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1.  Aberrant DNA Methylation of OLIG1, a Novel Prognostic Factor in Non-Small Cell Lung Cancer 
PLoS Medicine  2007;4(3):e108.
Background
Lung cancer is the leading cause of cancer-related death worldwide. Currently, tumor, node, metastasis (TNM) staging provides the most accurate prognostic parameter for patients with non-small cell lung cancer (NSCLC). However, the overall survival of patients with resectable tumors varies significantly, indicating the need for additional prognostic factors to better predict the outcome of the disease, particularly within a given TNM subset.
Methods and Findings
In this study, we investigated whether adenocarcinomas and squamous cell carcinomas could be differentiated based on their global aberrant DNA methylation patterns. We performed restriction landmark genomic scanning on 40 patient samples and identified 47 DNA methylation targets that together could distinguish the two lung cancer subgroups. The protein expression of one of those targets, oligodendrocyte transcription factor 1 (OLIG1), significantly correlated with survival in NSCLC patients, as shown by univariate and multivariate analyses. Furthermore, the hazard ratio for patients negative for OLIG1 protein was significantly higher than the one for those patients expressing the protein, even at low levels.
Conclusions
Multivariate analyses of our data confirmed that OLIG1 protein expression significantly correlates with overall survival in NSCLC patients, with a relative risk of 0.84 (95% confidence interval 0.77–0.91, p < 0.001) along with T and N stages, as indicated by a Cox proportional hazard model. Taken together, our results suggests that OLIG1 protein expression could be utilized as a novel prognostic factor, which could aid in deciding which NSCLC patients might benefit from more aggressive therapy. This is potentially of great significance, as the addition of postoperative adjuvant chemotherapy in T2N0 NSCLC patients is still controversial.
Christopher Plass and colleagues find thatOLIG1 expression correlates with survival in lung cancer patients and suggest that it could be used in deciding which patients are likely to benefit from more aggressive therapy.
Editors' Summary
Background.
Lung cancer is the commonest cause of cancer-related death worldwide. Most cases are of a type called non-small cell lung cancer (NSCLC). Like other cancers, treatment of NCSLC depends on the “TNM stage” at which the cancer is detected. Staging takes into account the size and local spread of the tumor (its T classification), whether nearby lymph nodes contain tumor cells (its N classification), and whether tumor cells have spread (metastasized) throughout the body (its M classification). Stage I tumors are confined to the lung and are removed surgically. Stage II tumors have spread to nearby lymph nodes and are treated with a combination of surgery and chemotherapy. Stage III tumors have spread throughout the chest, and stage IV tumors have metastasized around the body; patients with both of these stages are treated with chemotherapy alone. About 70% of patients with stage I or II lung cancer, but only 2% of patients with stage IV lung cancer, survive for five years after diagnosis.
Why Was This Study Done?
TNM staging is the best way to predict the likely outcome (prognosis) for patients with NSCLC, but survival times for patients with stage I and II tumors vary widely. Another prognostic marker—maybe a “molecular signature”—that could distinguish patients who are likely to respond to treatment from those whose cancer will inevitably progress would be very useful. Unlike normal cells, cancer cells divide uncontrollably and can move around the body. These behavioral changes are caused by alterations in the pattern of proteins expressed by the cells. But what causes these alterations? The answer in some cases is “epigenetic changes” or chemical modifications of genes. In cancer cells, methyl groups are aberrantly added to GC-rich gene regions. These so-called “CpG islands” lie near gene promoters (sequences that control the transcription of DNA into mRNA, the template for protein production), and their methylation stops the promoters working and silences the gene. In this study, the researchers have investigated whether aberrant methylation patterns vary between NSCLC subtypes and whether specific aberrant methylations are associated with survival and can, therefore, be used prognostically.
What Did the Researchers Do and Find?
The researchers used “restriction landmark genomic scanning” (RLGS) to catalog global aberrant DNA methylation patterns in human lung tumor samples. In RLGS, DNA is cut into fragments with a restriction enzyme (a protein that cuts at specific DNA sequences), end-labeled, and separated using two-dimensional gel electrophoresis to give a pattern of spots. Because methylation stops some restriction enzymes cutting their target sequence, normal lung tissue and lung tumor samples yield different patterns of spots. The researchers used these patterns to identify 47 DNA methylation targets (many in CpG islands) that together distinguished between adenocarcinomas and squamous cell carcinomas, two major types of NSCLCs. Next, they measured mRNA production from the genes with the greatest difference in methylation between adenocarcinomas and squamous cell carcinomas. OLIG1 (the gene that encodes a protein involved in nerve cell development) had one of the highest differences in mRNA production between these tumor types. Furthermore, three-quarters of NSCLCs had reduced or no expression of OLIG1 protein and, when the researchers analyzed the association between OLIG1 protein expression and overall survival in patients with NSCLC, reduced OLIG1 protein expression was associated with reduced survival.
What Do These Findings Mean?
These findings indicate that different types of NSCLC can be distinguished by examining their aberrant methylation patterns. This suggests that the establishment of different DNA methylation patterns might be related to the cell type from which the tumors developed. Alternatively, the different aberrant methylation patterns might reflect the different routes that these cells take to becoming tumor cells. This research identifies a potential new prognostic marker for NSCLC by showing that OLIG1 protein expression correlates with overall survival in patients with NSCLC. This correlation needs to be tested in a clinical setting to see if adding OLIG1 expression to the current prognostic parameters can lead to better treatment choices for early-stage lung cancer patients and ultimately improve these patients' overall survival.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040108.
Patient and professional information on lung cancer, including staging (in English and Spanish), is available from the US National Cancer Institute
The MedlinePlus encyclopedia has pages on non-small cell lung cancer (in English and Spanish)
Cancerbackup provides patient information on lung cancer
CancerQuest, provided by Emory University, has information about how cancer develops (in English, Spanish, Chinese and Russian)
Wikipedia pages on epigenetics (note that Wikipedia is a free online encyclopedia that anyone can edit)
The Epigenome Network of Excellence gives background information and the latest news about epigenetics (in several European languages)
doi:10.1371/journal.pmed.0040108
PMCID: PMC1831740  PMID: 17388669
2.  A Gene Expression Signature Predicts Survival of Patients with Stage I Non-Small Cell Lung Cancer 
PLoS Medicine  2006;3(12):e467.
Background
Lung cancer is the leading cause of cancer-related death in the United States. Nearly 50% of patients with stages I and II non-small cell lung cancer (NSCLC) will die from recurrent disease despite surgical resection. No reliable clinical or molecular predictors are currently available for identifying those at high risk for developing recurrent disease. As a consequence, it is not possible to select those high-risk patients for more aggressive therapies and assign less aggressive treatments to patients at low risk for recurrence.
Methods and Findings
In this study, we applied a meta-analysis of datasets from seven different microarray studies on NSCLC for differentially expressed genes related to survival time (under 2 y and over 5 y). A consensus set of 4,905 genes from these studies was selected, and systematic bias adjustment in the datasets was performed by distance-weighted discrimination (DWD). We identified a gene expression signature consisting of 64 genes that is highly predictive of which stage I lung cancer patients may benefit from more aggressive therapy. Kaplan-Meier analysis of the overall survival of stage I NSCLC patients with the 64-gene expression signature demonstrated that the high- and low-risk groups are significantly different in their overall survival. Of the 64 genes, 11 are related to cancer metastasis (APC, CDH8, IL8RB, LY6D, PCDHGA12, DSP, NID, ENPP2, CCR2, CASP8, and CASP10) and eight are involved in apoptosis (CASP8, CASP10, PIK3R1, BCL2, SON, INHA, PSEN1, and BIK).
Conclusions
Our results indicate that gene expression signatures from several datasets can be reconciled. The resulting signature is useful in predicting survival of stage I NSCLC and might be useful in informing treatment decisions.
Meta-analysis of several lung cancer gene expression studies yields a set of 64 genes whose expression profile is useful in predicting survival of patients with early-stage lung cancer and possibly informing treatment decisions.
Editors' Summary
Background.
Lung cancer is the commonest cause of cancer-related death worldwide. Most cases are of a type called non-small cell lung cancer (NSCLC) and are mainly caused by smoking. Like other cancers, how NSCLC is treated depends on the “stage” at which it is detected. Stage IA NSCLCs are small and confined to the lung and can be removed surgically; patients with slightly larger stage IB tumors often receive chemotherapy after surgery. In stage II NSCLC, cancer cells may be present in lymph nodes near the tumor. Surgery plus chemotherapy is the usual treatment for this stage and for some stage III NSCLCs. However, in this stage, the tumor can be present throughout the chest and surgery is not always possible. For such cases and in stage IV NSCLC, where the tumor has spread throughout the body, patients are treated with chemotherapy alone. The stage at which NSCLC is detected also determines how well patients respond to treatment. Those who can be treated surgically do much better than those who can't. So, whereas only 2% of patients with stage IV lung cancer survive for 5 years after diagnosis, about 70% of patients with stage I or II lung cancer live at least this long.
Why Was This Study Done?
Even stage I and II lung cancers often recur and there is no accurate way to identify the patients in which this will happen. If there was, these patients could be given aggressive chemotherapy, so the search is on for a “molecular signature” to help identify which NSCLCs are likely to recur. Unlike normal cells, cancer cells divide uncontrollably and can move around the body. These behavioral differences are caused by changes in their genetic material that alter their patterns of RNA transcription and protein expression. In this study, the researchers have investigated whether data from several microarray studies (a technique used to catalog the genes expressed in cells) can be pooled to construct a gene expression signature that predicts the survival of patients with stage I NSCLC.
What Did the Researchers Do and Find?
The researchers took the data from seven independent microarray studies (including a new study of their own) that recorded gene expression profiles related to survival time (less than 2 years and greater than 5 years) for stage I NSCLC. Because these studies had been done in different places with slightly different techniques, the researchers applied a statistical tool called distance-weighted discrimination to smooth out any systematic differences among the studies before identifying 64 genes whose expression was associated with survival. Most of these genes are involved in cell adhesion, cell motility, cell proliferation, and cell death, all processes that are altered in cancer cells. The researchers then developed a statistical model that allowed them to use the gene expression and survival data to calculate risk scores for nearly 200 patients in five of the datasets. When they separated the patients into high and low risk groups on the basis of these scores, the two groups were significantly different in terms of survival time. Indeed, the gene expression signature was better at predicting outcome than routine staging. Finally, the researchers validated the gene expression signature by showing that it predicted survival with more than 85% accuracy in two independent datasets.
What Do These Findings Mean?
The 64 gene expression signature identified here could help clinicians prepare treatment plans for patients with stage I NSCLC. Because it accurately predicts survival in patients with adenocarcinoma or squamous cell cancer (the two major subtypes of NSCLC), it potentially indicates which of these patients should receive aggressive chemotherapy and which can be spared this unpleasant treatment. Previous attempts to establish gene expression signatures to predict outcome have used data from small groups of patients and have failed when tested in additional patients. In contrast, this new signature seems to be generalizable. Nevertheless, its ability to predict outcomes must be confirmed in further studies before it is routinely adopted by oncologists for treatment planning.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0030467.
US National Cancer Institute information on lung cancer for patients and health professionals.
MedlinePlus encyclopedia entries on small-cell and non-small-cell lung cancer.
Cancer Research UK, information on patients about all aspects of lung cancer.
Wikipedia pages on DNA microarrays and expression profiling (note that Wikipedia is a free online encyclopedia that anyone can edit).
doi:10.1371/journal.pmed.0030467
PMCID: PMC1716187  PMID: 17194181
3.  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.
Background
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.
Conclusions
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
Background
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 http://dx.doi.org/10.1371/journal.pmed.1000378.
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)
doi:10.1371/journal.pmed.1000378
PMCID: PMC3001894  PMID: 21179495
4.  Hybrid Models Identified a 12-Gene Signature for Lung Cancer Prognosis and Chemoresponse Prediction 
PLoS ONE  2010;5(8):e12222.
Background
Lung cancer remains the leading cause of cancer-related deaths worldwide. The recurrence rate ranges from 35–50% among early stage non-small cell lung cancer patients. To date, there is no fully-validated and clinically applied prognostic gene signature for personalized treatment.
Methodology/Principal Findings
From genome-wide mRNA expression profiles generated on 256 lung adenocarcinoma patients, a 12-gene signature was identified using combinatorial gene selection methods, and a risk score algorithm was developed with Naïve Bayes. The 12-gene model generates significant patient stratification in the training cohort HLM & UM (n = 256; log-rank P = 6.96e-7) and two independent validation sets, MSK (n = 104; log-rank P = 9.88e-4) and DFCI (n = 82; log-rank P = 2.57e-4), using Kaplan-Meier analyses. This gene signature also stratifies stage I and IB lung adenocarcinoma patients into two distinct survival groups (log-rank P<0.04). The 12-gene risk score is more significant (hazard ratio = 4.19, 95% CI: [2.08, 8.46]) than other commonly used clinical factors except tumor stage (III vs. I) in multivariate Cox analyses. The 12-gene model is more accurate than previously published lung cancer gene signatures on the same datasets. Furthermore, this signature accurately predicts chemoresistance/chemosensitivity to Cisplatin, Carboplatin, Paclitaxel, Etoposide, Erlotinib, and Gefitinib in NCI-60 cancer cell lines (P<0.017). The identified 12 genes exhibit curated interactions with major lung cancer signaling hallmarks in functional pathway analysis. The expression patterns of the signature genes have been confirmed in RT-PCR analyses of independent tumor samples.
Conclusions/Significance
The results demonstrate the clinical utility of the identified gene signature in prognostic categorization. With this 12-gene risk score algorithm, early stage patients at high risk for tumor recurrence could be identified for adjuvant chemotherapy; whereas stage I and II patients at low risk could be spared the toxic side effects of chemotherapeutic drugs.
doi:10.1371/journal.pone.0012222
PMCID: PMC2923187  PMID: 20808922
5.  A Novel Five Gene Signature Derived from Stem-Like Side Population Cells Predicts Overall and Recurrence-Free Survival in NSCLC 
PLoS ONE  2012;7(8):e43589.
Gene expression profiling has been used to characterize prognosis in various cancers. Earlier studies had shown that side population cells isolated from Non-Small Cell Lung Cancer (NSCLC) cell lines exhibit cancer stem cell properties. In this study we apply a systems biology approach to gene expression profiling data from cancer stem like cells isolated from lung cancer cell lines to identify novel gene signatures that could predict prognosis. Microarray data from side population (SP) and main population (MP) cells isolated from 4 NSCLC lines (A549, H1650, H460, H1975) were used to examine gene expression profiles associated with stem like properties. Differentially expressed genes that were over or under-expressed at least two fold commonly in all 4 cell lines were identified. We found 354 were upregulated and 126 were downregulated in SP cells compared to MP cells; of these, 89 up and 62 downregulated genes (average 2 fold changes) were used for Principle Component Analysis (PCA) and MetaCore™ pathway analysis. The pathway analysis demonstrated representation of 4 up regulated genes (TOP2A, AURKB, BRRN1, CDK1) in chromosome condensation pathway and 1 down regulated gene FUS in chromosomal translocation. Microarray data was validated using qRT-PCR on the 5 selected genes and all showed robust correlation between microarray and qRT-PCR. Further, we analyzed two independent gene expression datasets that included 360 lung adenocarcinoma patients from NCI Director's Challenge Set for overall survival and 63 samples from Sungkyunkwan University (SKKU) for recurrence free survival. Kaplan-Meier and log-rank test analysis predicted poor survival of patients in both data sets. Our results suggest that genes involved in chromosome condensation are likely related with stem-like properties and might predict survival in lung adenocarcinoma. Our findings highlight a gene signature for effective identification of lung adenocarcinoma patients with poor prognosis and designing more aggressive therapies for such patients.
doi:10.1371/journal.pone.0043589
PMCID: PMC3430700  PMID: 22952714
6.  Combination of expression levels of miR-21 and miR-126 is associated with cancer-specific survival in clear-cell renal cell carcinoma 
BMC Cancer  2014;14:25.
Background
Renal cell carcinoma (RCC) is marked by high mortality rate. To date, no robust risk stratification by clinical or molecular prognosticators of cancer-specific survival (CSS) has been established for early stages. Transcriptional profiling of small non-coding RNA gene products (miRNAs) seems promising for prognostic stratification. The expression of miR-21 and miR-126 was analysed in a large cohort of RCC patients; a combined risk score (CRS)-model was constructed based on expression levels of both miRNAs.
Methods
Expression of miR-21 and miR-126 was evaluated by qRT-PCR in tumour and adjacent non-neoplastic tissue in n = 139 clear cell RCC patients. Relation of miR-21 and miR-126 expression with various clinical parameters was assessed. Parameters were analysed by uni- and multivariate COX regression. A factor derived from the z-score resulting from the COX model was determined for both miRs separately and a combined risk score (CRS) was calculated multiplying the relative expression of miR-21 and miR-126 by this factor. The best fitting COX model was selected by relative goodness-of-fit with the Akaike information criterion (AIC).
Results
RCC with and without miR-21 up- and miR-126 downregulation differed significantly in synchronous metastatic status and CSS. Upregulation of miR-21 and downregulation of miR-126 were independently prognostic. A combined risk score (CRS) based on the expression of both miRs showed high sensitivity and specificity in predicting CSS and prediction was independent from any other clinico-pathological parameter. Association of CRS with CSS was successfully validated in a testing cohort containing patients with high and low risk for progressive disease.
Conclusions
A combined expression level of miR-21 and miR-126 accurately predicted CSS in two independent RCC cohorts and seems feasible for clinical application in assessing prognosis.
doi:10.1186/1471-2407-14-25
PMCID: PMC3897948  PMID: 24428907
Renal cell carcinoma; RCC; Kidney cancer; miRNA; miR-21; miR-126; Prognosis; Profiling; Biomarker; Tumour markers
7.  Predicting Survival within the Lung Cancer Histopathological Hierarchy Using a Multi-Scale Genomic Model of Development 
PLoS Medicine  2006;3(7):e232.
Background
The histopathologic heterogeneity of lung cancer remains a significant confounding factor in its diagnosis and prognosis—spurring numerous recent efforts to find a molecular classification of the disease that has clinical relevance.
Methods and Findings
Molecular profiles of tumors from 186 patients representing four different lung cancer subtypes (and 17 normal lung tissue samples) were compared with a mouse lung development model using principal component analysis in both temporal and genomic domains. An algorithm for the classification of lung cancers using a multi-scale developmental framework was developed. Kaplan–Meier survival analysis was conducted for lung adenocarcinoma patient subgroups identified via their developmental association. We found multi-scale genomic similarities between four human lung cancer subtypes and the developing mouse lung that are prognostically meaningful. Significant association was observed between the localization of human lung cancer cases along the principal mouse lung development trajectory and the corresponding patient survival rate at three distinct levels of classical histopathologic resolution: among different lung cancer subtypes, among patients within the adenocarcinoma subtype, and within the stage I adenocarcinoma subclass. The earlier the genomic association between a human tumor profile and the mouse lung development sequence, the poorer the patient's prognosis. Furthermore, decomposing this principal lung development trajectory identified a gene set that was significantly enriched for pyrimidine metabolism and cell-adhesion functions specific to lung development and oncogenesis.
Conclusions
From a multi-scale disease modeling perspective, the molecular dynamics of murine lung development provide an effective framework that is not only data driven but also informed by the biology of development for elucidating the mechanisms of human lung cancer biology and its clinical outcome.
Editors' Summary
Background.
Lung cancer causes the most deaths from cancer worldwide—around a quarter of all cancer deaths—and the number of deaths is rising each year. There are a number of different types of the disease, whose names come from early descriptions of the cancer cells when seen under the microscope: carcinoid, small cell, and non–small cell, which make up 2%, 13%, and 86% of lung cancers, respectively. To make things more complicated, each of these cancer types can be subdivided further. It is important to distinguish the different types of cancer because they differ in their rates of growth and how they respond to treatment; for example, small cell lung cancer is the most rapidly progressing type of lung cancer. But although these current classifications of cancers are useful, researchers believe that if the underlying molecular changes in these cancers could be discovered then a more accurate way of classifying cancers, and hence predicting outcome and response to treatment, might be possible.
Why Was This Study Done?
Previous work has suggested that some cancers come from very immature cells, that is, cells that are present in the early stages of an animal's development from an embryo in the womb to an adult animal. Many animals have been closely studied so as to understand how they develop; the best studied model that is also relevant to human disease is the mouse, and researchers have previously studied lung development in mice in detail. This group of researchers wanted to see if there was any relation between the activity (known as expression) of mouse genes during the development of the lung and the expression of genes in human lung cancers, particularly whether they could use gene expression to try to predict the outcome of lung cancer in patients.
What Did the Researchers Do and Find?
They compared the gene expression in lung cancer samples from 186 patients with four different types of lung cancer (and in 17 normal lung tissue samples) to the gene expression found in normal mice during development. They found similarities between expression patterns in the lung cancer subtypes and the developing mouse lung, and that these similarities explain some of the different outcomes for the patients. In general, they found that when the gene expression in the human cancer was similar to that of very immature mouse lung cells, patients had a poor prognosis. When the gene expression in the human cancer was more similar to mature mouse lung cells, the prognosis was better. However, the researchers found that carcinoid tumors had rather different expression profiles compared to the other tumors.
  The researchers were also able to discover some specific gene types that seemed to have particularly strong associations between mouse development and the human cancers. Two of these gene types were ones that are involved in building and breaking down DNA itself, and ones involved in how cells stick together. This latter group of genes is thought to be involved in how cancers spread.
What Do These Findings Mean?
These results provide a new way of thinking about how to classify lung cancers, and also point to a few groups of genes that may be particularly important in the development of the tumor. However, before these results are used in any clinical assessment, further work will need to be done to work out whether they are true for other groups of patients.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0030232.
•  MedlinePlus has information from the United States National Library of Medicine and other government agencies and health-related organizations [MedlinePlus]
•  National Institute on Aging is also a good place to start looking for information [National Institute for Aging]
•  [The National Cancer Institute] and Lung Cancer Online [ Lung Cancer Online] have a wide range of information on lung cancer
Comparison of gene expression patterns in patients with lung cancer and in mouse lung development showed that those tumors associated with earlier mouse lung development had a poorer prognosis.
doi:10.1371/journal.pmed.0030232
PMCID: PMC1483910  PMID: 16800721
8.  Validation of a Molecular and Pathological Model for Five-Year Mortality Risk in Patients with Early Stage Lung Adenocarcinoma 
Journal of Thoracic Oncology  2014;10(1):67-73.
Introduction:
The aim of this study was to validate a molecular expression signature [cell cycle progression (CCP) score] that identifies patients with a higher risk of cancer-related death after surgical resection of early stage (I-II) lung adenocarcinoma in a large patient cohort and evaluate the effectiveness of combining CCP score and pathological stage for predicting lung cancer mortality.
Methods:
Formalin-fixed paraffin-embedded surgical tumor samples from 650 patients diagnosed with stage I and II adenocarcinoma who underwent definitive surgical treatment without adjuvant chemotherapy were analyzed for 31 proliferation genes by quantitative real-time polymerase chain reaction. The prognostic discrimination of the expression score was assessed by Cox proportional hazards analysis using 5-year lung cancer-specific death as primary outcome.
Results:
The CCP score was a significant predictor of lung cancer-specific mortality above clinical covariates [hazard ratio (HR) = 1.46 per interquartile range (95% confidence interval = 1.12–1.90; p = 0.0050)]. The prognostic score, a combination of CCP score and pathological stage, was a more significant indicator of lung cancer mortality risk than pathological stage in the full cohort (HR = 2.01; p = 2.8 × 10−11) and in stage I patients (HR = 1.67; p = 0.00027). Using the 85th percentile of the prognostic score as a threshold, there was a significant difference in lung cancer survival between low-risk and high-risk patient groups (p = 3.8 × 10−7).
Conclusions:
This study validates the CCP score and the prognostic score as independent predictors of lung cancer death in patients with early stage lung adenocarcinoma treated with surgery alone. Patients with resected stage I lung adenocarcinoma and a high prognostic score may be candidates for adjuvant therapy to reduce cancer-related mortality.
doi:10.1097/JTO.0000000000000365
PMCID: PMC4272230  PMID: 25396679
Carcinoma; Nonsmall cell lung cancer; Real-time polymerase chain reaction; Risk stratification
9.  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.
Background
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.
Conclusions
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
Background
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 http://dx.doi.org/10.1371/journal.pmed.1000307.
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
doi:10.1371/journal.pmed.1000307
PMCID: PMC2903589  PMID: 20644708
10.  Confirmation of gene expression-based prediction of survival in non-small cell lung cancer 
Clinical Relevance
It remains a critical issue to reliably identify specific patients at high risk for recurrence and metastasis of lung cancer. To date, there has been no clinically applied gene test for predicting lung cancer recurrence. This study validated a 35-gene prognostic signature in various cell types of non-small cell lung cancer. The analysis showed that the 35-gene signature could further stratify patients at stage 1A into distinct prognostic subgroups. This lung cancer prognostic signature is independent of traditional clinicopathological factors, including patient age, clinical stage, tumor differentiation, and tumor grade. This signature had better prognostic performance than other lung cancer signatures, including the 5-gene signature and the 133-gene signature in the studied cohorts. The gene expression and protein expression of the identified biomarkers were validated in real-time RT-PCR and Western blots analysis of clinical specimens. This study indicates that the 35-gene signature could be applied in clinics for patient stratification.
Purpose
It remains a critical challenge to determine the risk for recurrence in early stage non-small cell lung cancer (NSCLC) patients. Accurate gene expression signatures are needed to classify patients into high- and low-risk groups to improve the selection of patients for adjuvant therapy.
Experimental Design
Multiple published microarray datasets were used to evaluate our previously identified lung cancer prognostic gene signature. Expression of the signature genes was further validated with real-time RT-PCR and Western blot assays of snap frozen lung cancer tumor tissues.
Results
Our previously identified 35-gene signature stratified 264 patients with non-small cell lung cancer into high- and low-risk groups with distinct overall survival rates (P < 0.05, Kaplan-Meier analysis, log-rank tests). The 35-gene signature further stratified patients with clinical stage 1A diseases into poor prognostic and good prognostic subgroups (P = 0.0007, Kaplan-Meier analysis, log-rank tests). This signature is independent of other prognostic factors for non-small cell lung cancer, including age, sex, tumor differentiation, tumor grade, and tumor stage. The expression of the signature genes was validated with real-time RT-PCR analysis of lung cancer tumor specimens. Protein expression of two signature genes, TAL2 and ILF3, was confirmed in lung adenocarcinoma tumors by using Western blot analysis. These two biomarkers showed correlated mRNA and protein over-expression in lung cancer development and progression.
Conclusions
The results indicate that the identified 35-gene signature is an accurate predictor of survival in non-small cell lung cancer. It provides independent prognostic information in addition to traditional clinicopathological criteria.
doi:10.1158/1078-0432.CCR-08-0095
PMCID: PMC2605664  PMID: 19088038
molecular signature; non-small cell lung cancer; prognosis; microarray analysis; protein expression; Western blots
11.  Pathway-based identification of a smoking associated 6-gene signature predictive of lung cancer risk and survival 
Objective
Smoking is a prominent risk factor for lung cancer. However, it is not an established prognostic factor for lung cancer in clinics. To date, no gene test is available for diagnostic screening of lung cancer risk or prognostication of clinical outcome in smokers. This study sought to identify a smoking associated gene signature in order to provide a more precise diagnosis and prognosis of lung cancer in smokers.
Methods and materials
An implication network based methodology was used to identify biomarkers by modeling crosstalk with major lung cancer signaling pathways. Specifically, the methodology contains the following steps: 1) identifying genes significantly associated with lung cancer survival; 2) selecting candidate genes which are differentially expressed in smokers versus non-smokers from the survival genes identified in Step 1; 3) from these candidate genes, constructing gene coexpression networks based on prediction logic for the smoker group and the non-smoker group, respectively; 4) identifying smoking-mediated differential components, i.e., the unique gene coexpression patterns specific to each group; and 5) from the differential components, identifying genes directly co-expressed with major lung cancer signaling hallmarks.
Results
A smoking-associated 6-gene signature was identified for prognosis of lung cancer from a training cohort (n=256). The 6-gene signature could separate lung cancer patients into two risk groups with distinct post-operative survival (log-rank P < 0.04, Kaplan-Meier analyses) in three independent cohorts (n=427). The expression-defined prognostic prediction is strongly related to smoking association and smoking cessation (P < 0.02; Pearson’s Chi-squared tests). The 6-gene signature is an accurate prognostic factor (hazard ratio = 1.89, 95% CI: [1.04, 3.43]) compared to common clinical covariates in multivariate Cox analysis. The 6-gene signature also provides an accurate diagnosis of lung cancer with an overall accuracy of 73% in a cohort of smokers (n=164). The coexpression patterns derived from the implication networks were validated with interactions reported in the literature retrieved with STRING8, Ingenuity Pathway Analysis, and Pathway Studio.
Conclusions
The pathway-based approach identified a smoking-associated 6-gene signature that predicts lung cancer risk and survival. This gene signature has potential clinical implications in the diagnosis and prognosis of lung cancer in smokers.
doi:10.1016/j.artmed.2012.01.001
PMCID: PMC3351561  PMID: 22326768
implication networks based on prediction logic; gene coexpression networks based on formal logic; smoking; gene signature; lung cancer diagnosis and prognosis; signaling pathways
12.  Survival-Related Profile, Pathways, and Transcription Factors in Ovarian Cancer 
PLoS Medicine  2009;6(2):e1000024.
Background
Ovarian cancer has a poor prognosis due to advanced stage at presentation and either intrinsic or acquired resistance to classic cytotoxic drugs such as platinum and taxoids. Recent large clinical trials with different combinations and sequences of classic cytotoxic drugs indicate that further significant improvement in prognosis by this type of drugs is not to be expected. Currently a large number of drugs, targeting dysregulated molecular pathways in cancer cells have been developed and are introduced in the clinic. A major challenge is to identify those patients who will benefit from drugs targeting these specific dysregulated pathways.The aims of our study were (1) to develop a gene expression profile associated with overall survival in advanced stage serous ovarian cancer, (2) to assess the association of pathways and transcription factors with overall survival, and (3) to validate our identified profile and pathways/transcription factors in an independent set of ovarian cancers.
Methods and Findings
According to a randomized design, profiling of 157 advanced stage serous ovarian cancers was performed in duplicate using ∼35,000 70-mer oligonucleotide microarrays. A continuous predictor of overall survival was built taking into account well-known issues in microarray analysis, such as multiple testing and overfitting. A functional class scoring analysis was utilized to assess pathways/transcription factors for their association with overall survival. The prognostic value of genes that constitute our overall survival profile was validated on a fully independent, publicly available dataset of 118 well-defined primary serous ovarian cancers. Furthermore, functional class scoring analysis was also performed on this independent dataset to assess the similarities with results from our own dataset. An 86-gene overall survival profile discriminated between patients with unfavorable and favorable prognosis (median survival, 19 versus 41 mo, respectively; permutation p-value of log-rank statistic = 0.015) and maintained its independent prognostic value in multivariate analysis. Genes that composed the overall survival profile were also able to discriminate between the two risk groups in the independent dataset. In our dataset 17/167 pathways and 13/111 transcription factors were associated with overall survival, of which 16 and 12, respectively, were confirmed in the independent dataset.
Conclusions
Our study provides new clues to genes, pathways, and transcription factors that contribute to the clinical outcome of serous ovarian cancer and might be exploited in designing new treatment strategies.
Ate van der Zee and colleagues analyze the gene expression profiles of ovarian cancer samples from 157 patients, and identify an 86-gene expression profile that seems to predict overall survival.
Editors' Summary
Background.
Ovarian cancer kills more than 100,000 women every year and is one of the most frequent causes of cancer death in women in Western countries. Most ovarian cancers develop when an epithelial cell in one of the ovaries (two small organs in the pelvis that produce eggs) acquires genetic changes that allow it to grow uncontrollably and to spread around the body (metastasize). In its early stages, ovarian cancer is confined to the ovaries and can often be treated successfully by surgery alone. Unfortunately, early ovarian cancer rarely has symptoms so a third of women with ovarian cancer have advanced disease when they first visit their doctor with symptoms that include vague abdominal pains and mild digestive disturbances. That is, cancer cells have spread into their abdominal cavity and metastasized to other parts of the body (so-called stage III and IV disease). The outlook for women diagnosed with stage III and IV disease, which are treated with a combination of surgery and chemotherapy, is very poor. Only 30% of women with stage III, and 5% with stage IV, are still alive five years after their cancer is diagnosed.
Why Was This Study Done?
If the cellular pathways that determine the biological behavior of ovarian cancer could be identified, it might be possible to develop more effective treatments for women with stage III and IV disease. One way to identify these pathways is to use gene expression profiling (a technique that catalogs all the genes expressed by a cell) to compare gene expression patterns in the ovarian cancers of women who survive for different lengths of time. Genes with different expression levels in tumors with different outcomes could be targets for new treatments. For example, it might be worth developing inhibitors of proteins whose expression is greatest in tumors with short survival times. In this study, the researchers develop an expression profile that is associated with overall survival in advanced-stage serous ovarian cancer (more than half of ovarian cancers originate in serous cells, epithelial cells that secrete a watery fluid). The researchers also assess the association of various cellular pathways and transcription factors (proteins that control the expression of other proteins) with survival in this type of ovarian carcinoma.
What Did the Researchers Do and Find?
The researchers analyzed the gene expression profiles of tumor samples taken from 157 patients with advanced stage serous ovarian cancer and used the “supervised principal components” method to build a predictor of overall survival from these profiles and patient survival times. This 86-gene predictor discriminated between patients with favorable and unfavorable outcomes (average survival times of 41 and 19 months, respectively). It also discriminated between groups of patients with these two outcomes in an independent dataset collected from 118 additional serous ovarian cancers. Next, the researchers used “functional class scoring” analysis to assess the association between pathway and transcription factor expression in the tumor samples and overall survival. Seventeen of 167 KEGG pathways (“wiring” diagrams of molecular interactions, reactions and relations involved in cellular processes and human diseases listed in the Kyoto Encyclopedia of Genes and Genomes) were associated with survival, 16 of which were confirmed in the independent dataset. Finally, 13 of 111 analyzed transcription factors were associated with overall survival in the tumor samples, 12 of which were confirmed in the independent dataset.
What Do These Findings Mean?
These findings identify an 86-gene overall survival gene expression profile that seems to predict overall survival for women with advanced serous ovarian cancer. However, before this profile can be used clinically, further validation of the profile and more robust methods for determining gene expression profiles are needed. Importantly, these findings also provide new clues about the genes, pathways and transcription factors that contribute to the clinical outcome of serous ovarian cancer, clues that can now be exploited in the search for new treatment strategies. Finally, these findings suggest that it might eventually be possible to tailor therapies to the needs of individual patients by analyzing which pathways are activated in their tumors and thus improve survival times for women with advanced ovarian cancer.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000024.
This study is further discussed in a PLoS Medicine Perspective by Simon Gayther and Kate Lawrenson
See also a related PLoS Medicine Research Article by Huntsman and colleagues
The US National Cancer Institute provides a brief description of what cancer is and how it develops, and information on all aspects of ovarian cancer for patients and professionals (in English and Spanish)
The UK charity Cancerbackup provides general information about cancer, and more specific information about ovarian cancer
MedlinePlus also provides links to other information about ovarian cancer (in English and Spanish)
The KEGG Pathway database provides pathway maps of known molecular networks involved in a wide range of cellular processes
doi:10.1371/journal.pmed.1000024
PMCID: PMC2634794  PMID: 19192944
13.  RSF1 and Not Cyclin D1 Gene Amplification May Predict Lack of Benefit from Adjuvant Tamoxifen in High-Risk Pre-Menopausal Women in the MA.12 Randomized Clinical Trial 
PLoS ONE  2013;8(12):e81740.
Most women with estrogen receptor expressing breast cancers receiving anti-estrogens such as tamoxifen may not need or benefit from them. Besides the estrogen receptor, there are no predictive biomarkers to help select breast cancer patients for tamoxifen treatment. CCND1 (cyclin D1) gene amplification is a putative candidate tamoxifen predictive biomarker. The RSF1 (remodeling and spacing factor 1) gene is frequently co-amplified with CCND1 on chromosome 11q. We validated the predictive value of these biomarkers in the MA.12 randomized study of adjuvant tamoxifen vs. placebo in high-risk premenopausal early breast cancer. Premenopausal women with node-positive/high-risk node-negative early breast cancer received standard adjuvant chemotherapy and then were randomized to tamoxifen (20 mg/day) or placebo for 5 yrs. Overall survival (OS) and relapse-free survival (RFS) were evaluated. Fluorescent in-situ hybridization was performed on a tissue microarray of 495 breast tumors (74% of patients) to measure CCND1 and RSF1 copy number. A multivariate Cox model to obtain hazard ratios (HR) adjusting for clinico-pathologic factors was used to assess the effect of these biomarkers on Os and RFS. 672 women were followed for a median of 8.4 years. We were able to measure the DNA copy number of CCND1 in 442 patients and RSF1 in 413 patients. CCND1 gene amplification was observed in 8.7% and RSF1 in 6.8% of these patients, preferentially in estrogen receptor-positive breast cancers. No statistically significant interaction with treatment was observed for either CCND1 or RSF1 amplification, although patients with high RSF1 copy number did not show benefit from adjuvant tamoxifen (HR = 1.11, interaction p = 0.09). Unlike CCND1 amplification, RSF1 amplification may predict for outcome in high-risk premenopausal breast cancer patients treated with adjuvant tamoxifen.
doi:10.1371/journal.pone.0081740
PMCID: PMC3868649  PMID: 24367492
14.  COX-2/EGFR expression and survival among women with adenocarcinoma of the lung 
Carcinogenesis  2008;29(9):1781-1787.
Previous studies suggest that cyclooxygenase-2 (COX-2) expression may predict survival among patients with non-small cell lung cancer. COX-2 may interact with epidermal growth factor receptor (EGFR), suggesting that combined COX-2/EGFR expression may provide predictive value. The extent to which their independent or combined expression is associated with prognosis in women with adenocarcinoma of the lung is unknown. In the present study, we examined relationships between COX-2 expression (n = 238), EGFR expression (n = 158) and dual COX-2/EGFR expression (n = 157) and survival among women with adenocarcinoma of the lung. Overall survival was estimated by constructing Cox proportional hazards models adjusting for other significant variables and stratifying by stage at diagnosis and race. Clinical or demographic parameters were not associated with either COX-2 or EGFR expression. Patients with COX-2-positive tumors tended to have poorer prognosis than did patients with COX-2-negative tumors [hazard ratio (HR) 1.67, 95% confidence interval (CI) 1.01–2.78]. African-Americans with COX-2-positive tumors had a statistically non-significant higher risk of death than African-Americans with COX-2-negative tumors (HR 5.58, 95% CI 0.64–48.37). No association between COX-2 expression and survival was observed among Caucasians (HR 1.29, 95% CI 0.72–2.30). EGFR expression was associated with a 44% reduction in the risk of death (HR 0.56, 95% CI 0.32–0.98). COX-2−/EGFR+ tumor expression, but not COX-2+/EGFR+ tumor expression, was associated with survival when compared with other combined expression results. In conclusion, COX-2 and EGFR expression, but not combined COX-2+/EGFR+ expression, independently predict survival of women with adenocarcinoma of the lung.
doi:10.1093/carcin/bgn107
PMCID: PMC2527644  PMID: 18453539
15.  CYP24A1 Is an Independent Prognostic Marker of Survival in Patients with Lung Adenocarcinoma 
Purpose
The active form of vitamin D, 1α,25-dihydroxyvitamin D3 (1,25-D3) exerts antiproliferative effects in cancers, including lung adenocarcinoma (AC). CYP24A1 is overexpressed in many cancers and catabolizes 1,25-D3. The purpose of our study was to assess CYP24A1 as a prognostic marker and to study its relevance to antiproliferative activity of 1,25-D3 in lung AC cells.
Experimental Design
Tumors and corresponding normal specimens from 86 patients with lung AC (stages I–III) were available. AffymetrixR array data and subsequent confirmation by quantitative real time-PCR were used to determine CYP24A1 mRNA expression. A subsequent validation set of 101 lung AC was used to confirm CYP24A1 mRNA expression and its associations with clinical variables. The antiproliferative effects of 1,25-D3 were examined using lung cancer cell lines with high as well as low expression of CYP24A1 mRNA.
Results
CYP24A1 mRNA was elevated 8–50 fold in lung AC (compared to normal nonneoplastic lung) and significantly higher in poorly-differentiated cancers. At 5 years of follow-up, the probability of survival was 42% (high CYP24A1, n = 29) versus 81% (low CYP24A1, n = 57) (P = 0.007). The validation set of 101 tumors showed that CYP24A1 was independently prognostic of survival (multivariate Cox model adjusted for age, gender and stage, P = 0.001). A549 cells (high CYP24A1) were more resistant to antiproliferative effects of 1,25-D3 compared with SKLU-1 cells (low CYP24A1).
Conclusions
CYP24A1 overexpression is associated with poorer survival in lung AC. This may relate to abrogation of antiproliferative effects of 1,25-D3 in high CYP24A1 expressing lung AC.
doi:10.1158/1078-0432.CCR-10-1789
PMCID: PMC3058389  PMID: 21169243
16.  Gene-Expression Signature Predicts Postoperative Recurrence in Stage I Non-Small Cell Lung Cancer Patients 
PLoS ONE  2012;7(1):e30880.
About 30% stage I non-small cell lung cancer (NSCLC) patients undergoing resection will recur. Robust prognostic markers are required to better manage therapy options. The purpose of this study is to develop and validate a novel gene-expression signature that can predict tumor recurrence of stage I NSCLC patients. Cox proportional hazards regression analysis was performed to identify recurrence-related genes and a partial Cox regression model was used to generate a gene signature of recurrence in the training dataset −142 stage I lung adenocarcinomas without adjunctive therapy from the Director's Challenge Consortium. Four independent validation datasets, including GSE5843, GSE8894, and two other datasets provided by Mayo Clinic and Washington University, were used to assess the prediction accuracy by calculating the correlation between risk score estimated from gene expression and real recurrence-free survival time and AUC of time-dependent ROC analysis. Pathway-based survival analyses were also performed. 104 probesets correlated with recurrence in the training dataset. They are enriched in cell adhesion, apoptosis and regulation of cell proliferation. A 51-gene expression signature was identified to distinguish patients likely to develop tumor recurrence (Dxy = −0.83, P<1e-16) and this signature was validated in four independent datasets with AUC >85%. Multiple pathways including leukocyte transendothelial migration and cell adhesion were highly correlated with recurrence-free survival. The gene signature is highly predictive of recurrence in stage I NSCLC patients, which has important prognostic and therapeutic implications for the future management of these patients.
doi:10.1371/journal.pone.0030880
PMCID: PMC3264655  PMID: 22292069
17.  Intra-tumor Genetic Heterogeneity and Mortality in Head and Neck Cancer: Analysis of Data from The Cancer Genome Atlas 
PLoS Medicine  2015;12(2):e1001786.
Background
Although the involvement of intra-tumor genetic heterogeneity in tumor progression, treatment resistance, and metastasis is established, genetic heterogeneity is seldom examined in clinical trials or practice. Many studies of heterogeneity have had prespecified markers for tumor subpopulations, limiting their generalizability, or have involved massive efforts such as separate analysis of hundreds of individual cells, limiting their clinical use. We recently developed a general measure of intra-tumor genetic heterogeneity based on whole-exome sequencing (WES) of bulk tumor DNA, called mutant-allele tumor heterogeneity (MATH). Here, we examine data collected as part of a large, multi-institutional study to validate this measure and determine whether intra-tumor heterogeneity is itself related to mortality.
Methods and Findings
Clinical and WES data were obtained from The Cancer Genome Atlas in October 2013 for 305 patients with head and neck squamous cell carcinoma (HNSCC), from 14 institutions. Initial pathologic diagnoses were between 1992 and 2011 (median, 2008). Median time to death for 131 deceased patients was 14 mo; median follow-up of living patients was 22 mo. Tumor MATH values were calculated from WES results. Despite the multiple head and neck tumor subsites and the variety of treatments, we found in this retrospective analysis a substantial relation of high MATH values to decreased overall survival (Cox proportional hazards analysis: hazard ratio for high/low heterogeneity, 2.2; 95% CI 1.4 to 3.3). This relation of intra-tumor heterogeneity to survival was not due to intra-tumor heterogeneity’s associations with other clinical or molecular characteristics, including age, human papillomavirus status, tumor grade and TP53 mutation, and N classification. MATH improved prognostication over that provided by traditional clinical and molecular characteristics, maintained a significant relation to survival in multivariate analyses, and distinguished outcomes among patients having oral-cavity or laryngeal cancers even when standard disease staging was taken into account. Prospective studies, however, will be required before MATH can be used prognostically in clinical trials or practice. Such studies will need to examine homogeneously treated HNSCC at specific head and neck subsites, and determine the influence of cancer therapy on MATH values. Analysis of MATH and outcome in human-papillomavirus-positive oropharyngeal squamous cell carcinoma is particularly needed.
Conclusions
To our knowledge this study is the first to combine data from hundreds of patients, treated at multiple institutions, to document a relation between intra-tumor heterogeneity and overall survival in any type of cancer. We suggest applying the simply calculated MATH metric of heterogeneity to prospective studies of HNSCC and other tumor types.
In this study, Rocco and colleagues examine data collected as part of a large, multi-institutional study, to validate a measure of tumor heterogeneity called MATH and determine whether intra-tumor heterogeneity is itself related to mortality.
Editors’ Summary
Background
Normally, the cells in human tissues and organs only reproduce (a process called cell division) when new cells are needed for growth or to repair damaged tissues. But sometimes a cell somewhere in the body acquires a genetic change (mutation) that disrupts the control of cell division and allows the cell to grow continuously. As the mutated cell grows and divides, it accumulates additional mutations that allow it to grow even faster and eventually from a lump, or tumor (cancer). Other mutations subsequently allow the tumor to spread around the body (metastasize) and destroy healthy tissues. Tumors can arise anywhere in the body—there are more than 200 different types of cancer—and about one in three people will develop some form of cancer during their lifetime. Many cancers can now be successfully treated, however, and people often survive for years after a diagnosis of cancer before, eventually, dying from another disease.
Why Was This Study Done?
The gradual acquisition of mutations by tumor cells leads to the formation of subpopulations of cells, each carrying a different set of mutations. This “intra-tumor heterogeneity” can produce tumor subclones that grow particularly quickly, that metastasize aggressively, or that are resistant to cancer treatments. Consequently, researchers have hypothesized that high intra-tumor heterogeneity leads to worse clinical outcomes and have suggested that a simple measure of this heterogeneity would be a useful addition to the cancer staging system currently used by clinicians for predicting the likely outcome (prognosis) of patients with cancer. Here, the researchers investigate whether a measure of intra-tumor heterogeneity called “mutant-allele tumor heterogeneity” (MATH) is related to mortality (death) among patients with head and neck squamous cell carcinoma (HNSCC)—cancers that begin in the cells that line the moist surfaces inside the head and neck, such as cancers of the mouth and the larynx (voice box). MATH is based on whole-exome sequencing (WES) of tumor and matched normal DNA. WES uses powerful DNA-sequencing systems to determine the variations of all the coding regions (exons) of the known genes in the human genome (genetic blueprint).
What Did the Researchers Do and Find?
The researchers obtained clinical and WES data for 305 patients who were treated in 14 institutions, primarily in the US, after diagnosis of HNSCC from The Cancer Genome Atlas, a catalog established by the US National Institutes of Health to map the key genomic changes in major types and subtypes of cancer. They calculated tumor MATH values for the patients from their WES results and retrospectively analyzed whether there was an association between the MATH values and patient survival. Despite the patients having tumors at various subsites and being given different treatments, every 10% increase in MATH value corresponded to an 8.8% increased risk (hazard) of death. Using a previously defined MATH-value cutoff to distinguish high- from low-heterogeneity tumors, compared to patients with low-heterogeneity tumors, patients with high-heterogeneity tumors were more than twice as likely to die (a hazard ratio of 2.2). Other statistical analyses indicated that MATH provided improved prognostic information compared to that provided by established clinical and molecular characteristics and human papillomavirus (HPV) status (HPV-positive HNSCC at some subsites has a better prognosis than HPV-negative HNSCC). In particular, MATH provided prognostic information beyond that provided by standard disease staging among patients with mouth or laryngeal cancers.
What Do These Findings Mean?
By using data from more than 300 patients treated at multiple institutions, these findings validate the use of MATH as a measure of intra-tumor heterogeneity in HNSCC. Moreover, they provide one of the first large-scale demonstrations that intra-tumor heterogeneity is clinically important in the prognosis of any type of cancer. Before the MATH metric can be used in clinical trials or in clinical practice as a prognostic tool, its ability to predict outcomes needs to be tested in prospective studies that examine the relation between MATH and the outcomes of patients with identically treated HNSCC at specific head and neck subsites, that evaluate the use of MATH for prognostication in other tumor types, and that determine the influence of cancer treatments on MATH values. Nevertheless, these findings suggest that MATH should be considered as a biomarker for survival in HNSCC and other tumor types, and raise the possibility that clinicians could use MATH values to decide on the best treatment for individual patients and to choose patients for inclusion in clinical trials.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001786.
The US National Cancer Institute (NCI) provides information about cancer and how it develops and about head and neck cancer (in English and Spanish)
Cancer Research UK, a not-for-profit organization, provides general information about cancer and how it develops, and detailed information about head and neck cancer; the Merseyside Regional Head and Neck Cancer Centre provides patient stories about HNSCC
Wikipedia provides information about tumor heterogeneity, and about whole-exome sequencing (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
Information about The Cancer Genome Atlas is available
A PLOS Blog entry by Jessica Wapner explains more about MATH
doi:10.1371/journal.pmed.1001786
PMCID: PMC4323109  PMID: 25668320
18.  BRCA1: A Novel Prognostic Factor in Resected Non-Small-Cell Lung Cancer 
PLoS ONE  2007;2(11):e1129.
Background
Although early-stage non-small-cell lung cancer (NSCLC) is considered a potentially curable disease following complete resection, patients have a wide spectrum of survival according to stage (IB, II, IIIA). Within each stage, gene expression profiles can identify patients with a higher risk of recurrence. We hypothesized that altered mRNA expression in nine genes could help to predict disease outcome: excision repair cross-complementing 1 (ERCC1), myeloid zinc finger 1 (MZF1) and Twist1 (which regulate N-cadherin expression), ribonucleotide reductase subunit M1 (RRM1), thioredoxin-1 (TRX1), tyrosyl-DNA phosphodiesterase (Tdp1), nuclear factor of activated T cells (NFAT), BRCA1, and the human homolog of yeast budding uninhibited by benzimidazole (BubR1).
Methodology and Principal Findings
We performed real-time quantitative polymerase chain reaction (RT-QPCR) in frozen lung cancer tissue specimens from 126 chemonaive NSCLC patients who had undergone surgical resection and evaluated the association between gene expression levels and survival. For validation, we used paraffin-embedded specimens from 58 other NSCLC patients. A strong inter-gene correlation was observed between expression levels of all genes except NFAT. A Cox proportional hazards model indicated that along with disease stage, BRCA1 mRNA expression significantly correlated with overall survival (hazard ratio [HR], 1.98 [95% confidence interval (CI), 1.11-6]; P = 0.02). In the independent cohort of 58 patients, BRCA1 mRNA expression also significantly correlated with survival (HR, 2.4 [95%CI, 1.01-5.92]; P = 0.04).
Conclusions
Overexpression of BRCA1 mRNA was strongly associated with poor survival in NSCLC patients, and the validation of this finding in an independent data set further strengthened this association. Since BRCA1 mRNA expression has previously been linked to differential sensitivity to cisplatin and antimicrotubule drugs, BRCA1 mRNA expression may provide additional information for customizing adjuvant antimicrotubule-based chemotherapy, especially in stage IB, where the role of adjuvant chemotherapy has not been clearly demonstrated.
doi:10.1371/journal.pone.0001129
PMCID: PMC2042516  PMID: 17987116
19.  Validation of a Proliferation-based Expression Signature as Prognostic Marker in Early Stage Lung Adenocarcinoma 
PURPOSE
New prognostic markers to guide treatment decisions in early stage non-small cell lung cancer are necessary to improve patient outcomes. In this report, we assess the utility of a pre-defined mRNA expression signature of cell cycle progression genes (CCP score) to define 5-year risk of lung cancer related death in patients with early stage lung adenocarcinoma.
EXPERIMENTAL DESIGN
A CCP score was calculated from the mRNA expression levels of 31 proliferation genes in stage I and II tumor samples from two public microarray data sets (Director’s Consortium (DC) and GSE31210). The same gene set was tested by quantitative PCR in 381 formalin-fixed paraffin-embedded (FFPE) primary tumors. Association of the CCP score with outcome was assessed by Cox proportional hazards analysis.
RESULTS
In univariate analysis the CCP score was a strong predictor of cancer-specific survival in both the DC cohort (p=0.00014, HR 2.08, 95%CI 1.43–3.02) and GSE31210 (p=0.0010, HR 2.25, 95%CI 1.42–3.56). In multivariate analysis the CCP score remained the dominant prognostic marker in the presence of clinical variables (p=0.0022, HR 2.02, 95%CI 1.29–3.17 in DC, p=0.0026, HR 2.16, 95%CI 1.32–3.53 in GSE31210). On a quantitative PCR platform the CCP score maintained highly significant prognostic value in FFPE derived mRNA from clinical samples in both univariate (p=0.00033, HR 2.10, 95%CI 1.39–3.17) and multivariate analyses (p=0.0071, HR 1.92, 95%CI 1.18–3.10).
CONCLUSIONS
The CCP score is a significant predictor of lung cancer death in early stage lung adenocarcinoma treated with surgery and may be a valuable tool in selecting patients for adjuvant treatment.
doi:10.1158/1078-0432.CCR-13-0596
PMCID: PMC3834029  PMID: 24048333
non-small cell lung cancer; adenocarcinoma; prognosis; expression signature
20.  MicroRNA-31 predicts the presence of lymph node metastases and survival in lung adenocarcinoma patients 
Purpose
We performed genome-wide microRNA-sequencing (miRNA-seq) in primary cancer tissue from lung adenocarcinoma patients to identify markers for the presence of lymph node metastasis.
Experimental Design
Markers for lymph node metastasis identified by sequencing were validated in a separate cohort using QPCR. After additional validation in the TCGA dataset, functional characterization studies were performed in vitro.
Results
MiR-31 was upregulated in lung adenocarcinoma tissues from patients with lymph node metastases compared to those without lymph node metastases. We confirmed miR-31 to be up-regulated in lymph node positive patients in a separate patient cohort (p=0.009, t-test), and to be expressed higher in adenocarcinoma tissue than in matched normal adjacent lung tissues (p<0.0001, paired t-test). MiR-31 was then validated as a marker for lymph node metastasis in an external validation cohort of 233 lung adenocarcinoma cases of the TCGA (p=0.031, t-test). In vitro functional assays showed that miR-31 increases cell migration, invasion, and proliferation in an ERK1/2 signaling dependent manner. Of note, miR-31 was a significant predictor of survival in a multivariate cox regression model even when controlling for cancer staging. Exploratory in silico analysis showed that low expression of miR-31 is associated with excellent survival for T2N0 patients.
Conclusions
We applied microRNA-seq to study microRNomes in lung adenocarcinoma tissue samples for the first time and identified potentially a microRNA predicting the presence of lymph node metastasis and survival outcomes in lung adenocarcinoma patients.
doi:10.1158/1078-0432.CCR-13-0320
PMCID: PMC3823052  PMID: 23946296
miRNA-seq; lung adenocarcinoma; metastasis; nodal stage; biomarker
21.  Identifying Important Risk Factors for Survival in Systolic Heart Failure Patients Using Random Survival Forests 
Background
Heart failure survival models are typically constructed using Cox-proportional hazards regression. Regression modeling suffers from a number of limitations, including bias introduced by commonly used variable selection methods. We illustrate the value of an intuitive, robust approach to variable selection, random survival forests (RSF), in a large clinical cohort. RSF is a potentially powerful extension of Classification and Regression Trees (CART), with lower variance and bias.
Methods and Results
We studied 2231 adult systolic heart failure patients who underwent cardiopulmonary stress testing. During a mean follow-up of 5 years, 742 patients died. Thirty-nine demographic, cardiac and noncardiac co-morbidity, and stress testing variables were analyzed as potential predictors of all-cause mortality. A RSF of 2000 trees was constructed, with each tree constructed on a bootstrap sample from the original cohort. The most predictive variables were defined as those near the tree trunks (averaged over the forest). The RSF identified peak VO2, serum BUN, and treadmill exercise time as the three most important predictors of survival. The RSF predicted survival similarly to a conventional Cox-proportional hazards model (out-of-bag C-index of 0.705 for RSF vs 0.698 for Cox-proportional hazards model).
Conclusions
A random survival forests model in a cohort of heart failure patients performed as well as a traditional Cox-proportional hazard model, and may serve as a more intuitive approach for clinicians to identify important risk factors for all-cause mortality.
doi:10.1161/CIRCOUTCOMES.110.939371
PMCID: PMC3991475  PMID: 21098782
Heart failure; prognosis; statistical modeling; survival analyses
22.  Identification of Gene Signatures and Molecular Markers for Human Lung Cancer Prognosis using an In vitro Lung Carcinogenesis System 
Lung cancer continues to be a major deadly malignancy. The mortality of this disease could be reduced by improving the ability to predict cancer patients' survival. We hypothesized that genes differentially expressed among cells constituting an in vitro human lung carcinogenesis model consisting of normal, immortalized, transformed, and tumorigenic bronchial epithelial cells are relevant to the clinical outcome of non–small cell lung cancer (NSCLC). Multidimensional scaling, microarray, and functional pathways analyses of the transcriptomes of the above cells were done and combined with integrative genomics to incorporate the microarray data with published NSCLC data sets. Up-regulated (n = 301) and down-regulated genes (n = 358) displayed expression level variation across the in vitro model with progressive changes in cancer-related molecular functions. A subset of these genes (n = 584) separated lung adenocarcinoma clinical samples (n = 361) into two clusters with significant survival differences. Six genes, UBE2C, TPX2, MCM2, MCM6, FEN1, and SFN, selected by functional array analysis, were also effective in prognosis. The mRNA and protein levels of one these genes—UBE2C—were significantly up-regulated in NSCLC tissue relative to normal lung and increased progressively in lung lesions. Moreover, stage I NSCLC patients with positive UBE2C expression exhibited significantly poorer overall and progression-free survival than patients with negative expression. Our studies with this in vitro model have lead to the identification of a robust six-gene signature, which may be valuable for predicting the survival of lung adenocarcinoma patients. Moreover, one of those genes, UBE2C, seems to be a powerful biomarker for NSCLC survival prediction.
doi:10.1158/1940-6207.CAPR-09-0084
PMCID: PMC3382104  PMID: 19638491
23.  MicroRNA expression differentiates histology and predicts survival of lung cancer 
Purpose
The molecular drivers that determine histology in lung cancer are largely unknown. We investigated whether microRNA (miR) expression profiles can differentiate histological subtypes and predict survival for non-small cell lung cancer.
Experimental design
We analyzed miR expression in 165 adenocarcinoma (AD) and 125 squamous cell carcinoma (SQ) tissue samples from the Environmental And Genetics in Lung cancer Etiology (EAGLE) study using a custom oligo array with 440 human mature antisense miRs. We compared miR expression profiles using t-tests and F-tests and accounted for multiple testing using global permutation tests. We assessed the association of miR expression with tobacco smoking using Spearman correlation coefficients and linear regression models, and with clinical outcome using log-rank tests, Cox proportional hazards and survival risk prediction models, accounting for demographic and tumor characteristics.
Results
MiR expression profiles strongly differed between AD and SQ (global p<0.0001), particularly in the early stages, and included miRs located on chromosome loci most often altered in lung cancer (e.g., 3p21-22). Most miRs, including all members of the let-7 family, were down-regulated in SQ. Major findings were confirmed by QRT-PCR in EAGLE samples and in an independent set of lung cancer cases. In SQ, low expression of miRs down-regulated in the histology comparison was associated with 1.2 to 3.6-fold increased mortality risk. A 5-miR signature significantly predicted survival for SQ.
Conclusions
We identified a miR expression profile that strongly differentiated AD from SQ and had prognostic implications. These findings may lead to histology-based therapeutic approaches.
doi:10.1158/1078-0432.CCR-09-1736
PMCID: PMC3163170  PMID: 20068076
24.  A robust tool for discriminative analysis and feature selection in paired samples impacts the identification of the genes essential for reprogramming lung tissue to adenocarcinoma 
BMC Genomics  2011;12(Suppl 3):S24.
Background
Lung cancer is the leading cause of cancer deaths in the world. The most common type of lung cancer is lung adenocarcinoma (AC). The genetic mechanisms of the early stages and lung AC progression steps are poorly understood. There is currently no clinically applicable gene test for the early diagnosis and AC aggressiveness. Among the major reasons for the lack of reliable diagnostic biomarkers are the extraordinary heterogeneity of the cancer cells, complex and poorly understudied interactions of the AC cells with adjacent tissue and immune system, gene variation across patient cohorts, measurement variability, small sample sizes and sub-optimal analytical methods. We suggest that gene expression profiling of the primary tumours and adjacent tissues (PT-AT) handled with a rational statistical and bioinformatics strategy of biomarker prediction and validation could provide significant progress in the identification of clinical biomarkers of AC. To minimise sample-to-sample variability, repeated multivariate measurements in the same object (organ or tissue, e.g. PT-AT in lung) across patients should be designed, but prediction and validation on the genome scale with small sample size is a great methodical challenge.
Results
To analyse PT-AT relationships efficiently in the statistical modelling, we propose an Extreme Class Discrimination (ECD) feature selection method that identifies a sub-set of the most discriminative variables (e.g. expressed genes). Our method consists of a paired Cross-normalization (CN) step followed by a modified sign Wilcoxon test with multivariate adjustment carried out for each variable. Using an Affymetrix U133A microarray paired dataset of 27 AC patients, we reviewed the global reprogramming of the transcriptome in human lung AC tissue versus normal lung tissue, which is associated with about 2,300 genes discriminating the tissues with 100% accuracy. Cluster analysis applied to these genes resulted in four distinct gene groups which we classified as associated with (i) up-regulated genes in the mitotic cell cycle lung AC, (ii) silenced/suppressed gene specific for normal lung tissue, (iii) cell communication and cell motility and (iv) the immune system features. The genes related to mutagenesis, specific lung cancers, early stage of AC development, tumour aggressiveness and metabolic pathway alterations and adaptations of cancer cells are strongly enriched in the AC PT-AT discriminative gene set. Two AC diagnostic biomarkers SPP1 and CENPA were successfully validated on RT-RCR tissue array. ECD method was systematically compared to several alternative methods and proved to be of better performance and as well as it was validated by comparison of the predicted gene set with literature meta-signature.
Conclusions
We developed a method that identifies and selects highly discriminative variables from high dimensional data spaces of potential biomarkers based on a statistical analysis of paired samples when the number of samples is small. This method provides superior selection in comparison to conventional methods and can be widely used in different applications. Our method revealed at least 23 hundreds patho-biologically essential genes associated with the global transcriptional reprogramming of human lung epithelium cells and lung AC aggressiveness. This gene set includes many previously published AC biomarkers reflecting inherent disease complexity and specifies the mechanisms of carcinogenesis in the lung AC. SPP1, CENPA and many other PT-AT discriminative genes could be considered as the prospective diagnostic and prognostic biomarkers of lung AC.
doi:10.1186/1471-2164-12-S3-S24
PMCID: PMC3377915  PMID: 22369099
25.  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 http://compbio.cs.umn.edu/Net-Cox/.
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.
doi:10.1371/journal.pcbi.1002975
PMCID: PMC3605061  PMID: 23555212

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