CCR 20th Anniversary Commentary: Gene-Expression Signature in Breast Cancer--Where Did It Start and Where Are We Now?

Exploring public cancer gene expression signatures across bulk, single-cell and spatial transcriptomics data with signifinder Bioconductor package

Understanding cancer mechanisms, defining subtypes, predicting prognosis and assessing therapy efficacy are crucial aspects of cancer research. Gene-expression signatures derived from bulk gene expression data have played a significant role in these endeavors over the past decade. However, recent advancements in high-resolution transcriptomic technologies, such as single-cell RNA sequencing and spatial transcriptomics, have revealed the complex cellular heterogeneity within tumors, necessitating the development of computational tools to characterize tumor mass heterogeneity accurately. Thus we implemented signifinder, a novel R Bioconductor package designed to streamline the collection and use of cancer transcriptional signatures across bulk, single-cell, and spatial transcriptomics data. Leveraging publicly available signatures curated by signifinder, users can assess a wide range of tumor characteristics, including hallmark processes, therapy responses, and tumor microenvironment peculiarities. Through three case studies, we demonstrate the utility of transcriptional signatures in bulk, single-cell, and spatial transcriptomic data analyses, providing insights into cell-resolution transcriptional signatures in oncology. Signifinder represents a significant advancement in cancer transcriptomic data analysis, offering a comprehensive framework for interpreting high-resolution data and addressing tumor complexity.

signifinder enables the identification of tumor cell states and cancer expression signatures in bulk, single-cell and spatial transcriptomic data

Over the last decade, many studies and some clinical trials have proposed gene expression signatures as a valuable tool for understanding cancer mechanisms, defining subtypes, monitoring patient prognosis, and therapy efficacy. However, technical and biological concerns about reproducibility have been raised. Technical reproducibility is a major concern: we currently lack a computational implementation of the proposed signatures, which would provide detailed signature definition and assure reproducibility, dissemination, and usability of the classifier. Another concern regards intratumor heterogeneity, which has never been addressed when studying these types of biomarkers using bulk transcriptomics. With the aim of providing a tool able to improve the reproducibility and usability of gene expression signatures, we propose signifinder, an R package that provides the infrastructure to collect, implement, and compare expression-based signatures from cancer literature. The included signatures cover a wide range of biological processes from metabolism and programmed cell death, to morphological changes, such as quantification of epithelial or mesenchymal-like status. Collected signatures can score tumor cell characteristics, such as the predicted response to therapy or the survival association, and can quantify microenvironmental information, including hypoxia and immune response activity. signifinder has been used to characterize tumor samples and to investigate intra-tumor heterogeneity, extending its application to single-cell and spatial transcriptomic data. Through these higher-resolution technologies, it has become increasingly apparent that the single-sample score assessment obtained by transcriptional signatures is conditioned by the phenotypic and genetic intratumor heterogeneity of tumor masses. Since the characteristics of the most abundant cell type or clone might not necessarily predict the properties of mixed populations, signature prediction efficacy is lowered, thus impeding effective clinical diagnostics. Through signifinder, we offer general principles for interpreting and comparing transcriptional signatures, as well as suggestions for additional signatures that would allow for more complete and robust data inferences. We consider signifinder a useful tool to pave the way for reproducibility and comparison of transcriptional signatures in oncology.

A novel immune prognostic index for stratification of high-risk patients with early breast cancer

The prognostic value of current multigene assays for breast cancer is limited to hormone receptor-positive, human epidermal growth factor receptor 2-negative early breast cancer. Despite the prognostic significance of immune response-related genes in breast cancer, immune gene signatures have not been incorporated into most multigene assays. Here, using public gene expression microarray datasets, we classified breast cancer patients into three risk groups according to clinical risk and proliferation risk. We then developed the immune prognostic index based on expression of five immune response-related genes (TRAT1, IL2RB, CTLA4, IGHM and IL21R) and lymph node status to predict the risk of recurrence in the clinical and proliferation high-risk (CPH) group. The 10-year probability of disease-free survival (DFS) or distant metastasis-free survival (DMFS) of patients classified as high risk according to the immune prognostic index was significantly lower than those of patients classified as intermediate or low risk. Multivariate analysis revealed that the index is an independent prognostic factor for DFS or DMFS. Moreover, the C-index revealed that it is superior to clinicopathological variables for predicting prognosis. Its prognostic significance was also validated in independent datasets. The immune prognostic index identified low-risk patients among patients classified as CPH, regardless of the molecular subtype of breast cancer, and may overcome the limitations of current multigene assays.

Gene signature-based prediction of triple-negative breast cancer patient response to Neoadjuvant chemotherapy

Neoadjuvant chemotherapy is the current standard of care for large, advanced, and/or inoperable tumors, including triple‐negative breast cancer. Although the clinical benefits of neoadjuvant chemotherapy have been illustrated through numerous clinical trials, more than half of the patients do not experience therapeutic benefit and needlessly suffer from side effects. Currently, no clinically applicable biomarkers are available for predicting neoadjuvant chemotherapy response in triple‐negative breast cancer; the discovery of such a predictive biomarker or marker profile is an unmet need. In this study, we introduce a generic computational framework to calculate a response‐probability score (RPS), based on patient transcriptomic profiles, to predict their response to neoadjuvant chemotherapy. We first validated this framework in ER‐positive breast cancer patients and showed that it predicted neoadjuvant chemotherapy response with equal performance to several clinically used gene signatures, including Oncotype DX and MammaPrint. Then, we applied this framework to triple‐negative breast cancer data and, for each patient, we calculated a response probability score (TNBC‐RPS). Our results indicate that the TNBC‐RPS achieved the highest accuracy for predicting neoadjuvant chemotherapy response compared to previously proposed 143 gene signatures. When combined with additional clinical factors, the TNBC‐RPS achieved a high prediction accuracy for triple‐negative breast cancer patients, which was comparable to the prediction accuracy of Oncotype DX and MammaPrint in ER‐positive patients. In conclusion, the TNBC‐RPS accurately predicts neoadjuvant chemotherapy response in triple‐negative breast cancer patients and has the potential to be clinically used to aid physicians in stratifying patients for more effective neoadjuvant chemotherapy.

Identification of a prognostic LncRNA signature for ER-positive, ER-negative and triple-negative breast cancers

PURPOSE

The development of multi-gene signatures has led to improvements in identification of breast cancer patients at high risk of recurrence. The prognostic power of commercially available gene signatures is mostly restricted to estrogen receptor (ER)-positive breast cancer. On the contrary, immune-related gene signatures predict prognosis only in ER-negative breast cancer. This study aimed to develop a better prognostic signature for breast cancer.

METHODS

The expressions of long non-coding RNA (lncRNA) genes from 30 independent microarray datasets with a total of 4813 samples were analyzed. A prognostic lncRNA signature was developed based on likelihood-ratio Cox regression analysis. Survival analysis was used to compare the prognostic efficiencies of our signature and 10 previously reported prognostic gene signatures.

RESULTS

Cox regression analysis on 30 independent datasets showed that the 6-lncRNA signature identified in this study performed as well as five commercially available signatures in recurrence prediction for ER-positive breast cancer. In ER-negative breast cancer, this lncRNA signature was as prognostic as three immune-related gene signatures. Moreover, our lncRNA signature also demonstrated a good capacity to predict recurrence risk for triple-negative breast cancer. Function analysis showed that several lncRNAs in this signature were probably involved in cell proliferation and immune processes.

CONCLUSIONS

A six-LncRNA signature was identified that is prognostic for ER-positive, ER-negative, and triple-negative breast cancers and thus deserves further validation in prospective studies.

Gene expression signatures: A tool for analysis of breast cancer prognosis and therapy

Breast Cancer is the most predominant female cancer in developed as well as developing countries. The treatment strategies of breast cancers depends on an array of factors like age at diagnosis, menstrual status, dietary pattern, immunological response, genetic variations of the cancer cells etc. Recent technological advancements in cancer diagnosis lead to the emergence of gene expression pattern for better understanding of the tumor behavior. It has not only bolstered the prognosis, but also the early diagnosis and therapy. The accuracy in disease prognosis can be boosted when gene expression signatures are combined with traditional clinicopathological features. This review explains how the evolution of gene expression signatures for breast cancers, its advantages and future prospects. In addition, an overview of currently available gene expression signature analysis tools and consolidated information on their current status and specific benefits, that can be availed for breast cancer diagnosis are also discussed.

AR/ER Ratio Correlates with Expression of Proliferation Markers and with Distinct Subset of Breast Tumors

The co-expression of androgen (AR) and estrogen (ER) receptors, in terms of higher AR/ER ratio, has been recently associated with poor outcome in ER-positive (ER+) breast cancer (BC) patients. The aim of this study was to analyze if the biological aggressiveness, underlined in ER+ BC tumors with higher AR/ER ratio, could be due to higher expression of genes related to cell proliferation. On a cohort of 47 ER+ BC patients, the AR/ER ratio was assessed by immunohistochemistry and by mRNA analysis. The expression level of five gene proliferation markers was defined through TaqMan^®-qPCR assays. Results were validated using 979 BC cases obtained from gene expression public databases. ER+ BC tumors with ratios of AR/ER ≥ 2 have higher expression levels of cellular proliferation genes than tumors with ratios of AR/ER < 2, in both the 47 ER+ BC patients (P < 0.001) and in the validation cohort (P = 0.005). Moreover, BC cases with ratios of AR/ER ≥ 2 of the validation cohort were mainly assigned to luminal B and HER2-enriched molecular subtypes, typically characterized by higher proliferation and poorer prognosis. These data suggest that joint routine evaluation of AR and ER expression may identify a unique subset of tumors, which show higher levels of cellular proliferation and therefore a more aggressive behavior.

Emerging immune gene signatures as prognostic or predictive biomarkers in breast cancer

Several multigene assays have been developed to predict the risk of distant recurrence and response to adjuvant therapy in early breast cancer. However, the prognostic or predictive value of current proliferation gene signature-based assays are limited to hormone receptor-positive, human epidermal growth factor receptor 2-negative (HR+/HER2-) early breast cancer. Considerable discordance between the different assays in classifying patients into risk groups has also been reported, thus raising questions about the clinical utility of these assays for individual patients. Therefore, there still remains a need for better prognostic or predictive biomarkers for breast cancer. The role of immune cells comprising tumor microenvironment in tumor progression has been recognized. Accumulating evidences have shown that immune gene signatures and tumor-infiltrating lymphocytes (TILs) can be prognostic or predictive factors in breast cancer, particularly with regard to HER2+ and triple-negative breast cancer. In this review, I summarize current multigene assays for breast cancer and discuss recent progress in identifying novel breast cancer biomarkers, focusing on the emerging importance of immune gene signatures and TILs as prognostic or predictive biomarkers.

Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort

PURPOSE

More than 70% of patients with breast cancer present with node-negative disease, yet all undergo surgical axillary staging. We aimed to define predictors of nodal metastasis using clinicopathological characteristics (CLINICAL), gene expression data (GEX), and mixed features (MIXED) and to identify patients at low risk of metastasis who might be spared sentinel lymph node biopsy (SLNB).Experimental Design: Breast tumors (n = 3,023) from the population-based Sweden Cancerome Analysis Network-Breast initiative were profiled by RNA sequencing and linked to clinicopathologic characteristics. Seven machine-learning models present the discriminative ability of N0/N+ in development (n = 2,278) and independent validation cohorts (n = 745) stratified as ER⁺HER2^-, HER2⁺, and TNBC. Possible SLNB reduction rates are proposed by applying CLINICAL and MIXED predictors.

RESULTS

In the validation cohort, the MIXED predictor showed the highest area under ROC curves to assess nodal metastasis; AUC = 0.72. For the subgroups, the AUCs for MIXED, CLINICAL, and GEX predictors ranged from 0.66 to 0.72, 0.65 to 0.73, and 0.58 to 0.67, respectively. Enriched proliferation metagene and luminal B features were noticed in node-positive ER⁺HER2^- and HER2⁺ tumors, while upregulated basal-like features were observed in node-negative TNBC tumors. The SLNB reduction rates in patients with ER⁺HER2^- tumors were 6% to 7% higher for the MIXED predictor compared with the CLINICAL predictor accepting false negative rates of 5% to 10%.

CONCLUSIONS

Although CLINICAL and MIXED predictors of nodal metastasis had comparable accuracy, the MIXED predictor identified more node-negative patients. This translational approach holds promise for development of classifiers to reduce the rates of SLNB for patients at low risk of nodal involvement.

Schlafen-11 expression is associated with immune signatures and basal-like phenotype in breast cancer

PURPOSE

Breast cancer (BC) is a heterogeneous disorder, with variable response to systemic chemotherapy. Likewise, BC shows highly complex immune activation patterns, only in part reflecting classical histopathological subtyping. Schlafen-11 (SLFN11) is a nuclear protein we independently described as causal factor of sensitivity to DNA damaging agents (DDA) in cancer cell line models. SLFN11 has been reported as a predictive biomarker for DDA and PARP inhibitors in human neoplasms. SLFN11 has been implicated in several immune processes such as thymocyte maturation and antiviral response through the activation of interferon signaling pathway, suggesting its potential relevance as a link between immunity and cancer. In the present work, we investigated the transcriptional landscape of SLFN11, its potential prognostic value, and the clinico-pathological associations with its variability in BC.

METHODS

We assessed SLFN11 determinants in a gene expression meta-set of 5061 breast cancer patients annotated with clinical data and multigene signatures.

RESULTS

We found that 537 transcripts are highly correlated with SLFN11, identifying "immune response", "lymphocyte activation", and "T cell activation" as top Gene Ontology processes. We established a strong association of SLFN11 with stromal signatures of basal-like phenotype and response to chemotherapy in estrogen receptor negative (ER-) BC. We identified a distinct subgroup of patients, characterized by high SLFN11 levels, ER- status, basal-like phenotype, immune activation, and younger age. Finally, we observed an independent positive predictive role for SLFN11 in BC.

CONCLUSIONS

Our findings are suggestive of a relevant role for SLFN11 in BC and its immune and molecular variability.

Unmet Needs in Clinical Research in Breast Cancer: Where Do We Need to Go?

This CCR Focus highlights areas in breast cancer research with the greatest potential for clinical and therapeutic application. The articles in this CCR Focus address the state of the science in a broad range of areas with a focus on "hot" although sometimes controversial topics, unanswered questions, and unmet need. From mutational signatures, the cancer genomic revolution, and new inroads in immunotherapy for breast cancer to unique concerns of vulnerable populations as well as national and global health disparities, these works represent much of the promise of breast cancer research as well as the challenges in the coming years. Each review focuses not only on recent discoveries but also on putting the topic in context, including limitations to overcome. This overview is designed to further contextualize the highlighted issues within the broader research landscape. We also present new information from a poll of ALLIANCE for Clinical Trials in Oncology Breast Committee members regarding the most needed and viable potential future National Cancer Institute (NCI)-supported clinical trials in breast cancer. The great challenge is to translate the potential benefits of greater scientific knowledge reflected in this CCR Focus section into improvements in outcomes for individuals and populations with breast cancer. A unifying theme across the six articles contained in this CCR Focus is the increasingly recognized value and necessity of collaboration across disciplines from bench to bedside to populations. Only continued and iteratively amplified scientific, clinical, and governmental commitment to creating, testing, and implementing new knowledge will reduce the global morbidity and mortality of breast cancer. Clin Cancer Res; 23(11); 2611-6. ©2017 AACRSee all articles in this CCR Focus section, "Breast Cancer Research: From Base Pairs to Populations."

Translational Genomics: Practical Applications of the Genomic Revolution in Breast Cancer

The genomic revolution has fundamentally changed our perception of breast cancer. It is now apparent from DNA-based massively parallel sequencing data that at the genomic level, every breast cancer is unique and shaped by the mutational processes to which it was exposed during its lifetime. More than 90 breast cancer driver genes have been identified as recurrently mutated, and many occur at low frequency across the breast cancer population. Certain cancer genes are associated with traditionally defined histologic subtypes, but genomic intertumoral heterogeneity exists even between cancers that appear the same under the microscope. Most breast cancers contain subclonal populations, many of which harbor driver alterations, and subclonal structure is typically remodeled over time, across metastasis and as a consequence of treatment interventions. Genomics is deepening our understanding of breast cancer biology, contributing to an accelerated phase of targeted drug development and providing insights into resistance mechanisms. Genomics is also providing tools necessary to deliver personalized cancer medicine, but a number of challenges must still be addressed. Clin Cancer Res; 23(11); 2630-9. ©2017 AACRSee all articles in this CCR Focus section, "Breast Cancer Research: From Base Pairs to Populations."

Novel Predictors of Breast Cancer Survival Derived from miRNA Activity Analysis

Purpose: Breast cancer is among the leading causes of cancer-related death; discovery of novel prognostic markers is needed to improve outcomes. Combining systems biology and epidemiology, we investigated miRNA-associated genes and breast cancer survival in a well-characterized population-based study.Experimental Design: A recently developed algorithm, ActMiR, was used to identify key miRNA "activities" corresponding to target gene degradation, which were predictive of breast cancer mortality in published databases. We profiled miRNA-associated genes in tumors from our well-characterized population-based cohort of 606 women with first primary breast cancer. Cox proportional hazards models were used to estimate HRs and 95% confidence intervals (CI), after 15+ years of follow-up with 119 breast cancer-specific deaths.Results: miR-500a activity was identified as a key miRNA for estrogen receptor-positive breast cancer mortality using public databases. From a panel of 161 miR-500a-associated genes profiled, 73 were significantly associated with breast cancer-specific mortality (FDR < 0.05) in our population, among which two clusters were observed to have opposing directions of association. For example, high level of SUSD3 was associated with reduced breast cancer-specific mortality (HR = 0.3; 95% CI, 0.2-0.4), whereas the opposite was observed for TPX2 (HR = 2.7; 95% CI, 1.8-3.9). Most importantly, we identified set of genes for which associations with breast cancer-specific mortality were independent of known prognostic factors, including hormone receptor status and PAM50-derived risk-of-recurrence scores. These results are validated in independent datasets.Conclusions: We identified novel markers that may improve prognostic efficiency while shedding light on molecular mechanisms of breast cancer progression. Clin Cancer Res; 24(3); 581-91. ©2017 AACR.

Gene Expression Signatures and Immunohistochemical Subtypes Add Prognostic Value to Each Other in Breast Cancer Cohorts

Purpose: Gene signatures and Ki67 stratify the same breast tumor into opposing good/poor prognosis groups in approximately 20% of patients. Given this discrepancy, we hypothesized that the combination of a clinically relevant signature and IHC markers may provide more prognostic information than either classifier alone.Experimental Design: We assessed Ki67 alone or combined with ER, PR and HER2 (forming IHC subtypes), and the research versions of the Genomic Grade Index, 70-gene, cell-cycle score, recurrence score (RS), and PAM50 signatures on matching TMA/whole tumor sections and microarray data in two Swedish breast cancer cohorts of 379 and 209 patients, with median follow-up of 12.4 and 12.5 years, respectively. First, we fit Cox proportional hazards models and used the change in likelihood ratio (Δ LR) to determine the additional prognostic information provided by signatures beyond that of (i) Ki67 and (ii) IHC subtypes. Second and uniquely, we then assessed whether signatures could compete well with pathology-based IHC classifiers by calculating the additional prognostic information of Ki67/IHC subtypes beyond signatures.Results: In cohort 1, only RS and PAM50 provided additional prognostic information beyond Ki67 and IHC subtypes (Δ LR-χ2 Ki67: RS = 12.8, PAM50 = 20.7, IHC subtypes: RS = 12.9, PAM50 = 11.7). Conversely, IHC subtypes added prognostic information beyond all signatures except PAM50. Similar results were observed in cohort 2.Conclusions: RS and PAM50 provided more prognostic information than the IHC subtypes in all breast cancer patients; however, the IHC subtypes did not add any prognostic information to PAM50. Clin Cancer Res; 23(24); 7512-20. ©2017 AACR.

MELK: a potential novel therapeutic target for TNBC and other aggressive malignancies

INTRODUCTION

There is an unmet need in triple-negative breast cancer (TNBC) patients for targeted therapies. Maternal embryonic leucine zipper kinase (MELK) is a promising target for inhibition based on the abundance of correlative and functional data supporting its role in various cancer types. Areas covered: This review endeavors to outline the role of MELK in cancer. Studies covering a range of biological functions including proliferation, apoptosis, cancer stem cell phenotypes, epithelial-to-mesenchymal transition, metastasis, and therapy resistance are discussed here in order to understand the potential of MELK as a clinically significant target for TNBC patients. Expert opinion: Targeting MELK may offer a novel therapeutic opportunity in TNBC and other cancers. Despite the abundance of correlative data, there is still much we do not know. There are a lack of potent, specific inhibitors against MELK, as well as an insufficient understanding of MELK's downstream substrates. Addressing these issues is the first step toward identifying a patient population that could benefit from MELK inhibition in combination with other therapies.

Immune cell profiling in cancer: molecular approaches to cell-specific identification

The immune system has many important regulatory roles in cancer development and progression. Given the emergence of effective immune therapies against many cancers, reliable predictors of response are needed. One method of determining response is by evaluating immune cell populations from treated and untreated tumor samples. The amount of material obtained from tumor biopsies can be limited; therefore, gene-based or protein-based analyses may be attractive because they require minimal tissue. Cell-specific signatures are being analyzed with use of the latest technologies, including NanoString’s nCounter technology, intracellular staining flow cytometry, cytometry by time-of-flight, RNA-Seq, and barcoding antibody-based protein arrays. These signatures provide information about the contributions of specific types of immune cells to bulk tumor samples. To date, both tumor tissue and immune cells have been analyzed for molecular expression profiles that can assess genes and proteins that are specific to immune cells, yielding results of varying specificity. Here, we discuss the importance of profiling tumor tissue and immune cells to identify immune-cell-associated genes and proteins and specific gene profiles of immune cells. We also discuss the use of these signatures in cancer treatment and the challenges faced in molecular expression profiling of immune cell populations.

Heterogeneous Enhancement Patterns of Tumor-adjacent Parenchyma at MR Imaging Are Associated with Dysregulated Signaling Pathways and Poor Survival in Breast Cancer

Purpose To identify the molecular basis of quantitative imaging characteristics of tumor-adjacent parenchyma at dynamic contrast material-enhanced magnetic resonance (MR) imaging and to evaluate their prognostic value in breast cancer. Materials and Methods In this institutional review board-approved, HIPAA-compliant study, 10 quantitative imaging features depicting tumor-adjacent parenchymal enhancement patterns were extracted and screened for prognostic features in a discovery cohort of 60 patients. By using data from The Cancer Genome Atlas (TCGA), a radiogenomic map for the tumor-adjacent parenchymal tissue was created and molecular pathways associated with prognostic parenchymal imaging features were identified. Furthermore, a multigene signature of the parenchymal imaging feature was built in a training cohort (n = 126), and its prognostic relevance was evaluated in two independent cohorts (n = 879 and 159). Results One image feature measuring heterogeneity (ie, information measure of correlation) was significantly associated with prognosis (false-discovery rate < 0.1), and at a cutoff of 0.57 stratified patients into two groups with different recurrence-free survival rates (log-rank P = .024). The tumor necrosis factor signaling pathway was identified as the top enriched pathway (hypergeometric P < .0001) among genes associated with the image feature. A 73-gene signature based on the tumor profiles in TCGA achieved good association with the tumor-adjacent parenchymal image feature (R² = 0.873), which stratified patients into groups regarding recurrence-free survival (log-rank P = .029) and overall survival (log-rank P = .042) in an independent TCGA cohort. The prognostic value was confirmed in another independent cohort (Gene Expression Omnibus GSE 1456), with log-rank P = .00058 for recurrence-free survival and log-rank P = .0026 for overall survival. Conclusion Heterogeneous enhancement patterns of tumor-adjacent parenchyma at MR imaging are associated with the tumor necrosis signaling pathway and poor survival in breast cancer. ^© RSNA, 2017 Online supplemental material is available for this article.

Clinical outcomes linked to expression of gene subsets for protein hormones and their cognate receptors from LCM-procured breast carcinoma cells

PURPOSE

Certain peptide hormones and/or their cognate receptors influencing normal cellular pathways also have been detected in breast cancers. The hypothesis is that gene subsets of these regulatory molecules predict risk of breast carcinoma recurrence in patients with primary disease.

METHODS

Gene expression levels of 61 hormones and 81 receptors were determined by microarray with LCM-procured carcinoma cells of 247 de-identified biopsies. Univariable and multivariable Cox regressions were determined using expression levels of each hormone/receptor gene, individually or as a pair.

RESULTS

Molecular signatures for ER+/PR+, ER-/PR-, and ER- carcinoma cells deciphered by LASSO were externally validated at HRs (CI) of 2.8 (1.84-4.4), 1.53 (1.01-2.3), and 1.72 (1.15-2.56), respectively. Using LCM-procured breast carcinoma cells, a 16-gene molecular signature was derived for ER+/PR+ biopsies, whereas a 10-gene signature was deciphered for ER-/PR- cancers. Four genes, POMC, CALCR, AVPR1A, and GH1, of this 10-gene signature were identified in a 6-gene molecular signature for ER- specimens.

CONCLUSIONS

Applying these signatures, Kaplan-Meier plots definitively identified a cohort of patients with either ER-/PR- or ER- carcinomas that exhibited low risk of recurrence. In contrast, the ER+/PR+ signature identified a cohort of patients with high risk of breast cancer recurrence. Each of the three molecular signatures predicted clinical outcomes of breast cancer patients with greater accuracy than observed with either single-gene analysis or by ER/PR protein content alone. Collectively, our results suggest that gene expression profiles of breast carcinomas with suspected poor prognosis (ER-/PR-) have identified a subset of patients with decreased risk of recurrence.

The Role of Proliferation in Determining Response to Neoadjuvant Chemotherapy in Breast Cancer: A Gene Expression-Based Meta-Analysis

PURPOSE

To provide further insight into the role of proliferation and other cellular processes in chemosensitivity and resistance, we evaluated the association of a diverse set of gene expression signatures with response to neoadjuvant chemotherapy (NAC) in breast cancer.

EXPERIMENTAL DESIGN

Expression data from primary breast cancer biopsies for 1,419 patients in 17 studies prior to NAC were identified and aggregated using common normalization procedures. Clinicopathologic characteristics, including response to NAC, were collected. Scores for 125 previously published breast cancer-related gene expression signatures were calculated for each tumor.

RESULTS

Within each receptor-based subgroup or PAM50 subtype, breast tumors with high proliferation signature scores were significantly more likely to achieve pathologic complete response to NAC. To distinguish "proliferation-associated" from "proliferation-independent" signatures, we used correlation and linear modeling approaches. Most signatures associated with response to NAC were proliferation associated: 90.5% (38/42) in ER⁺/HER2^- and 63.3% (38/60) in triple-negative breast cancer (TNBC). Proliferation-independent signatures predictive of response to NAC in ER⁺/HER2^- breast cancer were related to immune activity, while those in TNBC comprised a diverse set of signatures, including immune, DNA damage, signaling pathways (PI3K, AKT, Ras, and EGFR), and "stemness" phenotypes.

CONCLUSIONS

Proliferation differences account for the vast majority of predictive capacity of gene expression signatures in neoadjuvant chemosensitivity for ER⁺/HER2^- breast cancers and, to a lesser extent, TNBCs. Immune activation signatures are proliferation-independent predictors of pathologic complete response in ER⁺/HER2^- breast cancers. In TNBCs, significant proliferation-independent signatures include gene sets that represent a diverse set of cellular processes. Clin Cancer Res; 22(24); 6039-50. ©2016 AACR.