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1.  Genetic Predictors of Response to Serotonergic and Noradrenergic Antidepressants in Major Depressive Disorder: A Genome-Wide Analysis of Individual-Level Data and a Meta-Analysis 
PLoS Medicine  2012;9(10):e1001326.
Testing whether genetic information could inform the selection of the best drug for patients with depression, Rudolf Uher and colleagues searched for genetic variants that could predict clinically meaningful responses to two major groups of antidepressants.
It has been suggested that outcomes of antidepressant treatment for major depressive disorder could be significantly improved if treatment choice is informed by genetic data. This study aims to test the hypothesis that common genetic variants can predict response to antidepressants in a clinically meaningful way.
Methods and Findings
The NEWMEDS consortium, an academia–industry partnership, assembled a database of over 2,000 European-ancestry individuals with major depressive disorder, prospectively measured treatment outcomes with serotonin reuptake inhibiting or noradrenaline reuptake inhibiting antidepressants and available genetic samples from five studies (three randomized controlled trials, one part-randomized controlled trial, and one treatment cohort study). After quality control, a dataset of 1,790 individuals with high-quality genome-wide genotyping provided adequate power to test the hypotheses that antidepressant response or a clinically significant differential response to the two classes of antidepressants could be predicted from a single common genetic polymorphism. None of the more than half million genetic markers significantly predicted response to antidepressants overall, serotonin reuptake inhibitors, or noradrenaline reuptake inhibitors, or differential response to the two types of antidepressants (genome-wide significance p<5×10−8). No biological pathways were significantly overrepresented in the results. No significant associations (genome-wide significance p<5×10−8) were detected in a meta-analysis of NEWMEDS and another large sample (STAR*D), with 2,897 individuals in total. Polygenic scoring found no convergence among multiple associations in NEWMEDS and STAR*D.
No single common genetic variant was associated with antidepressant response at a clinically relevant level in a European-ancestry cohort. Effects specific to particular antidepressant drugs could not be investigated in the current study.
Please see later in the article for the Editors' Summary
Editors' Summary
Genetic and environmental factors can influence a person's response to medications. Taking advantage of the recent advancements in genetics, scientists are working to match specific gene variations with responses to particular medications. Knowing whether a patient is likely to respond to a drug or have serious side effects would allow doctors to select the best treatment up front. Right now, there are only a handful of examples where a patient's version of a particular gene predicts their response to a particular drug. Some scientists believe that there will be many more such matches between genetic variants and treatment responses. Others think that because the action of most drugs is influenced by many different genes, a variant in one of those genes is unlikely to have measurable effect in most cases.
Why Was This Study Done?
One of the areas where patients' responses to available drugs vary widely is severe depression (or major depressive disorder). Prescription of an antidepressant is often the first step in treating the disease. However, less than half of patients get well taking the first antidepressant prescribed. Those who don't respond to the first drug need to, together with their doctors, try multiple courses of treatment to find the right drug and the right dose for them. For some patients none of the existing drugs work well.
To see whether genetic information could help improve the choice of antidepressant, researchers from universities and the pharmaceutical industry joined forces in this large study. They examined two ways to use genetic information to improve the treatment of depression. First, they searched all genes for common genetic variants that could predict which patients would not respond to the two major groups of antidepressants (serotonin reuptake inhibitors, or SRIs, and noradrenaline reuptake inhibitors, or NRIs). They hoped that this would help with the development of new drugs that could help these patients. Second, they looked for common genetic variants in all genes that could identify patients who responded to one of the two major groups of antidepressants. Such predictors would make it possible to know which drug to prescribe for which patient.
What Did the Researchers Do and Find?
The researchers selected 1,790 patients with severe depression who had participated in one of several research studies; 1,222 of the patients had been treated with an SRI, the remaining 568 with an NRI, and it was recorded how well the drugs worked for each patient. The researchers also had a detailed picture of the genetic make-up of each patient, with information for over half a million genetic variants. They then looked for an association between genetic variants and responses to drugs.
They found not a single genetic variant that could predict clearly whether a person would respond to antidepressants in general, to one of the two main groups (SRIs and NRIs), or much better to one than the other. They also didn't find any combination of variants in groups of genes that work together that could predict responses. Combining their data with those from another large study did not yield any robust predictors either.
What Do These Findings Mean?
This study was large enough that it should have been possible to find common genetic variants that by themselves could predict a clinically meaningful response to SRIs and/or NRIs, had such variants existed. The fact that the study failed to find such variants suggests that such variants do not exist. It is still possible, however, that variants that are less common could predict response, or that combinations of variants could. To find those, if they do exist, even larger studies will need to be done.
Additional Information
Please access these websites via the online version of this summary at
The National Institute of General Medical Sciences at the US National Institutes of Health has a fact sheet on personalized medicine
PubMed Health at the US National Library of Medicine has a page on major depressive disorder
Wikipedia has pages on major depressive disorder and pharmacogenetics, the study of how genetic variation affects response to certain drugs (note that Wikipedia is a free online encyclopedia that anyone can edit)
The UK National Health Service has comprehensive information pages on depression
PMCID: PMC3472989  PMID: 23091423
2.  The Role of the Toxicologic Pathologist in the Post-Genomic Era# 
Journal of Toxicologic Pathology  2013;26(2):105-110.
An era can be defined as a period in time identified by distinctive character, events, or practices. We are now in the genomic era. The pre-genomic era: There was a pre-genomic era. It started many years ago with novel and seminal animal experiments, primarily directed at studying cancer. It is marked by the development of the two-year rodent cancer bioassay and the ultimate realization that alternative approaches and short-term animal models were needed to replace this resource-intensive and time-consuming method for predicting human health risk. Many alternatives approaches and short-term animal models were proposed and tried but, to date, none have completely replaced our dependence upon the two-year rodent bioassay. However, the alternative approaches and models themselves have made tangible contributions to basic research, clinical medicine and to our understanding of cancer and they remain useful tools to address hypothesis-driven research questions. The pre-genomic era was a time when toxicologic pathologists played a major role in drug development, evaluating the cancer bioassay and the associated dose-setting toxicity studies, and exploring the utility of proposed alternative animal models. It was a time when there was shortage of qualified toxicologic pathologists. The genomic era: We are in the genomic era. It is a time when the genetic underpinnings of normal biological and pathologic processes are being discovered and documented. It is a time for sequencing entire genomes and deliberately silencing relevant segments of the mouse genome to see what each segment controls and if that silencing leads to increased susceptibility to disease. What remains to be charted in this genomic era is the complex interaction of genes, gene segments, post-translational modifications of encoded proteins, and environmental factors that affect genomic expression. In this current genomic era, the toxicologic pathologist has had to make room for a growing population of molecular biologists. In this present era newly emerging DVM and MD scientists enter the work arena with a PhD in pathology often based on some aspect of molecular biology or molecular pathology research. In molecular biology, the almost daily technological advances require one’s complete dedication to remain at the cutting edge of the science. Similarly, the practice of toxicologic pathology, like other morphological disciplines, is based largely on experience and requires dedicated daily examination of pathology material to maintain a well-trained eye capable of distilling specific information from stained tissue slides - a dedicated effort that cannot be well done as an intermezzo between other tasks. It is a rare individual that has true expertise in both molecular biology and pathology. In this genomic era, the newly emerging DVM-PhD or MD-PhD pathologist enters a marketplace without many job opportunities in contrast to the pre-genomic era. Many face an identity crisis needing to decide to become a competent pathologist or, alternatively, to become a competent molecular biologist. At the same time, more PhD molecular biologists without training in pathology are members of the research teams working in drug development and toxicology. How best can the toxicologic pathologist interact in the contemporary team approach in drug development, toxicology research and safety testing? Based on their biomedical training, toxicologic pathologists are in an ideal position to link data from the emerging technologies with their knowledge of pathobiology and toxicology. To enable this linkage and obtain the synergy it provides, the bench-level, slide-reading expert pathologist will need to have some basic understanding and appreciation of molecular biology methods and tools. On the other hand, it is not likely that the typical molecular biologist could competently evaluate and diagnose stained tissue slides from a toxicology study or a cancer bioassay. The post-genomic era: The post-genomic era will likely arrive approximately around 2050 at which time entire genomes from multiple species will exist in massive databases, data from thousands of robotic high throughput chemical screenings will exist in other databases, genetic toxicity and chemical structure-activity-relationships will reside in yet other databases. All databases will be linked and relevant information will be extracted and analyzed by appropriate algorithms following input of the latest molecular, submolecular, genetic, experimental, pathology and clinical data. Knowledge gained will permit the genetic components of many diseases to be amenable to therapeutic prevention and/or intervention. Much like computerized algorithms are currently used to forecast weather or to predict political elections, computerized sophisticated algorithms based largely on scientific data mining will categorize new drugs and chemicals relative to their health benefits versus their health risks for defined human populations and subpopulations. However, this form of a virtual toxicity study or cancer bioassay will only identify probabilities of adverse consequences from interaction of particular environmental and/or chemical/drug exposure(s) with specific genomic variables. Proof in many situations will require confirmation in intact in vivo mammalian animal models. The toxicologic pathologist in the post-genomic era will be the best suited scientist to confirm the data mining and its probability predictions for safety or adverse consequences with the actual tissue morphological features in test species that define specific test agent pathobiology and human health risk.
PMCID: PMC3695332  PMID: 23914052
genomic era; history of toxicologic pathology; molecular biology
3.  Number of Patients Studied Prior to Approval of New Medicines: A Database Analysis 
PLoS Medicine  2013;10(3):e1001407.
In an evaluation of medicines approved by the European Medicines Agency 2000 to 2010, Ruben Duijnhoven and colleagues find that the number of patients evaluated for medicines approved for chronic use are inadequate for evaluation of safety or long-term efficacy.
At the time of approval of a new medicine, there are few long-term data on the medicine's benefit–risk balance. Clinical trials are designed to demonstrate efficacy, but have major limitations with regard to safety in terms of patient exposure and length of follow-up. This study of the number of patients who had been administered medicines at the time of medicine approval by the European Medicines Agency aimed to determine the total number of patients studied, as well as the number of patients studied long term for chronic medication use, compared with the International Conference on Harmonisation's E1 guideline recommendations.
Methods and Findings
All medicines containing new molecular entities approved between 2000 and 2010 were included in the study, including orphan medicines as a separate category. The total number of patients studied before approval was extracted (main outcome). In addition, the number of patients with long-term use (6 or 12 mo) was determined for chronic medication. 200 unique new medicines were identified: 161 standard and 39 orphan medicines. The median total number of patients studied before approval was 1,708 (interquartile range [IQR] 968–3,195) for standard medicines and 438 (IQR 132–915) for orphan medicines. On average, chronic medication was studied in a larger number of patients (median 2,338, IQR 1,462–4,135) than medication for intermediate (878, IQR 513–1,559) or short-term use (1,315, IQR 609–2,420). Safety and efficacy of chronic use was studied in fewer than 1,000 patients for at least 6 and 12 mo in 46.4% and 58.3% of new medicines, respectively. Among the 84 medicines intended for chronic use, 68 (82.1%) met the guideline recommendations for 6-mo use (at least 300 participants studied for 6 mo and at least 1,000 participants studied for any length of time), whereas 67 (79.8%) of the medicines met the criteria for 12-mo patient exposure (at least 100 participants studied for 12 mo).
For medicines intended for chronic use, the number of patients studied before marketing is insufficient to evaluate safety and long-term efficacy. Both safety and efficacy require continued study after approval. New epidemiologic tools and legislative actions necessitate a review of the requirements for the number of patients studied prior to approval, particularly for chronic use, and adequate use of post-marketing studies.
Please see later in the article for the Editors' Summary
Editors' Summary
Before any new medicine is marketed for the treatment of a human disease, it has to go through extensive laboratory and clinical research. In the laboratory, scientists investigate the causes of diseases, identify potential new treatments, and test these interventions in disease models, some of which involve animals. The safety and efficacy of potential new interventions is then investigated in a series of clinical trials—studies in which the new treatment is tested in selected groups of patients under strictly controlled conditions, first to determine whether the drug is tolerated by humans and then to assess its efficacy. Finally, the results of these trials are reviewed by the government body responsible for drug approval; in the US, this body is the Food and Drug Administration, and in the European Union, the European Medicines Agency (EMA) is responsible for the scientific evaluation and approval of new medicines.
Why Was This Study Done?
Clinical trials are primarily designed to test the efficacy—the ability to produce the desired therapeutic effect—of new medicines. The number of patients needed to establish efficacy determines the size of a clinical trial, and the indications for which efficacy must be shown determine the trial's duration. However, identifying adverse effects of drugs generally requires the drug to be taken by more patients than are required to show efficacy, so the information about adverse effects is often relatively limited at the end of clinical testing. Consequently, when new medicines are approved, their benefit–risk ratios are often poorly defined, even though physicians need this information to decide which treatment to recommend to their patients. For the evaluation of risk or adverse effects of medicines being developed for chronic (long-term) treatment of non-life-threatening diseases, current guidelines recommend that at least 1,000–1,500 patients are exposed to the new drug and that 300 and 100 patients use the drug for six and twelve months, respectively, before approval. But are these guidelines being followed? In this database analysis, the researchers use data collected by the EMA to determine how many patients are exposed to new medicines before approval in the European Union and how many are exposed for extended periods of time to medicines intended for chronic use.
What Did the Researchers Do and Find?
Using the European Commission's Community Register of Medicinal Products, the researchers identified 161 standard medicines and 39 orphan medicines (medicines to treat or prevent rare life-threatening diseases) that contained new active substances and that were approved in the European Union between 2000 and 2010. They extracted information on the total number of patients studied and on the number exposed to the medicines for six months and twelve months before approval of each medicine from EMA's European public assessment reports. The average number of patients studied before approval was 1,708 for standard medicines and 438 for orphan medicines (marketing approval is easier to obtain for orphan medicines than for standard medicines to encourage drug companies to develop medicines that might otherwise be unprofitable). On average, medicines for chronic use (for example, asthma medications) were studied in more patients (2,338) than those for intermediate use such as anticancer drugs (878), or short-term use such as antibiotics (1,315). The safety and efficacy of chronic use was studied in fewer than 1,000 patients for at least six and twelve months in 46.4% and 58.4% of new medicines, respectively. Finally, among the 84 medicines intended for chronic use, 72 were studied in at least 300 patients for six months, and 70 were studied in at least 100 patients for twelve months.
What Do These Findings Mean?
These findings suggest that although the number of patients studied before approval is sufficient to determine the short-term efficacy of new medicines, it is insufficient to determine safety or long-term efficacy. Any move by drug approval bodies to require pharmaceutical companies to increase the total number of patients exposed to a drug, or the number exposed for extended periods of time to drugs intended for chronic use, would inevitably delay the entry of new products into the market, which likely would be unacceptable to patients and healthcare providers. Nevertheless, the researchers suggest that a reevaluation of the study size and long-term data requirements that need to be met for the approval of new medicines, particularly those designed for long-term use, is merited. They also stress the need for continued study of both the safety and efficacy of new medicines after approval and the importance of post-marketing studies that actively examine safety issues.
Additional Information
Please access these websites via the online version of this summary at
The European Medicines Agency (EMA) provides information about all aspects of the scientific evaluation and approval of new medicines in the European Union; its European public assessment reports are publicly available
The European Commission's Community Register of Medicinal Products is a publicly searchable database of medicinal products approved for human use in the European Union
The US Food and Drug Administration provides information about drug approval in the US for consumers and for health professionals
The US National Institutes of Health provides information (including personal stories) about clinical trials
PMCID: PMC3601954  PMID: 23526887
4.  The Cancer Genome Atlas Clinical Explorer: a web and mobile interface for identifying clinical–genomic driver associations 
Genome Medicine  2015;7:112.
The Cancer Genome Atlas (TCGA) project has generated genomic data sets covering over 20 malignancies. These data provide valuable insights into the underlying genetic and genomic basis of cancer. However, exploring the relationship among TCGA genomic results and clinical phenotype remains a challenge, particularly for individuals lacking formal bioinformatics training. Overcoming this hurdle is an important step toward the wider clinical translation of cancer genomic/proteomic data and implementation of precision cancer medicine. Several websites such as the cBio portal or University of California Santa Cruz genome browser make TCGA data accessible but lack interactive features for querying clinically relevant phenotypic associations with cancer drivers. To enable exploration of the clinical–genomic driver associations from TCGA data, we developed the Cancer Genome Atlas Clinical Explorer.
The Cancer Genome Atlas Clinical Explorer interface provides a straightforward platform to query TCGA data using one of the following methods: (1) searching for clinically relevant genes, micro RNAs, and proteins by name, cancer types, or clinical parameters; (2) searching for genomic/proteomic profile changes by clinical parameters in a cancer type; or (3) testing two-hit hypotheses. SQL queries run in the background and results are displayed on our portal in an easy-to-navigate interface according to user’s input. To derive these associations, we relied on elastic-net estimates of optimal multiple linear regularized regression and clinical parameters in the space of multiple genomic/proteomic features provided by TCGA data. Moreover, we identified and ranked gene/micro RNA/protein predictors of each clinical parameter for each cancer. The robustness of the results was estimated by bootstrapping. Overall, we identify associations of potential clinical relevance among genes/micro RNAs/proteins using our statistical analysis from 25 cancer types and 18 clinical parameters that include clinical stage or smoking history.
The Cancer Genome Atlas Clinical Explorer enables the cancer research community and others to explore clinically relevant associations inferred from TCGA data. With its accessible web and mobile interface, users can examine queries and test hypothesis regarding genomic/proteomic alterations across a broad spectrum of malignancies.
Electronic supplementary material
The online version of this article (doi:10.1186/s13073-015-0226-3) contains supplementary material, which is available to authorized users.
PMCID: PMC4624593  PMID: 26507825
5.  Evaluating Drug Prices, Availability, Affordability, and Price Components: Implications for Access to Drugs in Malaysia 
PLoS Medicine  2007;4(3):e82.
Malaysia's stable health care system is facing challenges with increasing medicine costs. To investigate these issues a survey was carried out to evaluate medicine prices, availability, affordability, and the structure of price components.
Methods and Findings
The methodology developed by the World Health Organization (WHO) and Health Action International (HAI) was used. Price and availability data for 48 medicines was collected from 20 public sector facilities, 32 private sector retail pharmacies and 20 dispensing doctors in four geographical regions of West Malaysia. Medicine prices were compared with international reference prices (IRPs) to obtain a median price ratio. The daily wage of the lowest paid unskilled government worker was used to gauge the affordability of medicines. Price component data were collected throughout the supply chain, and markups, taxes, and other distribution costs were identified. In private pharmacies, innovator brand (IB) prices were 16 times higher than the IRPs, while generics were 6.6 times higher. In dispensing doctor clinics, the figures were 15 times higher for innovator brands and 7.5 for generics. Dispensing doctors applied high markups of 50%–76% for IBs, and up to 316% for generics. Retail pharmacy markups were also high—25%–38% and 100%–140% for IBs and generics, respectively. In the public sector, where medicines are free, availability was low even for medicines on the National Essential Drugs List. For a month's treatment for peptic ulcer disease and hypertension people have to pay about a week's wages in the private sector.
The free market by definition does not control medicine prices, necessitating price monitoring and control mechanisms. Markups for generic products are greater than for IBs. Reducing the base price without controlling markups may increase profits for retailers and dispensing doctors without reducing the price paid by end users. To increase access and affordability, promotion of generic medicines and improved availability of medicines in the public sector are required.
Drug price and availability data were collected from West Malaysian public sector facilities, private sector retail pharmacies, and dispensing doctors. Mark-ups were higher on generic drugs than on innovator brands.
Editors' Summary
The World Health Organization has said that one-third of the people of the world cannot access the medicines they need. An important reason for this problem is that prices are often too high for people or government-funded health systems to afford. In developing countries, most people who need medicines have to pay for them out of their own pockets. Where the cost of drugs is covered by health systems, spending on medicines is a major part of the total healthcare budget. Governments use a variety of approaches to try to control the cost of drugs and make sure that essential medicines are affordable and not overpriced. According to the theory of “free market economics,” the costs of goods and services are determined by interactions between buyers and sellers and not by government intervention. However, free market economics does not work well at containing the costs of medicines, particularly new medicines, because new medicines are protected by patent law, which legally prevents others from making, using, or selling the medicine for a particular period of time. Therefore, without government intervention, there is nothing to help to push down prices.
Why Was This Study Done?
Malaysia is a middle-income country with a relatively effective public health system, but it is facing a rapid rise in drug costs. In Malaysia, medicine prices are determined by free-market economics, without any control by government. Government hospitals are expected to provide drugs free, but a substantial proportion of medicines are paid for by patients who buy them directly from private pharmacies or prescribing doctors. There is evidence that Malaysian patients have difficulties accessing the drugs they need and that cost is an important factor. Therefore, the researchers who wrote this paper wanted to examine the cost of different medicines in Malaysia, and their availability and affordability from different sources.
What Did the Researchers Do and Find?
In this research project, 48 drugs were studied, of which 28 were part of a “core list” identified by the World Health Organization as “essential drugs” on the basis of the global burden of disease. The remaining 20 reflected health care needs in Malaysia itself. The costs of each medicine were collected from government hospitals, private pharmacies, and dispensing doctors in four different regions of Malaysia. Data were collected for the “innovator brand” (made by the original patent holder) and for “generic” brands (an equivalent drug to the innovator brand, produced by a different company once the innovator brand no longer has an exclusive patent). The medicine prices were compared against international reference prices (IRP), which are the average prices offered by not-for-profit drug companies to developing countries. Finally, the researchers also compared the cost of the drugs with daily wages, in order to work out their “affordability.”
The researchers found that, irrespective of the source of medicines, prices were on average very much higher than the international reference price, ranging from 2.4 times the IRP for innovator brands accessed through public hospitals, to 16 times the IRP for innovator brands accessed through private pharmacies. The availability of medicines was also very poor, with only 25% of generic medicines available on average through the public sector. The affordability of many of the medicines studied was again very poor. For example, one month's supply of ranitidine (a drug for stomach ulcers) was equivalent to around three days' wages for a low-paid government worker, and one month's supply of fluoxetine (an antidepressant) would cost around 26 days' wages.
What Do These Findings Mean?
These results show that essential drugs are very expensive in Malaysia and are not universally available. Many people would not be able to pay for essential medicines. The cost of medicines in Malaysia seems to be much higher than in areas of India and Sri Lanka, although the researchers did not attempt to collect data in order to carry out an international comparison. It is possible that the high cost and low availability in Malaysia are the result of a lack of government regulation. Overall, the findings suggest that the government should set up mechanisms to prevent drug manufacturers from increasing prices too much and thus ensure greater access to essential medicines.
Additional Information.
Please access these Web sites via the online version of this summary at
Read a related PLoS Medicine Perspective article by Suzanne Hill
Information is available from the World Health Organization on Improving Access to Medicines
Information on medicine prices is available from Health Action International
Wikipedia has an entry on Patent (a type of intellectual property that is normally used to prevent other companies from selling a newly invented medicine). (Wikipedia is an internet encyclopedia anyone can edit.)
The Drugs for Neglected Diseases Initiative is an international collaboration between public organizations that aims to develop drugs for people suffering from neglected diseases
PMCID: PMC1831730  PMID: 17388660
6.  Genomics for Disease Treatment and Prevention 
The enormous advances in genetics and genomics of the past decade have the potential to revolutionize health care, including mental health care, and bring about a system predominantly characterized by the practice of genomic and personalized medicine. We briefly review the history of genetics and genomics and present heritability estimates for major chronic diseases of aging and neuropsychiatric disorders. We then assess the extent to which the results of genetic and genomic studies are currently being leveraged clinically for disease treatment and prevention and identify priority research areas in which further work is needed. Pharmacogenomics has emerged as one area of genomics that already has had notable impacts on disease treatment and the practice of medicine. Little evidence, however, for the clinical validity and utility of predictive testing based on genomic information is available, and thus has, to some extent, hindered broader-scale preventive efforts for common, complex diseases. Furthermore, although other disease areas have had greater success in identifying genetic factors responsible for various conditions, progress in identifying the genetic basis of neuropsychiatric diseases has lagged behind. We review social, economic, and policy issues relevant to genomic medicine, and find that a new model of health care based on proactive and preventive health planning and individualized treatment will require major advances in health care policy and administration. Specifically, incentives for relevant stakeholders are critical, as are realignment of incentives and education initiatives for physicians, and updates to pertinent legislation. Moreover, the translational behavioral and public health research necessary for fully integrating genomics into health care is lacking, and further work in these areas is needed. In short, while the pace of advances in genetic and genomic science and technology has been rapid, more work is needed to fully realize the potential for impacting disease treatment and prevention generally, and mental health specifically.
PMCID: PMC3073546  PMID: 21333845
genomics; genetic testing; genetic risk assessment; public health genomics; pharmacogenomics
7.  Discovery of small molecule cancer drugs: Successes, challenges and opportunities 
Molecular Oncology  2012;6(2):155-176.
The discovery and development of small molecule cancer drugs has been revolutionised over the last decade. Most notably, we have moved from a one-size-fits-all approach that emphasized cytotoxic chemotherapy to a personalised medicine strategy that focuses on the discovery and development of molecularly targeted drugs that exploit the particular genetic addictions, dependencies and vulnerabilities of cancer cells. These exploitable characteristics are increasingly being revealed by our expanding understanding of the abnormal biology and genetics of cancer cells, accelerated by cancer genome sequencing and other high-throughput genome-wide campaigns, including functional screens using RNA interference. In this review we provide an overview of contemporary approaches to the discovery of small molecule cancer drugs, highlighting successes, current challenges and future opportunities. We focus in particular on four key steps: Target validation and selection; chemical hit and lead generation; lead optimization to identify a clinical drug candidate; and finally hypothesis-driven, biomarker-led clinical trials. Although all of these steps are critical, we view target validation and selection and the conduct of biology-directed clinical trials as especially important areas upon which to focus to speed progress from gene to drug and to reduce the unacceptably high attrition rate during clinical development. Other challenges include expanding the envelope of druggability for less tractable targets, understanding and overcoming drug resistance, and designing intelligent and effective drug combinations. We discuss not only scientific and technical challenges, but also the assessment and mitigation of risks as well as organizational, cultural and funding problems for cancer drug discovery and development, together with solutions to overcome the ‘Valley of Death’ between basic research and approved medicines. We envisage a future in which addressing these challenges will enhance our rapid progress towards truly personalised medicine for cancer patients.
► Here we review small molecule cancer drug discovery and development. ► We focus on Target selection, hit identification, lead optimization and clinical trials. ► A particular emphasis of this article is personalized medicine.
PMCID: PMC3476506  PMID: 22440008
Small molecule cancer drug discovery and development; Target and validation selection; Hit identification; Lead optimization and clinical trials; Personalized medicine
8.  SuperPhy: predictive genomics for the bacterial pathogen Escherichia coli 
BMC Microbiology  2016;16:65.
Predictive genomics is the translation of raw genome sequence data into a phenotypic assessment of the organism. For bacterial pathogens, these phenotypes can range from environmental survivability, to the severity of human disease. Significant progress has been made in the development of generic tools for genomic analyses that are broadly applicable to all microorganisms; however, a fundamental missing component is the ability to analyze genomic data in the context of organism-specific phenotypic knowledge, which has been accumulated from decades of research and can provide a meaningful interpretation of genome sequence data.
In this study, we present SuperPhy, an online predictive genomics platform ( for Escherichia coli. The platform integrates the analytical tools and genome sequence data for all publicly available E. coli genomes and facilitates the upload of new genome sequences from users under public or private settings. SuperPhy provides real-time analyses of thousands of genome sequences with results that are understandable and useful to a wide community, including those in the fields of clinical medicine, epidemiology, ecology, and evolution. SuperPhy includes identification of: 1) virulence and antimicrobial resistance determinants 2) statistical associations between genotypes, biomarkers, geospatial distribution, host, source, and phylogenetic clade; 3) the identification of biomarkers for groups of genomes on the based presence/absence of specific genomic regions and single-nucleotide polymorphisms and 4) in silico Shiga-toxin subtype.
SuperPhy is a predictive genomics platform that attempts to provide an essential link between the vast amounts of genome information currently being generated and phenotypic knowledge in an organism-specific context.
PMCID: PMC4828761  PMID: 27067409
Comparative genomics; Bioinformatics; Anti-microbial resistance; Virulence factors; Epidemiology; Population genomics; Software
9.  From Molecules to Patients: The Clinical Applications of Translational Bioinformatics 
Yearbook of Medical Informatics  2015;10(1):164-169.
In order to realize the promise of personalized medicine, Translational Bioinformatics (TBI) research will need to continue to address implementation issues across the clinical spectrum. In this review, we aim to evaluate the expanding field of TBI towards clinical applications, and define common themes and current gaps in order to motivate future research.
Here we present the state-of-the-art of clinical implementation of TBI-based tools and resources. Our thematic analyses of a targeted literature search of recent TBI-related articles ranged across topics in genomics, data management, hypothesis generation, molecular epidemiology, diagnostics, therapeutics and personalized medicine.
Open areas of clinically-relevant TBI research identified in this review include developing data standards and best practices, publicly available resources, integrative systems-level approaches, user-friendly tools for clinical support, cloud computing solutions, emerging technologies and means to address pressing legal, ethical and social issues.
There is a need for further research bridging the gap from foundational TBI-based theories and methodologies to clinical implementation. We have organized the topic themes presented in this review into four conceptual foci – domain analyses, knowledge engineering, computational architectures and computation methods alongside three stages of knowledge development in order to orient future TBI efforts to accelerate the goals of personalized medicine.
PMCID: PMC4587059  PMID: 26293863
Translational bioinformatics; clinical informatics; personalized medicine; clinical research; translational science
10.  Paving the Way to Personalized Genomic Medicine: Steps to Successful Implementation 
Over the last decade there has been vast interest in and focus on the implementation of personalized genomic medicine. Although there is general agreement that personalized genomic medicine involves utilizing genome technology to assess individual risk and ensure the delivery of the “right treatment, for the right patient, at the right time,” different categories of stakeholders focus on different aspects of personalized genomic medicine and operationalize it in diverse ways. In order to move toward a clearer, more holistic understanding of the concept, this article begins by identifying and defining three major elements of personalized genomic medicine commonly discussed by stakeholders: molecular medicine, pharmacogenomics, and health information technology. The integration of these three elements has the potential to improve health and reduce health care costs, but it also raises many challenges. This article endeavors to address these challenges by identifying five strategic areas that will require significant investment for the successful integration of personalized genomics into clinical care: (1) health technology assessment; (2) health outcomes research; (3) education (of both health professionals and the public); (4) communication among stakeholders; and (5) the development of best practices and guidelines. While different countries and global regions display marked heterogeneity in funding of health care in the form of public, private, or blended payor systems, previous analyses of personalized genomic medicine and attendant technological innovations have been performed without due attention to this complexity. Hence, this article focuses on personalized genomic medicine in the United States as a model case study wherein a significant portion of health care payors represent private, nongovernment resources. Lessons learned from the present analysis of personalized genomic medicine could usefully inform health care systems in other global regions where payment for personalized genomic medicine will be enabled through private or hybrid public-private funding systems.
PMCID: PMC2809376  PMID: 20098629
Personalized Genomic Medicine; Personalized Medicine; Ethics; Genomics; Policy
11.  Issue Information 
Aims and Scope: Molecular Genetics & Genomic Medicine is a peer reviewed journal for rapid dissemination of high-quality research related to the dynamically developing areas of human, molecular and medical genetics. The journal publishes original research articles covering novel findings in phenotypic, molecular, biological, and genomic aspects of genomic variation, inherited disorders and birth defects. The broad publishing spectrum of Molecular Genetics & Genomic Medicine includes rare and common disorders from diagnosis to treatment. Examples of appropriate articles include reports of novel disease genes, functional studies of genetic variants, in-depth genotype-phenotype studies, genomic analysis of inherited disorders, molecular diagnostic methods, medical bioinformatics, ethical, legal, and social implications (ELSI), and novel approaches to clinical diagnosis. We anticipate that Molecular Genetics & Genomic Medicine will provide a high quality scientific home for next generation sequencing studies of rare and common disorders, which will make novel findings in this fascinating area easily and rapidly accessible to the scientific community. This will serve as the basis for translating next generation sequencing studies into individualized diagnostics and therapeutics, for day-to-day medical care.
Molecular Genetics & Genomic Medicine publishes original research articles, reviews, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented.
Molecular Genetics & Genomic Medicine is a Wiley Open Access journal, one of a series of peer reviewed titles publishing quality research with speed and efficiency. For further information visit the Wiley Open Access website.
PMCID: PMC3907910  PMID: 24498632
12.  Red Blood Cell Transfusion and Mortality in Trauma Patients: Risk-Stratified Analysis of an Observational Study 
PLoS Medicine  2014;11(6):e1001664.
Using a large multicentre cohort, Pablo Perel and colleagues evaluate the association of red blood cell transfusion with mortality according to the predicted risk of death for trauma patients.
Please see later in the article for the Editors' Summary
Haemorrhage is a common cause of death in trauma patients. Although transfusions are extensively used in the care of bleeding trauma patients, there is uncertainty about the balance of risks and benefits and how this balance depends on the baseline risk of death. Our objective was to evaluate the association of red blood cell (RBC) transfusion with mortality according to the predicted risk of death.
Methods and Findings
A secondary analysis of the CRASH-2 trial (which originally evaluated the effect of tranexamic acid on mortality in trauma patients) was conducted. The trial included 20,127 trauma patients with significant bleeding from 274 hospitals in 40 countries. We evaluated the association of RBC transfusion with mortality in four strata of predicted risk of death: <6%, 6%–20%, 21%–50%, and >50%. For this analysis the exposure considered was RBC transfusion, and the main outcome was death from all causes at 28 days. A total of 10,227 patients (50.8%) received at least one transfusion. We found strong evidence that the association of transfusion with all-cause mortality varied according to the predicted risk of death (p-value for interaction <0.0001). Transfusion was associated with an increase in all-cause mortality among patients with <6% and 6%–20% predicted risk of death (odds ratio [OR] 5.40, 95% CI 4.08–7.13, p<0.0001, and OR 2.31, 95% CI 1.96–2.73, p<0.0001, respectively), but with a decrease in all-cause mortality in patients with >50% predicted risk of death (OR 0.59, 95% CI 0.47–0.74, p<0.0001). Transfusion was associated with an increase in fatal and non-fatal vascular events (OR 2.58, 95% CI 2.05–3.24, p<0.0001). The risk associated with RBC transfusion was significantly increased for all the predicted risk of death categories, but the relative increase was higher for those with the lowest (<6%) predicted risk of death (p-value for interaction <0.0001). As this was an observational study, the results could have been affected by different types of confounding. In addition, we could not consider haemoglobin in our analysis. In sensitivity analyses, excluding patients who died early; conducting propensity score analysis adjusting by use of platelets, fresh frozen plasma, and cryoprecipitate; and adjusting for country produced results that were similar.
The association of transfusion with all-cause mortality appears to vary according to the predicted risk of death. Transfusion may reduce mortality in patients at high risk of death but increase mortality in those at low risk. The effect of transfusion in low-risk patients should be further tested in a randomised trial.
Trial registration NCT01746953
Please see later in the article for the Editors' Summary
Editors' Summary
Trauma—a serious injury to the body caused by violence or an accident—is a major global health problem. Every year, injuries caused by traffic collisions, falls, blows, and other traumatic events kill more than 5 million people (9% of annual global deaths). Indeed, for people between the ages of 5 and 44 years, injuries are among the top three causes of death in many countries. Trauma sometimes kills people through physical damage to the brain and other internal organs, but hemorrhage (serious uncontrolled bleeding) is responsible for 30%–40% of trauma-related deaths. Consequently, early trauma care focuses on minimizing hemorrhage (for example, by using compression to stop bleeding) and on restoring blood circulation after blood loss (health-care professionals refer to this as resuscitation). Red blood cell (RBC) transfusion is often used for the management of patients with trauma who are bleeding; other resuscitation products include isotonic saline and solutions of human blood proteins.
Why Was This Study Done?
Although RBC transfusion can save the lives of patients with trauma who are bleeding, there is considerable uncertainty regarding the balance of risks and benefits associated with this procedure. RBC transfusion, which is an expensive intervention, is associated with several potential adverse effects, including allergic reactions and infections. Moreover, blood supplies are limited, and the risks from transfusion are high in low- and middle-income countries, where most trauma-related deaths occur. In this study, which is a secondary analysis of data from a trial (CRASH-2) that evaluated the effect of tranexamic acid (which stops excessive bleeding) in patients with trauma, the researchers test the hypothesis that RBC transfusion may have a beneficial effect among patients at high risk of death following trauma but a harmful effect among those at low risk of death.
What Did the Researchers Do and Find?
The CRASH-2 trail included 20,127 patients with trauma and major bleeding treated in 274 hospitals in 40 countries. In their risk-stratified analysis, the researchers investigated the effect of RBC transfusion on CRASH-2 participants with a predicted risk of death (estimated using a validated model that included clinical variables such as heart rate and blood pressure) on admission to hospital of less than 6%, 6%–20%, 21%–50%, or more than 50%. That is, the researchers compared death rates among patients in each stratum of predicted risk of death who received a RBC transfusion with death rates among patients who did not receive a transfusion. Half the patients received at least one transfusion. Transfusion was associated with an increase in all-cause mortality at 28 days after trauma among patients with a predicted risk of death of less than 6% or of 6%–20%, but with a decrease in all-cause mortality among patients with a predicted risk of death of more than 50%. In absolute figures, compared to no transfusion, RBC transfusion was associated with 5.1 more deaths per 100 patients in the patient group with the lowest predicted risk of death but with 11.9 fewer deaths per 100 patients in the group with the highest predicted risk of death.
What Do These Findings Mean?
These findings show that RBC transfusion is associated with an increase in all-cause deaths among patients with trauma and major bleeding with a low predicted risk of death, but with a reduction in all-cause deaths among patients with a high predicted risk of death. In other words, these findings suggest that the effect of RBC transfusion on all-cause mortality may vary according to whether a patient with trauma has a high or low predicted risk of death. However, because the participants in the CRASH-2 trial were not randomly assigned to receive a RBC transfusion, it is not possible to conclude that receiving a RBC transfusion actually increased the death rate among patients with a low predicted risk of death. It might be that the patients with this level of predicted risk of death who received a transfusion shared other unknown characteristics (confounders) that were actually responsible for their increased death rate. Thus, to provide better guidance for clinicians caring for patients with trauma and hemorrhage, the hypothesis that RBC transfusion could be harmful among patients with trauma with a low predicted risk of death should be prospectively evaluated in a randomised controlled trial.
Additional Information
Please access these websites via the online version of this summary at
This study is further discussed in a PLOS Medicine Perspective by Druin Burch
The World Health Organization provides information on injuries and on violence and injury prevention (in several languages)
The US Centers for Disease Control and Prevention has information on injury and violence prevention and control
The National Trauma Institute, a US-based non-profit organization, provides information about hemorrhage after trauma and personal stories about surviving trauma
The UK National Health Service Choices website provides information about blood transfusion, including a personal story about transfusion after a serious road accident
The US National Heart, Lung, and Blood Institute also provides detailed information about blood transfusions
MedlinePlus provides links to further resources on injuries, bleeding, and blood transfusion (in English and Spanish)
More information in available about CRASH-2 (in several languages)
PMCID: PMC4060995  PMID: 24937305
13.  The Paradox of Equipoise: The Principle That Drives and Limits Therapeutic Discoveries in Clinical Research 
Progress in clinical medicine relies on the willingness of patients to take part in experimental clinical trials, particularly randomized controlled trials (RCTs). Before agreeing to enroll in clinical trials, patients require guarantees that they will not knowingly be harmed and will have the best possible chances of receiving the most favorable treatments. This guarantee is provided by the acknowledgment of uncertainty (equipoise), which removes ethical dilemmas and makes it easier for patients to enroll in clinical trials.
Since the design of clinical trials is mostly affected by clinical equipoise, the “clinical equipoise hypothesis” has been postulated. If the uncertainty requirement holds, this means that investigators cannot predict what they are going to discover in any individual trial that they undertake. In some instances, new treatments will be superior to standard treatments, while in others, standard treatments will be superior to experimental treatments, and in still others, no difference will be detected between new and standard treatments. It is hypothesized that there must be a relationship between the overall pattern of treatment successes and the uncertainties that RCTs are designed to address.
An analysis of published trials shows that the results cannot be predicted at the level of individual trials. However, the results also indicate that the overall pattern of discovery of treatment success across a series of trials is predictable and is consistent with clinical equipoise hypothesis. The analysis shows that we can discover no more than 25% to 50% of successful treatments when they are tested in RCTs. The analysis also indicates that this discovery rate is optimal in helping to preserve the clinical trial system; a high discovery rate (eg, a 90% to 100% probability of success) is neither feasible nor desirable since under these circumstances, neither the patient nor the researcher has an interest in randomization. This in turn would halt the RCT system as we know it.
The “principle or law of clinical discovery” described herein predicts the efficiency of the current system of RCTs at generating discoveries of new treatments. The principle is derived from the requirement for uncertainty or equipoise as a precondition for RCTs, the precept that paradoxically drives discoveries of new treatments while limiting the proportion and rate of new therapeutic discoveries.
PMCID: PMC2782889  PMID: 19910921
14.  Transforming the practice of medicine using genomics 
Recent studies have demonstrated the use of genomic data, particularly gene expression signatures, as clinical prognostic factors in complex diseases. Such studies herald the future for genomic medicine and the opportunity for personalized prognosis in a variety of clinical contexts that utilize genomescale molecular information. Several key areas represent logical and critical next steps in the use of complex genomic profiling data towards the goal of personalized medicine. First, analyses should be geared toward the development of molecular profiles that predict future events – such as major clinical events or the response, resistance, or adverse reaction to therapy. Secondly, these must move into actual clinical practice by forming the basis for the next generation of clinical trials that will employ these methodologies to stratify patients. Lastly, there remain formidable challenges is in the translation of genomic technologies into clinical medicine that will need to be addressed: professional and public education, health outcomes research, reimbursement, regulatory oversight and privacy protection.
PMCID: PMC2781216  PMID: 22461094
genomic medicine, personalized medicine, human genome.
15.  Faculty Survey to Assess Research Literacy and Evidence-Informed Practice Interest and Support at Pacific College of Oriental Medicine 
Context: Educating healthcare practitioners to understand, critically evaluate, and apply evidence to the clinical practice of complementary and alternative medicine has been an important initiative for the National Institutes of Health National Center for Complementary and Alternative Medicine.
Objective: To determine the self-assessed research skills and interest of faculty at Pacific College of Oriental Medicine (New York campus) and their likely support of, and participatory interest in, an evidence-based medicine (EBM) training program.
Design: The survey was administered in Survey Monkey. All questions were close-ended with 5-point Likert answers, except for one open-ended question at the end of the survey.
Setting: One of three campuses of Pacific College of Oriental Medicine (PCOM), the largest Chinese medicine college in the United States.
Participants: 102 faculty employed at PCOM.
Results: The response rate was 88.7%. Responses illustrated a generally high degree of interest and support for research, EBM, and institutional participation in research activities. Faculty who responded to the open-ended question (19.6% of respondents) expressed concerns about the relevance of research to Chinese medicine and the possibility of co-option by biomedicine.
Conclusions: While faculty were overall supportive and interested in research and EBM, the results are consistent with the hypothesis that success of EBM training programs could be enhanced by soliciting and addressing faculty concerns and by being inclusive of approaches that honor the traditions of Chinese medicine and its own forms of clinical evidence.
PMCID: PMC4155412  PMID: 25120170
16.  Survival-Related Profile, Pathways, and Transcription Factors in Ovarian Cancer 
PLoS Medicine  2009;6(2):e1000024.
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.
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
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
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
PMCID: PMC2634794  PMID: 19192944
17.  The National Lung Matrix Trial: translating the biology of stratification in advanced non-small-cell lung cancer 
Annals of Oncology  2015;26(12):2464-2469.
The National Lung Matrix Trial is currently the largest stratified medicine study in lung cancer. Utilizing a next-generation sequencing screening platform and an adaptive umbrella trial design, we will explore the activity of multiple biomarker/targeted therapy options in order to expand the precision medicine opportunities for patients with non-small-cell lung cancer.
The management of NSCLC has been transformed by stratified medicine. The National Lung Matrix Trial (NLMT) is a UK-wide study exploring the activity of rationally selected biomarker/targeted therapy combinations.
Patients and methods
The Cancer Research UK (CRUK) Stratified Medicine Programme 2 is undertaking the large volume national molecular pre-screening which integrates with the NLMT. At study initiation, there are eight drugs being used to target 18 molecular cohorts. The aim is to determine whether there is sufficient signal of activity in any drug–biomarker combination to warrant further investigation. A Bayesian adaptive design that gives a more realistic approach to decision making and flexibility to make conclusions without fixing the sample size was chosen. The screening platform is an adaptable 28-gene Nextera next-generation sequencing platform designed by Illumina, covering the range of molecular abnormalities being targeted. The adaptive design allows new biomarker–drug combination cohorts to be incorporated by substantial amendment. The pre-clinical justification for each biomarker–drug combination has been rigorously assessed creating molecular exclusion rules and a trumping strategy in patients harbouring concomitant actionable genetic abnormalities. Discrete routes of pathway activation or inactivation determined by cancer genome aberrations are treated as separate cohorts. Key translational analyses include the deep genomic analysis of pre- and post-treatment biopsies, the establishment of patient-derived xenograft models and longitudinal ctDNA collection, in order to define predictive biomarkers, mechanisms of resistance and early markers of response and relapse.
The SMP2 platform will provide large scale genetic screening to inform entry into the NLMT, a trial explicitly aimed at discovering novel actionable cohorts in NSCLC.
Clinical Trial ISRCTN
PMCID: PMC4658545  PMID: 26410619
National Lung Matrix Trial; non-small-cell lung cancer; stratified medicine; adaptive trial design; Umbrella Trial
18.  Liver Dysfunction and Phosphatidylinositol-3-Kinase Signalling in Early Sepsis: Experimental Studies in Rodent Models of Peritonitis 
PLoS Medicine  2012;9(11):e1001338.
Experimental studies in a rat model of fecal peritonitis conducted by Michael Bauer and colleagues show that in this model, changes in liver function occur early in the development of sepsis, with potential implications for prognosis and development of new therapeutic approaches.
Hepatic dysfunction and jaundice are traditionally viewed as late features of sepsis and portend poor outcomes. We hypothesized that changes in liver function occur early in the onset of sepsis, yet pass undetected by standard laboratory tests.
Methods and Findings
In a long-term rat model of faecal peritonitis, biotransformation and hepatobiliary transport were impaired, depending on subsequent disease severity, as early as 6 h after peritoneal contamination. Phosphatidylinositol-3-kinase (PI3K) signalling was simultaneously induced at this time point. At 15 h there was hepatocellular accumulation of bilirubin, bile acids, and xenobiotics, with disturbed bile acid conjugation and drug metabolism. Cholestasis was preceded by disruption of the bile acid and organic anion transport machinery at the canalicular pole. Inhibitors of PI3K partially prevented cytokine-induced loss of villi in cultured HepG2 cells. Notably, mice lacking the PI3Kγ gene were protected against cholestasis and impaired bile acid conjugation. This was partially confirmed by an increase in plasma bile acids (e.g., chenodeoxycholic acid [CDCA] and taurodeoxycholic acid [TDCA]) observed in 48 patients on the day severe sepsis was diagnosed; unlike bilirubin (area under the receiver-operating curve: 0.59), these bile acids predicted 28-d mortality with high sensitivity and specificity (area under the receiver-operating curve: CDCA: 0.77; TDCA: 0.72; CDCA+TDCA: 0.87).
Liver dysfunction is an early and commonplace event in the rat model of sepsis studied here; PI3K signalling seems to play a crucial role. All aspects of hepatic biotransformation are affected, with severity relating to subsequent prognosis. Detected changes significantly precede conventional markers and are reflected by early alterations in plasma bile acids. These observations carry important implications for the diagnosis of liver dysfunction and pharmacotherapy in the critically ill. Further clinical work is necessary to extend these concepts into clinical practice.
Please see later in the article for the Editors' Summary
Editors' Summary
Sepsis (blood poisoning)—a life-threatening condition caused by an inappropriate immune response to an infection—is a major global cause of death. Normally, when bacteria or other microbes enter the human body, the immune system efficiently destroys the invaders. In sepsis the immune system goes into overdrive, and the chemicals it releases into the blood to combat the infection trigger widespread inflammation (swelling). This leads to the formation of small blood clots and leaky blood vessels that block the flow of blood to vital organs such as the kidneys and liver. In the most severe cases, multiple organs fail and the patient dies. Anyone can get sepsis, but people with weakened immune systems, the very young, and the elderly are most vulnerable. Symptoms of sepsis include fever, chills, rapid breathing, a fast heart rate, and confusion. In its early stages, sepsis can be treated with antibiotics alone, but people with severe sepsis need to be admitted to an intensive care unit where the vital organs can be supported while the infection is treated.
Why Was This Study Done?
Thirty to fifty percent of people who develop severe sepsis die. If sepsis could be diagnosed in its early stages, it might be possible to save more people. Unfortunately, the symptoms of sepsis mimic those of other conditions, and, because sepsis tends to develop very quickly, it is often not diagnosed until it is too late to save the patient's life. The development of liver (hepatic) dysfunction and jaundice are both regarded as late features of sepsis (jaundice is yellowing of the skin and eyes caused by a build-up of bilirubin in the blood). However, the researchers hypothesized that changes in liver function occur early in sepsis and could, therefore, be used to improve the diagnosis and management of sepsis.
What Did the Researchers Do and Find?
The researchers induced sepsis in rats by injecting bacteria into the peritoneal cavity (the gap between the abdominal wall and the abdominal organs), separated the infected animals into predicted survivors and non-survivors based on their heart stroke volume measured using cardiac ultrasound, and then examined their liver function. The expression of genes encoding proteins involved in “biotransformation” and “hepatobiliary transport” (the processes that convert waste products and toxic chemicals into substances that can be conjugated to increase solubility and then excreted) was down-regulated within six hours of sepsis induction in the predicted non-survivors compared to the predicted survivors. Functional changes such as bilirubin and bile acid accumulation in the liver (cholestasis), poor excretion of xenobiotics (molecules not usually found in the body such as antibiotics), and disturbed bile acid conjugation were also seen in predicted non-survivors but not in survivors. Moreover, phosphatidylinositol-3-kinase (PI3K) signaling (which is involved in several immune processes) increased soon after sepsis induction in non-survivor but not in survivor animals. Notably, mice lacking the PI3Kγ gene did not develop cholestasis or show impaired bile acid conjugation after induction of sepsis. Finally, in human patients, plasma bile acids were increased in 48 patients on the day that severe sepsis was diagnosed, and these increases accurately predicted death in these patients.
What Do These Findings Mean?
These findings show that liver dysfunction is an early event in animal models of sepsis and that PI3K signalling plays a crucial role in the development of liver dysfunction. They show that all aspects of liver biotransformation are affected during sepsis and suggest that outcomes are related to the severity of these changes. The limited clinical data included in this study also support the hypothesis that changes in liver function occur early in sepsis, although these data need confirming and extending. Taken together, these findings suggest that liver function tests might aid early diagnosis of sepsis and might also provide information about likely outcomes. They also have important implications for the use of drugs in patients who are critically ill with sepsis, in that some of the drugs routinely administered to such patients may not be adequately detoxified and may, therefore, contribute to organ injury. Finally, these findings suggest that inhibition of PI3Kγ may alleviate sepsis-associated cholestasis.
Additional Information
Please access these websites via the online version of this summary at
This study is further discussed in a PLOS Medicine Perspective by John Marshall
The US National Institute of General Medical Sciences has a fact sheet on sepsis
The UK National Health Service Choices website has information about sepsis and about jaundice
The Surviving Sepsis Campaign, which was developed to improve the management, diagnosis, and treatment of sepsis, provides basic information about sepsis
The Sepsis Alliance, a US not-for-profit organization, also provides information about sepsis for patients and their families, including personal stories about sepsis
The not-for profit UK Sepsis Trust is another useful source of information about sepsis that includes patient stories
MedlinePlus provides links to additional resources about sepsis and jaundice (in English and Spanish)
PMCID: PMC3496669  PMID: 23152722
19.  Quantifying the Impoverishing Effects of Purchasing Medicines: A Cross-Country Comparison of the Affordability of Medicines in the Developing World 
PLoS Medicine  2010;7(8):e1000333.
Laurens Niëns and colleagues estimate the impoverishing effects of four medicines in 16 low- and middle-income countries using the impoverishment method as a metric of affordability and show that medicine purchases could impoverish large numbers of people.
Increasing attention is being paid to the affordability of medicines in low- and middle-income countries (LICs and MICs) where medicines are often highly priced in relation to income levels. The impoverishing effect of medicine purchases can be estimated by determining pre- and postpayment incomes, which are then compared to a poverty line. Here we estimate the impoverishing effects of four medicines in 16 LICs and MICs using the impoverishment method as a metric of affordability.
Methods and Findings
Affordability was assessed in terms of the proportion of the population being pushed below US$1.25 or US$2 per day poverty levels because of the purchase of medicines. The prices of salbutamol 100 mcg/dose inhaler, glibenclamide 5 mg cap/tab, atenolol 50 mg cap/tab, and amoxicillin 250 mg cap/tab were obtained from facility-based surveys undertaken using a standard measurement methodology. The World Bank's World Development Indicators provided household expenditure data and information on income distributions. In the countries studied, purchasing these medicines would impoverish large portions of the population (up to 86%). Originator brand products were less affordable than the lowest-priced generic equivalents. In the Philippines, for example, originator brand atenolol would push an additional 22% of the population below US$1.25 per day, whereas for the lowest priced generic equivalent this demographic shift is 7%. Given related prevalence figures, substantial numbers of people are affected by the unaffordability of medicines.
Comparing medicine prices to available income in LICs and MICs shows that medicine purchases by individuals in those countries could lead to the impoverishment of large numbers of people. Action is needed to improve medicine affordability, such as promoting the use of quality assured, low-priced generics, and establishing health insurance systems.
Please see later in the article for the Editors' Summary
Editors' Summary
In recent years, the international community has prioritized access to essential medicines, which has required focusing on the accessibility, availability, quality, and affordability of life-saving medicines and the development of appropriate data and research agendas to measure these components. Determining the degree of affordability of medicines, especially in low- and middle-income countries, is a complex process as the term affordability is vague. However, the cost of medicines is a major public health issue, especially as the majority of people in developing countries do not have health insurance and medicines freely provided through the public sector are often unavailable. Therefore, although countries have a legal obligation to make essential medicines available to those who need them at an affordable cost, poor people often have to pay for the medicines that they need when they are ill. Consequently, where medicine prices are high, people may have to forego treatment or they may go into debt if they decide to buy the necessary medicines.
Why Was This Study Done?
The researchers wanted to show the impact of the cost of medicines on poorer populations by undertaking an analysis that quantified the proportion of people who would be pushed into poverty (an income level of US$1.25 or US$2 a day) because their only option is to pay out-of-pocket expenses for the life-saving medicines they need. The researchers referred to this consequence as the “impoverishing effect of a medicine.”
What Did the Researchers Do and Find?
The researchers generated “impoverishment rates” of four medicines in 16 low- and middle-income countries by comparing households' daily per capita income before and after (the hypothetical) purchase of one of the following: a salbutamol 100 mcg/dose inhaler, glibenclamide 5 mg cap/tab, atenolol 50 mg cap/tab, and amoxicillin 250 mg cap/tab. This selection of drugs covers the treatment/management of three chronic diseases and one acute illness. The cost of each medicine was taken from standardized surveys, which report median patient prices for a selection of commonly used medicines in the private sector (the availability of essential medicines in the public sector is much lower so many people will depend on the private sector for their medicines) for both originator brand and lowest priced generic products. If the prepayment income was above the US$1.25 (or US$2) poverty line and the postpayment income fell below these lines, purchasing these medicines at current prices impoverishes people.
According to the results of this analysis, a substantial proportion (up to 86%) of the population in the countries studied would be pushed into poverty as a result of purchasing one of the four selected medicines. Furthermore, the lowest priced generic versions of each medicine were generally substantially more affordable than originator brand products. For example, in the Philippines, purchasing originator brand atenolol would push an additional 22% of the population below US$1.25 per day compared to 7% if the lowest priced generic equivalent was bought instead. In effect, purchasing essential medicines for both chronic and acute conditions could impoverish large numbers of people, especially if originator brand products are bought.
What Do These Findings Mean?
Although the purchasing of medicines represents only part of the costs associated with the management of an illness, it is clear that the high cost of medicines have catastrophic effects on poor people. In addition, as the treatment of chronic conditions often requires a combination of medicines, the cost of treating and managing a chronic condition such as asthma, diabetes, and cardiovascular disease is likely to be even more unaffordable than what is reported in this study. Therefore concerted action is urgently required to improve medicine affordability and prevent poor populations from being pushed further into poverty. Such action could include: governments, civil society organizations, and others making access to essential medicines more of a priority and to consider this strategy as an integral part of reducing poverty; the development, implementation, and enforcement of sound national and international price policies; actively promoting the use of quality assured, low-cost generic drugs; ensuring the availability of essential medicines in the public sector at little or no charge to poor people; establishing health insurance systems with outpatient medicine benefits; encouraging pharmaceutical companies to differentially price medicines that are still subject to patent restrictions.
Additional Information
Please access these Web sites via the online version of this summary at
For a comprehensive resource for medicine prices, availability, and affordability, see Health Action International
Guidelines about access to essential medicines and pharmaceutical policies can be found at WHO
Transparency Alliance provides more information about medicines
Access to essential medicines has become a key campaign topic; for more information see Médecins Sans Frontières (Doctors without Borders)
PMCID: PMC2930876  PMID: 20824175
20.  No evidence for mutations in NLRP7, NLRP2 or KHDC3L in women with unexplained recurrent pregnancy loss or infertility 
Are mutations in NLRP2/7 (NACHT, LRR and PYD domains-containing protein 2/7) or KHDC3L (KH Domain Containing 3 Like) associated with recurrent pregnancy loss (RPL) or infertility?
We found no evidence for mutations in NLRP2/7 or KHDC3L in unexplained RPL or infertility.
Mutations in NLRP7 and KHDC3L are known to cause biparental hydatidiform moles (BiHMs), a rare form of pregnancy loss. NLRP2, while not associated with the BiHM pathology, is known to cause recurrent Beckwith Weidemann Syndrome (BWS).
Ninety-four patients with well characterized, unexplained infertility were recruited over a 9-year period from three IVF clinics in Sweden. Blood samples from 24 patients with 3 or more consecutive miscarriages of unknown etiology were provided by the Recurrent Miscarriage Clinic at St Mary's Hospital, London, UK.
Patients were recruited into both cohorts following extensive clinical studies. Genomic DNA was isolated from peripheral blood and subject to Sanger sequencing of NLRP2, NLRP7 and KHDC3L. Sequence electropherograms were analyzed by Sequencher v5.0 software and variants compared with those observed in the 1000 Genomes, single nucleotide polymorphism database (dbSNP) and HapMap databases. Functional effects of non-synonymous variants were predicted using Polyphen-2 and sorting intolerant from tolerant (SIFT).
No disease-causing mutations were identified in NLRP2, NLRP7 and KHDC3L in our cohorts of unexplained infertility and RPL.
Due to the limited patient size, it is difficult to conclude if the low frequency single nucleotide polymorphisms observed in the present study are causative of the phenotype. The design of the present study therefore is only capable of detecting highly penetrant mutations.
The present study supports the hypothesis that mutations in NLRP7 and KHDC3L are specific for the BiHM phenotype and do not play a role in other adverse reproductive outcomes. Furthermore, to date, mutations in NLRP2 have only been associated with the imprinting disorder BWS in offspring and there is no evidence for a role in molar pregnancies, RPL or unexplained infertility.
This study was funded by the following sources: Estonian Ministry of Education and Research (Grant SF0180044s09), Enterprise Estonia (Grant EU30020); Mentored Resident research project (Department of Obstetrics and Gynecology, Baylor College of Medicine); Imperial NIHR Biomedical Research Centre; Grant Number C06RR029965 from the National Center for Research Resources (NCCR; NIH). No competing interests declared.
PMCID: PMC4262469  PMID: 25376457
recurrent pregnancy loss; unexplained infertility; NLRP2; NLRP7; KHDC3L
21.  Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism 
A comprehensive genome-scale metabolic network of Chlamydomonas reinhardtii, including a detailed account of light-driven metabolism, is reconstructed and validated. The model provides a new resource for research of C. reinhardtii metabolism and in algal biotechnology.
The genome-scale metabolic network of Chlamydomonas reinhardtii (iRC1080) was reconstructed, accounting for >32% of the estimated metabolic genes encoded in the genome, and including extensive details of lipid metabolic pathways.This is the first metabolic network to explicitly account for stoichiometry and wavelengths of metabolic photon usage, providing a new resource for research of C. reinhardtii metabolism and developments in algal biotechnology.Metabolic functional annotation and the largest transcript verification of a metabolic network to date was performed, at least partially verifying >90% of the transcripts accounted for in iRC1080. Analysis of the network supports hypotheses concerning the evolution of latent lipid pathways in C. reinhardtii, including very long-chain polyunsaturated fatty acid and ceramide synthesis pathways.A novel approach for modeling light-driven metabolism was developed that accounts for both light source intensity and spectral quality of emitted light. The constructs resulting from this approach, termed prism reactions, were shown to significantly improve the accuracy of model predictions, and their use was demonstrated for evaluation of light source efficiency and design.
Algae have garnered significant interest in recent years, especially for their potential application in biofuel production. The hallmark, model eukaryotic microalgae Chlamydomonas reinhardtii has been widely used to study photosynthesis, cell motility and phototaxis, cell wall biogenesis, and other fundamental cellular processes (Harris, 2001). Characterizing algal metabolism is key to engineering production strains and understanding photobiological phenomena. Based on extensive literature on C. reinhardtii metabolism, its genome sequence (Merchant et al, 2007), and gene functional annotation, we have reconstructed and experimentally validated the genome-scale metabolic network for this alga, iRC1080, the first network to account for detailed photon absorption permitting growth simulations under different light sources. iRC1080 accounts for 1080 genes, associated with 2190 reactions and 1068 unique metabolites and encompasses 83 subsystems distributed across 10 cellular compartments (Figure 1A). Its >32% coverage of estimated metabolic genes is a tremendous expansion over previous algal reconstructions (Boyle and Morgan, 2009; Manichaikul et al, 2009). The lipid metabolic pathways of iRC1080 are considerably expanded relative to existing networks, and chemical properties of all metabolites in these pathways are accounted for explicitly, providing sufficient detail to completely specify all individual molecular species: backbone molecule and stereochemical numbering of acyl-chain positions; acyl-chain length; and number, position, and cis–trans stereoisomerism of carbon–carbon double bonds. Such detail in lipid metabolism will be critical for model-driven metabolic engineering efforts.
We experimentally verified transcripts accounted for in the network under permissive growth conditions, detecting >90% of tested transcript models (Figure 1B) and providing validating evidence for the contents of iRC1080. We also analyzed the extent of transcript verification by specific metabolic subsystems. Some subsystems stood out as more poorly verified, including chloroplast and mitochondrial transport systems and sphingolipid metabolism, all of which exhibited <80% of transcripts detected, reflecting incomplete characterization of compartmental transporters and supporting a hypothesis of latent pathway evolution for ceramide synthesis in C. reinhardtii. Additional lines of evidence from the reconstruction effort similarly support this hypothesis including lack of ceramide synthetase and other annotation gaps downstream in sphingolipid metabolism. A similar hypothesis of latent pathway evolution was established for very long-chain fatty acids (VLCFAs) and their polyunsaturated analogs (VLCPUFAs) (Figure 1C), owing to the absence of this class of lipids in previous experimental measurements, lack of a candidate VLCFA elongase in the functional annotation, and additional downstream annotation gaps in arachidonic acid metabolism.
The network provides a detailed account of metabolic photon absorption by light-driven reactions, including photosystems I and II, light-dependent protochlorophyllide oxidoreductase, provitamin D3 photoconversion to vitamin D3, and rhodopsin photoisomerase; this network accounting permits the precise modeling of light-dependent metabolism. iRC1080 accounts for effective light spectral ranges through analysis of biochemical activity spectra (Figure 3A), either reaction activity or absorbance at varying light wavelengths. Defining effective spectral ranges associated with each photon-utilizing reaction enabled our network to model growth under different light sources via stoichiometric representation of the spectral composition of emitted light, termed prism reactions. Coefficients for different photon wavelengths in a prism reaction correspond to the ratios of photon flux in the defined effective spectral ranges to the total emitted photon flux from a given light source (Figure 3B). This approach distinguishes the amount of emitted photons that drive different metabolic reactions. We created prism reactions for most light sources that have been used in published studies for algal and plant growth including solar light, various light bulbs, and LEDs. We also included regulatory effects, resulting from lighting conditions insofar as published studies enabled. Light and dark conditions have been shown to affect metabolic enzyme activity in C. reinhardtii on multiple levels: transcriptional regulation, chloroplast RNA degradation, translational regulation, and thioredoxin-mediated enzyme regulation. Through application of our light model and prism reactions, we were able to closely recapitulate experimental growth measurements under solar, incandescent, and red LED lights. Through unbiased sampling, we were able to establish the tremendous statistical significance of the accuracy of growth predictions achievable through implementation of prism reactions. Finally, application of the photosynthetic model was demonstrated prospectively to evaluate light utilization efficiency under different light sources. The results suggest that, of the existing light sources, red LEDs provide the greatest efficiency, about three times as efficient as sunlight. Extending this analysis, the model was applied to design a maximally efficient LED spectrum for algal growth. The result was a 677-nm peak LED spectrum with a total incident photon flux of 360 μE/m2/s, suggesting that for the simple objective of maximizing growth efficiency, LED technology has already reached an effective theoretical optimum.
In summary, the C. reinhardtii metabolic network iRC1080 that we have reconstructed offers insight into the basic biology of this species and may be employed prospectively for genetic engineering design and light source design relevant to algal biotechnology. iRC1080 was used to analyze lipid metabolism and generate novel hypotheses about the evolution of latent pathways. The predictive capacity of metabolic models developed from iRC1080 was demonstrated in simulating mutant phenotypes and in evaluation of light source efficiency. Our network provides a broad knowledgebase of the biochemistry and genomics underlying global metabolism of a photoautotroph, and our modeling approach for light-driven metabolism exemplifies how integration of largely unvisited data types, such as physicochemical environmental parameters, can expand the diversity of applications of metabolic networks.
Metabolic network reconstruction encompasses existing knowledge about an organism's metabolism and genome annotation, providing a platform for omics data analysis and phenotype prediction. The model alga Chlamydomonas reinhardtii is employed to study diverse biological processes from photosynthesis to phototaxis. Recent heightened interest in this species results from an international movement to develop algal biofuels. Integrating biological and optical data, we reconstructed a genome-scale metabolic network for this alga and devised a novel light-modeling approach that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux. We experimentally verified transcripts accounted for in the network and physiologically validated model function through simulation and generation of new experimental growth data, providing high confidence in network contents and predictive applications. The network offers insight into algal metabolism and potential for genetic engineering and efficient light source design, a pioneering resource for studying light-driven metabolism and quantitative systems biology.
PMCID: PMC3202792  PMID: 21811229
Chlamydomonas reinhardtii; lipid metabolism; metabolic engineering; photobioreactor
22.  Predicting Survival within the Lung Cancer Histopathological Hierarchy Using a Multi-Scale Genomic Model of Development 
PLoS Medicine  2006;3(7):e232.
The histopathologic heterogeneity of lung cancer remains a significant confounding factor in its diagnosis and prognosis—spurring numerous recent efforts to find a molecular classification of the disease that has clinical relevance.
Methods and Findings
Molecular profiles of tumors from 186 patients representing four different lung cancer subtypes (and 17 normal lung tissue samples) were compared with a mouse lung development model using principal component analysis in both temporal and genomic domains. An algorithm for the classification of lung cancers using a multi-scale developmental framework was developed. Kaplan–Meier survival analysis was conducted for lung adenocarcinoma patient subgroups identified via their developmental association. We found multi-scale genomic similarities between four human lung cancer subtypes and the developing mouse lung that are prognostically meaningful. Significant association was observed between the localization of human lung cancer cases along the principal mouse lung development trajectory and the corresponding patient survival rate at three distinct levels of classical histopathologic resolution: among different lung cancer subtypes, among patients within the adenocarcinoma subtype, and within the stage I adenocarcinoma subclass. The earlier the genomic association between a human tumor profile and the mouse lung development sequence, the poorer the patient's prognosis. Furthermore, decomposing this principal lung development trajectory identified a gene set that was significantly enriched for pyrimidine metabolism and cell-adhesion functions specific to lung development and oncogenesis.
From a multi-scale disease modeling perspective, the molecular dynamics of murine lung development provide an effective framework that is not only data driven but also informed by the biology of development for elucidating the mechanisms of human lung cancer biology and its clinical outcome.
Editors' Summary
Lung cancer causes the most deaths from cancer worldwide—around a quarter of all cancer deaths—and the number of deaths is rising each year. There are a number of different types of the disease, whose names come from early descriptions of the cancer cells when seen under the microscope: carcinoid, small cell, and non–small cell, which make up 2%, 13%, and 86% of lung cancers, respectively. To make things more complicated, each of these cancer types can be subdivided further. It is important to distinguish the different types of cancer because they differ in their rates of growth and how they respond to treatment; for example, small cell lung cancer is the most rapidly progressing type of lung cancer. But although these current classifications of cancers are useful, researchers believe that if the underlying molecular changes in these cancers could be discovered then a more accurate way of classifying cancers, and hence predicting outcome and response to treatment, might be possible.
Why Was This Study Done?
Previous work has suggested that some cancers come from very immature cells, that is, cells that are present in the early stages of an animal's development from an embryo in the womb to an adult animal. Many animals have been closely studied so as to understand how they develop; the best studied model that is also relevant to human disease is the mouse, and researchers have previously studied lung development in mice in detail. This group of researchers wanted to see if there was any relation between the activity (known as expression) of mouse genes during the development of the lung and the expression of genes in human lung cancers, particularly whether they could use gene expression to try to predict the outcome of lung cancer in patients.
What Did the Researchers Do and Find?
They compared the gene expression in lung cancer samples from 186 patients with four different types of lung cancer (and in 17 normal lung tissue samples) to the gene expression found in normal mice during development. They found similarities between expression patterns in the lung cancer subtypes and the developing mouse lung, and that these similarities explain some of the different outcomes for the patients. In general, they found that when the gene expression in the human cancer was similar to that of very immature mouse lung cells, patients had a poor prognosis. When the gene expression in the human cancer was more similar to mature mouse lung cells, the prognosis was better. However, the researchers found that carcinoid tumors had rather different expression profiles compared to the other tumors.
  The researchers were also able to discover some specific gene types that seemed to have particularly strong associations between mouse development and the human cancers. Two of these gene types were ones that are involved in building and breaking down DNA itself, and ones involved in how cells stick together. This latter group of genes is thought to be involved in how cancers spread.
What Do These Findings Mean?
These results provide a new way of thinking about how to classify lung cancers, and also point to a few groups of genes that may be particularly important in the development of the tumor. However, before these results are used in any clinical assessment, further work will need to be done to work out whether they are true for other groups of patients.
Additional Information.
Please access these Web sites via the online version of this summary at
•  MedlinePlus has information from the United States National Library of Medicine and other government agencies and health-related organizations [MedlinePlus]
•  National Institute on Aging is also a good place to start looking for information [National Institute for Aging]
•  [The National Cancer Institute] and Lung Cancer Online [ Lung Cancer Online] have a wide range of information on lung cancer
Comparison of gene expression patterns in patients with lung cancer and in mouse lung development showed that those tumors associated with earlier mouse lung development had a poorer prognosis.
PMCID: PMC1483910  PMID: 16800721
23.  Systems Medicine: from molecular features and models to the clinic in COPD 
Journal of Translational Medicine  2014;12(Suppl 2):S4.
Background and hypothesis
Chronic Obstructive Pulmonary Disease (COPD) patients are characterized by heterogeneous clinical manifestations and patterns of disease progression. Two major factors that can be used to identify COPD subtypes are muscle dysfunction/wasting and co-morbidity patterns. We hypothesized that COPD heterogeneity is in part the result of complex interactions between several genes and pathways. We explored the possibility of using a Systems Medicine approach to identify such pathways, as well as to generate predictive computational models that may be used in clinic practice.
Objective and method
Our overarching goal is to generate clinically applicable predictive models that characterize COPD heterogeneity through a Systems Medicine approach. To this end we have developed a general framework, consisting of three steps/objectives: (1) feature identification, (2) model generation and statistical validation, and (3) application and validation of the predictive models in the clinical scenario. We used muscle dysfunction and co-morbidity as test cases for this framework.
In the study of muscle wasting we identified relevant features (genes) by a network analysis and generated predictive models that integrate mechanistic and probabilistic models. This allowed us to characterize muscle wasting as a general de-regulation of pathway interactions. In the co-morbidity analysis we identified relevant features (genes/pathways) by the integration of gene-disease and disease-disease associations. We further present a detailed characterization of co-morbidities in COPD patients that was implemented into a predictive model. In both use cases we were able to achieve predictive modeling but we also identified several key challenges, the most pressing being the validation and implementation into actual clinical practice.
The results confirm the potential of the Systems Medicine approach to study complex diseases and generate clinically relevant predictive models. Our study also highlights important obstacles and bottlenecks for such approaches (e.g. data availability and normalization of frameworks among others) and suggests specific proposals to overcome them.
PMCID: PMC4255907  PMID: 25471042
Chronic diseases; COPD; Disease heterogeneity; Systems Medicine; Predictive Modeling; Co-morbidity
24.  What do evidence-based secondary journals tell us about the publication of clinically important articles in primary healthcare journals? 
BMC Medicine  2004;2:33.
We conducted this analysis to determine i) which journals publish high-quality, clinically relevant studies in internal medicine, general/family practice, general practice nursing, and mental health; and ii) the proportion of clinically relevant articles in each journal.
We performed an analytic survey of a hand search of 170 general medicine, general healthcare, and specialty journals for 2000. Research staff assessed individual articles by using explicit criteria for scientific merit for healthcare application. Practitioners assessed the clinical importance of these articles. Outcome measures were the number of high-quality, clinically relevant studies published in the 170 journal titles and how many of these were published in each of four discipline-specific, secondary "evidence-based" journals (ACP Journal Club for internal medicine and its subspecialties; Evidence-Based Medicine for general/family practice; Evidence-Based Nursing for general practice nursing; and Evidence-Based Mental Health for all aspects of mental health). Original studies and review articles were classified for purpose: therapy and prevention, screening and diagnosis, prognosis, etiology and harm, economics and cost, clinical prediction guides, and qualitative studies.
We evaluated 60,352 articles from 170 journal titles. The pass criteria of high-quality methods and clinically relevant material were met by 3059 original articles and 1073 review articles. For ACP Journal Club (internal medicine), four titles supplied 56.5% of the articles and 27 titles supplied the other 43.5%. For Evidence-Based Medicine (general/family practice), five titles supplied 50.7% of the articles and 40 titles supplied the remaining 49.3%. For Evidence-Based Nursing (general practice nursing), seven titles supplied 51.0% of the articles and 34 additional titles supplied 49.0%. For Evidence-Based Mental Health (mental health), nine titles supplied 53.2% of the articles and 34 additional titles supplied 46.8%. For the disciplines of internal medicine, general/family practice, and mental health (but not general practice nursing), the number of clinically important articles was correlated withScience Citation Index (SCI) Impact Factors.
Although many clinical journals publish high-quality, clinically relevant and important original studies and systematic reviews, the articles for each discipline studied were concentrated in a small subset of journals. This subset varied according to healthcare discipline; however, many of the important articles for all disciplines in this study were published in broad-based healthcare journals rather than subspecialty or discipline-specific journals.
PMCID: PMC518974  PMID: 15350200
25.  The MedSeq Project: a randomized trial of integrating whole genome sequencing into clinical medicine 
Trials  2014;15:85.
Whole genome sequencing (WGS) is already being used in certain clinical and research settings, but its impact on patient well-being, health-care utilization, and clinical decision-making remains largely unstudied. It is also unknown how best to communicate sequencing results to physicians and patients to improve health. We describe the design of the MedSeq Project: the first randomized trials of WGS in clinical care.
This pair of randomized controlled trials compares WGS to standard of care in two clinical contexts: (a) disease-specific genomic medicine in a cardiomyopathy clinic and (b) general genomic medicine in primary care. We are recruiting 8 to 12 cardiologists, 8 to 12 primary care physicians, and approximately 200 of their patients. Patient participants in both the cardiology and primary care trials are randomly assigned to receive a family history assessment with or without WGS. Our laboratory delivers a genome report to physician participants that balances the needs to enhance understandability of genomic information and to convey its complexity. We provide an educational curriculum for physician participants and offer them a hotline to genetics professionals for guidance in interpreting and managing their patients’ genome reports. Using varied data sources, including surveys, semi-structured interviews, and review of clinical data, we measure the attitudes, behaviors and outcomes of physician and patient participants at multiple time points before and after the disclosure of these results.
The impact of emerging sequencing technologies on patient care is unclear. We have designed a process of interpreting WGS results and delivering them to physicians in a way that anticipates how we envision genomic medicine will evolve in the near future. That is, our WGS report provides clinically relevant information while communicating the complexity and uncertainty of WGS results to physicians and, through physicians, to their patients. This project will not only illuminate the impact of integrating genomic medicine into the clinical care of patients but also inform the design of future studies.
Trial registration identifier NCT01736566
PMCID: PMC4113228  PMID: 24645908
Whole genome sequencing; Genome report; Genomic medicine; Translational genomics; Primary care; Cardiomyopathy genetics

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