Search tips
Search criteria

Results 1-25 (1574456)

Clipboard (0)

Related Articles

1.  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
2.  Nuclear Receptor Expression Defines a Set of Prognostic Biomarkers for Lung Cancer 
PLoS Medicine  2010;7(12):e1000378.
David Mangelsdorf and colleagues show that nuclear receptor expression is strongly associated with clinical outcomes of lung cancer patients, and this expression profile is a potential prognostic signature for lung cancer patient survival time, particularly for individuals with early stage disease.
The identification of prognostic tumor biomarkers that also would have potential as therapeutic targets, particularly in patients with early stage disease, has been a long sought-after goal in the management and treatment of lung cancer. The nuclear receptor (NR) superfamily, which is composed of 48 transcription factors that govern complex physiologic and pathophysiologic processes, could represent a unique subset of these biomarkers. In fact, many members of this family are the targets of already identified selective receptor modulators, providing a direct link between individual tumor NR quantitation and selection of therapy. The goal of this study, which begins this overall strategy, was to investigate the association between mRNA expression of the NR superfamily and the clinical outcome for patients with lung cancer, and to test whether a tumor NR gene signature provided useful information (over available clinical data) for patients with lung cancer.
Methods and Findings
Using quantitative real-time PCR to study NR expression in 30 microdissected non-small-cell lung cancers (NSCLCs) and their pair-matched normal lung epithelium, we found great variability in NR expression among patients' tumor and non-involved lung epithelium, found a strong association between NR expression and clinical outcome, and identified an NR gene signature from both normal and tumor tissues that predicted patient survival time and disease recurrence. The NR signature derived from the initial 30 NSCLC samples was validated in two independent microarray datasets derived from 442 and 117 resected lung adenocarcinomas. The NR gene signature was also validated in 130 squamous cell carcinomas. The prognostic signature in tumors could be distilled to expression of two NRs, short heterodimer partner and progesterone receptor, as single gene predictors of NSCLC patient survival time, including for patients with stage I disease. Of equal interest, the studies of microdissected histologically normal epithelium and matched tumors identified expression in normal (but not tumor) epithelium of NGFIB3 and mineralocorticoid receptor as single gene predictors of good prognosis.
NR expression is strongly associated with clinical outcomes for patients with lung cancer, and this expression profile provides a unique prognostic signature for lung cancer patient survival time, particularly for those with early stage disease. This study highlights the potential use of NRs as a rational set of therapeutically tractable genes as theragnostic biomarkers, and specifically identifies short heterodimer partner and progesterone receptor in tumors, and NGFIB3 and MR in non-neoplastic lung epithelium, for future detailed translational study in lung cancer.
Please see later in the article for the Editors' Summary
Editors' Summary
Lung cancer, the most common cause of cancer-related death, kills 1.3 million people annually. Most lung cancers are “non-small-cell lung cancers” (NSCLCs), and most are caused by smoking. Exposure to chemicals in smoke causes changes in the genes of the cells lining the lungs that allow the cells to grow uncontrollably and to move around the body. How NSCLC is treated and responds to treatment depends on its “stage.” Stage I tumors, which are small and confined to the lung, are removed surgically, although chemotherapy is also sometimes given. Stage II tumors have spread to nearby lymph nodes and are treated with surgery and chemotherapy, as are some stage III tumors. However, because cancer cells in stage III tumors can be present throughout the chest, surgery is not always possible. For such cases, and for stage IV NSCLC, where the tumor has spread around the body, patients are treated with chemotherapy alone. About 70% of patients with stage I and II NSCLC but only 2% of patients with stage IV NSCLC survive for five years after diagnosis; more than 50% of patients have stage IV NSCLC at diagnosis.
Why Was This Study Done?
Patient responses to treatment vary considerably. Oncologists (doctors who treat cancer) would like to know which patients have a good prognosis (are likely to do well) to help them individualize their treatment. Consequently, the search is on for “prognostic tumor biomarkers,” molecules made by cancer cells that can be used to predict likely clinical outcomes. Such biomarkers, which may also be potential therapeutic targets, can be identified by analyzing the overall pattern of gene expression in a panel of tumors using a technique called microarray analysis and looking for associations between the expression of sets of genes and clinical outcomes. In this study, the researchers take a more directed approach to identifying prognostic biomarkers by investigating the association between the expression of the genes encoding nuclear receptors (NRs) and clinical outcome in patients with lung cancer. The NR superfamily contains 48 transcription factors (proteins that control the expression of other genes) that respond to several hormones and to diet-derived fats. NRs control many biological processes and are targets for several successful drugs, including some used to treat cancer.
What Did the Researchers Do and Find?
The researchers analyzed the expression of NR mRNAs using “quantitative real-time PCR” in 30 microdissected NSCLCs and in matched normal lung tissue samples (mRNA is the blueprint for protein production). They then used an approach called standard classification and regression tree analysis to build a prognostic model for NSCLC based on the expression data. This model predicted both survival time and disease recurrence among the patients from whom the tumors had been taken. The researchers validated their prognostic model in two large independent lung adenocarcinoma microarray datasets and in a squamous cell carcinoma dataset (adenocarcinomas and squamous cell carcinomas are two major NSCLC subtypes). Finally, they explored the roles of specific NRs in the prediction model. This analysis revealed that the ability of the NR signature in tumors to predict outcomes was mainly due to the expression of two NRs—the short heterodimer partner (SHP) and the progesterone receptor (PR). Expression of either gene could be used as a single gene predictor of the survival time of patients, including those with stage I disease. Similarly, the expression of either nerve growth factor induced gene B3 (NGFIB3) or mineralocorticoid receptor (MR) in normal tissue was a single gene predictor of a good prognosis.
What Do These Findings Mean?
These findings indicate that the expression of NR mRNA is strongly associated with clinical outcomes in patients with NSCLC. Furthermore, they identify a prognostic NR expression signature that provides information on the survival time of patients, including those with early stage disease. The signature needs to be confirmed in more patients before it can be used clinically, and researchers would like to establish whether changes in mRNA expression are reflected in changes in protein expression if NRs are to be targeted therapeutically. Nevertheless, these findings highlight the potential use of NRs as prognostic tumor biomarkers. Furthermore, they identify SHP and PR in tumors and two NRs in normal lung tissue as molecules that might provide new targets for the treatment of lung cancer and new insights into the early diagnosis, pathogenesis, and chemoprevention of lung cancer.
Additional Information
Please access these Web sites via the online version of this summary at
The Nuclear Receptor Signaling Atlas (NURSA) is consortium of scientists sponsored by the US National Institutes of Health that provides scientific reagents, datasets, and educational material on nuclear receptors and their co-regulators to the scientific community through a Web-based portal
The Cancer Prevention and Research Institute of Texas (CPRIT) provides information and resources to anyone interested in the prevention and treatment of lung and other cancers
The US National Cancer Institute provides detailed information for patients and professionals about all aspects of lung cancer, including information on non-small-cell carcinoma and on tumor markers (in English and Spanish)
Cancer Research UK also provides information about lung cancer and information on how cancer starts
MedlinePlus has links to other resources about lung cancer (in English and Spanish)
Wikipedia has a page on nuclear receptors (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
PMCID: PMC3001894  PMID: 21179495
3.  Network-based Survival Analysis Reveals Subnetwork Signatures for Predicting Outcomes of Ovarian Cancer Treatment 
PLoS Computational Biology  2013;9(3):e1002975.
Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets. Net-Cox integrates gene network information into the Cox's proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox was applied to analyze three independent gene expression datasets including the TCGA ovarian cancer dataset and two other public ovarian cancer datasets. Net-Cox with the network information from gene co-expression or functional relations identified highly consistent signature genes across the three datasets, and because of the better generalization across the datasets, Net-Cox also consistently improved the accuracy of survival prediction over the Cox models regularized by or . This study focused on analyzing the death and recurrence outcomes in the treatment of ovarian carcinoma to identify signature genes that can more reliably predict the events. The signature genes comprise dense protein-protein interaction subnetworks, enriched by extracellular matrix receptors and modulators or by nuclear signaling components downstream of extracellular signal-regulated kinases. In the laboratory validation of the signature genes, a tumor array experiment by protein staining on an independent patient cohort from Mayo Clinic showed that the protein expression of the signature gene FBN1 is a biomarker significantly associated with the early recurrence after 12 months of the treatment in the ovarian cancer patients who are initially sensitive to chemotherapy. Net-Cox toolbox is available at
Author Summary
Network-based computational models are attracting increasing attention in studying cancer genomics because molecular networks provide valuable information on the functional organizations of molecules in cells. Survival analysis mostly with the Cox proportional hazard model is widely used to predict or correlate gene expressions with time to an event of interest (outcome) in cancer genomics. Surprisingly, network-based survival analysis has not received enough attention. In this paper, we studied resistance to chemotherapy in ovarian cancer with a network-based Cox model, called Net-Cox. The experiments confirm that networks representing gene co-expression or functional relations can be used to improve the accuracy and the robustness of survival prediction of outcome in ovarian cancer treatment. The study also revealed subnetwork signatures that are enriched by extracellular matrix receptors and modulators and the downstream nuclear signaling components of extracellular signal-regulators, respectively. In particular, FBN1, which was detected as a signature gene of high confidence by Net-Cox with network information, was validated as a biomarker for predicting early recurrence in platinum-sensitive ovarian cancer patients in laboratory.
PMCID: PMC3605061  PMID: 23555212
4.  Differential Analysis of Ovarian and Endometrial Cancers Identifies a Methylator Phenotype 
PLoS ONE  2012;7(3):e32941.
Despite improved outcomes in the past 30 years, less than half of all women diagnosed with epithelial ovarian cancer live five years beyond their diagnosis. Although typically treated as a single disease, epithelial ovarian cancer includes several distinct histological subtypes, such as papillary serous and endometrioid carcinomas. To address whether the morphological differences seen in these carcinomas represent distinct characteristics at the molecular level we analyzed DNA methylation patterns in 11 papillary serous tumors, 9 endometrioid ovarian tumors, 4 normal fallopian tube samples and 6 normal endometrial tissues, plus 8 normal fallopian tube and 4 serous samples from TCGA. For comparison within the endometrioid subtype we added 6 primary uterine endometrioid tumors and 5 endometrioid metastases from uterus to ovary. Data was obtained from 27,578 CpG dinucleotides occurring in or near promoter regions of 14,495 genes. We identified 36 locations with significant increases or decreases in methylation in comparisons of serous tumors and normal fallopian tube samples. Moreover, unsupervised clustering techniques applied to all samples showed three major profiles comprising mostly normal samples, serous tumors, and endometrioid tumors including ovarian, uterine and metastatic origins. The clustering analysis identified 60 differentially methylated sites between the serous group and the normal group. An unrelated set of 25 serous tumors validated the reproducibility of the methylation patterns. In contrast, >1,000 genes were differentially methylated between endometrioid tumors and normal samples. This finding is consistent with a generalized regulatory disruption caused by a methylator phenotype. Through DNA methylation analyses we have identified genes with known roles in ovarian carcinoma etiology, whereas pathway analyses provided biological insight to the role of novel genes. Our finding of differences between serous and endometrioid ovarian tumors indicates that intervention strategies could be developed to specifically address subtypes of epithelial ovarian cancer.
PMCID: PMC3293923  PMID: 22403726
5.  The Preclinical Natural History of Serous Ovarian Cancer: Defining the Target for Early Detection 
PLoS Medicine  2009;6(7):e1000114.
Pat Brown and colleagues carry out a modeling study and define what properties a biomarker-based screening test would require in order to be clinically useful.
Ovarian cancer kills approximately 15,000 women in the United States every year, and more than 140,000 women worldwide. Most deaths from ovarian cancer are caused by tumors of the serous histological type, which are rarely diagnosed before the cancer has spread. Rational design of a potentially life-saving early detection and intervention strategy requires understanding the lesions we must detect in order to prevent lethal progression. Little is known about the natural history of lethal serous ovarian cancers before they become clinically apparent. We can learn about this occult period by studying the unsuspected serous cancers that are discovered in a small fraction of apparently healthy women who undergo prophylactic bilateral salpingo-oophorectomy (PBSO).
Methods and Findings
We developed models for the growth, progression, and detection of occult serous cancers on the basis of a comprehensive analysis of published data on serous cancers discovered by PBSO in BRCA1 mutation carriers. Our analysis yielded several critical insights into the early natural history of serous ovarian cancer. First, these cancers spend on average more than 4 y as in situ, stage I, or stage II cancers and approximately 1 y as stage III or IV cancers before they become clinically apparent. Second, for most of the occult period, serous cancers are less than 1 cm in diameter, and not visible on gross examination of the ovaries and Fallopian tubes. Third, the median diameter of a serous ovarian cancer when it progresses to an advanced stage (stage III or IV) is about 3 cm. Fourth, to achieve 50% sensitivity in detecting tumors before they advance to stage III, an annual screen would need to detect tumors of 1.3 cm in diameter; 80% detection sensitivity would require detecting tumors less than 0.4 cm in diameter. Fifth, to achieve a 50% reduction in serous ovarian cancer mortality with an annual screen, a test would need to detect tumors of 0.5 cm in diameter.
Our analysis has formalized essential conditions for successful early detection of serous ovarian cancer. Although the window of opportunity for early detection of these cancers lasts for several years, developing a test sufficiently sensitive and specific to take advantage of that opportunity will be a challenge. We estimated that the tumors we would need to detect to achieve even 50% sensitivity are more than 200 times smaller than the clinically apparent serous cancers typically used to evaluate performance of candidate biomarkers; none of the biomarker assays reported to date comes close to the required level of performance. Overcoming the signal-to-noise problem inherent in detection of tiny tumors will likely require discovery of truly cancer-specific biomarkers or development of novel approaches beyond traditional blood protein biomarkers. While this study was limited to ovarian cancers of serous histological type and to those arising in BRCA1 mutation carriers specifically, we believe that the results are relevant to other hereditary serous cancers and to sporadic ovarian cancers. A similar approach could be applied to other cancers to aid in defining their early natural history and to guide rational design of an early detection strategy.
Please see later in the article for Editors' Summary
Editors' Summary
Every year about 190,000 women develop ovarian cancer and more than 140,000 die from the disease. Ovarian cancer occurs when a cell on the surface of the ovaries (two small organs in the pelvis that produce eggs) or in the Fallopian tubes (which connect the ovaries to the womb) acquires genetic changes (mutations) that allow it to grow uncontrollably and to spread around the body (metastasize). For women whose cancer is diagnosed when it is confined to the site of origin—ovary or Fallopian tube—(stage I disease), the outlook is good; 70%–80% of these women survive for at least 5 y. However, very few ovarian cancers are diagnosed this early. Usually, by the time the cancer causes symptoms (often only vague abdominal pain and mild digestive disturbances), it has spread into the pelvis (stage II disease), into the space around the gut, stomach, and liver (stage III disease), or to distant organs (stage IV disease). Patients with advanced-stage ovarian cancer are treated with surgery and chemotherapy but, despite recent treatment improvements, only 15% of women diagnosed with stage IV disease survive for 5 y.
Why Was This Study Done?
Most deaths from ovarian cancer are caused by serous ovarian cancer, a tumor subtype that is rarely diagnosed before it has spread. Early detection of serous ovarian cancer would save the lives of many women but no one knows what these cancers look like before they spread or how long they grow before they become clinically apparent. Learning about this occult (hidden) period of ovarian cancer development by observing tumors from their birth to late-stage disease is not feasible. However, some aspects of the early natural history of ovarian cancer can be studied by using data collected from healthy women who have had their ovaries and Fallopian tubes removed (prophylactic bilateral salpingo-oophorectomy [PBSO]) because they have inherited a mutated version of the BRCA1 gene that increases their ovarian cancer risk. In a few of these women, unsuspected ovarian cancer is discovered during PBSO. In this study, the researchers identify and analyze the available reports on occult serous ovarian cancer found this way and then develop mathematical models describing the early natural history of ovarian cancer.
What Did the Researchers Do and Find?
The researchers first estimated the time period during which the detection of occult tumors might save lives using the data from these reports. Serous ovarian cancers, they estimated, spend more than 4 y as in situ (a very early stage of cancer development), stage I, or stage II cancers and about 1 y as stage III and IV cancers before they become clinically apparent. Next, the researchers used the data to develop mathematical models for the growth, progression, and diagnosis of serous ovarian cancer (the accuracy of which depends on the assumptions used to build the models and on the quality of the data fed into them). These models indicated that, for most of the occult period, serous cancers had a diameter of less than 1 cm (too small to be detected during surgery or by gross examination of the ovaries or Fallopian tubes) and that more than half of serous cancers had advanced to stage III/IV by the time they measured 3 cm across. Furthermore, to enable the detection of half of serous ovarian cancers before they reached stage III, an annual screening test would need to detect cancers with a diameter of 1.3 cm and to halve deaths from serous ovarian cancer, an annual screening test would need to detect 0.5-cm diameter tumors.
What Do These Findings Mean?
These findings suggest that the time period over which the early detection of serous ovarian cancer would save lives is surprisingly long. More soberingly, the authors find that a test that is sensitive and specific enough to take advantage of this “window of opportunity” would need to detect tumors hundreds of times smaller than clinically apparent serous cancers. So far no ovarian cancer-specific protein or other biomarker has been identified that could be used to develop a test that comes anywhere near this level of performance. Identification of truly ovarian cancer-specific biomarkers or novel strategies will be needed in order to take advantage of the window of opportunity. The stages prior to clinical presentation of other lethal cancers are still very poorly understood. Similar studies of the early natural history of these cancers could help guide the development of rational early detection strategies.
Additional Information
Please access these Web sites via the online version of this summary at
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. It also provides a fact sheet on BRCA1 mutations and cancer risk (in English and Spanish)
The UK charity Cancerbackup also provides information about all aspects of ovarian cancer
MedlinePlus provides a list of links to additional information about ovarian cancer (in English and Spanish)
The Canary Foundation is a nonprofit organization dedicated to development of effective strategies for early detection of cancers including ovarian cancer.
PMCID: PMC2711307  PMID: 19636370
6.  A taxonomy of epithelial human cancer and their metastases 
BMC Medical Genomics  2009;2:69.
Microarray technology has allowed to molecularly characterize many different cancer sites. This technology has the potential to individualize therapy and to discover new drug targets. However, due to technological differences and issues in standardized sample collection no study has evaluated the molecular profile of epithelial human cancer in a large number of samples and tissues. Additionally, it has not yet been extensively investigated whether metastases resemble their tissue of origin or tissue of destination.
We studied the expression profiles of a series of 1566 primary and 178 metastases by unsupervised hierarchical clustering. The clustering profile was subsequently investigated and correlated with clinico-pathological data. Statistical enrichment of clinico-pathological annotations of groups of samples was investigated using Fisher exact test. Gene set enrichment analysis (GSEA) and DAVID functional enrichment analysis were used to investigate the molecular pathways. Kaplan-Meier survival analysis and log-rank tests were used to investigate prognostic significance of gene signatures.
Large clusters corresponding to breast, gastrointestinal, ovarian and kidney primary tissues emerged from the data. Chromophobe renal cell carcinoma clustered together with follicular differentiated thyroid carcinoma, which supports recent morphological descriptions of thyroid follicular carcinoma-like tumors in the kidney and suggests that they represent a subtype of chromophobe carcinoma. We also found an expression signature identifying primary tumors of squamous cell histology in multiple tissues. Next, a subset of ovarian tumors enriched with endometrioid histology clustered together with endometrium tumors, confirming that they share their etiopathogenesis, which strongly differs from serous ovarian tumors. In addition, the clustering of colon and breast tumors correlated with clinico-pathological characteristics. Moreover, a signature was developed based on our unsupervised clustering of breast tumors and this was predictive for disease-specific survival in three independent studies. Next, the metastases from ovarian, breast, lung and vulva cluster with their tissue of origin while metastases from colon showed a bimodal distribution. A significant part clusters with tissue of origin while the remaining tumors cluster with the tissue of destination.
Our molecular taxonomy of epithelial human cancer indicates surprising correlations over tissues. This may have a significant impact on the classification of many cancer sites and may guide pathologists, both in research and daily practice. Moreover, these results based on unsupervised analysis yielded a signature predictive of clinical outcome in breast cancer. Additionally, we hypothesize that metastases from gastrointestinal origin either remember their tissue of origin or adapt to the tissue of destination. More specifically, colon metastases in the liver show strong evidence for such a bimodal tissue specific profile.
PMCID: PMC2806369  PMID: 20017941
7.  Multidrug Resistance-Linked Gene Signature Predicts Overall Survival of Patients With Primary Ovarian Serous Carcinoma 
Clinical Cancer Research  2012;18(11):3197-3206.
This study assesses the ability of multidrug resistance (MDR)-associated gene expression patterns to predict survival in patients with newly diagnosed carcinoma of the ovary. The scope of this research differs substantially from that of previous reports, as a very large set of genes was evaluated whose expression has been shown to affect response to chemotherapy.
Experimental Design
We applied a customized TaqMan Low Density Array, a highly sensitive and specific assay, to study the expression profiles of 380 MDR-linked genes in 80 tumor specimens collected at initial surgery to debulk primary serous carcinoma. The RNA expression profiles of these drug resistance genes were correlated with clinical outcomes.
Leave-one-out cross-validation was used to estimate the ability of MDR gene expression to predict survival. Although gene expression alone does not predict overall survival (P=0.06), four covariates (age, stage, CA125 level and surgical debulking) do (P=0.03). When gene expression was added to the covariates, we found an 11-gene signature that provides a major improvement in overall survival prediction (log-rank statistic P<0.003). The predictive power of this 11-gene signature was confirmed by dividing high and low risk patient groups, as defined by their clinical covariates, into four specific risk groups based on expression levels.
This study reveals an 11-gene signature that allows a more precise prognosis for patients with serous cancer of the ovary treated with carboplatin- and paclitaxel-based therapy. These 11 new targets offer opportunities for new therapies to improve clinical outcome in ovarian cancer.
PMCID: PMC3376649  PMID: 22492981
chemotherapy; gene expression profiling; multidrug resistance; ovarian cancer; risk prediction
8.  Spatial and Temporal Heterogeneity in High-Grade Serous Ovarian Cancer: A Phylogenetic Analysis 
PLoS Medicine  2015;12(2):e1001789.
The major clinical challenge in the treatment of high-grade serous ovarian cancer (HGSOC) is the development of progressive resistance to platinum-based chemotherapy. The objective of this study was to determine whether intra-tumour genetic heterogeneity resulting from clonal evolution and the emergence of subclonal tumour populations in HGSOC was associated with the development of resistant disease.
Methods and Findings
Evolutionary inference and phylogenetic quantification of heterogeneity was performed using the MEDICC algorithm on high-resolution whole genome copy number profiles and selected genome-wide sequencing of 135 spatially and temporally separated samples from 14 patients with HGSOC who received platinum-based chemotherapy. Samples were obtained from the clinical CTCR-OV03/04 studies, and patients were enrolled between 20 July 2007 and 22 October 2009. Median follow-up of the cohort was 31 mo (interquartile range 22–46 mo), censored after 26 October 2013. Outcome measures were overall survival (OS) and progression-free survival (PFS). There were marked differences in the degree of clonal expansion (CE) between patients (median 0.74, interquartile range 0.66–1.15), and dichotimization by median CE showed worse survival in CE-high cases (PFS 12.7 versus 10.1 mo, p = 0.009; OS 42.6 versus 23.5 mo, p = 0.003). Bootstrap analysis with resampling showed that the 95% confidence intervals for the hazard ratios for PFS and OS in the CE-high group were greater than 1.0. These data support a relationship between heterogeneity and survival but do not precisely determine its effect size. Relapsed tissue was available for two patients in the CE-high group, and phylogenetic analysis showed that the prevalent clonal population at clinical recurrence arose from early divergence events. A subclonal population marked by a NF1 deletion showed a progressive increase in tumour allele fraction during chemotherapy.
This study demonstrates that quantitative measures of intra-tumour heterogeneity may have predictive value for survival after chemotherapy treatment in HGSOC. Subclonal tumour populations are present in pre-treatment biopsies in HGSOC and can undergo expansion during chemotherapy, causing clinical relapse.
In this study, James Brenton and colleagues demonstrate that quantitative measures of intratumoural heterogeneity may have predictive value for survival after chemotherapy treatment in high-grade serous ovarian cancer.
Editors’ Summary
Every year, nearly 250,000 women develop ovarian cancer, and about 150,000 die from the disease. Ovarian cancer occurs when a cell on the surface of the ovaries (two small organs in the pelvis that produce eggs) or in the Fallopian tubes (which connect the ovaries to the womb) acquires genetic changes (mutations) that allow it to grow uncontrollably and to spread around the body (metastasize). For women whose ovarian cancer is diagnosed when it is confined to its site of origin, the outlook is good. About 90% of these women survive for at least five years. However, ovarian cancer is rarely diagnosed this early. Usually, by the time the cancer causes symptoms (often only vague abdominal pains and mild digestive disturbances), it has spread into the peritoneal cavity (the space around the gut, stomach, and liver) or has metastasized to distant organs. Patients with advanced ovarian cancer are treated with a combination of surgery and platinum-based chemotherapy, but only a quarter of such women are still alive five years after diagnosis, and the overall five-year survival rate for ovarian cancer is less than 50%.
Why Was This Study Done?
The major clinical challenge in the treatment of high-grade serous ovarian cancer (HGSOC; the most common type of ovarian cancer) is the development of resistance to platinum-based chemotherapy. If we knew how this resistance develops, it might be possible to improve the treatment of HGSOC. Tumors are thought to arise from a single mutated cell that accumulates additional mutations as it grows and divides. This process results in the formation of subpopulations of tumor cells, each with a different set of mutations. Experts think that this “intra-tumor heterogeneity” gives rise to tumor subclones that possess an evolutionary advantage over other subclones (they might, for example, grow faster or be resistant to chemotherapy) and that eventually dominate the tumor (“clonal expansion”). Here, the researchers investigate whether clonal evolution and the emergence of subclonal tumor populations explains the development of chemotherapy-resistant HGSOC by undertaking evolutionary inference and phylogenetic quantification of the heterogeneity of samples taken from women with HGSOC at different times and from different places in their body. Evolutionary inference and phylogenetic quantification are analytical approaches that can be used to reconstruct the evolutionary history (“family tree”) of a tumor.
What Did the Researchers Do and Find?
The researchers used an algorithm (a step-by-step procedure for data processing) called MEDICC to analyze detailed genetic data obtained from 135 spatially and temporally separated samples taken from 14 patients with HGSOC who had received platinum-based chemotherapy. The researchers report that there were marked differences in the degree of clonal expansion among the patients. When they split the patients into two groups based on the degree of clonal expansion in their tumors (CE-high and CE-low), patients with tumors classified as CE-high had a shorter progression-free survival time than patients with tumors classified as CE-high (10.1 months compared to 12.7 months) and a shorter overall survival time (23.5 months compared to 42.6 months). Moreover, a type of statistical analysis called bootstrap analysis, which tests for the robustness of the result, indicated that having CE-high tumors was likely to increase a patient’s risk of a poor outcome. Finally, phylogenetic analysis of samples taken from two patients before and after relapse and analysis of a NF1 deletion (NF1 encodes neurofibromin 1, a tumor suppressor protein that prevents uncontrolled cell growth; NF1 is frequently mutated in HGSOC) indicated that a resistant subclonal population was already present in the patients’ tumors before treatment began.
What Do These Findings Mean?
These findings show that clonal expansion occurs between diagnosis and relapse in HGSOC, that there are marked differences in the degree of clonal expansion among patients, and that a high degree of clonal expansion may have a negative effect on survival. The accuracy of these findings is limited by the small number of patients included in the study, and it is likely that the analyses reported here overestimate the effect of clonal expansion on patient outcomes. Nevertheless, the researchers suggest that, provided larger patient studies yield similar results, quantitative measures of intra-tumor heterogeneity might be useful as patient-specific prognostic markers in HGSOC. That is, measures of intra-tumor heterogeneity might eventually help clinicians to predict which of their patients with ovarian cancer are likely to have the best outcomes after platinum-based chemotherapy.
Additional Information
Please access these websites via the online version of this summary at
The US National Cancer Institute provides information about cancer and how it develops (in English and Spanish), including detailed information about ovarian cancer
Cancer Research UK, a not-for-profit organization, provides general information about cancer and how it develops, and detailed information about ovarian cancer
The UK National Health Service Choices website has information and personal stories about ovarian cancer
The not-for-profit organization provides personal stories about dealing with ovarian cancer; Eyes on the Prize, an online support group for women who have had cancers of the female reproductive system, also includes personal stories; the not-for-profit organization Ovarian Cancer Action also provides information, support, and personal stories about ovarian cancer
Wikipedia provides information about clonal evolution in cancer, tumor heterogeneity, and phylogenetics (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
More information about the MEDICC algorithm is available
PMCID: PMC4339382  PMID: 25710373
9.  Molecular signatures of epithelial ovarian cancer: analysis of associations with tumor characteristics and epidemiologic risk factors 
Six gene expression subtypes of invasive epithelial ovarian cancer were recently defined using microarrays by Tothill and colleagues. The Cancer Genome Atlas project (TCGA) subsequently replicated these subtypes and identified a signature predictive of survival in high grade serous cancers. We previously validated these signatures for use in formalin-fixed paraffin embedded (FFPE) tissues. The aim of the present study was to determine whether these signatures are associated with specific ovarian cancer risk factors, which would add to the evidence that they reflect the heterogeneous etiology of this disease.
We modeled signature-specific tumor characteristics and epidemiological risk factor relationships using multiple regression and multivariate response multiple regression models in 193 patients from a case-control study of epithelial ovarian cancer.
We observed associations between the Tothill gene expression subtype signatures and both age at diagnosis (p=0.0008) and race (p=0.008). Although most established epidemiologic risk factors were not associated with molecular signatures, there was an association between breast feeding (p=0.024) and first degree family history of breast or ovarian cancer (p=0.034) among the 106 high grade serous cases. Some of the above associations were validated using gene expression microarray data from the TCGA project. Weak associations were seen with age at menarche and duration of oral contraceptive use and the TCGA survival signature.
These data support the potential for genomic characterization to elucidate the etiologic heterogeneity of epithelial ovarian cancer.
This study suggests that molecular signatures may augment the ability to define etiologic subtypes of epithelial ovarian cancer.
PMCID: PMC3799825  PMID: 23917454
molecular signatures; ovarian cancer; epidemiology; tumor characteristics; TCGA
10.  The Anterior Gradient Homolog 3 (AGR3) Gene Is Associated with Differentiation and Survival in Ovarian Cancer 
Low-grade serous ovarian carcinoma is believed to arise from serous borderline ovarian tumors, yet the progression from serous borderline tumors to low-grade serous ovarian carcinoma remains poorly understood. The purpose of this study was to identify differentially expressed genes between the two groups. Expression profiles were generated from 6 human ovarian surface epithelia (HOSE), 8 serous borderline ovarian tumors (SBOT), 13 low-grade serous ovarian carcinomas (LG), and 24 high-grade serous ovarian carcinomas (HG). The anterior gradient homolog 3 (AGR3) gene was found to be highly upregulated in serous borderline ovarian tumors; this finding was validated by real-time quantitative RT-PCR, Western blotting, and immunohistochemistry. Anti-AGR3 immunohistochemistry was performed on an additional 56 LG and 103 HG tissues and the results were correlated with clinical data. Expression profiling determined that 1254 genes were differentially expressed (P < 0.005) between SBOT, LG and HG tumors. Serous borderline ovarian tumors exhibited robust positive staining for AGR3, with a lower percentage of tumor cells stained in LG and HG. Immunofluorescence staining indicated that AGR3 expression was limited to ciliated cells. Tumor samples with a high percentage (>10%) of AGR3 positively stained tumor cells were associated with improved longer median survival in both the LG (P = 0.013) and HG (P = 0.008) serous ovarian carcinoma groups. The progression of serous borderline ovarian tumors to low-grade serous ovarian carcinoma may involve the de-differentiation of ciliated cells. AGR3 could serve as a prognostic marker for survival in patients with low-grade and high-grade serous ovarian carcinomas.
PMCID: PMC3095702  PMID: 21451362
AGR3; BCMP11; ovarian cancer; biomarker; cilia
11.  An IL6-correlated signature in serous epithelial ovarian cancer associates with growth factor response 
BMC Genomics  2013;14:508.
Epithelial ovarian cancer (EOC) is one of the most lethal gynecological cancers; the majority of EOC is the serous histotype and diagnosed at advanced stage. IL6 is the cytokine that has been found most frequently associated with carcinogenesis and progression of serous EOCs. IL6 is a growth-promoting and anti-apoptotic factor, and high plasma levels of IL6 in advanced stage EOCs correlate with poor prognosis. The objective of the present study was to identify IL6 co-regulated genes and gene network/s in EOCs.
We applied bioinformatics tools on 7 publicly available data sets containing the gene expression profiles of 1262 EOC samples. By Pearson's correlation analysis we identified, in EOCs, an IL6-correlated gene signature containing 40 genes mainly associated with proliferation. 33 of 40 genes were also significantly correlated in low malignant potential (LMP) EOCs, while 7 genes, named C5AR1, FPR1, G0S2, IL8, KLF2, MMP19, and THBD were IL6-correlated only in advanced stage EOCs. Among the 40-gene signature EGFR ligand HBEGF, genes of the EGR family members and genes encoding for negative feedback regulators of growth factor signaling were included. The results obtained by Gene Set Enrichment and Ingenuity Pathway Analyses enabled the identification, respectively, of gene sets associated with ‘early growth factor response’ for the 40-gene signature, and a biological network related to ‘thrombosis and cardiovascular disease’ for the 7-gene signature. In agreement with these results, selected genes from the identified signatures were validated in vitro by real time RT-PCR in serous EOC cell lines upon stimulation with EGF.
Serous EOCs, independently of their aggressiveness, co-regulate IL6 expression together with that of genes associated to growth factor signaling, arguing for the hypothesis that common mechanism/s driven by EGFR ligands characterize both advanced-stage and LMP EOCs. Only advanced-stage EOCs appeared to be characterized by a scenario that involves genes which are so far associated with thrombosis and cardiovascular disease, thus suggesting that this pathway is implicated in the growth and/or spread of more aggressive tumors. We have discovered novel activated signaling pathways that drive the expression of IL6 and of co-regulated genes and are possibly involved in the pathobiology of EOCs.
PMCID: PMC3728068  PMID: 23889749
Epithelial ovarian cancer; IL6; Microarrays; Bioinformatics; Growth factor
12.  Clinical Relevance of Multidrug Resistance Gene Expression in Ovarian Serous Carcinoma Effusions 
Molecular pharmaceutics  2011;8(6):2080-2088.
The presence of tumor cells in effusions within serosal cavities is a clinical manifestation of advanced-stage cancer and is generally associated with poor survival. Identifying molecular targets may help to design efficient treatments to eradicate these aggressive cancer cells and improve patient survival. Using a state-of-the-art Taqman-based qRT-PCR assay, we investigated the multidrug resistance (MDR) transcriptome of 32 unpaired ovarian serous carcinoma effusion samples obtained at diagnosis or at disease recurrence following chemotherapy. MDR genes were selected a priori based on an extensive curation of the literature published during the last three decades. We found three gene signatures with a statistically significant correlation with overall survival (OS), response to treatment (complete response - CR vs. other), and progression free survival (PFS). The median log-rank p-values for the signatures were 0.023, 0.034, and 0.008, respectively. No correlation was found with residual tumor status after cytoreductive surgery, treatment (with or without chemotherapy) and stage defined according to the International Federation of Gynecology and Obstetrics. Further analyses demonstrated that gene expression alone can effectively predict the survival outcome of women with ovarian serous carcinoma (OS: log-rank p=0.0000 and PFS: log-rank p=0.002). Interestingly, the signature for overall survival is the same in patients at first presentation and those who had chemotherapy and relapsed. This pilot study highlights two new gene signatures that may help in optimizing the treatment for ovarian carcinoma patients with effusions.
PMCID: PMC3224865  PMID: 21761824
ovarian serous carcinoma; effusion; multidrug resistance; gene signature
13.  PAX2 Expression in Low Malignant Potential Ovarian Tumors and Low-Grade Ovarian Serous Carcinomas 
Ovarian tumors of low-malignant potential and low-grade ovarian serous carcinomas are thought to represent different stages on a tumorigenic continuum and to develop along pathways distinct from high-grade ovarian serous carcinoma. We performed gene expression profiling on 3 normal human ovarian surface epithelia samples, and 10 low-grade and 10 high-grade ovarian serous carcinomas. Analysis of gene expression profiles of these samples has identified 80 genes up-regulated and 232 genes down-regulated in low-grade ovarian serous carcinomas. PAX2 was found to be one of the most up-regulated genes in low-grade ovarian serous carcinoma. The up-regulation of PAX2 was validated by real-time quantitative RT-PCR, Western blot and immunohistochemical analyses. Real-time RT-PCR demonstrated a statistically significant difference in PAX2 mRNA expression (expressed as fold change in comparison to normal human ovarian surface epithelia) among ovarian tumors of low-malignant potential (1837.38, N=8), low-grade (183.12, N=17), and high-grade (3.72, N=23) carcinoma samples (p=0.015). Western blot analysis revealed strong PAX2 expression in ovarian tumors of low-malignant potential (67%, N=3) and low-grade carcinoma samples (50%, N=10) but no PAX2 protein expression in high-grade carcinomas (0%, N=10). Using immunohistochemistry, tumors of low-malignant potential (59%, N=17) and low-grade carcinoma (63%, N=16) samples expressed significantly stronger nuclear staining than high-grade ovarian carcinoma samples (9.1%, N=263). Furthermore, consistent with previous immunohistochemical findings, PAX2 expression was found to be expressed in the epithelial cells of fallopian tubes but not in normal ovarian surface epithelial cells. Our findings further support the two-tiered hypothesis that tumors of low-malignant potential and low-grade ovarian serous carcinoma are on a continuum and are distinct from high-grade ovarian carcinomas. Additionally, the absence of PAX2 expression in normal ovarian epithelia but expression in fallopian tube fimbria and ciliated epithelial inclusions would suggest the potential development of tumors of low-malignant potential and of low-grade ovarian serous carcinomas from secondary Müllerian structures.
PMCID: PMC2736318  PMID: 19525924
Biomarker; low-grade ovarian cancer; PAX2; borderline ovarian tumors; gene expression
14.  Molecular Subtyping of Serous Ovarian Tumors Reveals Multiple Connections to Intrinsic Breast Cancer Subtypes 
PLoS ONE  2014;9(9):e107643.
Transcriptional profiling of epithelial ovarian cancer has revealed molecular subtypes correlating to biological and clinical features. We aimed to determine gene expression differences between malignant, benign and borderline serous ovarian tumors, and investigate similarities with the well-established intrinsic molecular subtypes of breast cancer.
Global gene expression profiling using Illumina's HT12 Bead Arrays was applied to 59 fresh-frozen serous ovarian malignant, benign and borderline tumors. Nearest centroid classification was performed applying previously published gene profiles for the ovarian and breast cancer subtypes. Correlations to gene expression modules representing key biological breast cancer features were also sought. Validation was performed using an independent, publicly available dataset.
5,944 genes were significantly differentially expressed between benign and malignant serous ovarian tumors, with cell cycle processes enriched in the malignant subgroup. Borderline tumors were split between the two clusters. Significant correlations between the malignant serous tumors and the highly aggressive ovarian cancer signatures, and the basal-like breast cancer subtype were found. The benign and borderline serous tumors together were significantly correlated to the normal-like breast cancer subtype and the ovarian cancer signature derived from borderline tumors. The borderline tumors in the study dataset, in addition, also correlated significantly to the luminal A breast cancer subtype. These findings remained when analyzed in an independent dataset, supporting links between the molecular subtypes of ovarian cancer and breast cancer beyond those recently acknowledged.
These data link the transcriptional profiles of serous ovarian cancer to the intrinsic molecular subtypes of breast cancer, in line with the shared clinical and molecular features between high-grade serous ovarian cancer and basal-like breast cancer, and suggest that biomarkers and targeted therapies may overlap between these tumor subsets. The link between benign and borderline ovarian cancer and luminal breast cancer may indicate endocrine responsiveness in a subset of ovarian cancers.
PMCID: PMC4166462  PMID: 25226589
15.  Lysophosphatidic Acid-Induced Transcriptional Profile Represents Serous Epithelial Ovarian Carcinoma and Worsened Prognosis 
PLoS ONE  2009;4(5):e5583.
Lysophosphatidic acid (LPA) governs a number of physiologic and pathophysiological processes. Malignant ascites fluid is rich in LPA, and LPA receptors are aberrantly expressed by ovarian cancer cells, implicating LPA in the initiation and progression of ovarian cancer. However, there is an absence of systematic data critically analyzing the transcriptional changes induced by LPA in ovarian cancer.
Methodology and Principal Findings
In this study, gene expression profiling was used to examine LPA-mediated transcription by exogenously adding LPA to human epithelial ovarian cancer cells for 24 h to mimic long-term stimulation in the tumor microenvironment. The resultant transcriptional profile comprised a 39-gene signature that closely correlated to serous epithelial ovarian carcinoma. Hierarchical clustering of ovarian cancer patient specimens demonstrated that the signature is associated with worsened prognosis. Patients with LPA-signature-positive ovarian tumors have reduced disease-specific and progression-free survival times. They have a higher frequency of stage IIIc serous carcinoma and a greater proportion is deceased. Among the 39-gene signature, a group of seven genes associated with cell adhesion recapitulated the results. Out of those seven, claudin-1, an adhesion molecule and phenotypic epithelial marker, is the only independent biomarker of serous epithelial ovarian carcinoma. Knockdown of claudin-1 expression in ovarian cancer cells reduces LPA-mediated cellular adhesion, enhances suspended cells and reduces LPA-mediated migration.
The data suggest that transcriptional events mediated by LPA in the tumor microenvironment influence tumor progression through modulation of cell adhesion molecules like claudin-1 and, for the first time, report an LPA-mediated expression signature in ovarian cancer that predicts a worse prognosis.
PMCID: PMC2679144  PMID: 19440550
16.  A Six-Gene Signature Predicts Survival of Patients with Localized Pancreatic Ductal Adenocarcinoma 
PLoS Medicine  2010;7(7):e1000307.
Jen Jen Yeh and colleagues developed and validated a six-gene signature in patients with pancreatic ductal adenocarcinoma that may be used to better stage the disease in these patients and assist in treatment decisions.
Pancreatic ductal adenocarcinoma (PDAC) remains a lethal disease. For patients with localized PDAC, surgery is the best option, but with a median survival of less than 2 years and a difficult and prolonged postoperative course for most, there is an urgent need to better identify patients who have the most aggressive disease.
Methods and Findings
We analyzed the gene expression profiles of primary tumors from patients with localized compared to metastatic disease and identified a six-gene signature associated with metastatic disease. We evaluated the prognostic potential of this signature in a training set of 34 patients with localized and resected PDAC and selected a cut-point associated with outcome using X-tile. We then applied this cut-point to an independent test set of 67 patients with localized and resected PDAC and found that our signature was independently predictive of survival and superior to established clinical prognostic factors such as grade, tumor size, and nodal status, with a hazard ratio of 4.1 (95% confidence interval [CI] 1.7–10.0). Patients defined to be high-risk patients by the six-gene signature had a 1-year survival rate of 55% compared to 91% in the low-risk group.
Our six-gene signature may be used to better stage PDAC patients and assist in the difficult treatment decisions of surgery and to select patients whose tumor biology may benefit most from neoadjuvant therapy. The use of this six-gene signature should be investigated in prospective patient cohorts, and if confirmed, in future PDAC clinical trials, its potential as a biomarker should be investigated. Genes in this signature, or the pathways that they fall into, may represent new therapeutic targets.
Please see later in the article for the Editors' Summary
Editors' Summary
Pancreatic cancer kills nearly a quarter of a million people every year. It begins when a cell in the pancreas (an organ lying behind the stomach that produces digestive enzymes and hormones such as insulin, which controls blood sugar levels) acquires genetic changes that allow it to grow uncontrollably and to spread around the body (metastasize). Nearly all pancreatic cancers are “pancreatic ductal adenocarcinomas” (PDACs)—tumors that start in the cells that line the tubes in the pancreas that take digestive juices to the gut. Because PDAC rarely causes any symptoms early in its development, it has already metastasized in about half of patients before it is diagnosed. Consequently, the average survival time after a diagnosis of PDAC is only 5–8 months. At present, the only chance for cure is surgical removal (resection) of the tumor, part of the pancreas, and other nearby digestive organs. The operation that is needed for the majority of patients—the Whipple procedure—is only possible in the fifth of patients whose tumor is found when it is small enough to be resectable but even with postoperative chemotherapy, these patients only live for 23 months after surgery on average, possibly because they have micrometastases at the time of their operation.
Why Was This Study Done?
Despite this poor overall outcome, about a quarter of patients with resectable PDAC survive for more than 5 years after surgery. Might some patients, therefore, have a less aggressive form of PDAC determined by the biology of the primary (original) tumor? If this is the case, it would be useful to be able to stratify patients according to the aggressiveness of their disease so that patients with very aggressive disease could be given chemotherapy before surgery (neoadjuvant therapy) to kill any micrometastases. At present neoadjuvant therapy is given to patients with locally advanced, unresectable tumors. In this study, the researchers compare gene expression patterns in primary tumor samples collected from patients with localized PDAC and from patients with metastatic PDAC between 1999 and 2007 to try to identify molecular markers that distinguish between more and less aggressive PDACs.
What Did the Researchers Do and Find?
The researchers identified a six-gene signature that was associated with metastatic disease using a molecular biology approach called microarray hybridization and a statistical method called significance analysis of microarrays to analyze gene expression patterns in primary tumor samples from 15 patients with localized PDAC and 15 patients with metastatic disease. Next, they used a training set of tumor samples from another 34 patients with localized and resected PDAC, microarray hybridization, and a graphical method called X-tile to select a combination of expression levels of the six genes that discriminated optimally between high-risk (aggressive) and low-risk (less aggressive) tumors on the basis of patient survival (a “cut-point”). When the researchers applied this cut-point to an independent set of 67 tumor samples from patients with localized and resected PDAC, they found that 42 patients had high-risk tumors. These patients had an average survival time of 15 months; 55% of them were alive a year after surgery. The remaining 25 patients, who had low-risk tumors, had an average survival time of 49 months and 91% of them were alive a year after resection.
What Do These Findings Mean?
These and other findings identify a six-gene signature that can predict outcomes in patients with localized, resectable PDAC better than, and independently of, established clinical markers of outcome. If the predictive ability of this signature can be confirmed in additional patients, it could be used to help patients make decisions about their treatment. For example, a patient wondering whether to risk the Whipple procedure (2%–6% of patients die during this operation and more than 50% have serious postoperative complications), the knowledge that their tumor was low risk might help them decide to have the operation. Conversely, a patient in poor health with a high-risk tumor might decide to spare themselves the trauma of major surgery. The six-gene signature might also help clinicians decide which patients would benefit most from neoadjuvant therapy. Finally, the genes in this signature, or the biological pathways in which they participate, might represent new therapeutic targets for the treatment of PDAC.
Additional Information
Please access these Web sites via the online version of this summary at
The US National Cancer Institute provides information for patients and health professionals about all aspects of pancreatic cancer (in English and Spanish), including a booklet for patients
The American Cancer Society also provides detailed information about pancreatic cancer
The UK National Health Service and Cancer Research UK include information for patients on pancreatic cancer on their Web sites
MedlinePlus provides links to further resources on pancreatic cancer (in English and Spanish)
Cure Pancreatic Cancer provides information about scientific and medical research related to the diagnosis, treatment, cure, and prevention of pancreatic cancer
Pancreatic Cancer Action Network is a US organization that supports research, patient support, community outreach, and advocacy for a cure for pancreatic cancer
PMCID: PMC2903589  PMID: 20644708
17.  Insights into Endometrial Serous Carcinogenesis and Progression 
Endometrial serous carcinomas (ESC) constitute only approximately 10% of endometrial cancers, but have a substantially higher case-fatality rate than their more common endometrioid counterparts. The precise composite of factors driving endometrial serous carcinogenesis and progression remain largely unknown, but we attempt to review the current state of knowledge in this report. ESC probably do not evolve through a single pathway, and their underlying molecular events probably occur early in their evolution. TP53 gene mutations occur in 22.7 to 96% of cases, and p53 protein overexpression is seen in approximately 76%. By gene expression profiling, p16 is upregulated in ESC significantly above both normal endometrial cells and endometrioid carcinomas, and 92–100% of cases display diffuse expression of the p16 protein by immunohistochemistry (IHC). Together, these findings suggest dysregulation of both the p16INKA/Cyclin D-CDK/pRb-E2F and the ARF-MDM2-p53 cell cycle pathways in ESC. By IHC, HER2/neu is overexpressed (2+ or 3+) in approximately 32.1% of ESC, and approximately 54.5% of cases scored as 2+ or 3+ by IHC display c-erbB2 gene amplification as assessed by fluorescent in situ hybridization. Genetic instability, typically manifested as loss of heterozygosity in multiple chromosomes, is a common feature of ESC, and one study found loss of heterozygosity at 1p32-33 in 63% of cases. A subset of ESC display protein expression patterns that are characteristic of high grade endometrial carcinomas, including loss of the metastasis suppressor CD82 (KAI-1) and epithelial-to-mesenchymal transformation, the latter manifested as E-cadherin downregulation, P-cadherin upregulation, and expression of epithelial-to-mesenchymal transformation-related molecules such as zinc-finger E-box-binding homeobox 1 (ZEB1) and focal adhesion kinase. Preliminary data suggests differential patterns of expression in ESC of some isoforms of claudins, proteases, the tumor invasiveness and progression-associated oncofetal protein insulin-like growth factor II mRNA-binding protein 3 (IMP3), as well as a variety of other molecules. At the morphologic level, evidence that indicates that endometrial glandular dysplasia (EmGD) is the most likely morphologically recognizable precursor lesion to ESC is presented. We advocate use of the term endometrial intraepithelial carcinoma (EIC, or its other appellations) only as a morphologic descriptor and never as a diagnostic/pathologic statement of biologic potential. Given its potential for extrauterine extension, we consider the lesions described as EIC, when present in isolation, as examples of localized ESC, and patients should be managed as such. Morphologically normal, p53 immunoreactive endometrial cells (the so-called “p53 signatures”), show a statistically significant association with ESC, display p53 mutations in a significant subset, and form the start of a progression model, outlined herein, from p53 signatures to EmGD to localized ESC to the more conventionally invasive neoplasm. The identification of a morphologically-recognizable precursor holds the promise of early detection of ESC, with the attendant reduction in its overall associated mortality rate. Deciphering the molecular basis for endometrial serous carcinogenesis should uncover potential targets for diagnosis, therapy, and/or disease surveillance.
PMCID: PMC2655156  PMID: 19294001
Endometrial serous carcinoma; endometrial glandular dysplasia; endometrial intraepithelial carcinoma; p53; cadherins; claudins; CDKs; MDM2 and HER2/neu (erb-B2)
18.  Potential predictive markers of chemotherapy resistance in stage III ovarian serous carcinomas 
BMC Cancer  2009;9:368.
Chemotherapy resistance remains a major obstacle in the treatment of women with ovarian cancer. Establishing predictive markers of chemoresponse would help to individualize therapy and improve survival of ovarian cancer patients. Chemotherapy resistance in ovarian cancer has been studied thoroughly and several non-overlapping single genes, gene profiles and copy number alterations have been suggested as potential markers. The objective of this study was to explore genetic alterations behind chemotherapy resistance in ovarian cancer with the ultimate aim to find potential predictive markers.
To create the best opportunities for identifying genetic alterations of importance for resistance, we selected a homogenous tumor material concerning histology, stage and chemotherapy. Using high-resolution whole genome array comparative genomic hybridization (CGH), we analyzed the tumor genomes of 40 fresh-frozen stage III ovarian serous carcinomas, all uniformly treated with combination therapy paclitaxel/carboplatin. Fisher's exact test was used to identify significant differences. Subsequently, we examined four genes in the significant regions (EVI1, MDS1, SH3GL2, SH3KBP1) plus the ABCB1 gene with quantitative real-time polymerase chain reaction (QPCR) to evaluate the impact of DNA alterations on the transcriptional level.
We identified gain in 3q26.2, and losses in 6q11.2-12, 9p22.3, 9p22.2-22.1, 9p22.1-21.3, Xp22.2-22.12, Xp22.11-11.3, and Xp11.23-11.1 to be significantly associated with chemotherapy resistance. In the gene expression analysis, EVI1 expression differed between samples with gain versus without gain, exhibiting higher expression in the gain group.
In conclusion, we detected specific genetic alterations associated with resistance, of which some might be potential predictive markers of chemotherapy resistance in advanced ovarian serous carcinomas. Thus, further studies are required to validate these findings in an independent ovarian tumor series.
PMCID: PMC2770569  PMID: 19835627
19.  An Erythroid Differentiation Signature Predicts Response to Lenalidomide in Myelodysplastic Syndrome  
PLoS Medicine  2008;5(2):e35.
Lenalidomide is an effective new agent for the treatment of patients with myelodysplastic syndrome (MDS), an acquired hematopoietic disorder characterized by ineffective blood cell production and a predisposition to the development of leukemia. Patients with an interstitial deletion of Chromosome 5q have a high rate of response to lenalidomide, but most MDS patients lack this deletion. Approximately 25% of patients without 5q deletions also benefit from lenalidomide therapy, but response in these patients cannot be predicted by any currently available diagnostic assays. The aim of this study was to develop a method to predict lenalidomide response in order to avoid unnecessary toxicity in patients unlikely to benefit from treatment.
Methods and Findings
Using gene expression profiling, we identified a molecular signature that predicts lenalidomide response. The signature was defined in a set of 16 pretreatment bone marrow aspirates from MDS patients without 5q deletions, and validated in an independent set of 26 samples. The response signature consisted of a cohesive set of erythroid-specific genes with decreased expression in responders, suggesting that a defect in erythroid differentiation underlies lenalidomide response. Consistent with this observation, treatment with lenalidomide promoted erythroid differentiation of primary hematopoietic progenitor cells grown in vitro.
These studies indicate that lenalidomide-responsive patients have a defect in erythroid differentiation, and suggest a strategy for a clinical test to predict patients most likely to respond to the drug. The experiments further suggest that the efficacy of lenalidomide, whose mechanism of action in MDS is unknown, may be due to its ability to induce erythroid differentiation.
Using gene expression profiling, Azra Raza and colleagues identified a molecular signature that predicts response to lenalidomide in patients without Chromosome 5q deletions, which suggests that these patients have a defect in erythroid differentiation.
Editors' Summary
Myelodysplastic syndrome (MDS) is a group of disorders in which the bone marrow (the spongy material found inside bones) does not make enough healthy blood cells. Normally, immature cells in the bone marrow called hematopoietic stem cells mature (differentiate) into three types of blood cells: red blood cells (which carry oxygen around the body; people with too few red blood cells are “anemic”), white blood cells (which fight off infections), and platelets (which prevent bleeding by forming blood clots). In patients with MDS, the production of these mature cell types is defective. In addition, immature cells called leukemic blasts sometimes accumulate in the bone marrow and blood. Thus, although MDS itself is not a type of cancer, it often develops into leukemia (blood cancer). The cause of most cases of MDS, which affects mainly elderly people, is not known. Its symptoms include tiredness and breathlessness (signs of anemia), frequent infections, and easy bruising or bleeding. Patients are usually given supportive care to relieve their symptoms (for example, blood transfusions to top up their red blood cells). Chemotherapy can sometimes delay the progression of MDS to leukemia and a few patients can be helped with bone marrow transplantation.
Why Was This Study Done?
Recently, researchers have discovered that some people with MDS respond very well to a drug called lenalidomide. Three-quarters of patients whose MDS is characterized by the loss of a small part of Chromosome 5 need fewer blood transfusions after being given lenalidomide but only a quarter of people without this chromosomal defect respond to the drug. Unfortunately, most patients with MDS do not have this chromosome abnormality and there is no way to predict which of these patients are likely to respond to lenalidomide. Lenalidomide is a toxic drug that damages white blood cells and platelets, so it is important not to give it to people who might not benefit. In this study, the researchers have used gene expression profiling (a technique that catalogs all the genes expressed by a cell) to try to develop a way of predicting who will respond to lenalidomide
What Did the Researchers Do and Find?
The researchers obtained pre-treatment bone marrow samples from patients enrolled in two clinical trials of lenalidomide and compared the gene expression profiles of the bone marrow cells from the patients who subsequently responded to the drug with the profiles of cells from nonresponding patients. In all, 47 genes were more highly expressed in nonresponders than in responders. The researchers then asked whether the expression of any gene sets (collections of genes that code for proteins that work in a single pathway) was greater in the nonresponders than in the responders. This analysis revealed a “signature” of lenalidomide response consisting of a set of genes normally expressed during the differentiation of red blood cells (an “erythroid differentiation signature”). Decreased expression of this signature was associated with a response to lenalidomide in an independent set of patients (validation set). The researchers then used the response signature and the original set of samples to develop a single score that could distinguish individual responders from nonresponders. This score accurately predicted the response of three-quarters of the patients in the validation set to lenalidomide. Finally, the researchers showed that lenalidomide promotes the erythroid maturation of normal human hematopoietic stem cells grown in dishes and stimulates the expression of the lenalidomide response signature in these cells.
What Do These Findings Mean?
These findings indicate that patients with MDS who respond to lenalidomide have defective red blood cell differentiation. In addition, they suggest that it might be possible to use the response signature to develop a test that can predict which patients with MDS will benefit from treatment with lenalidomide. However, the preliminary predictive test described here will need to be tested in many more patients before it can be used as a routine clinical test. Finally, the researchers' last experiment suggests that lenalidomide may help people with MDS because it induces red blood cell differentiation. Lenalidomide therapy might, therefore be useful in other disorders in which red blood cell maturation is defective, including some forms of anemia.
Additional Information.
Please access these Web sites via the online version of this summary at
See a related PLoS Medicine Perspective article
The US National Cancer Institute provides information for patients about myelodysplastic syndrome (in English and Spanish)
The UK charity Cancerbackup provides information about myelodysplastic syndrome
The American Cancer Society and the Leukemia and Lymphoma Society provide additional information about myelodysplastic syndrome
The US Food and Drug Administration provides information for patients on lenalidomide
PMCID: PMC2235894  PMID: 18271621
20.  Identification of heterogeneity among soft tissue sarcomas by gene expression profiles from different tumors 
The heterogeneity that soft tissue sarcomas (STS) exhibit in their clinical behavior, even within histological subtypes, complicates patient care. Histological appearance is determined by gene expression. Morphologic features are generally good predictors of biologic behavior, however, metastatic propensity, tumor growth, and response to chemotherapy may be determined by gene expression patterns that do not correlate well with morphology. One approach to identify heterogeneity is to search for genetic markers that correlate with differences in tumor behavior. Alternatively, subsets may be identified based on gene expression patterns alone, independent of knowledge of clinical outcome. We have reported gene expression patterns that distinguish two subgroups of clear cell renal carcinoma (ccRCC), and other gene expression patterns that distinguish heterogeneity of serous ovarian carcinoma (OVCA) and aggressive fibromatosis (AF). In this study, gene expression in 53 samples of STS and AF [including 16 malignant fibrous histiocytoma (MFH), 9 leiomyosarcoma, 12 liposarcoma, 4 synovial sarcoma, and 12 samples of AF] was determined at Gene Logic Inc. (Gaithersburg, MD) using Affymetrix GeneChip® U_133 arrays containing approximately 40,000 genes/ESTs. Gene expression analysis was performed with the Gene Logic Genesis Enterprise System® Software and Expressionist software. Hierarchical clustering of the STS using our three previously reported gene sets, each generated subgroups within the STS that for some subtypes correlated with histology, and also suggested the existence of subsets of MFH. All three gene sets also recognized the same two subsets of the fibromatosis samples that we had found in our earlier study of AF. These results suggest that these subgroups may have biological significance, and that these gene sets may be useful for sub-classification of STS. In addition, several genes that are targets of some anti-tumor drugs were found to be differentially expressed in particular subsets of STS.
PMCID: PMC2412854  PMID: 18460215
21.  Genome-wide analysis of three-way interplay among gene expression, cancer cell invasion and anti-cancer compound sensitivity 
BMC Medicine  2013;11:106.
Chemosensitivity and tumor metastasis are two primary issues in cancer management. Cancer cells often exhibit a wide range of sensitivity to anti-cancer compounds. To gain insight on the genetic mechanism of drug sensitivity, one powerful approach is to employ the panel of 60 human cancer cell lines developed by the National Cancer Institute (NCI). Cancer cells also show a broad range of invasion ability. However, a genome-wide portrait on the contributing molecular factors to invasion heterogeneity is lacking.
Our lab performed an invasion assay on the NCI-60 panel. We identified invasion-associated (IA) genes by correlating our invasion profiling data with the Affymetrix gene expression data on NCI-60. We then employed the recently released chemosensitivity data of 99 anti-cancer drugs of known mechanism to investigate the gene-drug correlation, focusing on the IA genes. Afterwards, we collected data from four independent drug-testing experiments to validate our findings on compound response prediction. Finally, we obtained published clinical and molecular data from two recent adjuvant chemotherapy cohorts, one on lung cancer and one on breast cancer, to test the performance of our gene signature for patient outcome prediction.
First, we found 633 IA genes from the invasion-gene expression correlation study. Then, for each of the 99 drugs, we obtained a subset of IA genes whose expression levels correlated with drug-sensitivity profiles. We identified a set of eight genes (EGFR, ITGA3, MYLK, RAI14, AHNAK, GLS, IL32 and NNMT) showing significant gene-drug correlation with paclitaxel, docetaxel, erlotinib, everolimus and dasatinib. This eight-gene signature (derived from NCI-60) for chemosensitivity prediction was validated by a total of 107 independent drug tests on 78 tumor cell lines, most of which were outside of the NCI-60 panel. The eight-gene signature predicted relapse-free survival for the lung and breast cancer patients (log-rank P = 0.0263; 0.00021). Multivariate Cox regression yielded a hazard ratio of our signature of 5.33 (95% CI = 1.76 to 16.1) and 1.81 (95% CI = 1.19 to 2.76) respectively. The eight-gene signature features the cancer hallmark epidermal growth factor receptor (EGFR) and genes involved in cell adhesion, migration, invasion, tumor growth and progression.
Our study sheds light on the intricate three-way interplay among gene expression, invasion and compound-sensitivity. We report the finding of a unique signature that predicts chemotherapy survival for both lung and breast cancer. Augmenting the NCI-60 model with in vitro characterization of important phenotype-like invasion potential is a cost-effective approach to power the genomic chemosensitivity analysis.
PMCID: PMC3635895  PMID: 23590835
NCI-60; Invasion; Metastasis; Microarray; Chemotherapy
22.  ESRRA-C11orf20 Is a Recurrent Gene Fusion in Serous Ovarian Carcinoma 
PLoS Biology  2011;9(9):e1001156.
Many ovarian cancers have a chromosomal rearrangement that fuses two neighboring genes, ESRRA and c11orf20. Similar rearrangements may be common, important features of cancer genomes that have largely escaped detection.
Every year, ovarian cancer kills approximately 14,000 women in the United States and more than 140,000 women worldwide. Most of these deaths are caused by tumors of the serous histological type, which is rarely diagnosed before it has disseminated. By deep paired-end sequencing of mRNA from serous ovarian cancers, followed by deep sequencing of the corresponding genomic region, we identified a recurrent fusion transcript. The fusion transcript joins the 5′ exons of ESRRA, encoding a ligand-independent member of the nuclear-hormone receptor superfamily, to the 3′ exons of C11orf20, a conserved but uncharacterized gene located immediately upstream of ESRRA in the reference genome. To estimate the prevalence of the fusion, we tested 67 cases of serous ovarian cancer by RT-PCR and sequencing and confirmed its presence in 10 of these. Targeted resequencing of the corresponding genomic region from two fusion-positive tumor samples identified a nearly clonal chromosomal rearrangement positioning ESRRA upstream of C11orf20 in one tumor, and evidence of local copy number variation in the ESRRA locus in the second tumor. We hypothesize that the recurrent novel fusion transcript may play a role in pathogenesis of a substantial fraction of serous ovarian cancers and could provide a molecular marker for detection of the cancer. Gene fusions involving adjacent or nearby genes can readily escape detection but may play important roles in the development and progression of cancer.
Author Summary
Serous ovarian cancer, the most common form of ovarian cancer, is especially lethal because it is usually only detected at a late stage in its progression, after the cancer has spread to other tissues. We searched for molecular markers of this cancer that might provide a better way to detect tumors at a curable stage and that might provide targets for new treatments. Chromosomal rearrangements that fuse two genes to produce a recombinant gene that enhances growth or spread of the cancer are particularly specific biomarkers and have been found in many cancers. By “deep” sequencing of the RNA molecules that carry genetic information in serous ovarian cancers, we discovered a rearrangement that fuses the same two neighboring genes in at least 15% of these tumors. The two fused genes are ESRRA, which encodes a key regulator of gene expression, and an essentially uncharacterized gene, C11orf20, that is normally adjacent to the ESRRA gene. Chromosomal rearrangements that recombine parts of two nearby genes or even parts of a single gene may be a common, important feature of the cancer genome that eludes detection by most approaches to characterizing cancer genomes.
PMCID: PMC3176749  PMID: 21949640
23.  Time to Recurrence and Survival in Serous Ovarian Tumors Predicted from Integrated Genomic Profiles 
PLoS ONE  2011;6(11):e24709.
Serous ovarian cancer (SeOvCa) is an aggressive disease with differential and often inadequate therapeutic outcome after standard treatment. The Cancer Genome Atlas (TCGA) has provided rich molecular and genetic profiles from hundreds of primary surgical samples. These profiles confirm mutations of TP53 in ∼100% of patients and an extraordinarily complex profile of DNA copy number changes with considerable patient-to-patient diversity. This raises the joint challenge of exploiting all new available datasets and reducing their confounding complexity for the purpose of predicting clinical outcomes and identifying disease relevant pathway alterations. We therefore set out to use multi-data type genomic profiles (mRNA, DNA methylation, DNA copy-number alteration and microRNA) available from TCGA to identify prognostic signatures for the prediction of progression-free survival (PFS) and overall survival (OS).
Methodology/Principal Findings
We implemented a multivariate Cox Lasso model and median time-to-event prediction algorithm and applied it to two datasets integrated from the four genomic data types. We (1) selected features through cross-validation; (2) generated a prognostic index for patient risk stratification; and (3) directly predicted continuous clinical outcome measures, that is, the time to recurrence and survival time. We used Kaplan-Meier p-values, hazard ratios (HR), and concordance probability estimates (CPE) to assess prediction performance, comparing separate and integrated datasets. Data integration resulted in the best PFS signature (withheld data: p-value = 0.008; HR = 2.83; CPE = 0.72).
We provide a prediction tool that inputs genomic profiles of primary surgical samples and generates patient-specific predictions for the time to recurrence and survival, along with outcome risk predictions. Using integrated genomic profiles resulted in information gain for prediction of outcomes. Pathway analysis provided potential insights into functional changes affecting disease progression. The prognostic signatures, if prospectively validated, may be useful for interpreting therapeutic outcomes for clinical trials that aim to improve the therapy for SeOvCa patients.
PMCID: PMC3207809  PMID: 22073136
24.  Poor survival with wild-type TP53 ovarian cancer? 
Gynecologic oncology  2013;130(3):565-569.
To investigate whether wild-type TP53 status in high-grade serous ovarian carcinoma is associated with poorer survival.
Clinical and genomic data of 316 sequenced samples from The Cancer Genome Atlas (TCGA) ovarian high-grade serous carcinoma (HGOSC) study were downloaded from TCGA data portal. Association between wild-type TP53 and survival was analyzed with Kaplan Meier method and Cox regression. The diagnosis of HGOSC was evaluated by reviewing pathological reports and high-resolution hematoxylin and eosin (H&E) images from frozen sections. The authenticity of wild-type TP53 in these tumor samples was assessed by analyzing SNP array data with ASCAT algorithm, reverse phase protein array (RPPA) data and RNAseq data.
Fifteen patients with HGOSCs were identified to have wild-type TP53, which had significantly shorter survival and higher chemoresistance than those with mutated TP53. The authenticity of wild-type TP53 status in these fifteen patients was supported by SNP array, RPPA, and RNAseq data. Except four cases with mixed histology, the classification as high grade serous carcinomas was supported by pathological reports and H&E images. Using RNAseq data, it was found that EDA2R gene, a direct target of wild-type TP53, was highly up-regulated in samples with wild-type TP53 in comparison to samples with either nonsense or missense TP53 mutations.
Patients with wild-type TP53 high grade ovarian serous carcinomas appeared to have a poorer survival and were more chemoresistant than those with mutated TP53. Differentially expressed genes in these TP53 wild-type tumors may provide insight in the molecular mechanism in chemotherapy resistance.
PMCID: PMC4059202  PMID: 23800698
Ovarian Cancer; p53 mutation; wild-type p53; EDA2R; Survival Statistics; Collagen VI
25.  Evaluation of public cancer datasets and signatures identifies TP53 mutant signatures with robust prognostic and predictive value 
BMC Cancer  2015;15:179.
Systematic analysis of cancer gene-expression patterns using high-throughput transcriptional profiling technologies has led to the discovery and publication of hundreds of gene-expression signatures. However, few public signature values have been cross-validated over multiple studies for the prediction of cancer prognosis and chemosensitivity in the neoadjuvant setting.
To analyze the prognostic and predictive values of publicly available signatures, we have implemented a systematic method for high-throughput and efficient validation of a large number of datasets and gene-expression signatures. Using this method, we performed a meta-analysis including 351 publicly available signatures, 37,000 random signatures, and 31 breast cancer datasets. Survival analyses and pathologic responses were used to assess prediction of prognosis, chemoresponsiveness, and chemo-drug sensitivity.
Among 31 breast cancer datasets and 351 public signatures, we identified 22 validation datasets, two robust prognostic signatures (BRmet50 and PMID18271932Sig33) in breast cancer and one signature (PMID20813035Sig137) specific for prognosis prediction in patients with ER-negative tumors. The 22 validation datasets demonstrated enhanced ability to distinguish cancer gene profiles from random gene profiles. Both prognostic signatures are composed of genes associated with TP53 mutations and were able to stratify the good and poor prognostic groups successfully in 82%and 68% of the 22 validation datasets, respectively. We then assessed the abilities of the two signatures to predict treatment responses of breast cancer patients treated with commonly used chemotherapeutic regimens. Both BRmet50 and PMID18271932Sig33 retrospectively identified those patients with an insensitive response to neoadjuvant chemotherapy (mean positive predictive values 85%-88%). Among those patients predicted to be treatment sensitive, distant relapse-free survival (DRFS) was improved (negative predictive values 87%-88%). BRmet50 was further shown to prospectively predict taxane-anthracycline sensitivity in patients with HER2-negative (HER2-) breast cancer.
We have developed and applied a high-throughput screening method for public cancer signature validation. Using this method, we identified appropriate datasets for cross-validation and two robust signatures that differentiate TP53 mutation status and have prognostic and predictive value for breast cancer patients.
Electronic supplementary material
The online version of this article (doi:10.1186/s12885-015-1102-7) contains supplementary material, which is available to authorized users.
PMCID: PMC4404582  PMID: 25886164
Breast cancer; Gene expression profiles; Signatures; Meta-analysis; Prognosis; HER2− breast cancer; Chemosensitivity

Results 1-25 (1574456)