Published prognostic gene signatures in breast cancer have few genes in common. Here we provide a rationale for this observation by studying the prognostic power and the underlying biological pathways of different gene signatures.
Gene signatures to predict the development of metastases in estrogen receptor-positive and estrogen receptor-negative tumors were identified using 500 re-sampled training sets and mapping to Gene Ontology Biological Process to identify over-represented pathways. The Global Test program confirmed that gene expression profilings in the common pathways were associated with the metastasis of the patients.
The apoptotic pathway and cell division, or cell growth regulation and G-protein coupled receptor signal transduction, were most significantly associated with the metastatic capability of estrogen receptor-positive or estrogen-negative tumors, respectively. A gene signature derived of the common pathways predicted metastasis in an independent cohort. Mapping of the pathways represented by different published prognostic signatures showed that they share 53% of the identified pathways.
We show that divergent gene sets classifying patients for the same clinical endpoint represent similar biological processes and that pathway-derived signatures can be used to predict prognosis. Furthermore, our study reveals that the underlying biology related to aggressiveness of estrogen receptor subgroups of breast cancer is quite different.
Epidemiological studies indicate an increased risk of subsequent primary ovarian cancer from women with breast cancer. We have recently identified a 28-gene expression signature that predicts, with high accuracy, the clinical course in a large population of breast cancer patients. This prognostic gene signature also accurately predicts response to chemotherapy commonly used for treating breast cancer, including CMF, Tamoxifen, Paclitaxel, Docetaxel, and Doxorubicin (Adriamycin), in a panel of 60 cancer cell lines of nine different tissue origins. This prompted us to investigate whether this prognostic gene signature could also predict clinical outcome in other cancer types of epithelial origins, including ovarian cancer (n = 124), colon tumors (n = 74), and lung adenocarcinomas (n = 442). The results show that the gene expression signature contributes significantly more accurate (P < 0.05; compared with random prediction) prognostic information in multiple cancer types independent of established clinical parameters. Furthermore, the functional pathway analysis with curated database delineated a biological network with tight connections between the signature genes and numerous well established cancer hallmarks, indicating important roles of this prognostic gene signature in tumor genesis and progression.
prognostic gene signature; breast cancer; ovarian cancer; colon cancer; lung adenocarcinoma
The advent of global gene expression profiling has generated unprecedented insight into our molecular understanding of cancer, including breast cancer. For example, human breast cancer patients display significant diversity in terms of their survival, recurrence, metastasis as well as response to treatment. These patient outcomes can be predicted by the transcriptional programs of their individual breast tumors. Predictive gene signatures allow us to correctly classify human breast tumors into various risk groups as well as to more accurately target therapy to ensure more durable cancer treatment.
Here we present a novel algorithm to generate gene signatures with predictive potential. The method first classifies the expression intensity for each gene as determined by global gene expression profiling as low, average or high. The matrix containing the classified data for each gene is then used to score the expression of each gene based its individual ability to predict the patient characteristic of interest. Finally, all examined genes are ranked based on their predictive ability and the most highly ranked genes are included in the master gene signature, which is then ready for use as a predictor. This method was used to accurately predict the survival outcomes in a cohort of human breast cancer patients.
We confirmed the capacity of our algorithm to generate gene signatures with bona fide predictive ability. The simplicity of our algorithm will enable biological researchers to quickly generate valuable gene signatures without specialized software or extensive bioinformatics training.
Recently, expression profiling of breast carcinomas has revealed gene signatures that predict clinical outcome, and discerned prognostically relevant breast cancer subtypes. Measurement of the degree of genomic instability provides a very similar stratification of prognostic groups. We therefore hypothesized that these features are linked. We used gene expression profiling of 48 breast cancer specimens that profoundly differed in their degree of genomic instability and identified a set of 12 genes that defines the two groups. The biological and prognostic significance of this gene set was established through survival prediction in published datasets from patients with breast cancer. Of note, the gene expression signatures that define specific prognostic subtypes in other breast cancer datasets, such as luminal A and B, basal, normal-like, and ERBB2+, and prognostic signatures including MammaPrint® and Oncotype DX, predicted genomic instability in our samples. This remarkable congruence suggests a biological interdependence of poor-prognosis gene signatures, breast cancer subtypes, genomic instability, and clinical outcome.
Gene expression profiling of human breast tumors has uncovered several molecular signatures that can divide breast cancer patients into good and poor outcome groups. However, these signatures typically comprise many genes (~50-100), and the prognostic tests associated with identifying these signatures in patient tumor specimens require complicated methods, which are not routinely available in most hospital pathology laboratories, thus limiting their use. Hence, there is a need for more practical methods to predict patient survival.
We modified a feature selection algorithm and used survival analysis to derive a 2-gene signature that accurately predicts breast cancer patient survival.
We developed a tree based decision method that segregated patients into various risk groups using KIAA0191 expression in the context of E2F1 expression levels. This approach led to highly accurate survival predictions in a large cohort of breast cancer patients using only a 2-gene signature.
Our observations suggest a possible relationship between E2F1 and KIAA0191 expression that is relevant to the pathogenesis of breast cancer. Furthermore, our findings raise the prospect that the practicality of patient prognosis methods may be improved by reducing the number of genes required for analysis. Indeed, our E2F1/KIAA0191 2-gene signature would be highly amenable for an immunohistochemistry based test, which is commonly used in hospital laboratories.
Our goal was to identify gene signatures predictive of response to preoperative systemic chemotherapy (PST) with epirubicin/cyclophosphamide (EC) in patients with primary breast cancer.
Needle biopsies were obtained pre-treatment from 83 patients with breast cancer and mRNA was profiled on Affymetrix HG-U133A arrays. Response ranged from pathologically confirmed complete remission (pCR), to partial remission (PR), to stable or progressive disease, "No Change" (NC). A primary analysis was performed in breast tissue samples from 56 patients and 5 normal healthy individuals as a training cohort for predictive marker identification. Gene signatures identifying individuals most likely to respond completely to PST-EC were extracted by combining several statistical methods and filtering criteria. In order to optimize prediction of non responding tumors Student's t-test and Wilcoxon test were also applied. An independent cohort of 27 patients was used to challenge the predictive signatures. A k-Nearest neighbor algorithm as well as two independent linear partial least squares determinant analysis (PLS-DA) models based on the training cohort were selected for classification of the test samples. The average specificity of these predictions was greater than 74% for pCR, 100% for PR and greater than 62% for NC. All three classification models could identify all pCR cases.
The differential expression of 59 genes in the training and the test cohort demonstrated capability to predict response to PST-EC treatment. Based on the training cohort a classifier was constructed following a decision tree.
First, a transcriptional profile capable to distinguish cancerous from normal tissue was identified. Then, a "favorable outcome signature" (31 genes) and a "poor outcome signature" (26 genes) were extracted from the cancer specific signatures. This stepwise implementation could predict pCR and distinguish between NC and PR in a subsequent set of patients. Both PLS-DA models were implemented to discriminate all three response classes in one step.
In this study signatures were identified capable to predict clinical outcome in an independent set of primary breast cancer patients undergoing PST-EC.
breast cancer; preoperative chemotherapy; microarray; prognostic classification
Estrogen signaling plays an essential role in breast cancer progression, and estrogen receptor (ER) status has long been a marker of hormone responsiveness. However, ER status alone has been an incomplete predictor of endocrine therapy, as some ER+ tumors, nevertheless, have poor prognosis. Here we sought to use expression profiling of ER+ breast cancer cells to screen for a robust estrogen-regulated gene signature that may serve as a better indicator of cancer outcome. We identified 532 estrogen-induced genes and further developed a 73-gene signature that best separated a training set of 286 primary breast carcinomas into prognostic subtypes by stepwise cross-validation. Notably, this signature predicts clinical outcome in over 10 patient cohorts as well as their respective ER+ subcohorts. Further, this signature separates patients who have received endocrine therapy into two prognostic subgroups, suggesting its specificity as a measure of estrogen signaling, and thus hormone sensitivity. The 73-gene signature also provides additional predictive value for patient survival, independent of other clinical parameters, and outperforms other previously reported molecular outcome signatures. Taken together, these data demonstrate the power of using cell culture systems to screen for robust gene signatures of clinical relevance.
Activation of the DNA damage response pathway is a hallmark for early tumorigenesis, while loss of pathway activity is associated with disease progression. Thus we hypothesized that a gene expression signature associated with the DNA damage response may serve as a prognostic signature for outcome in cancer patients. We identified ionizing radiation-responsive transcripts in human lymphoblast cells derived from 12 individuals and used this signature to screen a panel of cancer data sets for the ability to predict long-term survival of cancer patients. We demonstrate that gene sets induced or repressed by ionizing radiation can predict clinical outcome in two independent breast cancer data sets, and we compare the radiation signature to previously described gene expression-based outcome predictors. While genes repressed in response to radiation likely represent the well-characterized proliferation signature predictive of breast cancer outcome, genes induced by radiation likely encode additional information representing other deregulated biological properties of tumors such as checkpoint or apoptotic responses.
The lack of markers to predict chemotherapy responses in patients poses a major handicap in cancer treatment. We searched for gene expression patterns that correlate with docetaxel or cisplatin response in a mouse model for breast cancer associated with BRCA1 deficiency. Array-based expression profiling did not identify a single marker gene predicting docetaxel response, despite an increase in Abcb1 (P-glycoprotein) expression that was sufficient to explain resistance in several poor responders. Intertumoral heterogeneity explained the inability to identify a predictive gene expression signature for docetaxel. To address this problem, we used a novel algorithm designed to detect differential gene expression in a subgroup of the poor responders which could identify tumors with increased Abcb1 transcript levels. In contrast, standard analytical tools, such as Significance Analysis of Microarrays (SAM), detected a marker only if it correlated with response in a substantial fraction of tumors. For example, low expression of the Xist gene correlated with cisplatin hypersensitivity in most tumors, and it also predicted long recurrence-free survival of HER2-negative, stage-III breast cancer patients treated with intensive platinum-based chemotherapy. Our findings may prove useful for selecting patients with high risk breast cancer who could benefit from platinum-based therapy.
breast cancer; BRCA1; chemotherapy response prediction; gene expression profiling; XIST
During the last years, several groups have identified prognostic gene expression signatures with apparently similar performances. However, signatures were never compared on an independent population of untreated breast cancer patients, where risk assessment was computed using the original algorithms and microarray platforms.
We compared three gene expression signatures, the 70-gene, the 76-gene and the Gene expression Grade Index (GGI) signatures, in terms of predicting distant metastasis free survival (DMFS) for the individual patient. To this end, we used the previously published TRANSBIG independent validation series of node-negative untreated primary breast cancer patients. We observed agreement in prediction for 135 of 198 patients (68%) when considering the three signatures. When comparing the signatures two by two, the agreement in prediction was 71% for the 70- and 76-gene signatures, 76% for the 76-gene signature and the GGI, and 88% for the 70-gene signature and the GGI. The three signatures had similar capabilities of predicting DMFS and added significant prognostic information to that provided by the classical parameters.
Despite the difference in development of these signatures and the limited overlap in gene identity, they showed similar prognostic performance, adding to the growing evidence that these prognostic signatures are of clinical relevance.
Current concepts conceive “breast cancer” as a complex disease that comprises several very different types of neoplasms. Nonetheless, breast cancer treatment has considerably improved through early diagnosis, adjuvant chemotherapy, and endocrine treatments. The limited prognostic power of classical classifiers determines considerable over-treatment of women who either do not benefit from, or do not at all need, chemotherapy. Several gene expression based molecular classifiers (signatures) have been developed for a more reliable prognostication. Gene expression profiling identifies profound differences in breast cancers, most probably as a consequence of different cellular origin and different driving mutations and can therefore distinguish the intrinsic propensity to metastasize. Existing signatures have been shown to be useful for treatment decisions, although they have been developed using relatively small sample numbers. Major improvements are expected from the use of large datasets, subtype specific signatures and from the re-introduction of functional information. We show that molecular signatures encounter clear limitations given by the intrinsic probabilistic nature of breast cancer metastasis. Already today, signatures are, however, useful for clinical decisions in specific cases, in particular if the personal inclination of the patient towards different treatment strategies is taken into account.
Breast cancer; Gene expression profiling; Metastasis; Stroma; Angiogenesis
The detection of circulating tumor cells (CTCs) in the peripheral blood and microarray gene expression profiling of the primary tumor are two promising new technologies able to provide valuable prognostic data for patients with breast cancer. Meta-analyses of several established prognostic breast cancer gene expression profiles in large patient cohorts have demonstrated that despite sharing few genes, their delineation of patients into “good prognosis” or “poor prognosis” are frequently very highly correlated, and combining prognostic profiles does not increase prognostic power. In the current study, we aimed to develop a novel profile which provided independent prognostic data by building a signature predictive of CTC status rather than outcome. Microarray gene expression data from an initial training cohort of 72 breast cancer patients for which CTC status had been determined in a previous study using a multimarker QPCR-based assay was used to develop a CTC-predictive profile. The generated profile was validated in two independent datasets of 49 and 123 patients and confirmed to be both predictive of CTC status, and independently prognostic. Importantly, the “CTC profile” also provided prognostic information independent of the well-established and powerful ‘70-gene’ prognostic breast cancer signature. This profile therefore has the potential to not only add prognostic information to currently-available microarray tests but in some circumstances even replace blood-based prognostic CTC tests at time of diagnosis for those patients already undergoing testing by multigene assays.
The clinical course of breast cancer is difficult to predict on the basis of established clinical and pathological prognostic criteria. Given the genetic complexity of breast carcinomas, it is not surprising that correlations with individual genetic abnormalities have also been disappointing. The use of gene expression profiles could result in more accurate and objective prognostication.
To this end, we used real-time quantitative RT-PCR assays to quantify the mRNA expression of a large panel (n = 47) of genes previously identified as candidate prognostic molecular markers in a series of 100 ERα-positive breast tumor samples from patients with known long-term follow-up. We identified a three-gene expression signature (BRCA2, DNMT3B and CCNE1) as an independent prognostic marker (P = 0.007 by univariate analysis; P = 0.006 by multivariate analysis). This "poor prognosis" signature was then tested on an independent panel of ERα-positive breast tumors from a well-defined cohort of 104 postmenopausal breast cancer patients treated with primary surgery followed by adjuvant tamoxifen alone: although this "poor prognosis" signature was associated with shorter relapse-free survival in univariate analysis (P = 0.029), it did not persist as an independent prognostic factor in multivariate analysis (P = 0.27).
Our results confirm the value of gene expression signatures in predicting the outcome of breast cancer.
Breast cancer; Gene expression profiling; Real-time RT-PCR quantification; Prognostic value
Breast cancer comprises a collection of diseases with distinctive clinical, histopathological, and molecular features. Importantly, tumors with similar histological features may display disparate clinical behaviors. Gene expression profiling using microarray technologies has improved our understanding of breast cancer biology and has led to the development of a breast cancer molecular taxonomy and of multigene 'signatures' to predict outcome and response to systemic therapies. The use of these prognostic and predictive signatures in routine clinical decision-making remains controversial. Here, we review the clinical relevance of microarray-based profiling of breast cancer and discuss its impact on patient management.
Robust transcriptional signatures in cancer can be identified by data similarity-driven meta-analysis of gene expression profiles. An unbiased data integration and interrogation strategy has not previously been available.
Methods and Findings
We implemented and performed a large meta-analysis of breast cancer gene expression profiles from 223 datasets containing 10,581 human breast cancer samples using a novel data similarity-based approach (iterative EXALT). Cancer gene expression signatures extracted from individual datasets were clustered by data similarity and consolidated into a meta-signature with a recurrent and concordant gene expression pattern. A retrospective survival analysis was performed to evaluate the predictive power of a novel meta-signature deduced from transcriptional profiling studies of human breast cancer. Validation cohorts consisting of 6,011 breast cancer patients from 21 different breast cancer datasets and 1,110 patients with other malignancies (lung and prostate cancer) were used to test the robustness of our findings. During the iterative EXALT analysis, 633 signatures were grouped by their data similarity and formed 121 signature clusters. From the 121 signature clusters, we identified a unique meta-signature (BRmet50) based on a cluster of 11 signatures sharing a phenotype related to highly aggressive breast cancer. In patients with breast cancer, there was a significant association between BRmet50 and disease outcome, and the prognostic power of BRmet50 was independent of common clinical and pathologic covariates. Furthermore, the prognostic value of BRmet50 was not specific to breast cancer, as it also predicted survival in prostate and lung cancers.
We have established and implemented a novel data similarity-driven meta-analysis strategy. Using this approach, we identified a transcriptional meta-signature (BRmet50) in breast cancer, and the prognostic performance of BRmet50 was robust and applicable across a wide range of cancer-patient populations.
Perhaps the major challenge in developing more effective therapeutic strategies for the treatment of breast cancer patients is confronting the heterogeneity of the disease, recognizing that breast cancer is not one disease but multiple disorders with distinct underlying mechanisms. Gene-expression profiling studies have been used to dissect this complexity, and our previous studies identified a series of intrinsic subtypes of breast cancer that define distinct populations of patients with respect to survival. Additional work has also used signatures of oncogenic pathway deregulation to dissect breast cancer heterogeneity as well as to suggest therapeutic opportunities linked to pathway activation.
We used genomic analyses to identify relations between breast cancer subtypes, pathway deregulation, and drug sensitivity. For these studies, we use three independent breast cancer gene-expression data sets to measure an individual tumor phenotype. Correlation between pathway status and subtype are examined and linked to predictions for response to conventional chemotherapies.
We reveal patterns of pathway activation characteristic of each molecular breast cancer subtype, including within the more aggressive subtypes in which novel therapeutic opportunities are critically needed. Whereas some oncogenic pathways have high correlations to breast cancer subtype (RAS, CTNNB1, p53, HER1), others have high variability of activity within a specific subtype (MYC, E2F3, SRC), reflecting biology independent of common clinical factors. Additionally, we combined these analyses with predictions of sensitivity to commonly used cytotoxic chemotherapies to provide additional opportunities for therapeutics specific to the intrinsic subtype that might be better aligned with the characteristics of the individual patient.
Genomic analyses can be used to dissect the heterogeneity of breast cancer. We use an integrated analysis of breast cancer that combines independent methods of genomic analyses to highlight the complexity of signaling pathways underlying different breast cancer phenotypes and to identify optimal therapeutic opportunities.
Gene-expression profiling has had a considerable impact on our understanding of breast cancer biology, and more recently on clinical care. Two statistical approaches underlie these advancements. Supervised analyses have led to the development of gene-expression signatures designed to predict survival and/or treatment response, which has resulted in the development of new clinical assays. Unsupervised analyses have identified numerous biological signatures including signatures of cell type of origin, signaling pathways, and of cellular proliferation. Included within these biological signatures are the molecular subtypes known as the ‘intrinsic’ subtypes of breast cancer. This classification has expanded our appreciation of the heterogeneity of breast cancer and has provided a way to sub-classify the disease in a manner that might have clinical utility. In this Review, we discuss the clinical utility of gene-expression-based assays and their technical potential as clinical tools vis-a-vis the performance of breast cancer biomarkers that are the current standard of care.
It is now accepted that breast cancer is not a single disease, but instead it is composed of a spectrum of tumor subtypes with distinct cellular origins, somatic changes, and etiologies. Gene expression profiling using DNA microarrays has contributed significantly to our understanding of the molecular heterogeneity of breast tumor formation, progression, and recurrence. For example, at least two clinical diagnostic assays exist (i.e., OncotypeDX RS and Mammaprint®) that are able to predict outcome in patients using patterns of gene expression and predetermined mathematical algorithms. In addition, a new molecular taxonomy based upon the inherent, or “intrinsic,” biology of breast tumors has been developed; this taxonomy is called the “intrinsic subtypes of breast cancer,” which now identifies five distinct tumor types and a normal breast-like group. Importantly, the intrinsic subtypes of breast cancer predict patient relapse, overall survival, and response to endocrine and chemotherapy regimens. Thus, most of the clinical behavior of a breast tumor is already written in its subtype profile. Here, we describe the discovery and basic biology of the intrinsic subtypes of breast cancer, and detail how this interacts with underlying genetic alternations, response to therapy, and the metastatic process.
Mammary tumors have a variety of cellular origins and display significant heterogeneity. A new molecular taxonomy defines five tumor subtypes and can predict patient relapse, survival, and responses to therapy.
Estrogen is a pivotal regulator of cell proliferation in the normal breast and breast cancer. Endocrine therapies targeting the estrogen receptor are effective in breast cancer, but their success is limited by intrinsic and acquired resistance.
With the goal of gaining mechanistic insights into estrogen action and endocrine resistance, we classified estrogen-regulated genes by function, and determined the relationship between functionally-related genesets and the response to tamoxifen in breast cancer patients. Estrogen-responsive genes were identified by transcript profiling of MCF-7 breast cancer cells. Pathway analysis based on functional annotation of these estrogen-regulated genes identified gene signatures with known or predicted roles in cell cycle control, cell growth (i.e. ribosome biogenesis and protein synthesis), cell death/survival signaling and transcriptional regulation. Since inducible expression of c-Myc in antiestrogen-arrested cells can recapitulate many of the effects of estrogen on molecular endpoints related to cell cycle progression, the estrogen-regulated genes that were also targets of c-Myc were identified using cells inducibly expressing c-Myc. Selected genes classified as estrogen and c-Myc targets displayed similar levels of regulation by estrogen and c-Myc and were not estrogen-regulated in the presence of siMyc. Genes regulated by c-Myc accounted for 50% of all acutely estrogen-regulated genes but comprised 85% (110/129 genes) in the cell growth signature. siRNA-mediated inhibition of c-Myc induction impaired estrogen regulation of ribosome biogenesis and protein synthesis, consistent with the prediction that estrogen regulates cell growth principally via c-Myc. The ‘cell cycle’, ‘cell growth’ and ‘cell death’ gene signatures each identified patients with an attenuated response in a cohort of 246 tamoxifen-treated patients. In multivariate analysis the cell death signature was predictive independent of the cell cycle and cell growth signatures.
These functionally-based gene signatures can stratify patients treated with tamoxifen into groups with differing outcome, and potentially identify distinct mechanisms of tamoxifen resistance.
It remains a critical issue to improve the survival rate in patients with recurrent or metastatic breast cancer. This study sought to develop a prognostic scheme based on a 28-gene signature in a broad patient population, including those with advanced disease. Clinically annotated transcriptional profiles of 1,734 breast cancer patients were obtained to validate the 28-gene signature in prognostic categorization. The 28-gene signature generated significant patient stratification with regard to breast cancer disease-free survival (log-rank P < 0.0001; n = 1,337) and overall survival (log-rank P < 0.0001; n = 806) in Kaplan-Meier analyses. The gene expression signature provides refined prognosis of disease-free survival (log-rank P < 0.006; Kaplan-Meier analysis) within each classic clinicopathologic factor-defined subgroup, including LN-, LN+, ER-, ER+, and tumor Grade II. Furthermore, it was investigated whether this gene signature predicts chemoresponse to drugs commonly used to treat breast cancer. The mRNA expression levels of this gene signature in NCI-60 cell lines were used to predict chemoresponse to CMF, Tamoxifen, Paclitaxel, Docetaxel, and Doxorubicin (Adriamycin). The 28-gene prognostic signature accurately (P < 0.02) predicted chemotherapeutic response to the studied drugs. This study confirmed the prognostic applicability of the breast cancer gene signature in a broad clinical setting. This prognostic signature is also predictive of drug response in cancer cell lines.
gene signature; breast cancer prognosis; chemosensitivity prediction; NCI-60 cell lines
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.
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.
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.
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.
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.
molecular signature; non-small cell lung cancer; prognosis; microarray analysis; protein expression; Western blots
Neoadjuvant chemotherapy for breast cancer allows individual tumor response to be assessed depending on molecular subtype, and to judge the impact of response to therapy on recurrence-free survival (RFS). The multicenter I-SPY 1 TRIAL evaluated patients with ≥3 cm tumors by using early imaging and molecular signatures, with outcomes of pathologic complete response (pCR) and RFS. The current analysis was performed using data from patients who had molecular profiles and did not receive trastuzumab. The various molecular classifiers tested were highly correlated. Categorization of breast cancer by molecular signatures enhanced the ability of pCR to predict improvement in RFS compared to the population as a whole. In multivariate analysis, the molecular signatures that added to the ability of HR and HER2 receptors, clinical stage, and pCR in predicting RFS included 70-gene signature, wound healing signature, p53 mutation signature, and PAM50 risk of recurrence. The low risk signatures were associated with significantly better prognosis, and also identified additional patients with a good prognosis within the no pCR group, primarily in the hormone receptor positive, HER-2 negative subgroup. The I-SPY 1 population is enriched for tumors with a poor prognosis but is still heterogeneous in terms of rates of pCR and RFS. The ability of pCR to predict RFS is better by subset than it is for the whole group. Molecular markers improve prediction of RFS by identifying additional patients with excellent prognosis within the no pCR group.
Electronic supplementary material
The online version of this article (doi:10.1007/s10549-011-1895-2) contains supplementary material, which is available to authorized users.
Breast cancer; Neoadjuvant chemotherapy; Molecular biomarkers; Pathologic complete response; Medicine & Public Health; Oncology
Multiple breast cancer gene expression profiles have been developed that appear to provide similar abilities to predict outcome and may outperform clinical-pathologic criteria; however, the extent to which seemingly disparate profiles provide additive prognostic information is not known, nor do we know whether prognostic profiles perform equally across clinically defined breast cancer subtypes. We evaluated whether combining the prognostic powers of standard breast cancer clinical variables with a large set of gene expression signatures could improve on our ability to predict patient outcomes.
Using clinical-pathological variables and a collection of 323 gene expression "modules", including 115 previously published signatures, we build multivariate Cox proportional hazards models using a dataset of 550 node-negative systemically untreated breast cancer patients. Models predictive of pathological complete response (pCR) to neoadjuvant chemotherapy were also built using this approach.
We identified statistically significant prognostic models for relapse-free survival (RFS) at 7 years for the entire population, and for the subgroups of patients with ER-positive, or Luminal tumors. Furthermore, we found that combined models that included both clinical and genomic parameters improved prognostication compared with models with either clinical or genomic variables alone. Finally, we were able to build statistically significant combined models for pathological complete response (pCR) predictions for the entire population.
Integration of gene expression signatures and clinical-pathological factors is an improved method over either variable type alone. Highly prognostic models could be created when using all patients, and for the subset of patients with lymph node-negative and ER-positive breast cancers. Other variables beyond gene expression and clinical-pathological variables, like gene mutation status or DNA copy number changes, will be needed to build robust prognostic models for ER-negative breast cancer patients. This combined clinical and genomics model approach can also be used to build predictors of therapy responsiveness, and could ultimately be applied to other tumor types.
Using valproic acid as an example, the authors demonstrate that drug response signatures derived from genome-wide expression data can identify individuals likely to respond to a drug, and propose that this method could select optimal populations for clinical trials of new therapies.
Drug response signatures that accurately reflect the cellular response to a drug can be generated from Connectivity Map and publically available gene expression data.Predictions from the drug response signature for valproic acid correlate with sensitivity to valproic acid in breast cancer cell lines and patient tumors grown in three-dimensional culture and mouse xenografts.The MATCH algorithm provides an efficient approach for using genome-wide gene expression data to identify a target population for a drug prior to clinical trials.MATCH can predict drug sensitivity in tumors without knowledge of mechanism of action.
Unlike traditional chemotherapy, targeted cancer therapies are expected to work in only a subset of people with a particular cancer. However, biomarkers of response are not always known before clinical trial initiation. We present MATCH (Merging genomic and pharmacologic Analyses for Therapy CHoice), an algorithm for using genome-wide gene expression data to identify and validate a genomic biomarker of sensitivity (see Figure 1). Our proof-of-principle example is valproic acid (VPA), but we also show that an estrogen blocking drug currently used for breast cancer and a B-RAF inhibitor in trials for melanoma give predictions that correspond to their clinical uses.
We use genome-wide gene expression data from treated and untreated samples from the Connectivity Map to generate a VPA response signature. We validate that the VPA signature can identify treated and untreated cells in an independent data set of normal cells and in independent samples from the Connectivity Map. The AUC for the ROC curve is 0.86. We then apply the VPA signature to publically available data sets from a panel of cancer cell lines and from primary tumor and normal tissue samples. These data suggest that there is a subset of women with breast cancer who will be sensitive to VPA. Finally, we validate that our predictions correlate with sensitivity to VPA in breast cancer cell lines grown in two-dimensional culture, primary breast tumor samples grown in three-dimensional culture, and in vivo mouse breast cancer xenografts. Together, these studies show that MATCH can identify cancer patients most likely to respond to a specific drug treatment.
Identifying the best drug for each cancer patient requires an efficient individualized strategy. We present MATCH (Merging genomic and pharmacologic Analyses for Therapy CHoice), an approach using public genomic resources and drug testing of fresh tumor samples to link drugs to patients. Valproic acid (VPA) is highlighted as a proof-of-principle. In order to predict specific tumor types with high probability of drug sensitivity, we create drug response signatures using publically available gene expression data and assess sensitivity in a data set of >40 cancer types. Next, we evaluate drug sensitivity in matched tumor and normal tissue and exclude cancer types that are no more sensitive than normal tissue. From these analyses, breast tumors are predicted to be sensitive to VPA. A meta-analysis across breast cancer data sets shows that aggressive subtypes are most likely to be sensitive to VPA, but all subtypes have sensitive tumors. MATCH predictions correlate significantly with growth inhibition in cancer cell lines and three-dimensional cultures of fresh tumor samples. MATCH accurately predicts reduction in tumor growth rate following VPA treatment in patient tumor xenografts. MATCH uses genomic analysis with in vitro testing of patient tumors to select optimal drug regimens before clinical trial initiation.
biomarkers; cancer; pharmacogenomics
Expression profiling has been extensively applied to the study of breast cancer and undoubtedly is changing the way breast cancer is perceived. Over the past few years, several groups have described prognostic “signatures” (gene lists) that are purported to be more accurate prognostic factors than well established clinical and pathological features. In addition, cDNA and oligonucleotide microarrays have also been used to devise predictive “signatures” in the setting of neoadjuvant chemotherapy setting. However, it seems that the enthusiasm with this new technology has led most of us to turn a blind eye to some serious methodological problems which are evident in landmark papers on breast cancer expression profiling. These issues include small and biased cohorts of patients, inappropriate statistical analysis and lack of thorough validation of the technology. In this review, we critically revisit the most relevant cDNA microarray studies on breast cancer prognosis and prediction published to date. Although the results are promising, further optimisation and standardisation of the technique and properly designed clinical trials are required before microarrays can reliably be used as tools for clinical decision making.