, Sudhir | Al-Qahtani, Mohammed H. | Moradi, Fatima A. | Rashidi, Omran M. | Awan, Zuhier A. | Kaya, Ibrahim Hamza | Al-Harazi, Olfat | Colak, Dilek | Alkousi, Nabila A. | Athanasopoulos, Takis | Bahmaid, Afnan O. | Alhwait, Etimad A. | Gari, Mamdooh A. | Alsehli, Haneen S. | Abbas, Mohammed M. | Alkaf, Mohammed H. | Kadam, Roaa | Dallol, Ashraf | Kalamegam, Gauthaman | Eldin, Hend F. Nour | Karim, Sajjad | Abusamra, Heba | Alhathli, Elham | Salem, Nada | Al-Qahtani, Mohammed H. | Kumar, Sudhir | Alsayed, Salma N. | Aljohani, Fawziah H. | Habeeb, Samaher M. | Almashali, Rawan A. | Basit, Sulman | Ahmed, Samia M. | Sharma, Rakesh | Agarwal, Ashok | Durairajanayagam, Damayanthi | Samanta, Luna | Abu-Elmagd, Muhammad | Abuzenadah, Adel M. | Sabanegh, Edmund S. | Assidi, Mourad | Al-Qahtani, Mohammed | Agarwal, Ashok | Sharma, Rakesh | Samanta, Luna | Durairajanayagam, Damayanthi | Assidi, Mourad | Abu-Elmagd, Muhammad | Al-Qahtani, Mohammed | Abuzenadah, Adel M. | Sabanegh, Edmund S. | Samanta, Luna | Agarwal, Ashok | Sharma, Rakesh | Cui, Zhihong | Assidi, Mourad | Abuzenadah, Adel M. | Abu-Elmagd, Muhammad | Al-Qahtani, Mohammed | Alboogmi, Alaa A. | Alansari, Nuha A. | Al-Quaiti, Maha M. | Ashgan, Fai T. | Bandah, Afnan | Jamal, Hasan S. | Rozi, Abdullraheem | Mirza, Zeenat | Abuzenadah, Adel M. | Karim, Sajjad | Al-Qahtani, Mohammed H. | Karim, Sajjad | Schulten, Hans-Juergen | Al Sayyad, Ahmad J. | Farsi, Hasan M. A. | Al-Maghrabi, Jaudah A. | Mirza, Zeenat | Alotibi, Reem | Al-Ahmadi, Alaa | Alansari, Nuha A. | Albogmi, Alaa A. | Al-Quaiti, Maha M. | Ashgan, Fai T. | Bandah, Afnan | Al-Qahtani, Mohammed H. | Ebiya, Rasha A. | Darwish, Samia M. | Montaser, Metwally M. | Abusamra, Heba | Bajic, Vladimir B. | Al-Maghrabi, Jaudah | Gomaa, Wafaey | Hanbazazh, Mehenaz | Al-Ahwal, Mahmoud | Al-Harbi, Asia | Al-Qahtani, Wejdan | Hakamy, Saher | Baba, Ghali | Buhmeida, Abdelbaset | Al-Qahtani, Mohammed | Al-Maghrabi, Jaudah | Al-Harbi, Abdullah | Al-Ahwal, Mahmoud | Al-Harbi, Asia | Al-Qahtani, Wejdan | Hakamy, Sahar | Baba, Ghalia | Buhmeida, Abdelbaset | Al-Qahtani, Mohammed | Alhathli, Elham M. | Karim, Sajjad | Salem, Nada | Eldin, Hend Nour | Abusamra, Heba | Kumar, Sudhir | Al-Qahtani, Mohammed H. | Alyamani, Aisha A. | Kalamegam, Gauthaman | Alhwait, Etimad A. | Gari, Mamdooh A. | Abbas, Mohammed M. | Alkaf, Mohammed H. | Alsehli, Haneen S. | Kadam, Roaa A. | Al-Qahtani, Mohammed | Gadi, Rawan | Buhmeida, Abdelbaset | Assidi, Mourad | Chaudhary, Adeel | Merdad, Leena | Alfakeeh, Saadiah M. | Alhwait, Etimad A. | Gari, Mamdooh A. | Abbas, Mohammed M. | Alkaf, Mohammed H. | Alsehli, Haneen S. | Kadam, Roaa | Kalamegam, Gauthaman | Ghazala, Rubi | Mathew, Shilu | Hamed, M. Haroon | Assidi, Mourad | Al-Qahtani, Mohammed | Qadri, Ishtiaq | Mathew, Shilu | Mira, Lobna | Shaabad, Manal | Hussain, Shireen | Assidi, Mourad | Abu-Elmagd, Muhammad | Al-Qahtani, Mohammed | Mathew, Shilu | Shaabad, Manal | Mira, Lobna | Hussain, Shireen | Assidi, Mourad | Abu-Elmagd, Muhammad | Al-Qahtani, Mohammed | Rebai, Ahmed | Assidi, Mourad | Buhmeida, Abdelbaset | Abu-Elmagd, Muhammad | Dallol, Ashraf | Shay, Jerry W. | Almutairi, Mikhlid H. | Ambers, Angie | Churchill, Jennifer | King, Jonathan | Stoljarova, Monika | Gill-King, Harrell | Assidi, Mourad | Abu-Elmagd, Muhammad | Buhmeida, Abdelbaset | Al-Qatani, Muhammad | Budowle, Bruce | Abu-Elmagd, Muhammad | Ahmed, Farid | Dallol, Ashraf | Assidi, Mourad | Almagd, Taha Abo | Hakamy, Sahar | Agarwal, Ashok | Al-Qahtani, Muhammad | Abuzenadah, Adel | Karim, Sajjad | Schulten, Hans-Juergen | Al Sayyad, Ahmad J. | Farsi, Hasan M. A. | Al-Maghrabi, Jaudah A. | Buhmaida, Abdelbaset | Mirza, Zeenat | Alotibi, Reem | Al-Ahmadi, Alaa | Alansari, Nuha A. | Albogmi, Alaa A. | Al-Quaiti, Maha M. | Ashgan, Fai T. | Bandah, Afnan | Al-Qahtani, Mohammed H. | Satar, Rukhsana | Rasool, Mahmood | Ahmad, Waseem | Nazam, Nazia | Lone, Mohamad I. | Naseer, Muhammad I. | Jamal, Mohammad S. | Zaidi, Syed K. | Pushparaj, Peter N. | Jafri, Mohammad A. | Ansari, Shakeel A. | Alqahtani, Mohammed H. | Bashier, Hanan | Al Qahtani, Abrar | Mathew, Shilu | Nour, Amal M. | Alkhatabi, Heba | Zenadah, Adel M. Abu | Buhmeida, Abdelbaset | Assidi, Mourad | Al Qahtani, Muhammed | Faheem, Muhammad | Mathew, Shilu | Mathew, Shiny | Pushparaj, Peter Natesan | Al-Qahtani, Mohammad H. | Alhadrami, Hani A. | Dallol, Ashraf | Abuzenadah, Adel | Hussein, Ibtessam R. | Chaudhary, Adeel G. | Bader, Rima S. | Bassiouni, Randa | Alquaiti, Maha | Ashgan, Fai | Schulten, Hans | Alama, Mohamed Nabil | Al Qahtani, Mohammad H. | Lone, Mohammad I. | Nizam, Nazia | Ahmad, Waseem | Jafri, Mohammad A. | Rasool, Mahmood | Ansari, Shakeel A. | Al-Qahtani, Muhammed H. | Alshihri, Eradah | Abu-Elmagd, Muhammad | Alharbi, Lina | Assidi, Mourad | Al-Qahtani, Mohammed | Mathew, Shilu | Natesan, Peter Pushparaj | Al Qahtani, Muhammed | Kalamegam, Gauthaman | Pushparaj, Peter Natesan | Khan, Fazal | Kadam, Roaa | Ahmed, Farid | Assidi, Mourad | Sait, Khalid Hussain Wali | Anfinan, Nisreen | Al Qahtani, Mohammed | Naseer, Muhammad I. | Chaudhary, Adeel G. | Jamal, Mohammad S. | Mathew, Shilu | Mira, Lobna S. | Pushparaj, Peter N. | Ansari, Shakeel A. | Rasool, Mahmood | AlQahtani, Mohammed H. | Naseer, Muhammad I. | Chaudhary, Adeel G. | Mathew, Shilu | Mira, Lobna S. | Jamal, Mohammad S. | Sogaty, Sameera | Bassiouni, Randa I. | Rasool, Mahmood | AlQahtani, Mohammed H. | Rasool, Mahmood | Ansari, Shakeel A. | Jamal, Mohammad S. | Pushparaj, Peter N. | Sibiani, Abdulrahman M. S. | Ahmad, Waseem | Buhmeida, Abdelbaset | Jafri, Mohammad A. | Warsi, Mohiuddin K. | Naseer, Muhammad I. | Al-Qahtani, Mohammed H. | Rubi | Kumar, Kundan | Naqvi, Ahmad A. T. | Ahmad, Faizan | Hassan, Md I. | Jamal, Mohammad S. | Rasool, Mahmood | AlQahtani, Mohammed H. | Ali, Ashraf | Jarullah, Jummanah | Rasool, Mahmood | Buhmeida, Abdelbasit | Khan, Shahida | Abdussami, Ghufrana | Mahfooz, Maryam | Kamal, Mohammad A. | Damanhouri, Ghazi A. | Jamal, Mohammad S. | Jarullah, Bushra | Jarullah, Jummanah | Jarullah, Mohammad S. S. | Ali, Ashraf | Rasool, Mahmood | Jamal, Mohammad S. | Assidi, Mourad | Abu-Elmagd, Muhammad | Bajouh, Osama | Pushparaj, Peter Natesan | Al-Qahtani, Mohammed | Abuzenadah, Adel | Jamal, Mohammad S. | Jarullah, Jummanah | Mathkoor, Abdulah E. A. | Alsalmi, Hashim M. A. | Oun, Anas M. M. | Damanhauri, Ghazi A. | Rasool, Mahmood | AlQahtani, Mohammed H. | Naseer, Muhammad I. | Rasool, Mahmood | Sogaty, Sameera | Chudhary, Adeel G. | Abutalib, Yousif A. | Merico, Daniele | Walker, Susan | Marshall, Christian R. | Zarrei, Mehdi | Scherer, Stephen W. | Al-Qahtani, Mohammad H. | Naseer, Muhammad I. | Faheem, Muhammad | Chaudhary, Adeel G. | Rasool, Mahmood | Kalamegam, Gauthaman | Ashgan, Fai Talal | Assidi, Mourad | Ahmed, Farid | Zaidi, Syed Kashif | Jan, Mohammed M. | Al-Qahtani, Mohammad H. | Al-Zahrani, Maryam | Lary, Sahira | Hakamy, Sahar | Dallol, Ashraf | Al-Ahwal, Mahmoud | Al-Maghrabi, Jaudah | Dermitzakis, Emmanuel | Abuzenadah, Adel | Buhmeida, Abdelbaset | Al-Qahtani, Mohammed | Al-refai, Abeer A. | Saleh, Mona | Yassien, Rehab I. | Kamel, Mahmmoud | Habeb, Rabab M. | Filimban, Najlaa | Dallol, Ashraf | Ghannam, Nadia | Al-Qahtani, Mohammed | Abuzenadah, Adel Mohammed | Bibi, Fehmida | Akhtar, Sana | Azhar, Esam I. | Yasir, Muhammad | Nasser, Muhammad I. | Jiman-Fatani, Asif A. | Sawan, Ali | Lahzah, Ruaa A. | Ali, Asho | Hassan, Syed A. | Hasnain, Seyed E. | Tayubi, Iftikhar A. | Abujabal, Hamza A. | Magrabi, Alaa O. | Khan, Fazal | Kalamegam, Gauthaman | Pushparaj, Peter Natesan | Abuzenada, Adel | Kumosani, Taha Abduallah | Barbour, Elie | Al-Qahtani, Mohammed | Shabaad, Manal | Mathew, Shilu | Dallol, Ashraf | Merdad, Adnan | Buhmeida, Abdelbaset | Al-Qahtani, Mohammed | Assidi, Mourad | Abu-Elmagd, Muhammad | Gauthaman, Kalamegam | Gari, Mamdooh | Chaudhary, Adeel | Abuzenadah, Adel | Pushparaj, Peter Natesan | Al-Qahtani, Mohammed | Hassan, Syed A. | Tayubi, Iftikhar A. | Aljahdali, Hani M. A. | Al Nono, Reham | Gari, Mamdooh | Alsehli, Haneen | Ahmed, Farid | Abbas, Mohammed | Kalamegam, Gauthaman | Al-Qahtani, Mohammed | Mathew, Shilu | Khan, Fazal | Rasool, Mahmood | Jamal, Mohammed Sarwar | Naseer, Muhammad Imran | Mirza, Zeenat | Karim, Sajjad | Ansari, Shakeel | Assidi, Mourad | Kalamegam, Gauthaman | Gari, Mamdooh | Chaudhary, Adeel | Abuzenadah, Adel | Pushparaj, Peter Natesan | Al-Qahtani, Mohammed | Abu-Elmagd, Muhammad | Kalamegam, Gauthaman | Kadam, Roaa | Alghamdi, Mansour A. | Shamy, Magdy | Costa, Max | Khoder, Mamdouh I. | Assidi, Mourad | Pushparaj, Peter Natesan | Gari, Mamdooh | Al-Qahtani, Mohammed | Kharrat, Najla | Belmabrouk, Sabrine | Abdelhedi, Rania | Benmarzoug, Riadh | Assidi, Mourad | Al Qahtani, Mohammed H. | Rebai, Ahmed | Dhamanhouri, Ghazi | Pushparaj, Peter Natesan | Noorwali, Abdelwahab | Alwasiyah, Mohammad Khalid | Bahamaid, Afnan | Alfakeeh, Saadiah | Alyamani, Aisha | Alsehli, Haneen | Abbas, Mohammed | Gari, Mamdooh | Mobasheri, Ali | Kalamegam, Gauthaman | Al-Qahtani, Mohammed | Faheem, Muhammad | Mathew, Shilu | Pushparaj, Peter Natesan | Al-Qahtani, Mohammad H. | Mathew, Shilu | Faheem, Muhammad | Mathew, Shiny | Pushparaj, Peter Natesan | Al-Qahtani, Mohammad H. | Jamal, Mohammad Sarwar | Zaidi, Syed Kashif | Khan, Raziuddin | Bhatia, Kanchan | Al-Qahtani, Mohammed H. | Ahmad, Saif | AslamTayubi, Iftikhar | Tripathi, Manish | Hassan, Syed Asif | Shrivastava, Rahul | Tayubi, Iftikhar A. | Hassan, Syed | Abujabal, Hamza A. S. | Shah, Ishani | Jarullah, Bushra | Jamal, Mohammad S. | Jarullah, Jummanah | Sheikh, Ishfaq A. | Ahmad, Ejaz | Jamal, Mohammad S. | Rehan, Mohd | Abu-Elmagd, Muhammad | Tayubi, Iftikhar A. | AlBasri, Samera F. | Bajouh, Osama S. | Turki, Rola F. | Abuzenadah, Adel M. | Damanhouri, Ghazi A. | Beg, Mohd A. | Al-Qahtani, Mohammed | Hammoudah, Sahar A. F. | AlHarbi, Khalid M. | El-Attar, Lama M. | Darwish, Ahmed M. Z. | Ibrahim, Sara M. | Dallol, Ashraf | Choudhry, Hani | Abuzenadah, Adel | Awlia, Jalaludden | Chaudhary, Adeel | Ahmed, Farid | Al-Qahtani, Mohammed | Jafri, Mohammad A. | Abu-Elmagd, Muhammad | Assidi, Mourad | Al-Qahtani, Mohammed | khan, Imran | Yasir, Muhammad | Azhar, Esam I. | Al-basri, Sameera | Barbour, Elie | Kumosani, Taha | Khan, Fazal | Kalamegam, Gauthaman | Pushparaj, Peter Natesan | Abuzenada, Adel | Kumosani, Taha Abduallah | Barbour, Elie | EL Sayed, Heba M. | Hafez, Eman A. | Schulten, Hans-Juergen | Elaimi, Aisha Hassan | Hussein, Ibtessam R. | Bassiouni, Randa Ibrahim | Alwasiyah, Mohammad Khalid | Wintle, Richard F. | Chaudhary, Adeel | Scherer, Stephen W. | Al-Qahtani, Mohammed | Mirza, Zeenat | Pillai, Vikram Gopalakrishna | Karim, Sajjad | Sharma, Sujata | Kaur, Punit | Srinivasan, Alagiri | Singh, Tej P. | Al-Qahtani, Mohammed | Alotibi, Reem | Al-Ahmadi, Alaa | Al-Adwani, Fatima | Hussein, Deema | Karim, Sajjad | Al-Sharif, Mona | Jamal, Awatif | Al-Ghamdi, Fahad | Al-Maghrabi, Jaudah | Baeesa, Saleh S. | Bangash, Mohammed | Chaudhary, Adeel | Schulten, Hans-Juergen | Al-Qahtani, Mohammed | Faheem, Muhammad | Pushparaj, Peter Natesan | Mathew, Shilu | Kumosani, Taha Abdullah | Kalamegam, Gauthaman | Al-Qahtani, Mohammed | Al-Allaf, Faisal A. | Abduljaleel, Zainularifeen | Alashwal, Abdullah | Taher, Mohiuddin M. | Bouazzaoui, Abdellatif | Abalkhail, Halah | Ba-Hammam, Faisal A. | Athar, Mohammad | Kalamegam, Gauthaman | Pushparaj, Peter Natesan | Abu-Elmagd, Muhammad | Ahmed, Farid | Sait, Khalid HussainWali | Anfinan, Nisreen | Gari, Mamdooh | Chaudhary, Adeel | Abuzenadah, Adel | Assidi, Mourad | Al-Qahtani, Mohammed | Mami, Naira Ben | Haffani, Yosr Z. | Medhioub, Mouna | Hamzaoui, Lamine | Cherif, Ameur | Azouz, Msadok | Kalamegam, Gauthaman | Khan, Fazal | Mathew, Shilu | Nasser, Mohammed Imran | Rasool, Mahmood | Ahmed, Farid | Pushparaj, Peter Natesan | Al-Qahtani, Mohammed | Turkistany, Shereen A. | Al-harbi, Lina M. | Dallol, Ashraf | Sabir, Jamal | Chaudhary, Adeel | Abuzenadah, Adel | Al-Madoudi, Basmah | Al-Aslani, Bayan | Al-Harbi, Khulud | Al-Jahdali, Rwan | Qudaih, Hanadi | Al Hamzy, Emad | Assidi, Mourad | Al Qahtani, Mohammed | Ilyas, Asad M. | Ahmed, Youssri | Gari, Mamdooh | Ahmed, Farid | Alqahtani, Mohammed | Salem, Nada | Karim, Sajjad | Alhathli, Elham M. | Abusamra, Heba | Eldin, Hend F. Nour | Al-Qahtani, Mohammed H. | Kumar, Sudhir | Al-Adwani, Fatima | Hussein, Deema | Al-Sharif, Mona | Jamal, Awatif | Al-Ghamdi, Fahad | Al-Maghrabi, Jaudah | Baeesa, Saleh S. | Bangash, Mohammed | Chaudhary, Adeel | Al-Qahtani, Mohammed | Schulten, Hans-Juergen | Alamandi, Alaa | Alotibi, Reem | Hussein, Deema | Karim, Sajjad | Al-Maghrabi, Jaudah | Al-Ghamdi, Fahad | Jamal, Awatif | Baeesa, Saleh S. | Bangash, Mohammed | Chaudhary, Adeel | Schulten, Hans-Juergen | Al-Qahtani, Mohammed | Subhi, Ohoud | Bagatian, Nadia | Karim, Sajjad | Al-Johari, Adel | Al-Hamour, Osman Abdel | Al-Aradati, Hosam | Al-Mutawa, Abdulmonem | Al-Mashat, Faisal | Al-Maghrabi, Jaudah | Schulten, Hans-Juergen | Al-Qahtani, Mohammad | Bagatian, Nadia | Subhi, Ohoud | Karim, Sajjad | Al-Johari, Adel | Al-Hamour, Osman Abdel | Al-Mutawa, Abdulmonem | Al-Aradati, Hosam | Al-Mashat, Faisal | Al-Qahtani, Mohammad | Schulten, Hans-Juergen | Al-Maghrabi, Jaudah | shah, Muhammad W. | Yasir, Muhammad | Azhar, Esam I | Al-Masoodi, Saad | Haffani, Yosr Z. | Azouz, Msadok | Khamla, Emna | Jlassi, Chaima | Masmoudi, Ahmed S. | Cherif, Ameur | Belbahri, Lassaad | Al-Khayyat, Shadi | Attas, Roba | Abu-Sanad, Atlal | Abuzinadah, Mohammed | Merdad, Adnan | Dallol, Ashraf | Chaudhary, Adeel | Al-Qahtani, Mohammed | Abuzenadah, Adel | Bouazzi, Habib | Trujillo, Carlos | Alwasiyah, Mohammad Khalid | Al-Qahtani, Mohammed | Alotaibi, Maha | Nassir, Rami | Sheikh, Ishfaq A. | Kamal, Mohammad A. | Jiffri, Essam H. | Ashraf, Ghulam M. | Beg, Mohd A. | Aziz, Mohammad A. | Ali, Rizwan | Rasool, Mahmood | Jamal, Mohammad S. | Samman, Nusaibah | Abdussami, Ghufrana | Periyasamy, Sathish | Warsi, Mohiuddin K. | Aldress, Mohammed | Al Otaibi, Majed | Al Yousef, Zeyad | Boudjelal, Mohamed | Buhmeida, Abdelbasit | Al-Qahtani, Mohammed H. | AlAbdulkarim, Ibrahim | Ghazala, Rubi | Mathew, Shilu | Hamed, M. Haroon | Assidi, Mourad | Al-Qahtani, Mohammed | Qadri, Ishtiaq | Sheikh, Ishfaq A. | Abu-Elmagd, Muhammad | Turki, Rola F. | Damanhouri, Ghazi A. | Beg, Mohd A. | Suhail, Mohd | Qureshi, Abid | Jamal, Adil | Pushparaj, Peter Natesan | Al-Qahtani, Mohammad | Qadri, Ishtiaq | El-Readi, Mahmoud Z. | Eid, Safaa Y. | Wink, Michael | Isa, Ahmed M. | Alnuaim, Lulu | Almutawa, Johara | Abu-Rafae, Basim | Alasiri, Saleh | Binsaleh, Saleh | Nazam, Nazia | Lone, Mohamad I. | Ahmad, Waseem | Ansari, Shakeel A. | Alqahtani, Mohamed H.
BMC Genomics  2016;17(Suppl 6):487.
Table of contents
O1 Regulation of genes by telomere length over long distances
Jerry W. Shay
O2 The microtubule destabilizer KIF2A regulates the postnatal establishment of neuronal circuits in addition to prenatal cell survival, cell migration, and axon elongation, and its loss leading to malformation of cortical development and severe epilepsy
Noriko Homma, Ruyun Zhou, Muhammad Imran Naseer, Adeel G. Chaudhary, Mohammed Al-Qahtani, Nobutaka Hirokawa
O3 Integration of metagenomics and metabolomics in gut microbiome research
Maryam Goudarzi, Albert J. Fornace Jr.
O4 A unique integrated system to discern pathogenesis of central nervous system tumors
Saleh Baeesa, Deema Hussain, Mohammed Bangash, Fahad Alghamdi, Hans-Juergen Schulten, Angel Carracedo, Ishaq Khan, Hanadi Qashqari, Nawal Madkhali, Mohamad Saka, Kulvinder S. Saini, Awatif Jamal, Jaudah Al-Maghrabi, Adel Abuzenadah, Adeel Chaudhary, Mohammed Al Qahtani, Ghazi Damanhouri
O5 RPL27A is a target of miR-595 and deficiency contributes to ribosomal dysgenesis
Heba Alkhatabi
O6 Next generation DNA sequencing panels for haemostatic and platelet disorders and for Fanconi anaemia in routine diagnostic service
Anne Goodeve, Laura Crookes, Nikolas Niksic, Nicholas Beauchamp
O7 Targeted sequencing panels and their utilization in personalized medicine
Adel M. Abuzenadah
O8 International biobanking in the era of precision medicine
Jim Vaught
O9 Biobank and biodata for clinical and forensic applications
Bruce Budowle, Mourad Assidi, Abdelbaset Buhmeida
O10 Tissue microarray technique: a powerful adjunct tool for molecular profiling of solid tumors
Jaudah Al-Maghrabi
O11 The CEGMR biobanking unit: achievements, challenges and future plans
Abdelbaset Buhmeida, Mourad Assidi, Leena Merdad
O12 Phylomedicine of tumors
Sudhir Kumar, Sayaka Miura, Karen Gomez
O13 Clinical implementation of pharmacogenomics for colorectal cancer treatment
Angel Carracedo, Mahmood Rasool
O14 From association to causality: translation of GWAS findings for genomic medicine
Ahmed Rebai
O15 E-GRASP: an interactive database and web application for efficient analysis of disease-associated genetic information
Sajjad Karim, Hend F Nour Eldin, Heba Abusamra, Elham M Alhathli, Nada Salem, Mohammed H Al-Qahtani, Sudhir Kumar
O16 The supercomputer facility “AZIZ” at KAU: utility and future prospects
Hossam Faheem
O17 New research into the causes of male infertility
Ashok Agarwa
O18 The Klinefelter syndrome: recent progress in pathophysiology and management
Eberhard Nieschlag, Joachim Wistuba, Oliver S. Damm, Mohd A. Beg, Taha A. Abdel-Meguid, Hisham A. Mosli, Osama S. Bajouh, Adel M. Abuzenadah, Mohammed H. Al-Qahtani
O19 A new look to reproductive medicine in the era of genomics
Serdar Coskun
P1 Wnt signalling receptors expression in Saudi breast cancer patients
Muhammad Abu-Elmagd, Abdelbaset Buhmeida, Ashraf Dallol, Jaudah Al-Maghrabi, Sahar Hakamy, Wejdan Al-Qahtani, Asia Al-Harbi, Shireen Hussain, Mourad Assidi, Mohammed Al-Qahtani, Adel Abuzenadah
P2 Analysis of oxidative stress interactome during spermatogenesis: a systems biology approach to reproduction
Burak Ozkosem, Rick DuBois
P3 Interleukin-18 gene variants are strongly associated with idiopathic recurrent pregnancy loss.
Safia S Messaoudi, Maryam T Dandana, Touhami Mahjoub, Wassim Y Almawi
P4 Effect of environmental factors on gene-gene and gene-environment reactions: model and theoretical study applied to environmental interventions using genotype
S. Abdalla, M. Nabil Al-Aama
P5 Genomics and transcriptomic analysis of imatinib resistance in gastrointestinal stromal tumor
Asmaa Elzawahry, Tsuyoshi Takahashi, Sachiyo Mimaki, Eisaku Furukawa, Rie Nakatsuka, Isao Kurosaka, Takahiko Nishigaki, Hiromi Nakamura, Satoshi Serada, Tetsuji Naka, Seiichi Hirota, Tatsuhiro Shibata, Katsuya Tsuchihara, Toshirou Nishida, Mamoru Kato
P6 In-Silico analysis of putative HCV epitopes against Pakistani human leukocyte antigen background: an approach towards development of future vaccines for Pakistani population
Sajid Mehmood, Naeem Mahmood Ashraf, Awais Asif, Muhammad Bilal, Malik Siddique Mehmood, Aadil Hussain
P7 Inhibition of AChE and BuChE with the natural compounds of Bacopa monerri for the treatment of Alzheimer’s disease: a bioinformatics approach
Qazi Mohammad Sajid Jamal, Mughees Uddin Siddiqui, Mohammad A. Alzohairy, Mohammad A. Al Karaawi
P8 Her2 expression in urothelial cell carcinoma of the bladder in Saudi Arabia
Taoufik Nedjadi, Jaudah Al-Maghrabi, Mourad Assidi, Heba Al-Khattabi, Adel Al-Ammari, Ahmed Al-Sayyad, Abdelbaset Buhmeida, Mohammed Al-Qahtani
P9 Association of angiotensinogen single nucleotide polymorphisms with Preeclampsia in patients from North Africa
Hédia Zitouni, Nozha Raguema, Marwa Ben Ali, Wided Malah, Raja Lfalah, Wassim Almawi, Touhami Mahjoub
P10 Systems biology analysis reveals relations between normal skin, benign nevi and malignant melanoma
Mohammed Elanbari, Andrey Ptitsyn
P11 The apoptotic effect of thymoquinone in Jurkat cells
Sana Mahjoub, Rabeb El Ghali, Bechir Achour, Nidhal Ben Amor, Mourad Assidi, Brahim N'siri, Hamid Morjani
P12 Sonic hedgehog contributes in bladder cancer invasion in Saudi Arabia
Taoufik Nedjadi, Adel Al-Ammari, Ahmed Al-Sayyad, Nada Salem, Esam Azhar, Jaudah Al-Maghrabi
P13 Association of Interleukin 18 gene promoter polymorphisms - 607A/C and -137 G/C with colorectal cancer onset in a sample of Tunisian population
Vera Chayeb, Maryam Dendena, Hedia Zitouni, Khedija Zouari-Limayem, Touhami Mahjoub
P14 Pathological expression of interleukin-6, -11, leukemia inhibitory factor and their receptors in tubal gestation with and without tubal cytomegalovirus infection
Bassem Refaat, Ahmed M Ashshi, Sarah A Batwa
P15 Phenotypic and genetic profiling of avian pathogenic and human diarrhegenic Escherichia coli in Egypt
Hazem Ramadan, Amal Awad, Ahmed Ateya
P16 Cancer-targeting dual gene virotherapy as a promising therapeutic strategy for treatment of hepatocellular carcinoma
Adel Galal Ahmed El-Shemi, Ahmad Ashshi, Mohammed Basalamah, Youjin Na, Chae-Ok YUN
P17 Cancer dual gene therapy with oncolytic adenoviruses expressing TRAIL and IL-12 transgenes markedly eradicated human hepatocellular carcinoma both in vitro and in vivo
Adel Galal Ahmed El-Shemi, Ahmad Ashshi, Mohammed Basalamah, Youjin Na, Chae-Ok Yun
P18 Therapy with paricalcitol attenuates tumor growth and augments tumoricidal and anti-oncogenic effects of 5-fluorouracil on animal model of colon cancer
Adel Galal El-Shemi, Bassem Refaat, Osama Kensara, Amr Abdelfattah
P19 The effects of Rubus idaeus extract on normal human lymphocytes and cancer cell line
Batol Imran Dheeb, Mohammed M. F. Al-Halbosiy, Rghad Kadhim Al lihabi, Basim Mohammed Khashman
P20 Etanercept, a TNF-alpha inhibitor, alleviates mechanical hypersensitivity and spontaneous pain in a rat model of chemotherapy-induced neuropathic pain
Djouhri, Laiche, Chaudhary Adeel, Nedjadi, Taoufik
P21 Sleeping beauty mutagenesis system identified genes and neuronal transcription factor network involved in pediatric solid tumour (medulloblastoma)
Hani Al-Afghani, Maria Łastowska, Haya H Al-Balool, Harsh Sheth, Emma Mercer, Jonathan M Coxhead, Chris PF Redfern, Heiko Peters, Alastair D Burt, Mauro Santibanez-Koref, Chris M Bacon, Louis Chesler, Alistair G Rust, David J Adams, Daniel Williamson, Steven C Clifford, Michael S Jackson
P22 Involvement of interleukin-1 in vitiligo pathogenesis
Mala Singh, Mohmmad Shoab Mansuri, Shahnawaz D. Jadeja, Hima Patel, Yogesh S. Marfatia, Rasheedunnisa Begum
P23 Cytogenetics abnormalities in 12,884 referred population for chromosomal analysis and the role of FISH in refining the diagnosis (cytogenetic experience 2004-2013)
Amal M Mohamed, Alaa K Kamel, Nivin A Helmy, Sayda A Hammad, Hesham F Kayed, Marwa I Shehab, Assad El Gerzawy, Maha M. Ead, Ola M Ead, Mona Mekkawy, Innas Mazen, Mona El-Ruby
P24 Analysis of binding properties of angiotensin-converting enzyme 2 through in silico method
S. M. A. Shahid, Qazi Mohammad Sajid Jamal, J. M. Arif, Mohtashim Lohani
P25 Relationship of genetics markers cis and trans to the β-S globin gene with fetal hemoglobin expression in Tunisian sickle cell patients
Moumni Imen, Chaouch Leila, Ouragini Houyem, Douzi Kais, Chaouachi Dorra Mellouli Fethi, Bejaoui Mohamed, Abbes Salem
P26 Analysis of estrogen receptor alpha gene polymorphisms in breast cancer: link to genetic predisposition in Sudanese women
Areeg Faggad, Amanuel T Gebreslasie, Hani Y Zaki, Badreldin E Abdalla
P27 KCNQI gene polymorphism and its association with CVD and T2DM in the Saudi population
Maha S AlShammari, Rhaya Al-Ali, Nader Al-Balawi , Mansour Al-Enazi, Ali Al-Muraikhi, Fadi Busaleh, Ali Al-Sahwan, Francis Borgio, Abdulazeez Sayyed, Amein Al-Ali, Sadananda Acharya
P28 Clinical, neuroimaging and cytogenetic study of a patient with microcephaly capillary malformation syndrome
Maha S. Zaki, Hala T. El-Bassyouni, Marwa I. Shehab
P29 Altered expression of CD200R1 on dendritic cells of patients with inflammatory bowel diseases: in silico investigations and clinical evaluations
Mohammed F. Elshal, Kaleemuddin M., Alia M. Aldahlawi, Omar Saadah,
J. Philip McCoy
P30 Development of real time PCR diagnostic protocol specific for the Saudi Arabian H1N1 viral strains
Adel E El-Tarras, Nabil S Awad, Abdulla A Alharthi, Mohamed M M Ibrahim
P31 Identification of novel genetic variations affecting Osteoarthritis patients
Haneen S Alsehli, Ashraf Dallol, Abdullah M Gari, Mohammed M Abbas, Roaa A Kadam, Mazen M. Gari, Mohmmed H Alkaff, Adel M Abuzenadah, Mamdooh A Gari
P32 An integrated database of GWAS SNVs and their evolutionary properties
Heba Abusamra, Sajjad Karim, Hend F Nour eldin, Elham M Alhathli, Nada Salem, Sudhir Kumar, Mohammed H Al-Qahtani
P33 Familial hypercholesterolemia in Saudi Arabia: prime time for a national registry and genetic analysis
Fatima A. Moradi, Omran M. Rashidi, Zuhier A. Awan
P34 Comparative genomics and network-based analyses of early hepatocellular carcinoma
Ibrahim Hamza Kaya, Olfat Al-Harazi, Dilek Colak
P35 A TALEN-based oncolytic viral vector approach to knock out ABCB1 gene mediated chemoresistance in cancer stem cells
Nabila A Alkousi, Takis Athanasopoulos
P36 Cartilage differentiation and gene expression of synovial fluid mesenchymal stem cells derived from osteoarthritis patients
Afnan O Bahmaid, Etimad A Alhwait, Mamdooh A Gari, Haneen S Alsehli, Mohammed M Abbas, Mohammed H Alkaf, Roaa Kadam, Ashraf Dallol, Gauthaman Kalamegam
P37 E-GRASP: Adding an evolutionary component to the genome-wide repository of associations (GRASP) resource
Hend F Nour Eldin, Sajjad Karim, Heba Abusamra, Elham Alhathli, Nada Salem, Mohammed H Al-Qahtani, Sudhir Kumar
P38 Screening of AGL gene mutation in Saudi family with glycogen storage disease Type III
Salma N Alsayed, Fawziah H Aljohani, Samaher M Habeeb, Rawan A Almashali, Sulman Basit, Samia M Ahmed
P39 High throughput proteomic data suggest modulation of cAMP dependent protein kinase A and mitochondrial function in infertile patients with varicocele
Rakesh Sharma, Ashok Agarwal, Damayanthi Durairajanayagam, Luna Samanta, Muhammad Abu-Elmagd, Adel M. Abuzenadah, Edmund S. Sabanegh, Mourad Assidi, Mohammed Al-Qahtani
P40 Significant protein profile alterations in men with primary and secondary infertility
Ashok Agarwal, Rakesh Sharma, Luna Samanta, Damayanthi Durairajanayagam, Mourad Assidi, Muhammad Abu-Elmagd, Mohammed Al-Qahtani, Adel M. Abuzenadah, Edmund S. Sabanegh
P41 Spermatozoa maturation in infertile patients involves compromised expression of heat shock proteins
Luna Samanta, Ashok Agarwal, Rakesh Sharma, Zhihong Cui, Mourad Assidi, Adel M. Abuzenadah, Muhammad Abu-Elmagd, Mohammed Al-Qahtani
P42 Array comparative genomic hybridization approach to search genomic answers for spontaneous recurrent abortion in Saudi Arabia
Alaa A Alboogmi, Nuha A Alansari, Maha M Al-Quaiti, Fai T Ashgan, Afnan Bandah, Hasan S Jamal, Abdullraheem Rozi, Zeenat Mirza, Adel M Abuzenadah, Sajjad Karim, Mohammed H Al-Qahtani
P43 Global gene expression profiling of Saudi kidney cancer patients
Sajjad Karim, Hans-Juergen Schulten, Ahmad J Al Sayyad, Hasan MA Farsi, Jaudah A Al-Maghrabi, Zeenat Mirza, Reem Alotibi, Alaa Al-Ahmadi, Nuha A Alansari, Alaa A Albogmi, Maha M Al-Quaiti, Fai T Ashgan, Afnan Bandah, Mohammed H Al-Qahtani
P44 Downregulated StAR gene and male reproductive dysfunction caused by nifedipine and ethosuximide
Rasha A Ebiya, Samia M Darwish, Metwally M. Montaser
P45 Clustering based gene expression feature selection method: A computational approach to enrich the classifier efficiency of differentially expressed genes
Heba Abusamra, Vladimir B. Bajic
P46 Prognostic significance of Osteopontin expression profile in colorectal carcinoma
Jaudah Al-Maghrabi, Wafaey Gomaa, Mehenaz Hanbazazh, Mahmoud Al-Ahwal, Asia Al-Harbi, Wejdan Al-Qahtani, Saher Hakamy, Ghali Baba, Abdelbaset Buhmeida, Mohammed Al-Qahtani
P47 High Glypican-3 expression pattern predicts longer disease-specific survival in colorectal carcinoma
Jaudah Al-Maghrabi, Abdullah Al-Harbi, Mahmoud Al-Ahwal, Asia Al-Harbi, Wejdan Al-Qahtani, Sahar Hakamy, Ghalia Baba, Abdelbaset Buhmeida, Mohammed Al-Qahtani
P48 An evolutionary re-assessment of GWAS single nucleotide variants implicated in the Cholesterol traits
Elham M Alhathli, Sajjad Karim, Nada Salem, Hend Nour Eldin, Heba Abusamra, Sudhir Kumar, Mohammed H Al-Qahtani
P49 Derivation and characterization of human Wharton’s jelly stem cells (hWJSCs) in vitro for future therapeutic applications
Aisha A Alyamani, Gauthaman Kalamegam, Etimad A Alhwait, Mamdooh A Gari, Mohammed M Abbas, Mohammed H Alkaf, Haneen S Alsehli, Roaa A Kadam, Mohammed Al-Qahtani
P50 Attitudes of healthcare students toward biomedical research in the post-genomic era
Rawan Gadi, Abdelbaset Buhmeida, Mourad Assidi , Adeel Chaudhary, Leena Merdad
P51 Evaluation of the immunomodulatory effects of thymoquinone on human bone marrow mesenchymal stem cells (BM-MSCs) from osteoarthritic patients
Saadiah M Alfakeeh, Etimad A Alhwait, Mamdooh A Gari, Mohammed M Abbas, Mohammed H Alkaf, Haneen S Alsehli, Roaa Kadam, Gauthaman Kalamegam
P52 Implication of IL-10 and IL-28 polymorphism with successful anti-HCV therapy and viral clearance
Rubi Ghazala, Shilu Mathew, M.Haroon Hamed, Mourad Assidi, Mohammed Al-Qahtani, Ishtiaq Qadri
P53 Selection of flavonoids against obesity protein (FTO) using in silico and in vitro approaches
Shilu Mathew, Lobna Mira, Manal Shaabad, Shireen Hussain, Mourad Assidi, Muhammad Abu-Elmagd, Mohammed Al-Qahtani
P54 Computational selection and in vitro validation of flavonoids as new antidepressant agents
Shilu Mathew, Manal Shaabad, Lobna Mira, Shireen Hussain, Mourad Assidi, Muhammad Abu-Elmagd, Mohammed Al-Qahtani
P55 In Silico prediction and prioritization of aging candidate genes associated with
progressive telomere shortening
Ahmed Rebai, Mourad Assidi, Abdelbaset Buhmeida, Muhammad Abu-Elmagd, Ashraf Dallol, Jerry W Shay
P56 Identification of new cancer testis antigen genes in diverse types of malignant human tumour cells
Mikhlid H Almutairi
P57 More comprehensive forensic genetic marker analyses for accurate human remains identification using massively parallel sequencing (MPS)
Angie Ambers, Jennifer Churchill, Jonathan King, Monika Stoljarova, Harrell Gill-King, Mourad Assidi, Muhammad Abu-Elmagd, Abdelbaset Buhmeida, Muhammad Al-Qatani, Bruce Budowle
P58 Flow cytometry approach towards treatment men infertility in Saudi Arabia
Muhammad Abu-Elmagd, Farid Ahmed, Ashraf Dallol, Mourad Assidi, Taha Abo Almagd, Sahar Hakamy, Ashok Agarwal, Muhammad Al-Qahtani, Adel Abuzenadah
P59 Tissue microarray based validation of CyclinD1 expression in renal cell carcinoma of Saudi kidney patients
Sajjad Karim, Hans-Juergen Schulten, Ahmad J Al Sayyad, Hasan MA Farsi, Jaudah A Al-Maghrabi, Abdelbaset Buhmaida, Zeenat Mirza, Reem Alotibi, Alaa Al-Ahmadi, Nuha A Alansari, Alaa A Albogmi, Maha M Al-Quaiti, Fai T Ashgan, Afnan Bandah, Mohammed H Al-Qahtani
P60 Assessment of gold nanoparticles in molecular diagnostics and DNA damage studies
Rukhsana Satar, Mahmood Rasool, Waseem Ahmad, Nazia Nazam, Mohamad I Lone, Muhammad I Naseer, Mohammad S Jamal, Syed K Zaidi, Peter N Pushparaj, Mohammad A Jafri, Shakeel A Ansari, Mohammed H Alqahtani
P61 Surfing the biospecimen management and processing workflow at CEGMR Biobank
Hanan Bashier, Abrar Al Qahtani, Shilu Mathew, Amal M. Nour, Heba Alkhatabi, Adel M. Abu Zenadah, Abdelbaset Buhmeida, Mourad Assidi, Muhammed Al Qahtani
P62 Autism Spectrum Disorder: knowledge, attitude and awareness in Jeddah, Kingdom of Saudi Arabia
Muhammad Faheem, Shilu Mathew, Shiny Mathew, Peter Natesan Pushparaj, Mohammad H. Al-Qahtani
P63 Simultaneous genetic screening of the coagulation pathway genes using the Thromboscan targeted sequencing panel
Hani A. Alhadrami, Ashraf Dallol, Adel Abuzenadah
P64 Genome wide array comparative genomic hybridization analysis in patients with syndromic congenital heart defects
Ibtessam R. Hussein, Adeel G. Chaudhary, Rima S Bader, Randa Bassiouni, Maha Alquaiti, Fai Ashgan, Hans Schulten, Mohamed Nabil Alama, Mohammad H. Al Qahtani
P65 Toxocogenetic evaluation of 1, 2-Dichloroethane in bone marrow, blood and cells of immune system using conventional, molecular and flowcytometric approaches
Mohammad I Lone, Nazia Nizam, Waseem Ahmad, Mohammad A Jafri, Mahmood Rasool, Shakeel A Ansari, Muhammed H Al-Qahtani
P66 Molecular cytogenetic diagnosis of sexual development disorders in newborn: A case of ambiguous genitalia
Eradah Alshihri, Muhammad Abu-Elmagd, Lina Alharbi, Mourad Assidi, Mohammed Al-Qahtani
P67 Identification of disease specific gene expression clusters and pathways in hepatocellular carcinoma using In Silico methodologies
Shilu Mathew, Peter Pushparaj Natesan, Muhammed Al Qahtani
P68 Human Wharton’s Jelly stem cell conditioned medium inhibits primary ovarian cancer cells in vitro: Identification of probable targets and mechanisms using systems biology
Gauthaman Kalamegam, Peter Natesan Pushparaj, Fazal Khan, Roaa Kadam, Farid Ahmed, Mourad Assidi, Khalid Hussain Wali Sait, Nisreen Anfinan, Mohammed Al Qahtani
P69 Mutation spectrum of ASPM (Abnormal Spindle-like, Microcephaly-associated) gene in Saudi Arabian population
Muhammad I Naseer, Adeel G Chaudhary, Mohammad S Jamal, Shilu Mathew, Lobna S Mira, Peter N Pushparaj, Shakeel A Ansari, Mahmood Rasool, Mohammed H AlQahtani
P70 Identification and characterization of novel genes and mutations of primary microcephaly in Saudi Arabian population
Muhammad I Naseer, Adeel G Chaudhary, Shilu Mathew, Lobna S Mira, Mohammad S Jamal, Sameera Sogaty, Randa I Bassiouni, Mahmood Rasool, Mohammed H AlQahtani
P71 Molecular genetic analysis of hereditary nonpolyposis colorectal cancer (Lynch Syndrome) in Saudi Arabian population
Mahmood Rasool, Shakeel A Ansari, Mohammad S Jamal, Peter N Pushparaj, Abdulrahman MS Sibiani, Waseem Ahmad, Abdelbaset Buhmeida, Mohammad A Jafri, Mohiuddin K Warsi, Muhammad I Naseer, Mohammed H Al-Qahtani
P72 Function predication of hypothetical proteins from genome database of chlamydia trachomatis
Rubi, Kundan Kumar, Ahmad AT Naqvi, Faizan Ahmad, Md I Hassan, Mohammad S Jamal, Mahmood Rasool, Mohammed H AlQahtani
P73 Transcription factors as novel molecular targets for skin cancer
Ashraf Ali, Jummanah Jarullah, Mahmood Rasool, Abdelbasit Buhmeida, Shahida Khan, Ghufrana Abdussami, Maryam Mahfooz, Mohammad A Kamal, Ghazi A Damanhouri, Mohammad S Jamal
P74 An In Silico analysis of Plumbagin binding to apoptosis executioner: Caspase-3 and Caspase-7
Bushra Jarullah, Jummanah Jarullah, Mohammad SS Jarullah, Ashraf Ali, Mahmood Rasool, Mohammad S Jamal
P75 Single cell genomics applications for preimplantation genetic screening optimization: Comparative analysis of whole genome amplification technologies
Mourad Assidi, Muhammad Abu-Elmagd, Osama Bajouh, Peter Natesan Pushparaj, Mohammed Al-Qahtani, Adel Abuzenadah
P76 ZFP36 regulates miRs-34a in anti-IgM triggered immature B cells
Mohammad S Jamal, Jummanah Jarullah, Abdulah EA Mathkoor, Hashim MA Alsalmi, Anas MM Oun, Ghazi A Damanhauri, Mahmood Rasool, Mohammed H AlQahtani
P77 Identification of a novel mutation in the STAMBP gene in a family with microcephaly-capillary malformation syndrome
Muhammad I. Naseer, Mahmood Rasool, Sameera Sogaty, Adeel G. Chudhary, Yousif A. Abutalib, Daniele Merico, Susan Walker, Christian R. Marshall, Mehdi Zarrei, Stephen W. Scherer, Mohammad H. Al-Qahtani
P78 Copy number variations in Saudi patients with intellectual disability and epilepsy
Muhammad I. Naseer, Muhammad Faheem, Adeel G. Chaudhary, Mahmood Rasool, Gauthaman Kalamegam, Fai Talal Ashgan, Mourad Assidi, Farid Ahmed, Syed Kashif Zaidi, Mohammed M. Jan, Mohammad H. Al-Qahtani
P79 Prognostic significance of CD44 expression profile in colorectal carcinoma
Maryam Al-Zahrani, Sahira Lary, Sahar Hakamy, Ashraf Dallol, Mahmoud Al-Ahwal, Jaudah Al-Maghrabi, Emmanuel Dermitzakis, Adel Abuzenadah, Abdelbaset Buhmeida, Mohammed Al-Qahtani
P80 Association of the endothelial nitric oxide synthase (eNOS) gene G894T polymorphism with hypertension risk and complications
Abeer A Al-refai, Mona Saleh, Rehab I Yassien, Mahmmoud Kamel, Rabab M Habeb
P81 SNPs array to screen genetic variation among diabetic patients
Najlaa Filimban, Ashraf Dallol, Nadia Ghannam, Mohammed Al-Qahtani, Adel Mohammed Abuzenadah
P82 Detection and genotyping of Helicobacter pylori among gastric cancer patients from Saudi Arabian population
Fehmida Bibi, Sana Akhtar, Esam I. Azhar, Muhammad Yasir, Muhammad I. Nasser, Asif A. Jiman-Fatani, Ali Sawan
P83 Antimicrobial drug resistance and molecular detection of susceptibility to Fluoroquinolones among clinical isolates of Salmonella species from Jeddah-Saudi Arabia
Ruaa A Lahzah, Asho Ali
P84 Identification of the toxic and virulence nature of MAP1138c protein of Mycobacterium avium subsp. paratuberculosis
Syed A Hassan, Seyed E Hasnain, Iftikhar A Tayubi, Hamza A Abujabal, Alaa O Magrabi
P85 In vitro and in silico evaluation of miR137 in human breast cancer
Fazal Khan, Gauthaman Kalamegam, Peter Natesan Pushparaj, Adel Abuzenada, Taha Abduallah Kumosani, Elie Barbour, Mohammed Al-Qahtani
P86 Auruka gene is over-expressed in Saudi breast cancer
Manal Shabaad, Shilu Mathew, Ashraf Dallol, Adnan Merdad, Abdelbaset Buhmeida, Mohammed Al-Qahtani
P87 The potential of immunogenomics in personalized healthcare
Mourad Assidi, Muhammad Abu-Elmagd, Kalamegam Gauthaman, Mamdooh Gari, Adeel Chaudhary, Adel Abuzenadah, Peter Natesan Pushparaj, Mohammed Al-Qahtani
P88 In Silico physiochemical and structural characterization of a putative ORF MAP0591 and its implication in the pathogenesis of Mycobacterium paratuberculosis in ruminants and humans
Syed A Hassan, Iftikhar A Tayubi, Hani MA Aljahdali
P89 Effects of heat shock on human bone marrow mesenchymal stem cells (BM-MSCs): Implications in regenerative medicine
Reham Al Nono, Mamdooh Gari, Haneen Alsehli, Farid Ahmed, Mohammed Abbas, Gauthaman Kalamegam, Mohammed Al-Qahtani
P90 In Silico analyses of the molecular targets of Resveratrol unravels its importance in mast cell mediated allergic responses
Shilu Mathew, Fazal Khan, Mahmood Rasool, Mohammed Sarwar Jamal, Muhammad Imran Naseer, Zeenat Mirza, Sajjad Karim, Shakeel Ansari, Mourad Assidi, Gauthaman Kalamegam, Mamdooh Gari, Adeel Chaudhary, Adel Abuzenadah, Peter Natesan Pushparaj, Mohammed Al-Qahtani
P91 Effects of environmental particulate matter on bone-marrow mesenchymal stem cells
Muhammad Abu-Elmagd, Gauthaman Kalamegam, Roaa Kadam, Mansour A Alghamdi, Magdy Shamy, Max Costa, Mamdouh I Khoder, Mourad Assidi, Peter Natesan Pushparaj, Mamdooh Gari, Mohammed Al-Qahtani
P92 Distinctive charge clusters in human virus proteomes
Najla Kharrat, Sabrine Belmabrouk, Rania Abdelhedi, Riadh Benmarzoug, Mourad Assidi, Mohammed H. Al Qahtani, Ahmed Rebai
P93 In vitro experimental model and approach in identification of new biomarkers of inflammatory forms of arthritis
Ghazi Dhamanhouri, Peter Natesan Pushparaj, Abdelwahab Noorwali, Mohammad Khalid Alwasiyah, Afnan Bahamaid, Saadiah Alfakeeh, Aisha Alyamani, Haneen Alsehli, Mohammed Abbas, Mamdooh Gari, Ali Mobasheri, Gauthaman Kalamegam, Mohammed Al-Qahtani
P94 Molecular docking of GABAA receptor subunit γ-2 with novel anti-epileptic compounds
Muhammad Faheem, Shilu Mathew, Peter Natesan Pushparaj, Mohammad H. Al-Qahtani
P95 Breast cancer knowledge, awareness, and practices among Saudi females residing in Jeddah
Shilu Mathew, Muhammad Faheem, Shiny Mathew, Peter Natesan Pushparaj, Mohammad H. Al-Qahtani
P96 Anti-inflammatory role of Sesamin by Attenuation of Iba1/TNF-α/ICAM-1/iNOS signaling in Diabetic Retinopathy
Mohammad Sarwar Jamal, Syed Kashif Zaidi, Raziuddin Khan, Kanchan Bhatia, Mohammed H. Al-Qahtani, Saif Ahmad
P97 Identification of drug lead molecule against vp35 protein of Ebola virus: An In-Silico approach
Iftikhar AslamTayubi, Manish Tripathi, Syed Asif Hassan, Rahul Shrivastava
P98 An approach to personalized medicine from SNP-calling through disease analysis using whole exome-sequencing of three sub-continental populations
Iftikhar A Tayubi, Syed Hassan, Hamza A.S Abujabal
P99 Low versus high frequency of Glucose –6 – Phosphate Dehydrogenase (G6PD) deficiency in urban against tribal population of Gujarat – A signal to natural selection
Ishani Shah, Bushra Jarullah, Mohammad S Jamal, Jummanah Jarullah
P100 Spontaneous preterm birth and single nucleotide gene polymorphisms: a recent update
Ishfaq A Sheikh, Ejaz Ahmad, Mohammad S Jamal, Mohd Rehan, Muhammad Abu-Elmagd, Iftikhar A Tayubi, Samera F AlBasri, Osama S Bajouh, Rola F Turki, Adel M Abuzenadah, Ghazi A Damanhouri, Mohd A Beg, Mohammed Al-Qahtani
P101 Prevalence of congenital heart diseases among Down syndrome cases in Saudi Arabia: role of molecular genetics in the pathogenesis
Sahar AF Hammoudah, Khalid M AlHarbi, Lama M El-Attar, Ahmed MZ Darwish
P102 Combinatorial efficacy of specific pathway inhibitors in breast cancer cells
Sara M Ibrahim, Ashraf Dallol, Hani Choudhry, Adel Abuzenadah, Jalaludden Awlia, Adeel Chaudhary, Farid Ahmed, Mohammed Al-Qahtani
P103 MiR-143 and miR-145 cluster as potential replacement medicine for the treatment of cancer
Mohammad A Jafri, Muhammad Abu-Elmagd, Mourad Assidi, Mohammed Al-Qahtani
P104 Metagenomic profile of gut microbiota during pregnancy in Saudi population
Imran khan, Muhammad Yasir, Esam I. Azhar, Sameera Al-basri, Elie Barbour, Taha Kumosani
P105 Exploration of anticancer targets of selected metabolites of Phoenix dactylifera L. using systems biological approaches
Fazal Khan, Gauthaman Kalamegam, Peter Natesan Pushparaj, Adel Abuzenada, Taha Abduallah Kumosani, Elie Barbour
P106 CD226 and CD40 gene polymorphism in susceptibility to Juvenile rheumatoid arthritis in Egyptian patients
Heba M. EL Sayed, Eman A. Hafez
P107 Paediatric exome sequencing in autism spectrum disorder ascertained in Saudi families
Hans-Juergen Schulten, Aisha Hassan Elaimi, Ibtessam R Hussein, Randa Ibrahim Bassiouni, Mohammad Khalid Alwasiyah, Richard F Wintle, Adeel Chaudhary, Stephen W Scherer, Mohammed Al-Qahtani
P108 Crystal structure of the complex formed between Phospholipase A2 and the central core hydrophobic fragment of Alzheimer’s β- amyloid peptide: a reductionist approach
Zeenat Mirza, Vikram Gopalakrishna Pillai, Sajjad Karim, Sujata Sharma, Punit Kaur, Alagiri Srinivasan, Tej P Singh, Mohammed Al-Qahtani
P109 Differential expression profiling between meningiomas from female and male patients
Reem Alotibi, Alaa Al-Ahmadi, Fatima Al-Adwani, Deema Hussein, Sajjad Karim, Mona Al-Sharif, Awatif Jamal, Fahad Al-Ghamdi, Jaudah Al-Maghrabi, Saleh S Baeesa, Mohammed Bangash, Adeel Chaudhary, Hans-Juergen Schulten, Mohammed Al-Qahtani
P110 Neurospheres as models of early brain development and therapeutics
Muhammad Faheem, Peter Natesan Pushparaj, Shilu Mathew, Taha Abdullah Kumosani, Gauthaman Kalamegam, Mohammed Al-Qahtani
P111 Identification of a recurrent causative missense mutation p.(W577C) at the LDLR exon 12 in familial hypercholesterolemia affected Saudi families
Faisal A Al-Allaf, Zainularifeen Abduljaleel, Abdullah Alashwal, Mohiuddin M. Taher, Abdellatif Bouazzaoui, Halah Abalkhail, Faisal A. Ba-Hammam, Mohammad Athar
P112 Epithelial ovarian carcinoma (EOC): Systems oncological approach to identify diagnostic, prognostic and therapeutic biomarkers
Gauthaman Kalamegam, Peter Natesan Pushparaj, Muhammad Abu-Elmagd, Farid Ahmed Khalid HussainWali Sait, Nisreen Anfinan, Mamdooh Gari, Adeel Chaudhary, Adel Abuzenadah, Mourad Assidi, Mohammed Al-Qahtani
P113 Crohn’s disease phenotype in northern Tunisian population
Naira Ben Mami, Yosr Z Haffani, Mouna Medhioub, Lamine Hamzaoui, Ameur Cherif, Msadok Azouz
P114 Establishment of In Silico approaches to decipher the potential toxicity and mechanism of action of drug candidates and environmental agents
Gauthaman Kalamegam, Fazal Khan, Shilu Mathew, Mohammed Imran Nasser, Mahmood Rasool, Farid Ahmed, Peter Natesan Pushparaj, Mohammed Al-Qahtani
P115 1q Gain predicts poor prognosis marker for young breast cancer patients
Shereen A Turkistany, Lina M Al-harbi, Ashraf Dallol, Jamal Sabir, Adeel Chaudhary, Adel Abuzenadah
P116 Disorders of sex chromosomes in a diagnostic genomic medicine unit in Saudi Arabia: Prevalence, diagnosis and future guidelines
Basmah Al-Madoudi, Bayan Al-Aslani, Khulud Al-Harbi, Rwan Al-Jahdali, Hanadi Qudaih, Emad Al Hamzy, Mourad Assidi, Mohammed Al Qahtani
P117 Combination of WYE354 and Sunitinib demonstrate synergistic inhibition of acute myeloid leukemia in vitro
Asad M Ilyas, Youssri Ahmed, Mamdooh Gari, Farid Ahmed, Mohammed Alqahtani
P118 Integrated use of evolutionary information in GWAS reveals important SNPs in Asthma
Nada Salem, Sajjad Karim, Elham M Alhathli, Heba Abusamra, Hend F Nour Eldin, Mohammed H Al-Qahtani, Sudhir Kumar
P119 Assessment of BRAF, IDH1, IDH2, and EGFR mutations in a series of primary brain tumors
Fatima Al-Adwani, Deema Hussein, Mona Al-Sharif, Awatif Jamal, Fahad Al-Ghamdi, Jaudah Al-Maghrabi, Saleh S Baeesa, Mohammed Bangash, Adeel Chaudhary, Mohammed Al-Qahtani, Hans-Juergen Schulten
P120 Expression profiles distinguish oligodendrogliomas from glioblastoma multiformes with or without oligodendroglioma component
Alaa Alamandi, Reem Alotibi, Deema Hussein, Sajjad Karim, Jaudah Al-Maghrabi, Fahad Al-Ghamdi, Awatif Jamal, Saleh S Baeesa, Mohammed Bangash, Adeel Chaudhary, Hans-Juergen Schulten, Mohammed Al-Qahtani
P121 Hierarchical clustering in thyroid goiters and hyperplastic lesions
Ohoud Subhi, Nadia Bagatian, Sajjad Karim, Adel Al-Johari, Osman Abdel Al-Hamour, Hosam Al-Aradati, Abdulmonem Al-Mutawa, Faisal Al-Mashat, Jaudah Al-Maghrabi, Hans-Juergen Schulten, Mohammad Al-Qahtani
P122 Differential expression analysis in thyroiditis and papillary thyroid carcinomas with or without coexisting thyroiditis
Nadia Bagatian, Ohoud Subhi, Sajjad Karim, Adel Al-Johari, Osman Abdel Al-Hamour, Abdulmonem Al-Mutawa, Hosam Al-Aradati, Faisal Al-Mashat, Mohammad Al-Qahtani, Hans-Juergen Schulten, Jaudah Al-Maghrabi
P123 Metagenomic analysis of waste water microbiome in Sausdi Arabia
Muhammad W shah, Muhammad Yasir, Esam I Azhar, Saad Al-Masoodi
P124 Molecular characterization of Helicobacter pylori from faecal samples of Tunisian patients with gastric cancer
Yosr Z Haffani, Msadok Azouz, Emna Khamla, Chaima Jlassi, Ahmed S. Masmoudi, Ameur Cherif, Lassaad Belbahri
P125 Diagnostic application of the oncoscan© panel for the identification of hereditary cancer syndrome
Shadi Al-Khayyat, Roba Attas, Atlal Abu-Sanad, Mohammed Abuzinadah, Adnan MerdadAshraf Dallol, Adeel Chaudhary, Mohammed Al-Qahtani, Adel Abuzenadah
P126 Characterization of clinical and neurocognitive features in a family with a novel OGT gene missense mutation c. 1193G > A/ (p. Ala319Thr)
Habib Bouazzi, Carlos Trujillo, Mohammad Khalid Alwasiyah, Mohammed Al-Qahtani
P127 Case report: a rare homozygous deletion mutation of TMEM70 gene associated with 3-Methylglutaconic Aciduria and cataract in a Saudi patient
Maha Alotaibi, Rami Nassir
P128 Isolation and purification of antimicrobial milk proteins
Ishfaq A Sheikh, Mohammad A Kamal, Essam H Jiffri, Ghulam M Ashraf, Mohd A Beg
P129 Integrated analysis reveals association of ATP8B1 gene with colorectal cancer
Mohammad A Aziz, Rizwan Ali, Mahmood Rasool, Mohammad S Jamal, Nusaibah samman, Ghufrana Abdussami, Sathish Periyasamy, Mohiuddin K Warsi, Mohammed Aldress, Majed Al Otaibi, Zeyad Al Yousef, Mohamed Boudjelal, Abdelbasit Buhmeida, Mohammed H Al-Qahtani, Ibrahim AlAbdulkarim
P130 Implication of IL-10 and IL-28 polymorphism with successful anti-HCV therapy and viral clearance
Rubi Ghazala, Shilu Mathew, M. Haroon Hamed, Mourad Assidi, Mohammed Al-Qahtani, Ishtiaq Qadri
P131 Interactions of endocrine disruptor di-(2-ethylhexyl) phthalate (DEHP) and its metabolite mono-2-ethylhexyl phthalate (MEHP) with progesterone receptor
Ishfaq A Sheikh, Muhammad Abu-Elmagd, Rola F Turki, Ghazi A Damanhouri, Mohd A. Beg
P132 Association of HCV nucleotide polymorphism in the development of hepatocellular carcinoma
Mohd Suhail, Abid Qureshi, Adil Jamal, Peter Natesan Pushparaj, Mohammad Al-Qahtani, Ishtiaq Qadri
P133 Gene expression profiling by DNA microarrays in colon cancer treated with chelidonine alkaloid
Mahmoud Z El-Readi, Safaa Y Eid, Michael Wink
P134 Successful in vitro fertilization after eight failed trials
Ahmed M. Isa, Lulu Alnuaim, Johara Almutawa, Basim Abu-Rafae, Saleh Alasiri, Saleh Binsaleh
P135 Genetic sensitivity analysis using SCGE, cell cycle and mitochondrial membrane potential in OPs stressed leukocytes in Rattus norvegicus through flow cytometric input
Nazia Nazam, Mohamad I Lone, Waseem Ahmad, Shakeel A Ansari, Mohamed H Alqahtani
doi:10.1186/s12864-016-2858-0
PMCID: PMC4959372  PMID: 27454254
3.  Microenvironmental Heterogeneity Parallels Breast Cancer Progression: A Histology–Genomic Integration Analysis 
PLoS Medicine  2016;13(2):e1001961.
Background
The intra-tumor diversity of cancer cells is under intense investigation; however, little is known about the heterogeneity of the tumor microenvironment that is key to cancer progression and evolution. We aimed to assess the degree of microenvironmental heterogeneity in breast cancer and correlate this with genomic and clinical parameters.
Methods and Findings
We developed a quantitative measure of microenvironmental heterogeneity along three spatial dimensions (3-D) in solid tumors, termed the tumor ecosystem diversity index (EDI), using fully automated histology image analysis coupled with statistical measures commonly used in ecology. This measure was compared with disease-specific survival, key mutations, genome-wide copy number, and expression profiling data in a retrospective study of 510 breast cancer patients as a test set and 516 breast cancer patients as an independent validation set. In high-grade (grade 3) breast cancers, we uncovered a striking link between high microenvironmental heterogeneity measured by EDI and a poor prognosis that cannot be explained by tumor size, genomics, or any other data types. However, this association was not observed in low-grade (grade 1 and 2) breast cancers. The prognostic value of EDI was superior to known prognostic factors and was enhanced with the addition of TP53 mutation status (multivariate analysis test set, p = 9 × 10−4, hazard ratio = 1.47, 95% CI 1.17–1.84; validation set, p = 0.0011, hazard ratio = 1.78, 95% CI 1.26–2.52). Integration with genome-wide profiling data identified losses of specific genes on 4p14 and 5q13 that were enriched in grade 3 tumors with high microenvironmental diversity that also substratified patients into poor prognostic groups. Limitations of this study include the number of cell types included in the model, that EDI has prognostic value only in grade 3 tumors, and that our spatial heterogeneity measure was dependent on spatial scale and tumor size.
Conclusions
To our knowledge, this is the first study to couple unbiased measures of microenvironmental heterogeneity with genomic alterations to predict breast cancer clinical outcome. We propose a clinically relevant role of microenvironmental heterogeneity for advanced breast tumors, and highlight that ecological statistics can be translated into medical advances for identifying a new type of biomarker and, furthermore, for understanding the synergistic interplay of microenvironmental heterogeneity with genomic alterations in cancer cells.
A novel approach that maps tumor microenvironment heterogeneity and couples this with genetic information to provide superior prognosis in breast cancer.
Editors' Summary
Background
The human body contains millions of cells, all of which grow, divide, and die in an orderly fashion to build tissues during early life and to replace worn-out or dying cells and repair injuries during adult life. Sometimes, however, normal cells acquire genetic changes (mutations) that allow them to divide uncontrollably and to move around the body (metastasize), resulting in cancer. Because any cell in the body can acquire the mutations needed for cancer development, there are many types of cancer. For example, breast cancer, the most common cancer in women, begins when the cells in the breast that normally make milk become altered. Moreover, different types of cancer progress and evolve differently—some cancers grow quickly and kill their “host” soon after diagnosis, whereas others can be successfully treated with drugs, surgery, or radiotherapy. The behavior of individual cancers depends both on the characteristics of the cancer cells within the tumor and on the interactions between the cancer cells and the normal stromal cells (the connective tissue cells of organs) and other cells (for example, immune cells) that surround and feed cancer cells (the tumor microenvironment).
Why Was This Study Done?
Although recent studies have highlighted the importance of the tumor microenvironment for disease-related outcomes, little is known about how the heterogeneity of the tumor microenvironment—the diversity of non-cancer cells within the tumor—affects outcomes. Mathematical modeling suggests that tumors with heterogeneous and homogeneous microenvironments have different growth patterns and that heterogeneous microenvironments are more likely to be associated with aggressive cancers than homogenous microenvironments. However, the lack of methods to quantify the spatial variability and cellular composition across solid tumors has prevented confirmation of these predictions. Here, the researchers develop a computational system for quantifying microenvironmental heterogeneity in breast cancer based on tumor morphology (shape and form) in histological sections (tissue samples taken from tumors that are examined microscopically). They then use this system to analyze the associations between clinical outcomes, molecular changes, and microenvironmental heterogeneity in breast cancer.
What Did the Researchers Do and Find?
The researchers used automated image analysis and statistical analysis to develop the ecosystem diversity index (EDI), a numerical measure of microenvironmental heterogeneity in solid tumors. They compared the EDI with prognosis (likely outcome), key mutations, genome-wide copy number (tumor cells often contain abnormal numbers of copies of specific genes), and expression profiling data (the expression of several key proteins is altered in tumors) in a test set of 510 samples from patients with breast cancer and in a validation set of 516 additional samples. Among high-grade breast cancers (grade 3 cancers; the grade of a cancer indicates what the cells look like; high-grade breast cancers have a poor prognosis), but not among low-grade breast cancers (grades 1 and 2), a high EDI (high microenvironmental heterogeneity) was associated with a poor prognosis. Specifically, patients with grade 3 tumors and a high EDI had a ten-year disease-specific survival rate of 51%, whereas the remaining patients with grade 3 tumors had a ten-year survival rate of 70%. Notably, the combination of a high EDI with specific DNA alterations—mutations in a gene called TP53 and loss of genes on Chromosomes 4p14 and 5q13—improved the accuracy of prognosis among patients with grade 3 breast cancer and stratified them into subgroups with disease-specific five-year survival rates of 35%, 9%, and 32%, respectively.
What Do These Findings Mean?
These findings establish a method for measuring the spatial heterogeneity of the microenvironment of solid tumors and suggest that the measurement of tumor microenvironmental heterogeneity can be coupled with information about genomic alterations to provide an accurate way to predict outcomes among patients with high-grade breast cancer. The association between EDI, specific genomic alterations, and outcomes needs to be confirmed in additional patients. However, these findings suggest that microenvironmental heterogeneity might provide an additional biomarker to help clinicians identify those patients with advanced breast cancer who have a particularly bad prognosis. The ability to identify these patients is important because it will help clinicians target aggressive treatments to individuals with a poor prognosis and avoid the overtreatment of patients whose prognosis is more favorable. Finally, and more generally, these findings describe a new way to investigate the interactions between the tumor microenvironment and genomic alterations in cancer cells.
Additional Information
This list of resources contains links that can be accessed when viewing the PDF on a device or via the online version of the article at http://dx.doi.org/10.1371/journal.pmed.1001961.
The US National Cancer Institute provides comprehensive information about cancer and its development (in English and Spanish), including detailed information about breast cancer and an online booklet for patients
Cancer Research UK, a not-for-profit organization, provides information about cancer, including detailed information about breast cancer and a science blog on the tumor microenvironment
Breast Cancer Now is a not-for-profit organization that provides up-to-date information about breast cancer (in English and Spanish)
The UK National Health Service Choices website has information and personal stories about breast cancer; the not-for-profit organization Healthtalkonline also provides personal stories about dealing with breast cancer
Wikipedia has a page about the tumor microenvironment (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
doi:10.1371/journal.pmed.1001961
PMCID: PMC4755617  PMID: 26881778
4.  Protein localization as a principal feature of the etiology and comorbidity of genetic diseases 
Proteins localized within the same subcellular compartment tend to be functionally associated. This study shows that subcellular localization and network distance between disease-associated proteins provide complementary information explaining patterns of disease comorbidity.
A positive correlation was found between subcellular localization of disease-associated protein pairs and measures of comorbidity.A higher comorbidity tendency was found for disease-associated protein pairs that are positioned within a shorter distance in the protein interaction network.The integration of subcellular localization information with protein interaction network sheds light onto the potential molecular connections underlying comorbidity patterns and will help to understand the mechanisms of human disease.
It was shown that the emergence of phenotypically similar diseases are triggered as a result of molecular connections between disease-causing genes (Oti and Brunner, 2007; Zaghloul and Katsanis, 2010). From a genetics, perspective diseases are associated with certain genes (Goh et al, 2007; Feldman et al, 2008), whereas from a proteomics perspective phenotypically similar diseases are connected via biological modules such as protein–protein interactions (PPIs) or molecular pathways (Lage et al, 2007; Jiang et al, 2008; Wu et al, 2008; Linghu et al, 2009; Suthram et al, 2010). These molecular connections between diseases were observed on the population level as well: diseases connected through molecular connections such as shared genes, PPIs, and metabolic pathways tend to show elevated comorbidity (Rzhetsky et al, 2007; Lee et al, 2008; Zhernakova et al, 2009; Park et al, 2009a, 2009b). While these findings constitute a step toward improving our understanding of the mechanism of disease progression, there are still many more molecule-level connections between disease pairs that need to be explored in order to establish a firmer comorbidity association.
Subcellular localization provides spatial information of proteins in the cell; proteins target subcellular localizations to interact with appropriate partners and form functional complexes in signaling pathways and metabolic processes (Au et al, 2007). Abnormal protein localizations are known to lead to the loss of functional effects in diseases (Luheshi et al, 2008; Laurila and Vihinen, 2009). For example, mis-localizations of nuclear/cytoplasmic transport have been detected in many types of carcinoma cells (Kau et al, 2004). A proper identification of protein subcellular localization can hence be useful in discovering disease-associated proteins (Giallourakis et al, 2005; Calvo and Mootha, 2010). With this understanding, we postulate that disease-associated proteins connected by subcellular localizations could also explain the phenotypic similarities between diseases. Furthermore, such connections may also couple to disease progressions that contribute to multiple disease manifestation, that is, comorbidity.
Protein subcellular localization has been extensively studied through various methods to determine a variety of protein functions. To the best of our knowledge, the connection between diseases and subcellular localizations are yet to be studied systematically. To resolve this we constructed, for the first time, a human Disease-associated Protein and subcellular Localization (DPL) matrix (top panel in Box 1). Our DPL matrix provides the ‘cellular localization map of diseases' that represents the spatial index of diseases in the cell. We found that each disease shows unique characteristics of subcellular localization profile in the DPL matrix. We were interested in determining whether subsets of 1284 human diseases exhibit distinct enrichment profiles across subcellular localizations. We calculated pairwise correlations and performed a hierarchical clustering of the enrichments of the 1284 diseases across 10 different subcellular localizations.
Our DPL matrix revealed that 778 diseases (∼62%, P=1.40 × 10−3) are enriched in a single localization and 273 diseases (∼21%, P=3.45 × 10−3) are enriched in dual localizations. In the DPL matrix, certain disease-associated proteins are likely to be found in membrane-bounded organelles such as mitochondria, lysosome, and peroxisome, indicating that the mutations of proteins localized to these compartments are connected to the pathophysiological conditions of those organelles. Meanwhile, certain disease-associated proteins in the DPL matrix are enriched in dual localizations, such as extracellular/plasma membrane or endoplasmic reticulum/Golgi. Although these two pairs of subcellular localizations appear to be distinct compartments at first, they are functionally related compartments in close proximity during protein translocation process in the cell, and thus are likely to share interacting protein partners (Gandhi et al, 2006).
Comorbidity represents the co-occurrence of multiple diseases in the same individual (Lee et al, 2008; Hidalgo et al, 2009; Park et al, 2009a). Many comorbid disease pairs have been shown to share common genes in the human disease network. For example, Diabetes and Alzheimer's disease share a risk factor in angiotensin I converting enzyme, and frequently occur together in an individual. In such instances, comorbidity can be partially attributed to the disease connections on the molecular level. To explore the impact of protein subcellular localization on comorbidity, we hypothesized that certain disease pairs could also be connected via subcellular localization by the molecular connections between the disease-associated proteins (bottom panel in Box 1).
We found a positive correlation between subcellular localization similarity and relative risk (Figure 3B, Pearson's correlation coefficient between relative risk and subcellular localization similarity=0.81, P=2.96 × 10−5). The subcellular localization similarity represents the correlation of subcellular localization profiles between disease pairs. To our surprise, when we compared the relative risk of disease pairs linked via various molecular connections, we found that disease pairs connected by subcellular localization showed a near three-fold higher comorbidity tendency (with link distances equal to 2 or 3) when compared with random pairs (Figure 3E).
We then assessed quantitatively the impact of network distances and subcellular localizations on the comorbidity tendency of disease pairs. We expected the proteins associated with comorbid disease pairs to be located closely in the protein interaction network via fewer links compared with random disease pairs. Indeed, a higher comorbidity tendency was found when two disease-associated proteins were positioned within a shorter distance (gray plots in Figure 3F). Moreover, when subcellular localization information was combined with small network distances, the comorbidity tendency increased dramatically (orange plots in Figure 3F). It suggests that subcellular localization and close network distances, two conceptually distinct molecular connections, contributed synergistically to the comorbidity tendency.
Disease progression is not restricted to the mutation of disease-causing genes, but also affected by molecular connections in ‘disease modules,' resulting in comorbidity (Fraser, 2006; Lee et al, 2008). In this study, for the first time we applied subcellular localization information to elucidate the molecular connections between comorbid diseases. We believe that, based on our finding, our approach helps to define the boundaries of ‘disease modules.' Taken together, integration of diverse molecular connections should improve the molecular level understanding of hitherto unexplained comorbid disease pairs and help us in expanding the scope of our knowledge of the mechanism of human disease progression.
Proteins targeting the same subcellular localization tend to participate in mutual protein–protein interactions (PPIs) and are often functionally associated. Here, we investigated the relationship between disease-associated proteins and their subcellular localizations, based on the assumption that protein pairs associated with phenotypically similar diseases are more likely to be connected via subcellular localization. The spatial constraints from subcellular localization significantly strengthened the disease associations of the proteins connected by subcellular localizations. In particular, certain disease types were more prevalent in specific subcellular localizations. We analyzed the enrichment of disease phenotypes within subcellular localizations, and found that there exists a significant correlation between disease classes and subcellular localizations. Furthermore, we found that two diseases displayed high comorbidity when disease-associated proteins were connected via subcellular localization. We newly explained 7584 disease pairs by using the context of protein subcellular localization, which had not been identified using shared genes or PPIs only. Our result establishes a direct correlation between protein subcellular localization and disease association, and helps to understand the mechanism of human disease progression.
doi:10.1038/msb.2011.29
PMCID: PMC3130560  PMID: 21613983
cellular networks; comorbidity; human disease; subcellular localization
5.  Automated identification of pathways from quantitative genetic interaction data 
We present a novel Bayesian learning method that reconstructs large detailed gene networks from quantitative genetic interaction (GI) data.The method uses global reasoning to handle missing and ambiguous measurements, and provide confidence estimates for each prediction.Applied to a recent data set over genes relevant to protein folding, the learned networks reflect known biological pathways, including details such as pathway ordering and directionality of relationships.The reconstructed networks also suggest novel relationships, including the placement of SGT2 in the tail-anchored biogenesis pathway, a finding that we experimentally validated.
Recent developments have enabled large-scale quantitative measurement of genetic interactions (GIs) that report on the extent to which the activity of one gene is dependent on a second. It has long been recognized (Avery and Wasserman, 1992; Hartman et al, 2001; Segre et al, 2004; Tong et al, 2004; Drees et al, 2005; Schuldiner et al, 2005; St Onge et al, 2007; Costanzo et al, 2010) that functional dependencies revealed by GI data can provide rich information regarding underlying biological pathways. Further, the precise phenotypic measurements provided by quantitative GI data can provide evidence for even more detailed aspects of pathway structure, such as differentiating between full and partial dependence between two genes (Drees et al, 2005; Schuldiner et al, 2005; St Onge et al, 2007; Jonikas et al, 2009) (Figure 1A). As GI data sets become available for a range of quantitative phenotypes and organisms, such patterns will allow researchers to elucidate pathways important to a diverse set of biological processes.
We present a new method that exploits the high-quality, quantitative nature of recent GI assays to automatically reconstruct detailed multi-gene pathway structures, including the organization of a large set of genes into coherent pathways, the connectivity and ordering within each pathway, and the directionality of each relationship. We introduce activity pathway networks (APNs), which represent functional dependencies among a set of genes in the form of a network. We present an automatic method to efficiently reconstruct APNs over large sets of genes based on quantitative GI measurements. This method handles uncertainty in the data arising from noise, missing measurements, and data points with ambiguous interpretations, by performing global reasoning that combines evidence from multiple data points. In addition, because some structure choices remain uncertain even when jointly considering all measurements, our method maintains multiple likely networks, and allows computation of confidence estimates over each structure choice.
We applied our APN reconstruction method to the recent high-quality GI data set of Jonikas et al (2009), which examined the functional interaction between genes that contribute to protein folding in the ER. Specifically, Jonikas et al used the cell's endogenous sensor (the unfolded protein response), to first identify several hundred yeast genes with functions in endoplasmic reticulum folding and then systematically characterized their functional interdependencies by measuring unfolded protein response levels in double mutants. Our analysis produced an ensemble of 500 likelihood-weighted APNs over 178 genes (Figure 2).
We performed an aggregate evaluation of our results by comparing to known biological relationships between gene pairs, including participation in pathways according to the Kyoto Encyclopedia of Genes and Genomes (KEGG), correlation of chemical genomic profiles in a recent high-throughput assay (Hillenmeyer et al, 2008) and similarity of Gene Ontology (GO) annotations. In each evaluation performed, our reconstructed APNs were significantly more consistent with the known relationships than either the raw GI values or the Pearson correlation between profiles of GI values.
Importantly, our approach provides not only an improved means for defining pairs or groups of related genes, but also enables the identification of detailed multi-gene network structures. In many cases, our method successfully reconstructed known cellular pathways, including the ER-associated degradation (ERAD) pathway, and the biosynthesis of N-linked glycans, ranking them among the highest confidence structures. In-depth examination of the learned network structures indicates agreement with many known details of these pathways. In addition, quantitative analysis indicates that our learned APNs are indicative of ordering within KEGG-annotated biological pathways.
Our results also suggest several novel relationships, including placement of uncharacterized genes into pathways, and novel relationships between characterized genes. These include the dependence of the J domain chaperone JEM1 on the PDI homolog MPD1, dependence of the Ubiquitin-recycling enzyme DOA4 on N-linked glycosylation, and the dependence of the E3 Ubiquitin ligase DOA10 on the signal peptidase complex subunit SPC2. Our APNs also place the poorly characterized TPR-containing protein SGT2 upstream of the tail-anchored protein biogenesis machinery components GET3, GET4, and MDY2 (also known as GET5), suggesting that SGT2 has a function in the insertion of tail-anchored proteins into membranes. Consistent with this prediction, our experimental analysis shows that sgt2Δ cells show a defect in localization of the tail-anchored protein GFP-Sed5 from punctuate Golgi structures to a more diffuse pattern, as seen in other genes involved in this pathway.
Our results show that multi-gene, detailed pathway networks can be reconstructed from quantitative GI data, providing a concrete computational manifestation to intuitions that have traditionally accompanied the manual interpretation of such data. Ongoing technological developments in both genetics and imaging are enabling the measurement of GI data at a genome-wide scale, using high-accuracy quantitative phenotypes that relate to a range of particular biological functions. Methods based on RNAi will soon allow collection of similar data for human cell lines and other mammalian systems (Moffat et al, 2006). Thus, computational methods for analyzing GI data could have an important function in mapping pathways involved in complex biological systems including human cells.
High-throughput quantitative genetic interaction (GI) measurements provide detailed information regarding the structure of the underlying biological pathways by reporting on functional dependencies between genes. However, the analytical tools for fully exploiting such information lag behind the ability to collect these data. We present a novel Bayesian learning method that uses quantitative phenotypes of double knockout organisms to automatically reconstruct detailed pathway structures. We applied our method to a recent data set that measures GIs for endoplasmic reticulum (ER) genes, using the unfolded protein response as a quantitative phenotype. The results provided reconstructions of known functional pathways including N-linked glycosylation and ER-associated protein degradation. It also contained novel relationships, such as the placement of SGT2 in the tail-anchored biogenesis pathway, a finding that we experimentally validated. Our approach should be readily applicable to the next generation of quantitative GI data sets, as assays become available for additional phenotypes and eventually higher-level organisms.
doi:10.1038/msb.2010.27
PMCID: PMC2913392  PMID: 20531408
computational biology; genetic interaction; pathway reconstruction; probabilistic methods
6.  Human genome meeting 2016 
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Human Genomics  2016;10(Suppl 1):12.
Table of contents
O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder
A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson
O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents
R. Polimanti, J. Gelernter
O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort
X. Lin, I. Y. Lim, Y. Wu, A. L. Teh, L. Chen, I. M. Aris, S. E. Soh, M. T. Tint, J. L. MacIsaac, F. Yap, K. Kwek, S. M. Saw, M. S. Kobor, M. J. Meaney, K. M. Godfrey, Y. S. Chong, J. D. Holbrook, Y. S. Lee, P. D. Gluckman, N. Karnani, GUSTO study group
O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus
A. Kapoor, D. Lee, A. Chakravarti
O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells
C. Maercker, F. Graf, M. Boutros
O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies
G. Stamoulis, F. Santoni, P. Makrythanasis, A. Letourneau, M. Guipponi, N. Panousis, M. Garieri, P. Ribaux, E. Falconnet, C. Borel, S. E. Antonarakis
O7 Role of microRNA in LCL to IPSC reprogramming
S. Kumar, J. Curran, J. Blangero
O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease
S. Chatterjee, A. Kapoor, J. Akiyama, D. Auer, C. Berrios, L. Pennacchio, A. Chakravarti
O9 Metabolomic profiling for the diagnosis of neurometabolic disorders
T. R. Donti, G. Cappuccio, M. Miller, P. Atwal, A. Kennedy, A. Cardon, C. Bacino, L. Emrick, J. Hertecant, F. Baumer, B. Porter, M. Bainbridge, P. Bonnen, B. Graham, R. Sutton, Q. Sun, S. Elsea
O10 A novel causal methylation network approach to Alzheimer’s disease
Z. Hu, P. Wang, Y. Zhu, J. Zhao, M. Xiong, David A Bennett
O11 A microRNA signature identifies subtypes of triple-negative breast cancer and reveals MIR-342-3P as regulator of a lactate metabolic pathway
A. Hidalgo-Miranda, S. Romero-Cordoba, S. Rodriguez-Cuevas, R. Rebollar-Vega, E. Tagliabue, M. Iorio, E. D’Ippolito, S. Baroni
O12 Transcriptome analysis identifies genes, enhancer RNAs and repetitive elements that are recurrently deregulated across multiple cancer types
B. Kaczkowski, Y. Tanaka, H. Kawaji, A. Sandelin, R. Andersson, M. Itoh, T. Lassmann, the FANTOM5 consortium, Y. Hayashizaki, P. Carninci, A. R. R. Forrest
O13 Elevated mutation and widespread loss of constraint at regulatory and architectural binding sites across 11 tumour types
C. A. Semple
O14 Exome sequencing provides evidence of pathogenicity for genes implicated in colorectal cancer
E. A. Rosenthal, B. Shirts, L. Amendola, C. Gallego, M. Horike-Pyne, A. Burt, P. Robertson, P. Beyers, C. Nefcy, D. Veenstra, F. Hisama, R. Bennett, M. Dorschner, D. Nickerson, J. Smith, K. Patterson, D. Crosslin, R. Nassir, N. Zubair, T. Harrison, U. Peters, G. Jarvik, NHLBI GO Exome Sequencing Project
O15 The tandem duplicator phenotype as a distinct genomic configuration in cancer
F. Menghi, K. Inaki, X. Woo, P. Kumar, K. Grzeda, A. Malhotra, H. Kim, D. Ucar, P. Shreckengast, K. Karuturi, J. Keck, J. Chuang, E. T. Liu
O16 Modeling genetic interactions associated with molecular subtypes of breast cancer
B. Ji, A. Tyler, G. Ananda, G. Carter
O17 Recurrent somatic mutation in the MYC associated factor X in brain tumors
H. Nikbakht, M. Montagne, M. Zeinieh, A. Harutyunyan, M. Mcconechy, N. Jabado, P. Lavigne, J. Majewski
O18 Predictive biomarkers to metastatic pancreatic cancer treatment
J. B. Goldstein, M. Overman, G. Varadhachary, R. Shroff, R. Wolff, M. Javle, A. Futreal, D. Fogelman
O19 DDIT4 gene expression as a prognostic marker in several malignant tumors
L. Bravo, W. Fajardo, H. Gomez, C. Castaneda, C. Rolfo, J. A. Pinto
O20 Spatial organization of the genome and genomic alterations in human cancers
K. C. Akdemir, L. Chin, A. Futreal, ICGC PCAWG Structural Alterations Group
O21 Landscape of targeted therapies in solid tumors
S. Patterson, C. Statz, S. Mockus
O22 Genomic analysis reveals novel drivers and progression pathways in skin basal cell carcinoma
S. N. Nikolaev, X. I. Bonilla, L. Parmentier, B. King, F. Bezrukov, G. Kaya, V. Zoete, V. Seplyarskiy, H. Sharpe, T. McKee, A. Letourneau, P. Ribaux, K. Popadin, N. Basset-Seguin, R. Ben Chaabene, F. Santoni, M. Andrianova, M. Guipponi, M. Garieri, C. Verdan, K. Grosdemange, O. Sumara, M. Eilers, I. Aifantis, O. Michielin, F. de Sauvage, S. Antonarakis
O23 Identification of differential biomarkers of hepatocellular carcinoma and cholangiocarcinoma via transcriptome microarray meta-analysis
S. Likhitrattanapisal
O24 Clinical validity and actionability of multigene tests for hereditary cancers in a large multi-center study
S. Lincoln, A. Kurian, A. Desmond, S. Yang, Y. Kobayashi, J. Ford, L. Ellisen
O25 Correlation with tumor ploidy status is essential for correct determination of genome-wide copy number changes by SNP array
T. L. Peters, K. R. Alvarez, E. F. Hollingsworth, D. H. Lopez-Terrada
O26 Nanochannel based next-generation mapping for interrogation of clinically relevant structural variation
A. Hastie, Z. Dzakula, A. W. Pang, E. T. Lam, T. Anantharaman, M. Saghbini, H. Cao, BioNano Genomics
O27 Mutation spectrum in a pulmonary arterial hypertension (PAH) cohort and identification of associated truncating mutations in TBX4
C. Gonzaga-Jauregui, L. Ma, A. King, E. Berman Rosenzweig, U. Krishnan, J. G. Reid, J. D. Overton, F. Dewey, W. K. Chung
O28 NORTH CAROLINA macular dystrophy (MCDR1): mutations found affecting PRDM13
K. Small, A. DeLuca, F. Cremers, R. A. Lewis, V. Puech, B. Bakall, R. Silva-Garcia, K. Rohrschneider, M. Leys, F. S. Shaya, E. Stone
O29 PhenoDB and genematcher, solving unsolved whole exome sequencing data
N. L. Sobreira, F. Schiettecatte, H. Ling, E. Pugh, D. Witmer, K. Hetrick, P. Zhang, K. Doheny, D. Valle, A. Hamosh
O30 Baylor-Johns Hopkins Center for Mendelian genomics: a four year review
S. N. Jhangiani, Z. Coban Akdemir, M. N. Bainbridge, W. Charng, W. Wiszniewski, T. Gambin, E. Karaca, Y. Bayram, M. K. Eldomery, J. Posey, H. Doddapaneni, J. Hu, V. R. Sutton, D. M. Muzny, E. A. Boerwinkle, D. Valle, J. R. Lupski, R. A. Gibbs
O31 Using read overlap assembly to accurately identify structural genetic differences in an ashkenazi jewish trio
S. Shekar, W. Salerno, A. English, A. Mangubat, J. Bruestle
O32 Legal interoperability: a sine qua non for international data sharing
A. Thorogood, B. M. Knoppers, Global Alliance for Genomics and Health - Regulatory and Ethics Working Group
O33 High throughput screening platform of competent sineups: that can enhance translation activities of therapeutic target
H. Takahashi, K. R. Nitta, A. Kozhuharova, A. M. Suzuki, H. Sharma, D. Cotella, C. Santoro, S. Zucchelli, S. Gustincich, P. Carninci
O34 The undiagnosed diseases network international (UDNI): clinical and laboratory research to meet patient needs
J. J. Mulvihill, G. Baynam, W. Gahl, S. C. Groft, K. Kosaki, P. Lasko, B. Melegh, D. Taruscio
O36 Performance of computational algorithms in pathogenicity predictions for activating variants in oncogenes versus loss of function mutations in tumor suppressor genes
R. Ghosh, S. Plon
O37 Identification and electronic health record incorporation of clinically actionable pharmacogenomic variants using prospective targeted sequencing
S. Scherer, X. Qin, R. Sanghvi, K. Walker, T. Chiang, D. Muzny, L. Wang, J. Black, E. Boerwinkle, R. Weinshilboum, R. Gibbs
O38 Melanoma reprogramming state correlates with response to CTLA-4 blockade in metastatic melanoma
T. Karpinets, T. Calderone, K. Wani, X. Yu, C. Creasy, C. Haymaker, M. Forget, V. Nanda, J. Roszik, J. Wargo, L. Haydu, X. Song, A. Lazar, J. Gershenwald, M. Davies, C. Bernatchez, J. Zhang, A. Futreal, S. Woodman
O39 Data-driven refinement of complex disease classification from integration of heterogeneous functional genomics data in GeneWeaver
E. J. Chesler, T. Reynolds, J. A. Bubier, C. Phillips, M. A. Langston, E. J. Baker
O40 A general statistic framework for genome-based disease risk prediction
M. Xiong, L. Ma, N. Lin, C. Amos
O41 Integrative large-scale causal network analysis of imaging and genomic data and its application in schizophrenia studies
N. Lin, P. Wang, Y. Zhu, J. Zhao, V. Calhoun, M. Xiong
O42 Big data and NGS data analysis: the cloud to the rescue
O. Dobretsberger, M. Egger, F. Leimgruber
O43 Cpipe: a convergent clinical exome pipeline specialised for targeted sequencing
S. Sadedin, A. Oshlack, Melbourne Genomics Health Alliance
O44 A Bayesian classification of biomedical images using feature extraction from deep neural networks implemented on lung cancer data
V. A. A. Antonio, N. Ono, Clark Kendrick C. Go
O45 MAV-SEQ: an interactive platform for the Management, Analysis, and Visualization of sequence data
Z. Ahmed, M. Bolisetty, S. Zeeshan, E. Anguiano, D. Ucar
O47 Allele specific enhancer in EPAS1 intronic regions may contribute to high altitude adaptation of Tibetans
C. Zeng, J. Shao
O48 Nanochannel based next-generation mapping for structural variation detection and comparison in trios and populations
H. Cao, A. Hastie, A. W. Pang, E. T. Lam, T. Liang, K. Pham, M. Saghbini, Z. Dzakula
O49 Archaic introgression in indigenous populations of Malaysia revealed by whole genome sequencing
Y. Chee-Wei, L. Dongsheng, W. Lai-Ping, D. Lian, R. O. Twee Hee, Y. Yunus, F. Aghakhanian, S. S. Mokhtar, C. V. Lok-Yung, J. Bhak, M. Phipps, X. Shuhua, T. Yik-Ying, V. Kumar, H. Boon-Peng
O50 Breast and ovarian cancer prevention: is it time for population-based mutation screening of high risk genes?
I. Campbell, M.-A. Young, P. James, Lifepool
O53 Comprehensive coverage from low DNA input using novel NGS library preparation methods for WGS and WGBS
C. Schumacher, S. Sandhu, T. Harkins, V. Makarov
O54 Methods for large scale construction of robust PCR-free libraries for sequencing on Illumina HiSeqX platform
H. DoddapaneniR. Glenn, Z. Momin, B. Dilrukshi, H. Chao, Q. Meng, B. Gudenkauf, R. Kshitij, J. Jayaseelan, C. Nessner, S. Lee, K. Blankenberg, L. Lewis, J. Hu, Y. Han, H. Dinh, S. Jireh, K. Walker, E. Boerwinkle, D. Muzny, R. Gibbs
O55 Rapid capture methods for clinical sequencing
J. Hu, K. Walker, C. Buhay, X. Liu, Q. Wang, R. Sanghvi, H. Doddapaneni, Y. Ding, N. Veeraraghavan, Y. Yang, E. Boerwinkle, A. L. Beaudet, C. M. Eng, D. M. Muzny, R. A. Gibbs
O56 A diploid personal human genome model for better genomes from diverse sequence data
K. C. C. Worley, Y. Liu, D. S. T. Hughes, S. C. Murali, R. A. Harris, A. C. English, X. Qin, O. A. Hampton, P. Larsen, C. Beck, Y. Han, M. Wang, H. Doddapaneni, C. L. Kovar, W. J. Salerno, A. Yoder, S. Richards, J. Rogers, J. R. Lupski, D. M. Muzny, R. A. Gibbs
O57 Development of PacBio long range capture for detection of pathogenic structural variants
Q. Meng, M. Bainbridge, M. Wang, H. Doddapaneni, Y. Han, D. Muzny, R. Gibbs
O58 Rhesus macaques exhibit more non-synonymous variation but greater impact of purifying selection than humans
R. A. Harris, M. Raveenedran, C. Xue, M. Dahdouli, L. Cox, G. Fan, B. Ferguson, J. Hovarth, Z. Johnson, S. Kanthaswamy, M. Kubisch, M. Platt, D. Smith, E. Vallender, R. Wiseman, X. Liu, J. Below, D. Muzny, R. Gibbs, F. Yu, J. Rogers
O59 Assessing RNA structure disruption induced by single-nucleotide variation
J. Lin, Y. Zhang, Z. Ouyang
P1 A meta-analysis of genome-wide association studies of mitochondrial dna copy number
A. Moore, Z. Wang, J. Hofmann, M. Purdue, R. Stolzenberg-Solomon, S. Weinstein, D. Albanes, C.-S. Liu, W.-L. Cheng, T.-T. Lin, Q. Lan, N. Rothman, S. Berndt
P2 Missense polymorphic genetic combinations underlying down syndrome susceptibility
E. S. Chen
P4 The evaluation of alteration of ELAM-1 expression in the endometriosis patients
H. Bahrami, A. Khoshzaban, S. Heidari Keshal
P5 Obesity and the incidence of apolipoprotein E polymorphisms in an assorted population from Saudi Arabia population
K. K. R. Alharbi
P6 Genome-associated personalized antithrombotical therapy for patients with high risk of thrombosis and bleeding
M. Zhalbinova, A. Akilzhanova, S. Rakhimova, M. Bekbosynova, S. Myrzakhmetova
P7 Frequency of Xmn1 polymorphism among sickle cell carrier cases in UAE population
M. Matar
P8 Differentiating inflammatory bowel diseases by using genomic data: dimension of the problem and network organization
N. Mili, R. Molinari, Y. Ma, S. Guerrier
P9 Vulnerability of genetic variants to the risk of autism among Saudi children
N. Elhawary, M. Tayeb, N. Bogari, N. Qotb
P10 Chromatin profiles from ex vivo purified dopaminergic neurons establish a promising model to support studies of neurological function and dysfunction
S. A. McClymont, P. W. Hook, L. A. Goff, A. McCallion
P11 Utilization of a sensitized chemical mutagenesis screen to identify genetic modifiers of retinal dysplasia in homozygous Nr2e3rd7 mice
Y. Kong, J. R. Charette, W. L. Hicks, J. K. Naggert, L. Zhao, P. M. Nishina
P12 Ion torrent next generation sequencing of recessive polycystic kidney disease in Saudi patients
B. M. Edrees, M. Athar, F. A. Al-Allaf, M. M. Taher, W. Khan, A. Bouazzaoui, N. A. Harbi, R. Safar, H. Al-Edressi, A. Anazi, N. Altayeb, M. A. Ahmed, K. Alansary, Z. Abduljaleel
P13 Digital expression profiling of Purkinje neurons and dendrites in different subcellular compartments
A. Kratz, P. Beguin, S. Poulain, M. Kaneko, C. Takahiko, A. Matsunaga, S. Kato, A. M. Suzuki, N. Bertin, T. Lassmann, R. Vigot, P. Carninci, C. Plessy, T. Launey
P14 The evolution of imperfection and imperfection of evolution: the functional and functionless fractions of the human genome
D. Graur
P16 Species-independent identification of known and novel recurrent genomic entities in multiple cancer patients
J. Friis-Nielsen, J. M. Izarzugaza, S. Brunak
P18 Discovery of active gene modules which are densely conserved across multiple cancer types reveal their prognostic power and mutually exclusive mutation patterns
B. S. Soibam
P19 Whole exome sequencing of dysplastic leukoplakia tissue indicates sequential accumulation of somatic mutations from oral precancer to cancer
D. Das, N. Biswas, S. Das, S. Sarkar, A. Maitra, C. Panda, P. Majumder
P21 Epigenetic mechanisms of carcinogensis by hereditary breast cancer genes
J. J. Gruber, N. Jaeger, M. Snyder
P22 RNA direct: a novel RNA enrichment strategy applied to transcripts associated with solid tumors
K. Patel, S. Bowman, T. Davis, D. Kraushaar, A. Emerman, S. Russello, N. Henig, C. Hendrickson
P23 RNA sequencing identifies gene mutations for neuroblastoma
K. Zhang
P24 Participation of SFRP1 in the modulation of TMPRSS2-ERG fusion gene in prostate cancer cell lines
M. Rodriguez-Dorantes, C. D. Cruz-Hernandez, C. D. P. Garcia-Tobilla, S. Solorzano-Rosales
P25 Targeted Methylation Sequencing of Prostate Cancer
N. Jäger, J. Chen, R. Haile, M. Hitchins, J. D. Brooks, M. Snyder
P26 Mutant TPMT alleles in children with acute lymphoblastic leukemia from México City and Yucatán, Mexico
S. Jiménez-Morales, M. Ramírez, J. Nuñez, V. Bekker, Y. Leal, E. Jiménez, A. Medina, A. Hidalgo, J. Mejía
P28 Genetic modifiers of Alström syndrome
J. Naggert, G. B. Collin, K. DeMauro, R. Hanusek, P. M. Nishina
P31 Association of genomic variants with the occurrence of angiotensin-converting-enzyme inhibitor (ACEI)-induced coughing among Filipinos
E. M. Cutiongco De La Paz, R. Sy, J. Nevado, P. Reganit, L. Santos, J. D. Magno, F. E. Punzalan , D. Ona , E. Llanes, R. L. Santos-Cortes , R. Tiongco, J. Aherrera, L. Abrahan, P. Pagauitan-Alan; Philippine Cardiogenomics Study Group
P32 The use of “humanized” mouse models to validate disease association of a de novo GARS variant and to test a novel gene therapy strategy for Charcot-Marie-Tooth disease type 2D
K. H. Morelli, J. S. Domire, N. Pyne, S. Harper, R. Burgess
P34 Molecular regulation of chondrogenic human induced pluripotent stem cells
M. A. Gari, A. Dallol, H. Alsehli, A. Gari, M. Gari, A. Abuzenadah
P35 Molecular profiling of hematologic malignancies: implementation of a variant assessment algorithm for next generation sequencing data analysis and clinical reporting
M. Thomas, M. Sukhai, S. Garg, M. Misyura, T. Zhang, A. Schuh, T. Stockley, S. Kamel-Reid
P36 Accessing genomic evidence for clinical variants at NCBI
S. Sherry, C. Xiao, D. Slotta, K. Rodarmer, M. Feolo, M. Kimelman, G. Godynskiy, C. O’Sullivan, E. Yaschenko
P37 NGS-SWIFT: a cloud-based variant analysis framework using control-accessed sequencing data from DBGAP/SRA
C. Xiao, E. Yaschenko, S. Sherry
P38 Computational assessment of drug induced hepatotoxicity through gene expression profiling
C. Rangel-Escareño, H. Rueda-Zarate
P40 Flowr: robust and efficient pipelines using a simple language-agnostic approach;ultraseq; fast modular pipeline for somatic variation calling using flowr
S. Seth, S. Amin, X. Song, X. Mao, H. Sun, R. G. Verhaak, A. Futreal, J. Zhang
P41 Applying “Big data” technologies to the rapid analysis of heterogenous large cohort data
S. J. Whiite, T. Chiang, A. English, J. Farek, Z. Kahn, W. Salerno, N. Veeraraghavan, E. Boerwinkle, R. Gibbs
P42 FANTOM5 web resource for the large-scale genome-wide transcription start site activity profiles of wide-range of mammalian cells
T. Kasukawa, M. Lizio, J. Harshbarger, S. Hisashi, J. Severin, A. Imad, S. Sahin, T. C. Freeman, K. Baillie, A. Sandelin, P. Carninci, A. R. R. Forrest, H. Kawaji, The FANTOM Consortium
P43 Rapid and scalable typing of structural variants for disease cohorts
W. Salerno, A. English, S. N. Shekar, A. Mangubat, J. Bruestle, E. Boerwinkle, R. A. Gibbs
P44 Polymorphism of glutathione S-transferases and sulphotransferases genes in an Arab population
A. H. Salem, M. Ali, A. Ibrahim, M. Ibrahim
P46 Genetic divergence of CYP3A5*3 pharmacogenomic marker for native and admixed Mexican populations
J. C. Fernandez-Lopez, V. Bonifaz-Peña, C. Rangel-Escareño, A. Hidalgo-Miranda, A. V. Contreras
P47 Whole exome sequence meta-analysis of 13 white blood cell, red blood cell, and platelet traits
L. Polfus, CHARGE and NHLBI Exome Sequence Project Working Groups
P48 Association of adipoq gene with type 2 diabetes and related phenotypes in african american men and women: The jackson heart study
S. Davis, R. Xu, S. Gebeab, P Riestra, A Gaye, R. Khan, J. Wilson, A. Bidulescu
P49 Common variants in casr gene are associated with serum calcium levels in koreans
S. H. Jung, N. Vinayagamoorthy, S. H. Yim, Y. J. Chung
P50 Inference of multiple-wave population admixture by modeling decay of linkage disequilibrium with multiple exponential functions
Y. Zhou, S. Xu
P51 A Bayesian framework for generalized linear mixed models in genome-wide association studies
X. Wang, V. Philip, G. Carter
P52 Targeted sequencing approach for the identification of the genetic causes of hereditary hearing impairment
A. A. Abuzenadah, M. Gari, R. Turki, A. Dallol
P53 Identification of enhancer sequences by ATAC-seq open chromatin profiling
A. Uyar, A. Kaygun, S. Zaman, E. Marquez, J. George, D. Ucar
P54 Direct enrichment for the rapid preparation of targeted NGS libraries
C. L. Hendrickson, A. Emerman, D. Kraushaar, S. Bowman, N. Henig, T. Davis, S. Russello, K. Patel
P56 Performance of the Agilent D5000 and High Sensitivity D5000 ScreenTape assays for the Agilent 4200 Tapestation System
R. Nitsche, L. Prieto-Lafuente
P57 ClinVar: a multi-source archive for variant interpretation
M. Landrum, J. Lee, W. Rubinstein, D. Maglott
P59 Association of functional variants and protein physical interactions of human MUTY homolog linked with familial adenomatous polyposis and colorectal cancer syndrome
Z. Abduljaleel, W. Khan, F. A. Al-Allaf, M. Athar , M. M. Taher, N. Shahzad
P60 Modification of the microbiom constitution in the gut using chicken IgY antibodies resulted in a reduction of acute graft-versus-host disease after experimental bone marrow transplantation
A. Bouazzaoui, E. Huber, A. Dan, F. A. Al-Allaf, W. Herr, G. Sprotte, J. Köstler, A. Hiergeist, A. Gessner, R. Andreesen, E. Holler
P61 Compound heterozygous mutation in the LDLR gene in Saudi patients suffering severe hypercholesterolemia
F. Al-Allaf, A. Alashwal, Z. Abduljaleel, M. Taher, A. Bouazzaoui, H. Abalkhail, A. Al-Allaf, R. Bamardadh, M. Athar
doi:10.1186/s40246-016-0063-5
PMCID: PMC4896275  PMID: 27294413
7.  Survival-Related Profile, Pathways, and Transcription Factors in Ovarian Cancer 
PLoS Medicine  2009;6(2):e1000024.
Background
Ovarian cancer has a poor prognosis due to advanced stage at presentation and either intrinsic or acquired resistance to classic cytotoxic drugs such as platinum and taxoids. Recent large clinical trials with different combinations and sequences of classic cytotoxic drugs indicate that further significant improvement in prognosis by this type of drugs is not to be expected. Currently a large number of drugs, targeting dysregulated molecular pathways in cancer cells have been developed and are introduced in the clinic. A major challenge is to identify those patients who will benefit from drugs targeting these specific dysregulated pathways.The aims of our study were (1) to develop a gene expression profile associated with overall survival in advanced stage serous ovarian cancer, (2) to assess the association of pathways and transcription factors with overall survival, and (3) to validate our identified profile and pathways/transcription factors in an independent set of ovarian cancers.
Methods and Findings
According to a randomized design, profiling of 157 advanced stage serous ovarian cancers was performed in duplicate using ∼35,000 70-mer oligonucleotide microarrays. A continuous predictor of overall survival was built taking into account well-known issues in microarray analysis, such as multiple testing and overfitting. A functional class scoring analysis was utilized to assess pathways/transcription factors for their association with overall survival. The prognostic value of genes that constitute our overall survival profile was validated on a fully independent, publicly available dataset of 118 well-defined primary serous ovarian cancers. Furthermore, functional class scoring analysis was also performed on this independent dataset to assess the similarities with results from our own dataset. An 86-gene overall survival profile discriminated between patients with unfavorable and favorable prognosis (median survival, 19 versus 41 mo, respectively; permutation p-value of log-rank statistic = 0.015) and maintained its independent prognostic value in multivariate analysis. Genes that composed the overall survival profile were also able to discriminate between the two risk groups in the independent dataset. In our dataset 17/167 pathways and 13/111 transcription factors were associated with overall survival, of which 16 and 12, respectively, were confirmed in the independent dataset.
Conclusions
Our study provides new clues to genes, pathways, and transcription factors that contribute to the clinical outcome of serous ovarian cancer and might be exploited in designing new treatment strategies.
Ate van der Zee and colleagues analyze the gene expression profiles of ovarian cancer samples from 157 patients, and identify an 86-gene expression profile that seems to predict overall survival.
Editors' Summary
Background.
Ovarian cancer kills more than 100,000 women every year and is one of the most frequent causes of cancer death in women in Western countries. Most ovarian cancers develop when an epithelial cell in one of the ovaries (two small organs in the pelvis that produce eggs) acquires genetic changes that allow it to grow uncontrollably and to spread around the body (metastasize). In its early stages, ovarian cancer is confined to the ovaries and can often be treated successfully by surgery alone. Unfortunately, early ovarian cancer rarely has symptoms so a third of women with ovarian cancer have advanced disease when they first visit their doctor with symptoms that include vague abdominal pains and mild digestive disturbances. That is, cancer cells have spread into their abdominal cavity and metastasized to other parts of the body (so-called stage III and IV disease). The outlook for women diagnosed with stage III and IV disease, which are treated with a combination of surgery and chemotherapy, is very poor. Only 30% of women with stage III, and 5% with stage IV, are still alive five years after their cancer is diagnosed.
Why Was This Study Done?
If the cellular pathways that determine the biological behavior of ovarian cancer could be identified, it might be possible to develop more effective treatments for women with stage III and IV disease. One way to identify these pathways is to use gene expression profiling (a technique that catalogs all the genes expressed by a cell) to compare gene expression patterns in the ovarian cancers of women who survive for different lengths of time. Genes with different expression levels in tumors with different outcomes could be targets for new treatments. For example, it might be worth developing inhibitors of proteins whose expression is greatest in tumors with short survival times. In this study, the researchers develop an expression profile that is associated with overall survival in advanced-stage serous ovarian cancer (more than half of ovarian cancers originate in serous cells, epithelial cells that secrete a watery fluid). The researchers also assess the association of various cellular pathways and transcription factors (proteins that control the expression of other proteins) with survival in this type of ovarian carcinoma.
What Did the Researchers Do and Find?
The researchers analyzed the gene expression profiles of tumor samples taken from 157 patients with advanced stage serous ovarian cancer and used the “supervised principal components” method to build a predictor of overall survival from these profiles and patient survival times. This 86-gene predictor discriminated between patients with favorable and unfavorable outcomes (average survival times of 41 and 19 months, respectively). It also discriminated between groups of patients with these two outcomes in an independent dataset collected from 118 additional serous ovarian cancers. Next, the researchers used “functional class scoring” analysis to assess the association between pathway and transcription factor expression in the tumor samples and overall survival. Seventeen of 167 KEGG pathways (“wiring” diagrams of molecular interactions, reactions and relations involved in cellular processes and human diseases listed in the Kyoto Encyclopedia of Genes and Genomes) were associated with survival, 16 of which were confirmed in the independent dataset. Finally, 13 of 111 analyzed transcription factors were associated with overall survival in the tumor samples, 12 of which were confirmed in the independent dataset.
What Do These Findings Mean?
These findings identify an 86-gene overall survival gene expression profile that seems to predict overall survival for women with advanced serous ovarian cancer. However, before this profile can be used clinically, further validation of the profile and more robust methods for determining gene expression profiles are needed. Importantly, these findings also provide new clues about the genes, pathways and transcription factors that contribute to the clinical outcome of serous ovarian cancer, clues that can now be exploited in the search for new treatment strategies. Finally, these findings suggest that it might eventually be possible to tailor therapies to the needs of individual patients by analyzing which pathways are activated in their tumors and thus improve survival times for women with advanced ovarian cancer.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000024.
This study is further discussed in a PLoS Medicine Perspective by Simon Gayther and Kate Lawrenson
See also a related PLoS Medicine Research Article by Huntsman and colleagues
The US National Cancer Institute provides a brief description of what cancer is and how it develops, and information on all aspects of ovarian cancer for patients and professionals (in English and Spanish)
The UK charity Cancerbackup provides general information about cancer, and more specific information about ovarian cancer
MedlinePlus also provides links to other information about ovarian cancer (in English and Spanish)
The KEGG Pathway database provides pathway maps of known molecular networks involved in a wide range of cellular processes
doi:10.1371/journal.pmed.1000024
PMCID: PMC2634794  PMID: 19192944
8.  DMAP: a connectivity map database to enable identification of novel drug repositioning candidates 
BMC Bioinformatics  2015;16(Suppl 13):S4.
Background
Drug repositioning is a cost-efficient and time-saving process to drug development compared to traditional techniques. A systematic method to drug repositioning is to identify candidate drug's gene expression profiles on target disease models and determine how similar these profiles are to approved drugs. Databases such as the CMAP have been developed recently to help with systematic drug repositioning.
Methods
To overcome the limitation of connectivity maps on data coverage, we constructed a comprehensive in silico drug-protein connectivity map called DMAP, which contains directed drug-to-protein effects and effect scores. The drug-to-protein effect scores are compiled from all database entries between the drug and protein have been previously observed and provide a confidence measure on the quality of such drug-to-protein effects.
Results
In DMAP, we have compiled the direct effects between 24,121 PubChem Compound ID (CID), which were mapped from 289,571 chemical entities recognized from public literature, and 5,196 reviewed Uniprot proteins. DMAP compiles a total of 438,004 chemical-to-protein effect relationships. Compared to CMAP, DMAP shows an increase of 221 folds in the number of chemicals and 1.92 fold in the number of ATC codes. Furthermore, by overlapping DMAP chemicals with the approved drugs with known indications from the TTD database and literature, we obtained 982 drugs and 622 diseases; meanwhile, we only obtained 394 drugs with known indication from CMAP. To validate the feasibility of applying new DMAP for systematic drug repositioning, we compared the performance of DMAP and the well-known CMAP database on two popular computational techniques: drug-drug-similarity-based method with leave-one-out validation and Kolmogorov-Smirnov scoring based method. In drug-drug-similarity-based method, the drug repositioning prediction using DMAP achieved an Area-Under-Curve (AUC) score of 0.82, compared with that using CMAP, AUC = 0.64. For Kolmogorov-Smirnov scoring based method, with DMAP, we were able to retrieve several drug indications which could not be retrieved using CMAP. DMAP data can be queried using the existing C2MAP server or downloaded freely at: http://bio.informatics.iupui.edu/cmaps
Conclusions
Reliable measurements of how drug affect disease-related proteins are critical to ongoing drug development in the genome medicine era. We demonstrated that DMAP can help drug development professionals assess drug-to-protein relationship data and improve chances of success for systematic drug repositioning efforts.
doi:10.1186/1471-2105-16-S13-S4
PMCID: PMC4597058  PMID: 26423722
9.  Asporin Is a Fibroblast-Derived TGF-β1 Inhibitor and a Tumor Suppressor Associated with Good Prognosis in Breast Cancer 
PLoS Medicine  2015;12(9):e1001871.
Background
Breast cancer is a leading malignancy affecting the female population worldwide. Most morbidity is caused by metastases that remain incurable to date. TGF-β1 has been identified as a key driving force behind metastatic breast cancer, with promising therapeutic implications.
Methods and Findings
Employing immunohistochemistry (IHC) analysis, we report, to our knowledge for the first time, that asporin is overexpressed in the stroma of most human breast cancers and is not expressed in normal breast tissue. In vitro, asporin is secreted by breast fibroblasts upon exposure to conditioned medium from some but not all human breast cancer cells. While hormone receptor (HR) positive cells cause strong asporin expression, triple-negative breast cancer (TNBC) cells suppress it. Further, our findings show that soluble IL-1β, secreted by TNBC cells, is responsible for inhibiting asporin in normal and cancer-associated fibroblasts. Using recombinant protein, as well as a synthetic peptide fragment, we demonstrate the ability of asporin to inhibit TGF-β1-mediated SMAD2 phosphorylation, epithelial to mesenchymal transition, and stemness in breast cancer cells. In two in vivo murine models of TNBC, we observed that tumors expressing asporin exhibit significantly reduced growth (2-fold; p = 0.01) and metastatic properties (3-fold; p = 0.045). A retrospective IHC study performed on human breast carcinoma (n = 180) demonstrates that asporin expression is lowest in TNBC and HER2+ tumors, while HR+ tumors have significantly higher asporin expression (4-fold; p = 0.001). Assessment of asporin expression and patient outcome (n = 60; 10-y follow-up) shows that low protein levels in the primary breast lesion significantly delineate patients with bad outcome regardless of the tumor HR status (area under the curve = 0.87; 95% CI 0.78–0.96; p = 0.0001). Survival analysis, based on gene expression (n = 375; 25-y follow-up), confirmed that low asporin levels are associated with a reduced likelihood of survival (hazard ratio = 0.58; 95% CI 0.37–0.91; p = 0.017). Although these data highlight the potential of asporin to serve as a prognostic marker, confirmation of the clinical value would require a prospective study on a much larger patient cohort.
Conclusions
Our data show that asporin is a stroma-derived inhibitor of TGF-β1 and a tumor suppressor in breast cancer. High asporin expression is significantly associated with less aggressive tumors, stratifying patients according to the clinical outcome. Future pre-clinical studies should consider options for increasing asporin expression in TNBC as a promising strategy for targeted therapy.
Andrei Turtoi and colleagues describe a mechanistic role for stroma-derived asporin in breast cancer development.
Editors' Summary
Background
Breast cancer is the most common cancer in women worldwide. Nearly 1.7 million new cases were diagnosed in 2012, and half a million women died from the disease. Breast cancer begins when cells in the breast that normally make milk (epithelial cells) acquire genetic changes that allow them to divide uncontrollably and to move around the body (metastasize). Uncontrolled cell division leads to the formation of a lump that can be detected by mammography (a breast X-ray) or by manual breast examination. Breast cancer is treated by surgical removal of the lump or, if the cancer has started to spread, by removal of the whole breast (mastectomy). After surgery, women often receive chemotherapy or radiotherapy to kill any remaining cancer cells, and women whose tumors express receptors for the female sex hormones estrogen and progesterone or for HER2, a growth factor receptor, are treated with drugs that block these receptors; estrogen, progesterone, and HER2 all control breast cell growth. Nowadays, the prognosis (outlook) for women living in high-income countries who develop breast cancer is generally good—nearly 90% of such women are still alive five years after diagnosis.
Why Was This Study Done?
The cells surrounding cancer cells—cancer-associated fibroblasts and other components of the stroma—support cancer growth and metastasis and are good targets for new cancer therapies. But, although there is mounting evidence that cancer cells actively adapt the stroma so that it produces various factors the tumor needs to grow and spread, very few molecules produced by the stroma that might serve as targets for drug development have been identified. Here, the researchers investigate whether a molecule called asporin might represent one such target. Asporin, which is highly expressed in the stroma of breast tumors, inhibits a growth factor called TGF-β1. TGF-β1 is involved in maintaining healthy joints, but is also a key molecule in the development of metastatic breast cancer. Most particularly, it modulates an important step in metastasis called the epithelial to mesenchymal transition and it regulates “stemness” in cancer cells. Stem cells are a special type of cell that can multiply indefinitely; tumor cells often look and behave very much like stem cells.
What Did the Researchers Do and Find?
Using a technique called immunohistochemistry, the researchers first showed that asporin is highly expressed in the stroma of most human breast cancers but not in normal breast tissue. Next, they showed that breast fibroblasts secrete asporin when exposed to conditioned medium from some human breast cancer cell lines (breast cancer cells adapted to grow continuously in the laboratory; conditioned medium is the solution in which cells have been grown). Specifically, conditioned medium from hormone receptor positive cells induced strong asporin expression by breast fibroblasts, whereas medium from breast cancer cells not expressing estrogen or progesterone receptors or HER2 receptors (triple-negative breast cancer cells) suppressed asporin expression. Other experiments showed that TGF-β1 secreted by breast cancer cells induces asporin expression in breast fibroblasts, and that asporin, in turn, inhibits TGF-β1-mediated induction of the epithelial to mesenchymal transition and stemness in breast cancer cells. Triple negative breast cancers appear to inhibit stromal expression of asporin at least in part via expression of the soluble signaling protein interleukin-1β. Notably, in mouse models of triple-negative breast cancer, tumors engineered to express asporin grew slower and metastasized less than tumors not expressing asporin. Finally, among women with breast cancer, asporin expression was low in triple-negative and HER2-positive tumors but significantly higher in hormone receptor positive tumors, and low asporin levels in primary breast lesions were associated with a reduced likelihood of survival independent of hormone receptor and HER2 expression.
What Do These Findings Mean?
These findings suggest that asporin is a stroma-derived inhibitor of TGF-β1 and a tumor suppressor in breast cancer. Importantly, they also provide preliminary evidence that high asporin expression is associated with less aggressive tumors (hormone receptor positive tumors), whereas low asporin expression is associated with more aggressive tumors (triple negative tumors and HER2-positive tumors). Thus, asporin expression might provide a new prognostic marker for breast cancer. However, before asporin can be used as a biomarker to predict outcomes in women with breast cancer and to identify those women in need of more aggressive treatment, these findings need to be confirmed in large prospective clinical studies. If these findings are confirmed, methods for increasing asporin expression in the stromal tissues of triple negative breast cancer could be a promising strategy for targeted therapy for this group of breast cancers, which currently have a poor prognosis.
Additional Information
This list of resources contains links that can be accessed when viewing the PDF on a device or via the online version of the article at http://dx.doi.org/10.1371/journal.pmed.1001871.
The US National Cancer Institute provides comprehensive information about cancer (in English and Spanish), including detailed information for patients and professionals about breast cancer and an online booklet for patients
Cancer Research UK, a not-for-profit organization, provides information about cancer; its detailed information about breast cancer includes sections on tests for hormone receptors and HER2, on treatments that target hormone receptors and treatments that target HER2, and on triple negative breast cancer
Breastcancer.org is a not-for-profit organization that provides up-to-date information about breast cancer (in English and Spanish), including information on hormone receptor status, HER2 status, and triple negative breast cancer
The UK National Health Service Choices website has information and personal stories about breast cancer; the not-for-profit organization Healthtalk.org also provides personal stories about dealing with breast cancer
doi:10.1371/journal.pmed.1001871
PMCID: PMC4556693  PMID: 26327350
10.  Cancer Screening with Digital Mammography for Women at Average Risk for Breast Cancer, Magnetic Resonance Imaging (MRI) for Women at High Risk 
Executive Summary
Objective
The purpose of this review is to determine the effectiveness of 2 separate modalities, digital mammography (DM) and magnetic resonance imaging (MRI), relative to film mammography (FM), in the screening of women asymptomatic for breast cancer. A third analysis assesses the effectiveness and safety of the combination of MRI plus mammography (MRI plus FM) in screening of women at high risk. An economic analysis was also conducted.
Research Questions
How does the sensitivity and specificity of DM compare to FM?
How does the sensitivity and specificity of MRI compare to FM?
How do the recall rates compare among these screening modalities, and what effect might this have on radiation exposure? What are the risks associated with radiation exposure?
How does the sensitivity and specificity of the combination of MRI plus FM compare to either MRI or FM alone?
What are the economic considerations?
Clinical Need
The effectiveness of FM with respect to breast cancer mortality in the screening of asymptomatic average- risk women over the age of 50 has been established. However, based on a Medical Advisory Secretariat review completed in March 2006, screening is not recommended for women between the ages of 40 and 49 years. Guidelines published by the Canadian Task Force on Preventive Care recommend mammography screening every 1 to 2 years for women aged 50 years and over, hence, the inclusion of such women in organized breast cancer screening programs. In addition to the uncertainty of the effectiveness of mammography screening from the age of 40 years, there is concern over the risks associated with mammographic screening for the 10 years between the ages of 40 and 49 years.
The lack of effectiveness of mammography screening starting at the age of 40 years (with respect to breast cancer mortality) is based on the assumption that the ability to detect cancer decreases with increased breast tissue density. As breast density is highest in the premenopausal years (approximately 23% of postmenopausal and 53% of premenopausal women having at least 50% of the breast occupied by high density), mammography screening is not promoted in Canada nor in many other countries for women under the age of 50 at average risk for breast cancer. It is important to note, however, that screening of premenopausal women (i.e., younger than 50 years of age) at high risk for breast cancer by virtue of a family history of cancer or a known genetic predisposition (e.g., having tested positive for the breast cancer genes BRCA1 and/or BRCA2) is appropriate. Thus, this review will assess the effectiveness of breast cancer screening with modalities other than film mammography, specifically DM and MRI, for both pre/perimenopausal and postmenopausal age groups.
International estimates of the epidemiology of breast cancer show that the incidence of breast cancer is increasing for all ages combined whereas mortality is decreasing, though at a slower rate. The observed decreases in mortality rates may be attributable to screening, in addition to advances in breast cancer therapy over time. Decreases in mortality attributable to screening may be a result of the earlier detection and treatment of invasive cancers, in addition to the increased detection of ductal carcinoma in situ (DCIS), of which certain subpathologies are less lethal. Evidence from the Surveillance, Epidemiology and End Results (better known as SEER) cancer registry in the United States, indicates that the age-adjusted incidence of DCIS has increased almost 10-fold over a 20 year period, from 2.7 to 25 per 100,000.
There is a 4-fold lower incidence of breast cancer in the 40 to 49 year age group than in the 50 to 69 year age group (approximately 140 per 100,000 versus 500 per 100,000 women, respectively). The sensitivity of FM is also lower among younger women (approximately 75%) than for women aged over 50 years (approximately 85%). Specificity is approximately 80% for younger women versus 90% for women over 50 years. The increased density of breast tissue in younger women is likely responsible for the decreased accuracy of FM.
Treatment options for breast cancer vary with the stage of disease (based on tumor size, involvement of surrounding tissue, and number of affected axillary lymph nodes) and its pathology, and may include a combination of surgery, chemotherapy and/or radiotherapy. Surgery is the first-line intervention for biopsy-confirmed tumors. The subsequent use of radiation, chemotherapy or hormonal treatments is dependent on the histopathologic characteristics of the tumor and the type of surgery. There is controversy regarding the optimal treatment of DCIS, which is considered a noninvasive tumour.
Women at high risk for breast cancer are defined as genetic carriers of the more commonly known breast cancer genes (BRCA1, BRCA2 TP53), first degree relatives of carriers, women with varying degrees of high risk family histories, and/or women with greater than 20% lifetime risk for breast cancer based on existing risk models. Genetic carriers for this disease, primarily women with BRCA1 or BRCA2 mutations, have a lifetime probability of approximately 85% of developing breast cancer. Preventive options for these women include surgical interventions such as prophylactic mastectomy and/or oophorectomy, i.e., removal of the breasts and/or ovaries. Therefore, it is important to evaluate the benefits and risks of different screening modalities, to identify additional options for these women.
This Medical Advisory Secretariat review is the second of 2 parts on breast cancer screening, and concentrates on the evaluation of both DM and MRI relative to FM, the standard of care. Part I of this review (March 2006) addressed the effectiveness of screening mammography in 40 to 49 year old average-risk women. The overall objective of the present review is to determine the optimal screening modality based on the evidence.
Evidence Review Strategy
The Medical Advisory Secretariat followed its standard procedures and searched the following electronic databases: Ovid MEDLINE, EMBASE, Ovid MEDLINE In-Process & Other Non-Indexed Citations, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews and The International Network of Agencies for Health Technology Assessment database. The subject headings and keywords searched included breast cancer, breast neoplasms, mass screening, digital mammography, magnetic resonance imaging. The detailed search strategies can be viewed in Appendix 1.
Included in this review are articles specific to screening and do not include evidence on diagnostic mammography. The search was further restricted to English-language articles published between January 1996 and April 2006. Excluded were case reports, comments, editorials, nonsystematic reviews, and letters.
Digital Mammography: In total, 224 articles specific to DM screening were identified. These were examined against the inclusion/exclusion criteria described below, resulting in the selection and review of 5 health technology assessments (HTAs) (plus 1 update) and 4 articles specific to screening with DM.
Magnetic Resonance Imaging: In total, 193 articles specific to MRI were identified. These were examined against the inclusion/exclusion criteria described below, resulting in the selection and review of 2 HTAs and 7 articles specific to screening with MRI.
The evaluation of the addition of FM to MRI in the screening of women at high risk for breast cancer was also conducted within the context of standard search procedures of the Medical Advisory Secretariat. as outlined above. The subject headings and keywords searched included the concepts of breast cancer, magnetic resonance imaging, mass screening, and high risk/predisposition to breast cancer. The search was further restricted to English-language articles published between September 2007 and January 15, 2010. Case reports, comments, editorials, nonsystematic reviews, and letters were not excluded.
MRI plus mammography: In total, 243 articles specific to MRI plus FM screening were identified. These were examined against the inclusion/exclusion criteria described below, resulting in the selection and review of 2 previous HTAs, and 1 systematic review of 11 paired design studies.
Inclusion Criteria
English-language articles, and English or French-language HTAs published from January 1996 to April 2006, inclusive.
Articles specific to screening of women with no personal history of breast cancer.
Studies in which DM or MRI were compared with FM, and where the specific outcomes of interest were reported.
Randomized controlled trials (RCTs) or paired studies only for assessment of DM.
Prospective, paired studies only for assessment of MRI.
Exclusion Criteria
Studies in which outcomes were not specific to those of interest in this report.
Studies in which women had been previously diagnosed with breast cancer.
Studies in which the intervention (DM or MRI) was not compared with FM.
Studies assessing DM with a sample size of less than 500.
Intervention
Digital mammography.
Magnetic resonance imaging.
Comparator
Screening with film mammography.
Outcomes of Interest
Breast cancer mortality (although no studies were found with such long follow-up).
Sensitivity.
Specificity.
Recall rates.
Summary of Findings
Digital Mammography
There is moderate quality evidence that DM is significantly more sensitive than FM in the screening of asymptomatic women aged less than 50 years, those who are premenopausal or perimenopausal, and those with heterogeneously or extremely dense breast tissue (regardless of age).
It is not known what effect these differences in sensitivity will have on the more important effectiveness outcome measure of breast cancer mortality, as there was no evidence of such an assessment.
Other factors have been set out to promote DM, for example, issues of recall rates and reading and examination times. Our analysis did not show that recall rates were necessarily improved in DM, though examination times were lower than for FM. Other factors including storage and retrieval of screens were not the subject of this analysis.
Magnetic Resonance Imaging
There is moderate quality evidence that the sensitivity of MRI is significantly higher than that of FM in the screening of women at high risk for breast cancer based on genetic or familial factors, regardless of age.
Radiation Risk Review
Cancer Care Ontario conducted a review of the evidence on radiation risk in screening with mammography women at high risk for breast cancer. From this review of recent literature and risk assessment that considered the potential impact of screening mammography in cohorts of women who start screening at an earlier age or who are at increased risk of developing breast cancer due to genetic susceptibility, the following conclusions can be drawn:
For women over 50 years of age, the benefits of mammography greatly outweigh the risk of radiation-induced breast cancer irrespective of the level of a woman’s inherent breast cancer risk.
Annual mammography for women aged 30 – 39 years who carry a breast cancer susceptibility gene or who have a strong family breast cancer history (defined as a first degree relative diagnosed in their thirties) has a favourable benefit:risk ratio. Mammography is estimated to detect 16 to 18 breast cancer cases for every one induced by radiation (Table 1). Initiation of screening at age 35 for this same group would increase the benefit:risk ratio to an even more favourable level of 34-50 cases detected for each one potentially induced.
Mammography for women under 30 years of age has an unfavourable benefit:risk ratio due to the challenges of detecting cancer in younger breasts, the aggressiveness of cancers at this age, the potential for radiation susceptibility at younger ages and a greater cumulative radiation exposure.
Mammography when used in combination with MRI for women who carry a strong breast cancer susceptibility (e.g., BRCA1/2 carriers), which if begun at age 35 and continued for 35 years, may confer greatly improved benefit:risk ratios which were estimated to be about 220 to one.
While there is considerable uncertainty in the risk of radiation-induced breast cancer, the risk expressed in published studies is almost certainly conservative as the radiation dose absorbed by women receiving mammography recently has been substantially reduced by newer technology.
A CCO update of the mammography radiation risk literature for 2008 and 2009 gave rise to one article by Barrington de Gonzales et al. published in 2009 (Barrington de Gonzales et al., 2009, JNCI, vol. 101: 205-209). This article focuses on estimating the risk of radiation-induced breast cancer for mammographic screening of young women at high risk for breast cancer (with BRCA gene mutations). Based on an assumption of a 15% to 25% or less reduction in mortality from mammography in these high risk women, the authors conclude that such a reduction is not substantially greater than the risk of radiation-induced breast cancer mortality when screening before the age of 34 years. That is, there would be no net benefit from annual mammographic screening of BRCA mutation carriers at ages 25-29 years; the net benefit would be zero or small if screening occurs in 30-34 year olds, and there would be some net benefit at age 35 years or older.
The Addition of Mammography to Magnetic Resonance Imaging
The effects of the addition of FM to MRI screening of high risk women was also assessed, with inclusion and exclusion criteria as follows:
Inclusion Criteria
English-language articles and English or French-language HTAs published from September 2007 to January 15, 2010.
Articles specific to screening of women at high risk for breast cancer, regardless of the definition of high risk.
Studies in which accuracy data for the combination of MRI plus FM are available to be compared to that of MRI and FM alone.
RCTs or prospective, paired studies only.
Studies in which women were previously diagnosed with breast cancer were also included.
Exclusion Criteria
Studies in which outcomes were not specific to those of interest in this report.
Studies in which there was insufficient data on the accuracy of MRI plus FM.
Intervention
Both MRI and FM.
Comparators
Screening with MRI alone and FM alone.
Outcomes of Interest
Sensitivity.
Specificity.
Summary of Findings
Magnetic Resonance Imaging Plus Mammography
Moderate GRADE Level Evidence that the sensitivity of MRI plus mammography is significantly higher than that of MRI or FM alone, although the specificity remains either unchanged or decreases in the screening of women at high risk for breast cancer based on genetic/familial factors, regardless of age.
These studies include women at high risk defined as BRCA1/2 or TP53 carriers, first degree relatives of carriers, women with varying degrees of high risk family histories, and/or >20% lifetime risk based on existing risk models. This definition of high risk accounts for approximately 2% of the female adult population in Ontario.
PMCID: PMC3377503  PMID: 23074406
11.  Automatic Filtering and Substantiation of Drug Safety Signals 
PLoS Computational Biology  2012;8(4):e1002457.
Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be investigated by placing the pharmaco-epidemiologically detected adverse drug reaction in an information-rich context and by exploiting all currently available biomedical knowledge to substantiate it. We present a computational framework for the biological annotation of potential adverse drug reactions. First, the proposed framework investigates previous evidences on the drug-event association in the context of biomedical literature (signal filtering). Then, it seeks to provide a biological explanation (signal substantiation) by exploring mechanistic connections that might explain why a drug produces a specific adverse reaction. The mechanistic connections include the activity of the drug, related compounds and drug metabolites on protein targets, the association of protein targets to clinical events, and the annotation of proteins (both protein targets and proteins associated with clinical events) to biological pathways. Hence, the workflows for signal filtering and substantiation integrate modules for literature and database mining, in silico drug-target profiling, and analyses based on gene-disease networks and biological pathways. Application examples of these workflows carried out on selected cases of drug safety signals are discussed. The methodology and workflows presented offer a novel approach to explore the molecular mechanisms underlying adverse drug reactions.
Author Summary
Adverse drug reactions (ADRs) constitute a major cause of morbidity and mortality worldwide. Due to the relevance of ADRs for both public health and pharmaceutical industry, it is important to develop efficient ways to monitor ADRs in the population. In addition, it is also essential to comprehend why a drug produces an adverse effect. To unravel the molecular mechanisms of ADRs, it is necessary to consider the ADR in the context of current biomedical knowledge that might explain it. Nowadays there are plenty of information sources that can be exploited in order to accomplish this goal. Nevertheless, the fragmentation of information and, more importantly, the diverse knowledge domains that need to be traversed, pose challenges to the task of exploring the molecular mechanisms of ADRs. We present a novel computational framework to aid in the collection and exploration of evidences that support the causal inference of ADRs detected by mining clinical records. This framework was implemented as publicly available tools integrating state-of-the-art bioinformatics methods for the analysis of drugs, targets, biological processes and clinical events. The availability of such tools for in silico experiments will facilitate research on the mechanisms that underlie ADR, contributing to the development of safer drugs.
doi:10.1371/journal.pcbi.1002457
PMCID: PMC3320573  PMID: 22496632
12.  Human Disease-Drug Network Based on Genomic Expression Profiles 
PLoS ONE  2009;4(8):e6536.
Background
Drug repositioning offers the possibility of faster development times and reduced risks in drug discovery. With the rapid development of high-throughput technologies and ever-increasing accumulation of whole genome-level datasets, an increasing number of diseases and drugs can be comprehensively characterized by the changes they induce in gene expression, protein, metabolites and phenotypes.
Methodology/Principal Findings
We performed a systematic, large-scale analysis of genomic expression profiles of human diseases and drugs to create a disease-drug network. A network of 170,027 significant interactions was extracted from the ∼24.5 million comparisons between ∼7,000 publicly available transcriptomic profiles. The network includes 645 disease-disease, 5,008 disease-drug, and 164,374 drug-drug relationships. At least 60% of the disease-disease pairs were in the same disease area as determined by the Medical Subject Headings (MeSH) disease classification tree. The remaining can drive a molecular level nosology by discovering relationships between seemingly unrelated diseases, such as a connection between bipolar disorder and hereditary spastic paraplegia, and a connection between actinic keratosis and cancer. Among the 5,008 disease-drug links, connections with negative scores suggest new indications for existing drugs, such as the use of some antimalaria drugs for Crohn's disease, and a variety of existing drugs for Huntington's disease; while the positive scoring connections can aid in drug side effect identification, such as tamoxifen's undesired carcinogenic property. From the ∼37K drug-drug relationships, we discover relationships that aid in target and pathway deconvolution, such as 1) KCNMA1 as a potential molecular target of lobeline, and 2) both apoptotic DNA fragmentation and G2/M DNA damage checkpoint regulation as potential pathway targets of daunorubicin.
Conclusions/Significance
We have automatically generated thousands of disease and drug expression profiles using GEO datasets, and constructed a large scale disease-drug network for effective and efficient drug repositioning as well as drug target/pathway identification.
doi:10.1371/journal.pone.0006536
PMCID: PMC2715883  PMID: 19657382
13.  Molecular Concepts Analysis Links Tumors, Pathways, Mechanisms, and Drugs1 * 
Neoplasia (New York, N.Y.)  2007;9(5):443-454.
Global molecular profiling of cancers has shown broad utility in delineating pathways and processes underlying disease, in predicting prognosis and response to therapy, and in suggesting novel treatments. To gain further insights from such data, we have integrated and analyzed a comprehensive collection of “molecular concepts” representing > 2500 cancer-related gene expression signatures from Oncomine and manual curation of the literature, drug treatment signatures from the Connectivity Map, target gene sets from genome-scale regulatory motif analyses, and reference gene sets from several gene and protein annotation databases. We computed pairwise association analysis on all 13,364 molecular concepts and identified > 290,000 significant associations, generating hypotheses that link cancer types and subtypes, pathways, mechanisms, and drugs. To navigate a network of associations, we developed an analysis platform, the Molecular Concepts Map. We demonstrate the utility of the approach by highlighting molecular concepts analyses of Myc pathway activation, breast cancer relapse, and retinoic acid treatment.
PMCID: PMC1877973  PMID: 17534450
Cancer; bioinformatics; gene expression signature; network; oncomine
14.  TarNet: An Evidence-Based Database for Natural Medicine Research 
PLoS ONE  2016;11(6):e0157222.
Background
Complex diseases seriously threaten human health. Drug discovery approaches based on “single genes, single drugs, and single targets” are limited in targeting complex diseases. The development of new multicomponent drugs for complex diseases is imperative, and the establishment of a suitable solution for drug group-target protein network analysis is a key scientific problem that must be addressed. Herbal medicines have formed the basis of sophisticated systems of traditional medicine and have given rise to some key drugs that remain in use today. The search for new molecules is currently taking a different route, whereby scientific principles of ethnobotany and ethnopharmacognosy are being used by chemists in the discovery of different sources and classes of compounds.
Results
In this study, we developed TarNet, a manually curated database and platform of traditional medicinal plants with natural compounds that includes potential bio-target information. We gathered information on proteins that are related to or affected by medicinal plant ingredients and data on protein–protein interactions (PPIs). TarNet includes in-depth information on both plant–compound–protein relationships and PPIs. Additionally, TarNet can provide researchers with network construction analyses of biological pathways and protein–protein interactions (PPIs) associated with specific diseases. Researchers can upload a gene or protein list mapped to our PPI database that has been manually curated to generate relevant networks. Multiple functions are accessible for network topological calculations, subnetwork analyses, pathway analyses, and compound–protein relationships.
Conclusions
TarNet will serve as a useful analytical tool that will provide information on medicinal plant compound-affected proteins (potential targets) and system-level analyses for systems biology and network pharmacology researchers. TarNet is freely available at http://www.herbbol.org:8001/tarnet, and detailed tutorials on the program are also available.
doi:10.1371/journal.pone.0157222
PMCID: PMC4919029  PMID: 27337171
15.  Structural and functional protein network analyses predict novel signaling functions for rhodopsin 
Proteomic analyses, literature mining, and structural data were combined to generate an extensive signaling network linked to the visual G protein-coupled receptor rhodopsin. Network analysis suggests novel signaling routes to cytoskeleton dynamics and vesicular trafficking.
Using a shotgun proteomic approach, we identified the protein inventory of the light sensing outer segment of the mammalian photoreceptor.These data, combined with literature mining, structural modeling, and computational analysis, offer a comprehensive view of signal transduction downstream of the visual G protein-coupled receptor rhodopsin.The network suggests novel signaling branches downstream of rhodopsin to cytoskeleton dynamics and vesicular trafficking.The network serves as a basis for elucidating physiological principles of photoreceptor function and suggests potential disease-associated proteins.
Photoreceptor cells are neurons capable of converting light into electrical signals. The rod outer segment (ROS) region of the photoreceptor cells is a cellular structure made of a stack of around 800 closed membrane disks loaded with rhodopsin (Liang et al, 2003; Nickell et al, 2007). In disc membranes, rhodopsin arranges itself into paracrystalline dimer arrays, enabling optimal association with the heterotrimeric G protein transducin as well as additional regulatory components (Ciarkowski et al, 2005). Disruption of these highly regulated structures and processes by germline mutations is the cause of severe blinding diseases such as retinitis pigmentosa, macular degeneration, or congenital stationary night blindness (Berger et al, 2010).
Traditionally, signal transduction networks have been studied by combining biochemical and genetic experiments addressing the relations among a small number of components. More recently, large throughput experiments using different techniques like two hybrid or co-immunoprecipitation coupled to mass spectrometry have added a new level of complexity (Ito et al, 2001; Gavin et al, 2002, 2006; Ho et al, 2002; Rual et al, 2005; Stelzl et al, 2005). However, in these studies, space, time, and the fact that many interactions detected for a particular protein are not compatible, are not taken into consideration. Structural information can help discriminate between direct and indirect interactions and more importantly it can determine if two or more predicted partners of any given protein or complex can simultaneously bind a target or rather compete for the same interaction surface (Kim et al, 2006).
In this work, we build a functional and dynamic interaction network centered on rhodopsin on a systems level, using six steps: In step 1, we experimentally identified the proteomic inventory of the porcine ROS, and we compared our data set with a recent proteomic study from bovine ROS (Kwok et al, 2008). The union of the two data sets was defined as the ‘initial experimental ROS proteome'. After removal of contaminants and applying filtering methods, a ‘core ROS proteome', consisting of 355 proteins, was defined.
In step 2, proteins of the core ROS proteome were assigned to six functional modules: (1) vision, signaling, transporters, and channels; (2) outer segment structure and morphogenesis; (3) housekeeping; (4) cytoskeleton and polarity; (5) vesicles formation and trafficking, and (6) metabolism.
In step 3, a protein-protein interaction network was constructed based on the literature mining. Since for most of the interactions experimental evidence was co-immunoprecipitation, or pull-down experiments, and in addition many of the edges in the network are supported by single experimental evidence, often derived from high-throughput approaches, we refer to this network, as ‘fuzzy ROS interactome'. Structural information was used to predict binary interactions, based on the finding that similar domain pairs are likely to interact in a similar way (‘nature repeats itself') (Aloy and Russell, 2002). To increase the confidence in the resulting network, edges supported by a single evidence not coming from yeast two-hybrid experiments were removed, exception being interactions where the evidence was the existence of a three-dimensional structure of the complex itself, or of a highly homologous complex. This curated static network (‘high-confidence ROS interactome') comprises 660 edges linking the majority of the nodes. By considering only edges supported by at least one evidence of direct binary interaction, we end up with a ‘high-confidence binary ROS interactome'. We next extended the published core pathway (Dell'Orco et al, 2009) using evidence from our high-confidence network. We find several new direct binary links to different cellular functional processes (Figure 4): the active rhodopsin interacts with Rac1 and the GTP form of Rho. There is also a connection between active rhodopsin and Arf4, as well as PDEδ with Rab13 and the GTP-bound form of Arl3 that links the vision cycle to vesicle trafficking and structure. We see a connection between PDEδ with prenyl-modified proteins, such as several small GTPases, as well as with rhodopsin kinase. Further, our network reveals several direct binary connections between Ca2+-regulated proteins and cytoskeleton proteins; these are CaMK2A with actinin, calmodulin with GAP43 and S1008, and PKC with 14-3-3 family members.
In step 4, part of the network was experimentally validated using three different approaches to identify physical protein associations that would occur under physiological conditions: (i) Co-segregation/co-sedimentation experiments, (ii) immunoprecipitations combined with mass spectrometry and/or subsequent immunoblotting, and (iii) utilizing the glycosylated N-terminus of rhodopsin to isolate its associated protein partners by Concanavalin A affinity purification. In total, 60 co-purification and co-elution experiments supported interactions that were already in our literature network, and new evidence from 175 co-IP experiments in this work was added. Next, we aimed to provide additional independent experimental confirmation for two of the novel networks and functional links proposed based on the network analysis: (i) the proposed complex between Rac1/RhoA/CRMP-2/tubulin/and ROCK II in ROS was investigated by culturing retinal explants in the presence of an ROCK II-specific inhibitor (Figure 6). While morphology of the retinas treated with ROCK II inhibitor appeared normal, immunohistochemistry analyses revealed several alterations on the protein level. (ii) We supported the hypothesis that PDEδ could function as a GDI for Rac1 in ROS, by demonstrating that PDEδ and Rac1 co localize in ROS and that PDEδ could dissociate Rac1 from ROS membranes in vitro.
In step 5, we use structural information to distinguish between mutually compatible (‘AND') or excluded (‘XOR') interactions. This enables breaking a network of nodes and edges into functional machines or sub-networks/modules. In the vision branch, both ‘AND' and ‘XOR' gates synergize. This may allow dynamic tuning of light and dark states. However, all connections from the vision module to other modules are ‘XOR' connections suggesting that competition, in connection with local protein concentration changes, could be important for transmitting signals from the core vision module.
In the last step, we map and functionally characterize the known mutations that produce blindness.
In summary, this represents the first comprehensive, dynamic, and integrative rhodopsin signaling network, which can be the basis for integrating and mapping newly discovered disease mutants, to guide protein or signaling branch-specific therapies.
Orchestration of signaling, photoreceptor structural integrity, and maintenance needed for mammalian vision remain enigmatic. By integrating three proteomic data sets, literature mining, computational analyses, and structural information, we have generated a multiscale signal transduction network linked to the visual G protein-coupled receptor (GPCR) rhodopsin, the major protein component of rod outer segments. This network was complemented by domain decomposition of protein–protein interactions and then qualified for mutually exclusive or mutually compatible interactions and ternary complex formation using structural data. The resulting information not only offers a comprehensive view of signal transduction induced by this GPCR but also suggests novel signaling routes to cytoskeleton dynamics and vesicular trafficking, predicting an important level of regulation through small GTPases. Further, it demonstrates a specific disease susceptibility of the core visual pathway due to the uniqueness of its components present mainly in the eye. As a comprehensive multiscale network, it can serve as a basis to elucidate the physiological principles of photoreceptor function, identify potential disease-associated genes and proteins, and guide the development of therapies that target specific branches of the signaling pathway.
doi:10.1038/msb.2011.83
PMCID: PMC3261702  PMID: 22108793
protein interaction network; rhodopsin signaling; structural modeling
16.  A pharmacogenomic method for individualized prediction of drug sensitivity 
Using valproic acid as an example, the authors demonstrate that drug response signatures derived from genome-wide expression data can identify individuals likely to respond to a drug, and propose that this method could select optimal populations for clinical trials of new therapies.
Drug response signatures that accurately reflect the cellular response to a drug can be generated from Connectivity Map and publically available gene expression data.Predictions from the drug response signature for valproic acid correlate with sensitivity to valproic acid in breast cancer cell lines and patient tumors grown in three-dimensional culture and mouse xenografts.The MATCH algorithm provides an efficient approach for using genome-wide gene expression data to identify a target population for a drug prior to clinical trials.MATCH can predict drug sensitivity in tumors without knowledge of mechanism of action.
Unlike traditional chemotherapy, targeted cancer therapies are expected to work in only a subset of people with a particular cancer. However, biomarkers of response are not always known before clinical trial initiation. We present MATCH (Merging genomic and pharmacologic Analyses for Therapy CHoice), an algorithm for using genome-wide gene expression data to identify and validate a genomic biomarker of sensitivity (see Figure 1). Our proof-of-principle example is valproic acid (VPA), but we also show that an estrogen blocking drug currently used for breast cancer and a B-RAF inhibitor in trials for melanoma give predictions that correspond to their clinical uses.
We use genome-wide gene expression data from treated and untreated samples from the Connectivity Map to generate a VPA response signature. We validate that the VPA signature can identify treated and untreated cells in an independent data set of normal cells and in independent samples from the Connectivity Map. The AUC for the ROC curve is 0.86. We then apply the VPA signature to publically available data sets from a panel of cancer cell lines and from primary tumor and normal tissue samples. These data suggest that there is a subset of women with breast cancer who will be sensitive to VPA. Finally, we validate that our predictions correlate with sensitivity to VPA in breast cancer cell lines grown in two-dimensional culture, primary breast tumor samples grown in three-dimensional culture, and in vivo mouse breast cancer xenografts. Together, these studies show that MATCH can identify cancer patients most likely to respond to a specific drug treatment.
Identifying the best drug for each cancer patient requires an efficient individualized strategy. We present MATCH (Merging genomic and pharmacologic Analyses for Therapy CHoice), an approach using public genomic resources and drug testing of fresh tumor samples to link drugs to patients. Valproic acid (VPA) is highlighted as a proof-of-principle. In order to predict specific tumor types with high probability of drug sensitivity, we create drug response signatures using publically available gene expression data and assess sensitivity in a data set of >40 cancer types. Next, we evaluate drug sensitivity in matched tumor and normal tissue and exclude cancer types that are no more sensitive than normal tissue. From these analyses, breast tumors are predicted to be sensitive to VPA. A meta-analysis across breast cancer data sets shows that aggressive subtypes are most likely to be sensitive to VPA, but all subtypes have sensitive tumors. MATCH predictions correlate significantly with growth inhibition in cancer cell lines and three-dimensional cultures of fresh tumor samples. MATCH accurately predicts reduction in tumor growth rate following VPA treatment in patient tumor xenografts. MATCH uses genomic analysis with in vitro testing of patient tumors to select optimal drug regimens before clinical trial initiation.
doi:10.1038/msb.2011.47
PMCID: PMC3159972  PMID: 21772261
biomarkers; cancer; pharmacogenomics
17.  IPAD: the Integrated Pathway Analysis Database for Systematic Enrichment Analysis 
BMC Bioinformatics  2012;13(Suppl 15):S7.
Background
Next-Generation Sequencing (NGS) technologies and Genome-Wide Association Studies (GWAS) generate millions of reads and hundreds of datasets, and there is an urgent need for a better way to accurately interpret and distill such large amounts of data. Extensive pathway and network analysis allow for the discovery of highly significant pathways from a set of disease vs. healthy samples in the NGS and GWAS. Knowledge of activation of these processes will lead to elucidation of the complex biological pathways affected by drug treatment, to patient stratification studies of new and existing drug treatments, and to understanding the underlying anti-cancer drug effects. There are approximately 141 biological human pathway resources as of Jan 2012 according to the Pathguide database. However, most currently available resources do not contain disease, drug or organ specificity information such as disease-pathway, drug-pathway, and organ-pathway associations. Systematically integrating pathway, disease, drug and organ specificity together becomes increasingly crucial for understanding the interrelationships between signaling, metabolic and regulatory pathway, drug action, disease susceptibility, and organ specificity from high-throughput omics data (genomics, transcriptomics, proteomics and metabolomics).
Results
We designed the Integrated Pathway Analysis Database for Systematic Enrichment Analysis (IPAD, http://bioinfo.hsc.unt.edu/ipad), defining inter-association between pathway, disease, drug and organ specificity, based on six criteria: 1) comprehensive pathway coverage; 2) gene/protein to pathway/disease/drug/organ association; 3) inter-association between pathway, disease, drug, and organ; 4) multiple and quantitative measurement of enrichment and inter-association; 5) assessment of enrichment and inter-association analysis with the context of the existing biological knowledge and a "gold standard" constructed from reputable and reliable sources; and 6) cross-linking of multiple available data sources.
IPAD is a comprehensive database covering about 22,498 genes, 25,469 proteins, 1956 pathways, 6704 diseases, 5615 drugs, and 52 organs integrated from databases including the BioCarta, KEGG, NCI-Nature curated, Reactome, CTD, PharmGKB, DrugBank, PharmGKB, and HOMER. The database has a web-based user interface that allows users to perform enrichment analysis from genes/proteins/molecules and inter-association analysis from a pathway, disease, drug, and organ.
Moreover, the quality of the database was validated with the context of the existing biological knowledge and a "gold standard" constructed from reputable and reliable sources. Two case studies were also presented to demonstrate: 1) self-validation of enrichment analysis and inter-association analysis on brain-specific markers, and 2) identification of previously undiscovered components by the enrichment analysis from a prostate cancer study.
Conclusions
IPAD is a new resource for analyzing, identifying, and validating pathway, disease, drug, organ specificity and their inter-associations. The statistical method we developed for enrichment and similarity measurement and the two criteria we described for setting the threshold parameters can be extended to other enrichment applications. Enriched pathways, diseases, drugs, organs and their inter-associations can be searched, displayed, and downloaded from our online user interface. The current IPAD database can help users address a wide range of biological pathway related, disease susceptibility related, drug target related and organ specificity related questions in human disease studies.
doi:10.1186/1471-2105-13-S15-S7
PMCID: PMC3439721  PMID: 23046449
18.  DEAR1 Is a Dominant Regulator of Acinar Morphogenesis and an Independent Predictor of Local Recurrence-Free Survival in Early-Onset Breast Cancer 
PLoS Medicine  2009;6(5):e1000068.
Ann Killary and colleagues describe a new gene that is genetically altered in breast tumors, and that may provide a new breast cancer prognostic marker.
Background
Breast cancer in young women tends to have a natural history of aggressive disease for which rates of recurrence are higher than in breast cancers detected later in life. Little is known about the genetic pathways that underlie early-onset breast cancer. Here we report the discovery of DEAR1 (ductal epithelium–associated RING Chromosome 1), a novel gene encoding a member of the TRIM (tripartite motif) subfamily of RING finger proteins, and provide evidence for its role as a dominant regulator of acinar morphogenesis in the mammary gland and as an independent predictor of local recurrence-free survival in early-onset breast cancer.
Methods and Findings
Suppression subtractive hybridization identified DEAR1 as a novel gene mapping to a region of high-frequency loss of heterozygosity (LOH) in a number of histologically diverse human cancers within Chromosome 1p35.1. In the breast epithelium, DEAR1 expression is limited to the ductal and glandular epithelium and is down-regulated in transition to ductal carcinoma in situ (DCIS), an early histologic stage in breast tumorigenesis. DEAR1 missense mutations and homozygous deletion (HD) were discovered in breast cancer cell lines and tumor samples. Introduction of the DEAR1 wild type and not the missense mutant alleles to complement a mutation in a breast cancer cell line, derived from a 36-year-old female with invasive breast cancer, initiated acinar morphogenesis in three-dimensional (3D) basement membrane culture and restored tissue architecture reminiscent of normal acinar structures in the mammary gland in vivo. Stable knockdown of DEAR1 in immortalized human mammary epithelial cells (HMECs) recapitulated the growth in 3D culture of breast cancer cell lines containing mutated DEAR1, in that shDEAR1 clones demonstrated disruption of tissue architecture, loss of apical basal polarity, diffuse apoptosis, and failure of lumen formation. Furthermore, immunohistochemical staining of a tissue microarray from a cohort of 123 young female breast cancer patients with a 20-year follow-up indicated that in early-onset breast cancer, DEAR1 expression serves as an independent predictor of local recurrence-free survival and correlates significantly with strong family history of breast cancer and the triple-negative phenotype (ER−, PR−, HER-2−) of breast cancers with poor prognosis.
Conclusions
Our data provide compelling evidence for the genetic alteration and loss of expression of DEAR1 in breast cancer, for the functional role of DEAR1 in the dominant regulation of acinar morphogenesis in 3D culture, and for the potential utility of an immunohistochemical assay for DEAR1 expression as an independent prognostic marker for stratification of early-onset disease.
Editors' Summary
Background
Each year, more than one million women discover that they have breast cancer. This type of cancer begins when cells in the breast that line the milk-producing glands or the tubes that take the milk to the nipples (glandular and ductal epithelial cells, respectively) acquire genetic changes that allow them to grow uncontrollably and to move around the body (metastasize). The uncontrolled division leads to the formation of a lump that can be detected by mammography (a breast X-ray) or by manual breast examination. Breast cancer is treated by surgical removal of the lump or, if the cancer has started to spread, by removal of the whole breast (mastectomy). Surgery is usually followed by radiotherapy or chemotherapy. These “adjuvant” therapies are designed to kill any remaining cancer cells but can make patients very ill. Generally speaking, the outlook for women with breast cancer is good. In the US, for example, nearly 90% of affected women are still alive five years after their diagnosis.
Why Was This Study Done?
Although breast cancer is usually diagnosed in women in their 50s or 60s, some women develop breast cancer much earlier. In these women, the disease is often very aggressive. Compared to older women, young women with breast cancer have a lower overall survival rate and their cancer is more likely to recur locally or to metastasize. It would be useful to be able to recognize those younger women at the greatest risk of cancer recurrence so that they could be offered intensive surveillance and adjuvant therapy; those women at a lower risk could have gentler treatments. To achieve this type of “stratification,” the genetic changes that underlie breast cancer in young women need to be identified. In this study, the researchers discover a gene that is genetically altered (by mutations or deletion) in early-onset breast cancer and then investigate whether its expression can predict outcomes in women with this disease.
What Did the Researchers Do and Find?
The researchers used “suppression subtractive hybridization” to identify a new gene in a region of human Chromosome 1 where loss of heterozygosity (LOH; a genetic alteration associated with cancer development) frequently occurs. They called the gene DEAR1 (ductal epithelium-associated RING Chromosome 1) to indicate that it is expressed in ductal and glandular epithelial cells and encodes a “RING finger” protein (specifically, a subtype called a TRIM protein; RING finger proteins such as BRCA1 and BRCA2 have been implicated in early cancer development and in a large fraction of inherited breast cancers). DEAR1 expression was reduced or lost in several ductal carcinomas in situ (a local abnormality that can develop into breast cancer) and advanced breast cancers, the researchers report. Furthermore, many breast tumors carried DEAR1 missense mutations (genetic changes that interfere with the normal function of the DEAR1 protein) or had lost both copies of DEAR1 (the human genome contains two copies of most genes). To determine the function of DEAR1, the researchers replaced a normal copy of DEAR1 into a breast cancer cell that had a mutation in DEAR1. They then examined the growth of these genetically manipulated cells in special three-dimensional cultures. The breast cancer cells without DEAR1 grew rapidly without an organized structure while the breast cancer cells containing the introduced copy of DEAR1 formed structures that resembled normal breast acini (sac-like structures that secrete milk). In normal human mammary epithelial cells, the researchers silenced DEAR1 expression and also showed that without DEAR1, the normal mammary cells lost their ability to form proper acini. Finally, the researchers report that DEAR1 expression (detected “immunohistochemically”) was frequently lost in women who had had early-onset breast cancer and that the loss of DEAR1 expression correlated with reduced local recurrence-free survival, a strong family history of breast cancer and with a breast cancer subtype that has a poor outcome.
What Do These Findings Mean?
These findings indicate that genetic alteration and loss of expression of DEAR1 are common in breast cancer. Although laboratory experiments may not necessarily reflect what happens in people, the results from the three-dimensional culture of breast epithelial cells suggest that DEAR1 may regulate the normal acinar structure of the breast. Consequently, loss of DEAR1 expression could be an early event in breast cancer development. Most importantly, the correlation between DEAR1 expression and both local recurrence in early-onset breast cancer and a breast cancer subtype with a poor outcome suggests that it might be possible to use DEAR1 expression to identify women with early-onset breast cancer who have an increased risk of local recurrence so that they get the most appropriate treatment for their cancer.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000068.
This study is further discussed in a PLoS Medicine Perspective by Senthil Muthuswamy
The US National Cancer Institute provides detailed information for patients and health professionals on all aspects of breast cancer, including information on genetic alterations in breast cancer (in English and Spanish)
The MedlinePlus Encyclopedia provides information for patients about breast cancer; MedlinePlus also provides links to many other breast cancer resources (in English and Spanish)
The UK charities Cancerbackup (now merged with MacMillan Cancer Support) and Cancer Research UK also provide detailed information about breast cancer
doi:10.1371/journal.pmed.1000068
PMCID: PMC2673042  PMID: 19536326
19.  MalaCards: an integrated compendium for diseases and their annotation 
Comprehensive disease classification, integration and annotation are crucial for biomedical discovery. At present, disease compilation is incomplete, heterogeneous and often lacking systematic inquiry mechanisms. We introduce MalaCards, an integrated database of human maladies and their annotations, modeled on the architecture and strategy of the GeneCards database of human genes. MalaCards mines and merges 44 data sources to generate a computerized card for each of 16 919 human diseases. Each MalaCard contains disease-specific prioritized annotations, as well as inter-disease connections, empowered by the GeneCards relational database, its searches and GeneDecks set analyses. First, we generate a disease list from 15 ranked sources, using disease-name unification heuristics. Next, we use four schemes to populate MalaCards sections: (i) directly interrogating disease resources, to establish integrated disease names, synonyms, summaries, drugs/therapeutics, clinical features, genetic tests and anatomical context; (ii) searching GeneCards for related publications, and for associated genes with corresponding relevance scores; (iii) analyzing disease-associated gene sets in GeneDecks to yield affiliated pathways, phenotypes, compounds and GO terms, sorted by a composite relevance score and presented with GeneCards links; and (iv) searching within MalaCards itself, e.g. for additional related diseases and anatomical context. The latter forms the basis for the construction of a disease network, based on shared MalaCards annotations, embodying associations based on etiology, clinical features and clinical conditions. This broadly disposed network has a power-law degree distribution, suggesting that this might be an inherent property of such networks. Work in progress includes hierarchical malady classification, ontological mapping and disease set analyses, striving to make MalaCards an even more effective tool for biomedical research.
Database URL: http://www.malacards.org/
doi:10.1093/database/bat018
PMCID: PMC3625956  PMID: 23584832
20.  The BARD1 Cys557Ser Variant and Breast Cancer Risk in Iceland 
PLoS Medicine  2006;3(7):e217.
Background
Most, if not all, of the cellular functions of the BRCA1 protein are mediated through heterodimeric complexes composed of BRCA1 and a related protein, BARD1. Some breast-cancer-associated BRCA1 missense mutations disrupt the function of the BRCA1/BARD1 complex. It is therefore pertinent to determine whether variants of BARD1 confer susceptibility to breast cancer. Recently, a missense BARD1 variant, Cys557Ser, was reported to be at increased frequencies in breast cancer families. We investigated the role of the BARD1 Cys557Ser variant in a population-based cohort of 1,090 Icelandic patients with invasive breast cancer and 703 controls. We then used a computerized genealogy of the Icelandic population to study the relationships between the Cys557Ser variant and familial clustering of breast cancer.
Methods and Findings
The Cys557Ser allele was present at a frequency of 0.028 in patients with invasive breast cancer and 0.016 in controls (odds ratio [OR] = 1.82, 95% confidence interval [CI] 1.11–3.01, p = 0.014). The alleleic frequency was 0.037 in a high-predisposition group of cases defined by having a family history of breast cancer, early onset of breast cancer, or multiple primary breast cancers (OR = 2.41, 95% CI 1.22–4.75, p = 0.015). Carriers of the common Icelandic BRCA2 999del5 mutation were found to have their risk of breast cancer further increased if they also carried the BARD1 variant: the frequency of the BARD1 variant allele was 0.047 (OR = 3.11, 95% CI 1.16–8.40, p = 0.046) in 999del5 carriers with breast cancer. This suggests that the lifetime probability of a BARD1 Cys557Ser/BRCA2 999del5 double carrier developing breast cancer could approach certainty. Cys557Ser carriers, with or without the BRCA2 mutation, had an increased risk of subsequent primary breast tumors after the first breast cancer diagnosis compared to non-carriers. Lobular and medullary breast carcinomas were overrepresented amongst Cys557Ser carriers. We found that an excess of ancestors of contemporary carriers lived in a single county in the southeast of Iceland and that all carriers shared a SNP haplotype, which is suggestive of a founder event. Cys557Ser was found on the same SNP haplotype background in the HapMap Project CEPH sample of Utah residents.
Conclusions
Our findings suggest that BARD1 Cys557Ser is an ancient variant that confers risk of single and multiple primary breast cancers, and this risk extends to carriers of the BRCA2 999del5 mutation.
Editors' Summary
Background.
About 13% of women (one in eight women) will develop breast cancer during their lifetime, but many factors affect the likelihood of any individual woman developing this disease, for example, whether she has had children and at what age, when she started and stopped her periods, and her exposure to certain chemicals or radiation. She may also have inherited a defective gene that affects her risk of developing breast cancer. Some 5%–10% of all breast cancers are familial, or inherited. In 20% of these cases, the gene that is defective is BRCA1 or BRCA2. Inheriting a defective copy of one of these genes greatly increases a woman's risk of developing breast cancer, while researchers think that the other inherited genes that predispose to breast cancer—most of which have not been identified yet—have a much weaker effect. These are described as low-penetrance genes. Inheriting one such gene only slightly increases breast cancer risk; a woman has to inherit several to increase her lifetime risk of cancer significantly.
Why Was This Study Done?
It is important to identify these additional predisposing gene variants because they might provide insights into why breast cancer develops, how to prevent it, and how to treat it. To find low-penetrance genes, researchers do case–control association studies. They find a large group of women with breast cancer (cases) and a similar group of women without cancer (controls), and examine how often a specific gene variant occurs in the two groups. If the variant is found more often in the cases than in the controls, it might be a variant that increases a woman's risk of developing breast cancer.
What Did the Researchers Do and Find?
The researchers involved in this study recruited Icelandic women who had had breast cancer and unaffected women, and looked for a specific variant—the Cys557Ser allele—of a gene called BARD1. They chose BARD1 because the protein it encodes interacts with the protein encoded by BRCA1. Because defects in BRCA1 increase the risk of breast cancer, defects in an interacting protein might have a similar effect. In addition, the Cys557Ser allele has been implicated in breast cancer in other studies. The researchers found that the Cys557Ser allele was nearly twice as common in women with breast cancer as in control women. It was also more common (but not by much) in women who had a family history of breast cancer or who had developed breast cancer more than once. And having the Cys557Ser allele seemed to increase the already high risk of breast cancer in women who had a BRCA2 variant (known as BRCA2 999del5) that accounts for 40% of inherited breast cancer risk in Iceland.
What Do These Findings Mean?
These results indicate that inheriting the BARD1 Cys557Ser allele increases a woman's breast cancer risk but that she is unlikely to have a family history of the disease. Because carrying the Cys557Ser allele only slightly increases a woman's risk of breast cancer, for most women there is no clinical reason to test for this variant. Eventually, when all the low-penetrance genes that contribute to breast cancer risk have been identified, it might be helpful to screen women for the full set to determine whether they are at high risk of developing breast cancer. This will not happen for many years, however, since there might be tens or hundreds of these genes. For women who carry BRCA2 999del5, the situation might be different. It might be worth testing these women for the BARD1 Cys557Ser allele, the researchers explain, because the lifetime probability of developing breast cancer in women carrying both variants might approach 100%. This finding has clinical implications in terms of counseling and monitoring, as does the observation that Cys557Ser carriers have an increased risk of a second, independent breast cancer compared to non-carriers. However, all these findings need to be confirmed in other groups of patients before anyone is routinely tested for the BARD1 Cys557Ser allele.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0030217.
• MedlinePlus pages about breast cancer
• Information on breast cancer from the United States National Cancer Institute
• Information on inherited breast cancer from the United States National Human Genome Research Institute
• United States National Cancer Institute information on genetic testing for BRCA1 and BRCA2 variants
• GeneTests pages on the involvement of BRCA1 and BRCA2 in hereditary breast and ovarian cancer
• Cancer Research UK's page on breast cancer statistics
In a population-based cohort of 1090 Icelandic patients, a Cys557Ser missense variant of the BARD1 gene, which interacts with BRCA1, increased the risk of single and multiple primary breast cancers.
doi:10.1371/journal.pmed.0030217
PMCID: PMC1479388  PMID: 16768547
21.  PREDOSE: A Semantic Web Platform for Drug Abuse Epidemiology using Social Media 
Journal of biomedical informatics  2013;46(6):10.1016/j.jbi.2013.07.007.
Objectives
The role of social media in biomedical knowledge mining, including clinical, medical and healthcare informatics, prescription drug abuse epidemiology and drug pharmacology, has become increasingly significant in recent years. Social media offers opportunities for people to share opinions and experiences freely in online communities, which may contribute information beyond the knowledge of domain professionals. This paper describes the development of a novel Semantic Web platform called PREDOSE (PREscription Drug abuse Online Surveillance and Epidemiology), which is designed to facilitate the epidemiologic study of prescription (and related) drug abuse practices using social media. PREDOSE uses web forum posts and domain knowledge, modeled in a manually created Drug Abuse Ontology (DAO) (pronounced dow), to facilitate the extraction of semantic information from User Generated Content (UGC). A combination of lexical, pattern-based and semantics-based techniques is used together with the domain knowledge to extract fine-grained semantic information from UGC. In a previous study, PREDOSE was used to obtain the datasets from which new knowledge in drug abuse research was derived. Here, we report on various platform enhancements, including an updated DAO, new components for relationship and triple extraction, and tools for content analysis, trend detection and emerging patterns exploration, which enhance the capabilities of the PREDOSE platform. Given these enhancements, PREDOSE is now more equipped to impact drug abuse research by alleviating traditional labor-intensive content analysis tasks.
Methods
Using custom web crawlers that scrape UGC from publicly available web forums, PREDOSE first automates the collection of web-based social media content for subsequent semantic annotation. The annotation scheme is modeled in the DAO, and includes domain specific knowledge such as prescription (and related) drugs, methods of preparation, side effects, routes of administration, etc. The DAO is also used to help recognize three types of data, namely: 1) entities, 2) relationships and 3) triples. PREDOSE then uses a combination of lexical and semantic-based techniques to extract entities and relationships from the scraped content, and a top-down approach for triple extraction that uses patterns expressed in the DAO. In addition, PREDOSE uses publicly available lexicons to identify initial sentiment expressions in text, and then a probabilistic optimization algorithm (from related research) to extract the final sentiment expressions. Together, these techniques enable the capture of fine-grained semantic information from UGC, and querying, search, trend analysis and overall content analysis of social media related to prescription drug abuse. Moreover, extracted data are also made available to domain experts for the creation of training and test sets for use in evaluation and refinements in information extraction techniques.
Results
A recent evaluation of the information extraction techniques applied in the PREDOSE platform indicates 85% precision and 72% recall in entity identification, on a manually created gold standard dataset. In another study, PREDOSE achieved 36% precision in relationship identification and 33% precision in triple extraction, through manual evaluation by domain experts. Given the complexity of the relationship and triple extraction tasks and the abstruse nature of social media texts, we interpret these as favorable initial results. Extracted semantic information is currently in use in an online discovery support system, by prescription drug abuse researchers at the Center for Interventions, Treatment and Addictions Research (CITAR) at Wright State University.
Conclusion
A comprehensive platform for entity, relationship, triple and sentiment extraction from such abstruse texts has never been developed for drug abuse research. PREDOSE has already demonstrated the importance of mining social media by providing data from which new findings in drug abuse research were uncovered. Given the recent platform enhancements, including the refined DAO, components for relationship and triple extraction, and tools for content, trend and emerging pattern analysis, it is expected that PREDOSE will play a significant role in advancing drug abuse epidemiology in future.
doi:10.1016/j.jbi.2013.07.007
PMCID: PMC3844051  PMID: 23892295
Entity Identification; Relationship Extraction; Triple Extraction; Sentiment Extraction; Semantic Web; Drug Abuse Ontology; Prescription Drug Abuse; Epidemiology
22.  An integrative analysis of cellular contexts, miRNAs and mRNAs reveals network clusters associated with antiestrogen-resistant breast cancer cells 
BMC Genomics  2012;13:732.
Background
A major goal of the field of systems biology is to translate genome-wide profiling data (e.g., mRNAs, miRNAs) into interpretable functional networks. However, employing a systems biology approach to better understand the complexities underlying drug resistance phenotypes in cancer continues to represent a significant challenge to the field. Previously, we derived two drug-resistant breast cancer sublines (tamoxifen- and fulvestrant-resistant cell lines) from the MCF7 breast cancer cell line and performed genome-wide mRNA and microRNA profiling to identify differential molecular pathways underlying acquired resistance to these important antiestrogens. In the current study, to further define molecular characteristics of acquired antiestrogen resistance we constructed an “integrative network”. We combined joint miRNA-mRNA expression profiles, cancer contexts, miRNA-target mRNA relationships, and miRNA upstream regulators. In particular, to reduce the probability of false positive connections in the network, experimentally validated, rather than prediction-oriented, databases were utilized to obtain connectivity. Also, to improve biological interpretation, cancer contexts were incorporated into the network connectivity.
Results
Based on the integrative network, we extracted “substructures” (network clusters) representing the drug resistant states (tamoxifen- or fulvestrant-resistance cells) compared to drug sensitive state (parental MCF7 cells). We identified un-described network clusters that contribute to antiestrogen resistance consisting of miR-146a, -27a, -145, -21, -155, -15a, -125b, and let-7s, in addition to the previously described miR-221/222.
Conclusions
By integrating miRNA-related network, gene/miRNA expression and text-mining, the current study provides a computational-based systems biology approach for further investigating the molecular mechanism underlying antiestrogen resistance in breast cancer cells. In addition, new miRNA clusters that contribute to antiestrogen resistance were identified, and they warrant further investigation.
doi:10.1186/1471-2164-13-732
PMCID: PMC3560207  PMID: 23270413
Bioinformatics; miRNA; Network; Breast cancer; Antiestrogen resistance
23.  IIS – Integrated Interactome System: A Web-Based Platform for the Annotation, Analysis and Visualization of Protein-Metabolite-Gene-Drug Interactions by Integrating a Variety of Data Sources and Tools 
PLoS ONE  2014;9(6):e100385.
Background
High-throughput screening of physical, genetic and chemical-genetic interactions brings important perspectives in the Systems Biology field, as the analysis of these interactions provides new insights into protein/gene function, cellular metabolic variations and the validation of therapeutic targets and drug design. However, such analysis depends on a pipeline connecting different tools that can automatically integrate data from diverse sources and result in a more comprehensive dataset that can be properly interpreted.
Results
We describe here the Integrated Interactome System (IIS), an integrative platform with a web-based interface for the annotation, analysis and visualization of the interaction profiles of proteins/genes, metabolites and drugs of interest. IIS works in four connected modules: (i) Submission module, which receives raw data derived from Sanger sequencing (e.g. two-hybrid system); (ii) Search module, which enables the user to search for the processed reads to be assembled into contigs/singlets, or for lists of proteins/genes, metabolites and drugs of interest, and add them to the project; (iii) Annotation module, which assigns annotations from several databases for the contigs/singlets or lists of proteins/genes, generating tables with automatic annotation that can be manually curated; and (iv) Interactome module, which maps the contigs/singlets or the uploaded lists to entries in our integrated database, building networks that gather novel identified interactions, protein and metabolite expression/concentration levels, subcellular localization and computed topological metrics, GO biological processes and KEGG pathways enrichment. This module generates a XGMML file that can be imported into Cytoscape or be visualized directly on the web.
Conclusions
We have developed IIS by the integration of diverse databases following the need of appropriate tools for a systematic analysis of physical, genetic and chemical-genetic interactions. IIS was validated with yeast two-hybrid, proteomics and metabolomics datasets, but it is also extendable to other datasets. IIS is freely available online at: http://www.lge.ibi.unicamp.br/lnbio/IIS/.
doi:10.1371/journal.pone.0100385
PMCID: PMC4065059  PMID: 24949626
24.  Cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action 
Chemogenomic screens were performed in both budding and fission yeasts, allowing for a cross-species comparison of drug–gene interaction networks.Drug–module interactions were more conserved than individual drug–gene interactions.Combination of data from both species can improve drug–module predictions and helps identify a compound's mode of action.
Understanding the molecular effects of chemical compounds in living cells is an important step toward rational therapeutics. Drug discovery aims to find compounds that will target a specific pathway or pathogen with minimal side effects. However, even when an effective drug is found, its mode of action (MoA) is typically not well understood. The lack of knowledge regarding a drug's MoA makes the drug discovery process slow and rational therapeutics incredibly difficult. More recently, different high-throughput methods have been developed that attempt to discern how a compound exerts its effects in cells. One of these methods relies on measuring the growth of cells carrying different mutations in the presence of the compounds of interest, commonly referred to as chemogenomics (Wuster and Babu, 2008). The differential growth of the different mutants provides clues as to what the compounds target in the cell (Figure 2). For example, if a drug inhibits a branch in a vital two-branch pathway, then mutations in the second branch might result in cell death if the mutants are grown in the presence of the drug (Figure 2C). As these compound–mutant functional interactions are expected to be relatively rare, one can assume that the growth rate of a mutant–drug combination should generally be equal to the product of the growth rate of the untreated mutant with the growth rate of the drug-treated wild type. This expectation is defined as the neutral model and deviations from this provide a quantitative score that allow us to make informed predictions regarding a drug's MoA (Figure 2B; Parsons et al, 2006).
The availability of these high-throughput approaches now allows us to perform cross-species studies of functional interactions between compounds and genes. In this study, we have performed a quantitative analysis of compound–gene interactions for two fungal species (budding yeast (S. cerevisiae) and fission yeast (S. pombe)) that diverged from each other approximately 500–700 million years ago. A collection of 2957 compounds from the National Cancer Institute (NCI) were screened in both species for inhibition of wild-type cell growth. A total of 132 were found to be bioactive in both fungi and 9, along with 12 additional well-characterized drugs, were selected for subsequent screening. Mutant libraries of 727 and 438 gene deletions were used for S. cerevisiae and S. pombe, respectively, and these were selected based on availability of genetic interaction data from previous studies (Collins et al, 2007; Roguev et al, 2008; Fiedler et al, 2009) and contain an overlap of 190 one-to-one orthologs that can be directly compared. Deviations from the neutral expectation were quantified as drug–gene interactions scores (D-scores) for the 21 compounds against the deletion libraries. Replicates of both screens showed very high correlations (S. cerevisiae r=0.72, S. pombe r=0.76) and reproduced well previously known compound–gene interactions (Supplementary information). We then compared the D-scores for the 190 one-to-one orthologs present in the data set of both species. Despite the high reproducibility, we observed a very poor conservation of these compound–gene interaction scores across these species (r=0.13, Figure 4A).
Previous work had shown that, across these same species, genetic interactions within protein complexes were much more conserved than average genetic interactions (Roguev et al, 2008). Similarly we observed a higher cross-species conservation of the compound–module (complex or pathway) interactions than the overall compound–gene interactions. Specifically, the data derived from fission yeast were a poor predictor of S. cerevisaie drug–gene interactions, but a good predictor of budding yeast compound–module connections (Figure 4B). Also, a combined score from both species improved the prediction of compound–module interactions, above the accuracy observed with the S. cerevisae information alone, but this improvement was not observed for the prediction of drug–gene interactions (Figure 4B). Data from both species were used to predict drug–module interactions, and one specific interaction (compound NSC-207895 interaction with DNA repair complexes) was experimentally verified by showing that the compound activates the DNA damage repair pathway in three species (S. cerevisiae, S. pombe and H. sapiens).
To understand why the combination of chemogenomic data from two species might improve drug–module interaction predictions, we also analyzed previously published cross-species genetic–interaction data. We observed a significant correlation between the conservation of drug–gene and gene–gene interactions among the one-to-one orthologs (r=0.28, P-value=0.0078). Additionally, the strongest interactions of benomyl (a microtubule inhibitor) were to complexes that also had strong and conserved genetic interactions with microtubules (Figure 4C). We hypothesize that a significant number of the compound–gene interactions obtained from chemogenomic studies are not direct interactions with the physical target of the compounds, but include many indirect interactions that genetically interact with the main target(s). This would explain why the compound interaction networks show similar evolutionary patterns as the genetic interactions networks.
In summary, these results shed some light on the interplay between the evolution of genetic networks and the evolution of drug response. Understanding how genetic variability across different species might result in different sensitivity to drugs should improve our capacity to design treatments. Concretely, we hope that this line of research might one day help us create drugs and drug combinations that specifically affect a pathogen or diseased tissue, but not the host.
We present a cross-species chemogenomic screening platform using libraries of haploid deletion mutants from two yeast species, Saccharomyces cerevisiae and Schizosaccharomyces pombe. We screened a set of compounds of known and unknown mode of action (MoA) and derived quantitative drug scores (or D-scores), identifying mutants that are either sensitive or resistant to particular compounds. We found that compound–functional module relationships are more conserved than individual compound–gene interactions between these two species. Furthermore, we observed that combining data from both species allows for more accurate prediction of MoA. Finally, using this platform, we identified a novel small molecule that acts as a DNA damaging agent and demonstrate that its MoA is conserved in human cells.
doi:10.1038/msb.2010.107
PMCID: PMC3018166  PMID: 21179023
chemogenomics; evolution; modularity
25.  N348I in the Connection Domain of HIV-1 Reverse Transcriptase Confers Zidovudine and Nevirapine Resistance 
PLoS Medicine  2007;4(12):e335.
Background
The catalytically active 66-kDa subunit of the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT) consists of DNA polymerase, connection, and ribonuclease H (RNase H) domains. Almost all known RT inhibitor resistance mutations identified to date map to the polymerase domain of the enzyme. However, the connection and RNase H domains are not routinely analysed in clinical samples and none of the genotyping assays available for patient management sequence the entire RT coding region. The British Columbia Centre for Excellence in HIV/AIDS (the Centre) genotypes clinical isolates up to codon 400 in RT, and our retrospective statistical analyses of the Centre's database have identified an N348I mutation in the RT connection domain in treatment-experienced individuals. The objective of this multidisciplinary study was to establish the in vivo relevance of this mutation and its role in drug resistance.
Methods and Findings
The prevalence of N348I in clinical isolates, the time taken for it to emerge under selective drug pressure, and its association with changes in viral load, specific drug treatment, and known drug resistance mutations was analysed from genotypes, viral loads, and treatment histories from the Centre's database. N348I increased in prevalence from below 1% in 368 treatment-naïve individuals to 12.1% in 1,009 treatment-experienced patients (p = 7.7 × 10−12). N348I appeared early in therapy and was highly associated with thymidine analogue mutations (TAMs) M41L and T215Y/F (p < 0.001), the lamivudine resistance mutations M184V/I (p < 0.001), and non-nucleoside RTI (NNRTI) resistance mutations K103N and Y181C/I (p < 0.001). The association with TAMs and NNRTI resistance mutations was consistent with the selection of N348I in patients treated with regimens that included both zidovudine and nevirapine (odds ratio 2.62, 95% confidence interval 1.43–4.81). The appearance of N348I was associated with a significant increase in viral load (p < 0.001), which was as large as the viral load increases observed for any of the TAMs. However, this analysis did not account for the simultaneous selection of other RT or protease inhibitor resistance mutations on viral load. To delineate the role of this mutation in RT inhibitor resistance, N348I was introduced into HIV-1 molecular clones containing different genetic backbones. N348I decreased zidovudine susceptibility 2- to 4-fold in the context of wild-type HIV-1 or when combined with TAMs. N348I also decreased susceptibility to nevirapine (7.4-fold) and efavirenz (2.5-fold) and significantly potentiated resistance to these drugs when combined with K103N. Biochemical analyses of recombinant RT containing N348I provide supporting evidence for the role of this mutation in zidovudine and NNRTI resistance and give some insight into the molecular mechanism of resistance.
Conclusions
This study provides the first in vivo evidence that treatment with RT inhibitors can select a mutation (i.e., N348I) outside the polymerase domain of the HIV-1 RT that confers dual-class resistance. Its emergence, which can happen early during therapy, may significantly impact on a patient's response to antiretroviral therapies containing zidovudine and nevirapine. This study also provides compelling evidence for investigating the role of other mutations in the connection and RNase H domains in virological failure.
Analyzing HIV sequences from a Canadian cohort, Gilda Tachedjian and colleagues identify a common mutation in a little-studied domain of reverse transcriptase that confers resistance to two drug classes.
Editors' Summary
Background.
In the 1980s, infection with the human immunodeficiency virus (HIV), which causes acquired immunodeficiency syndrome (AIDS), was a death sentence. Although the first antiretroviral drugs (compounds that block HIV's life cycle) were developed quickly, single antiretrovirals only transiently suppress HIV infection. HIV rapidly accumulates random changes (mutations) in its genetic material, some of which make it drug resistant. Nowadays, there are many different antiretrovirals. Some inhibit the viral protease, an enzyme used to assemble new viruses. Others block reverse transcriptase (RT), which makes replicates of the genes of the virus. Nucleoside/nucleotide RT inhibitors (NRTIs; for example, zidovudine—also called AZT—and lamivudine) and non-nucleoside RT inhibitors (NNRTIs; for example, nevirapine and efavirenz) interfere with the activity of RT by binding to different sites in its so-called “DNA polymerase domain,” the part of the enzyme that constructs copies of the viral genes. Highly active antiretroviral therapy (HAART), which was introduced in the mid 1990s, combines several antiretrovirals (usually a protease inhibitor and two NRTIs or an NNRTI and two NRTIs) so that the replication of any virus that develops resistance to one drug is inhibited by the other drugs in the mix. When treated with HAART, HIV infection is usually a chronic, stable condition rather than a fatal disease.
Why Was This Study Done?
Unfortunately, HIV that is resistant to drugs still develops in some patients. To improve the prevention and management of drug resistance, a better understanding of the mutations that cause resistance is needed. Resistance to RT inhibitors usually involves mutations in the DNA polymerase domain that reduce the efficacy of NRTIs (including thymidine analogue mutations—also known as TAMs—and lamivudine-resistance mutations) and NNRTIs. Blood tests that detect these resistance mutations (genotype tests) have been used for several years to guide individualized selection of HIV drugs. Recently, however, mutations outside the DNA polymerase domain have also been implicated in resistance to RT inhibitors. In this study, the researchers have used data and samples collected since the mid 1990s by Canada's British Columbia Centre for Excellence in HIV/AIDS to investigate the clinical relevance of a mutation called N348I. This mutation changes an asparagine (a type of amino acid) to an isoleucine in a region of RT known as the connection domain. The researchers have also investigated how this mutation causes resistance to RT inhibitors in laboratory tests.
What Did the Researchers Do and Find?
The researchers analyzed the first two-thirds of the RT gene in viruses isolated from a large number of the Centre's patients. Virus carrying the N348I mutation was present in less than one in 100 patients whose HIV infection had never been treated, but in more than one in 10 treatment-experienced patients. The mutation appeared early in therapy, often in viruses that had TAMs, a lamivudine-resistance mutation called M184V/I, and/or NNRTI resistance mutations. Patients treated with zidovudine and nevirapine were 2.6 times more likely to have the N348I mutation than patients not treated with these drugs. Furthermore, the appearance of the N348I mutation often coincided with an increase in viral load, although other mutations that appeared at a similar time could have contributed to this increase. When the researchers introduced the N348I mutation into HIV growing in the laboratory, they found that it decreased the susceptibility of the virus to zidovudine and to NNRTIs.
What Do These Findings Mean?
These findings show that the treatment of patients with RT inhibitors can select a drug-resistant HIV variant that has a mutation outside the enzyme's DNA polymerase domain. Because this N348I mutation, which is commonly selected in vivo and has also been seen in other studies, confers resistance to two classes of RT inhibitors and can emerge early during therapy, it could have a large impact on patient responses to antiviral regimens that contain zidovudine and nevirapine. Although these findings do not show that the N348I mutation alone causes treatment failure, they may have implications for genotypic and phenotypic resistance testing, which is often used to guide treatment decisions. At present, genotype tests for resistance to RT inhibitors look for mutations only in the DNA polymerase domain of RT. This study is the first to demonstrate that it might be worth looking for the N348I mutation (and for other mutations outside the DNA polymerase domain) to improve the ability of genotypic and phenotypic resistance tests to predict treatment outcomes.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040335.
Information is available from the US National Institute of Allergy and Infectious Diseases on HIV infection and AIDS
HIV InSite has comprehensive information on all aspects of HIV/AIDS, including links to fact sheets (in English, French, and Spanish) about antiretrovirals, and chapters explaining antiretroviral resistance testing
NAM, a UK registered charity, provides information about all aspects of HIV and AIDS, including fact sheets on types of HIV drugs, drug resistance, and resistance tests (in English, Spanish, French, Portuguese, and Russian)
The US Centers for Disease Control and Prevention provides information on HIV/AIDS and on treatment (in English and Spanish)
AIDSinfo, a service of the US Department of Health and Human Services provides information for patients on HIV and its treatment
doi:10.1371/journal.pmed.0040335
PMCID: PMC2100143  PMID: 18052601

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