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Identification of prognosis-relevant subgroups in patients with chemoresistant triple-negative breast cancer.

ABSTRACT: Patients with triple-negative breast cancer (TNBC) and residual disease after neoadjuvant chemotherapy generally have worse outcome; however, some patients with residual tumor after neoadjuvant chemotherapy do not relapse. We hypothesize that there are subgroups of patients with chemoresistant TNBC with different prognosis.Forty-nine chemoresistant cases from 111 patients with TNBC treated with neoadjuvant chemotherapy (M.D. Anderson Cancer Center, Houston, TX) constituted the discovery cohort, and 25 chemoresistant samples from 47 neoadjuvant chemotherapy-treated TNBC (The Methodist Hospital, Houston, TX) were chosen for validation. Extended validation was carried out in 269 operable TNBC predicted to be chemoresistant by expression pattern from published datasets.We established a seven-gene prognostic signature using dChip and gene set enrichment analyses. In the independent validation cohort, the classifier predicted correctly with positive predictive value of 75.0% and negative predictive value (i.e., relapse-free survival; RFS) of 76.9% at 3 years. Those predicted to relapse had a HR of 4.67 [95% confidence interval (CI): 1.27-17.15] for relapse in 3 years. In extended validation, patients predicted not to relapse exhibited 3-year RFS of 78.9%, whereas the 3-year RFS was 48.5% for patients predicted to relapse, with HR of 2.61 (95% CI: 1.52-4.49). The TNBC subgroup that predicted to have relatively favorable prognosis was characterized by high expression of "luminal-like" genes [androgen-receptor (AR) and GATA3], whereas the subgroup with worse prognosis was characterized by expression of cancer stem-cell markers.We developed a clinically relevant signature for patients with chemoresistant TNBC. For these women, new therapeutic strategies like targeting AR activation or cancer stem cells may need to be developed.

SUBMITTER: Yu KD

PROVIDER: S-EPMC3655097 | biostudies-literature | 2013 May

REPOSITORIES: biostudies-literature

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Identification of prognosis-relevant subgroups in patients with chemoresistant triple-negative breast cancer.

Yu Ke-Da KD Zhu Rui R Zhan Ming M Rodriguez Angel A AA Yang Wei W Wong Stephen S Makris Andreas A Lehmann Brian D BD Chen Xi X Mayer Ingrid I Pietenpol Jennifer A JA Shao Zhi-Ming ZM Symmans W Fraser WF Chang Jenny C JC

Clinical cancer research : an official journal of the American Association for Cancer Research 20130402 10

<h4>Purpose</h4>Patients with triple-negative breast cancer (TNBC) and residual disease after neoadjuvant chemotherapy generally have worse outcome; however, some patients with residual tumor after neoadjuvant chemotherapy do not relapse. We hypothesize that there are subgroups of patients with chemoresistant TNBC with different prognosis.<h4>Experimental design</h4>Forty-nine chemoresistant cases from 111 patients with TNBC treated with neoadjuvant chemotherapy (M.D. Anderson Cancer Center, Hou ...[more]

PMID: 23549873

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Project description:PurposeWhile chemotherapy remains the standard treatment for triple-negative breast cancer (TNBC), identifying and managing chemoresistant tumors has proven elusive. We sought to discover hallmarks and therapeutically actionable features of refractory TNBC through molecular analysis of primary chemoresistant TNBC specimens.Experimental designWe performed transcriptional profiling of tumors from a phase II clinical trial of platinum chemotherapy for advanced TNBC (TBCRC-009), revealing a gene expression signature that identified de novo chemorefractory tumors. We then employed pharmacogenomic data mining, proteomic and other molecular studies to define the therapeutic vulnerabilities of these tumors.ResultsWe reveal the RAS-GTPase-activating protein (RAS-GAP) RASAL2 as an upregulated factor that mediates chemotherapy resistance but also an exquisite collateral sensitivity to combination MAP kinase kinase (MEK1/2) and EGFR inhibitors in TNBC. Mechanistically, RASAL2 GAP activity is required to confer kinase inhibitor sensitivity, as RASAL2-high TNBCs sustain basal RAS activity through suppression of negative feedback regulators SPRY1/2, together with EGFR upregulation. Consequently, RASAL2 expression results in failed feedback compensation upon co-inhibition of MEK1/2 and EGFR that induces synergistic apoptosis in vitro and in vivo. In patients with TNBC, high RASAL2 levels predict clinical chemotherapy response and long-term outcomes, and are associated via direct transcriptional regulation with activated oncogenic Yes-Associated Protein (YAP). Accordingly, chemorefractory patient-derived TNBC models exhibit YAP activation, high RASAL2 expression, and tumor regression in response to MEK/EGFR inhibitor combinations despite well-tolerated intermittent dosing.ConclusionsThese findings identify RASAL2 as a mediator of TNBC chemoresistance that rewires MAPK feedback and cross-talk to confer profound collateral sensitivity to combination MEK1/2 and EGFR inhibitors.

Project description:BackgroundAnti-Androgen Receptor (AR) therapy holds promise for a subset of AR expressing triple-negative breast cancer (TNBC) patients. However, current AR assays are suboptimal in detecting the dynamic range of AR expression, contributing to its controversial role in TNBC disease prognosis. This study is aimed at evaluating the feasibility of qRT-PCR to sensitively and robustly detect AR mRNA levels for prognostication.MethodsmRNA expression profiling was performed on FFPE blocks from a retrospective cohort of 101 TNBC patients using qRT-PCR and compared with AR protein expression by immunohistochemistry . Statistical analyses included Spearman's rank correlation, Chi-square and Kaplan-Meier analyses. Distant Metastasis Free Survival was used as the end point in survival analysis.ResultsAR mRNA expression was observed in 34/101 patients (34%) whereas 12/80 cases (15%) were positive by IHC. qRT-PCR could thus detect more AR positive patients as compared to IHC, with 75% (9/12) concordance between the two methods. Co-expression of GATA3 and FOXA1 mRNA was observed in 85 and 88% of AR mRNA positive tumors, respectively. AR mRNA positivity was significantly correlated with age at disease onset (p = 0.02), high FOXA1/GATA3 (p < 0.05) and distant recurrence. AR mRNA positive patients had poorer DMFS (43%; p = 0.002). DMFS dropped further to 26% (p = 0.006) in AR (+)/high FOXA1/GATA3 patients. AR mRNA expression together with node positivity had the worst DMFS (23%; p < 0.0001) compared to patients who were either positive for any one of these, or negative for both AR and node status. Low Ki67 mRNA with AR mRNA positivity also had poorer DMFS (39%; p = 0.001) compared to patients expressing low Ki67 with no AR mRNA expression.ConclusionqRT-PCR was more sensitive and reliable in detecting the dynamic expression levels of AR compared to IHC and this variation could be explained by the higher sensitivity of the former method. High AR mRNA expression was strongly associated with expression of AR protein, high FOXA1/GATA3 mRNA, and with poor prognosis. qRT-PCR was more efficient in detecting the AR positive cases compared to IHC. A distinct signature involving high GATA3/FOXA1, low Ki67, and node positivity in AR mRNA positive tumors correlated with poor prognosis. Thus, AR mRNA screening can serve as an effective prognostic marker along with offering potential targeted therapy options for TNBC.

Project description:BackgroundTriple-negative breast cancer (TNBC) is a very heterogeneous disease. Several gene expression and mutation profiling approaches were used to classify it, and all converged to the identification of distinct molecular subtypes, with some overlapping across different approaches. However, a standardised tool to routinely classify TNBC in the clinics and guide personalised treatment is lacking. We aimed at defining a specific gene signature for each of the six TNBC subtypes proposed by Lehman et al. in 2011 (basal-like 1 (BL1); basal-like 2 (BL2); mesenchymal (M); immunomodulatory (IM); mesenchymal stem-like (MSL); and luminal androgen receptor (LAR)), to be able to accurately predict them.MethodsLehman's TNBCtype subtyping tool was applied to RNA-sequencing data from 482 TNBC (GSE164458), and a minimal subtype-specific gene signature was defined by combining two class comparison techniques with seven attribute selection methods. Several machine learning algorithms for subtype prediction were used, and the best classifier was applied on microarray data from 72 Italian TNBC and on the TNBC subset of the BRCA-TCGA data set.ResultsWe identified two signatures with the 120 and 81 top up- and downregulated genes that define the six TNBC subtypes, with prediction accuracy ranging from 88.6 to 89.4%, and even improving after removal of the least important genes. Network analysis was used to identify highly interconnected genes within each subgroup. Two druggable matrix metalloproteinases were found in the BL1 and BL2 subsets, and several druggable targets were complementary to androgen receptor or aromatase in the LAR subset. Several secondary drug-target interactions were found among the upregulated genes in the M, IM and MSL subsets.ConclusionsOur study took full advantage of available TNBC data sets to stratify samples and genes into distinct subtypes, according to gene expression profiles. The development of a data mining approach to acquire a large amount of information from several data sets has allowed us to identify a well-determined minimal number of genes that may help in the recognition of TNBC subtypes. These genes, most of which have been previously found to be associated with breast cancer, have the potential to become novel diagnostic markers and/or therapeutic targets for specific TNBC subsets.

Project description:At present, a large number of exciting results have been found regarding energy metabolism within the triple‑negative breast cancer (TNBC) field. Apart from aerobic glycolysis, a number of other catabolic pathways have also been demonstrated to participate in energy generation. However, the prognostic value of energy metabolism for TNBC currently remains unclear. In the present study, the association between gene expression profiles of energy metabolism and outcomes in patients with TNBC was examined using datasets obtained from the Gene Expression Omnibus and The Cancer Genome Atlas. In total, four robust TNBC subtypes were identified on the basis of negative matrix factorization clustering and gene expression patterns, which exhibited distinct immunological, molecular and prognostic (disease‑free survival) features. The differentially expressed genes were subsequently identified from the subgroup that demonstrated the poorest prognosis compared with the remaining 3 subgroups, where their biological functions were assessed further by means of gene ontology enrichment analysis. Any signatures found to be associated with energy metabolism were then established using the Cox proportional hazards model to assess patient prognosis. According to results of Kaplan‑Meier analysis, the constructed signature consisting of eight genes that were associated with energy metabolism distinguished patient outcomes into low‑ and high‑risk groups. In addition, this signature, which was found to be markedly associated with the clinical characteristics of the patients, served as an independent factor in predicting TNBC patient prognosis. According to gene set enrichment analysis, the gene sets related to the high‑risk group participated in the MAPK signal transduction pathway, focal adhesion and extracellular matrix receptor interaction, whilst those related to the low‑risk group were revealed to be mainly associated with mismatch repair and propanoate metabolism. Findings from the present study shed new light on the role of energy metabolism within TNBC, where the eight‑gene signature associated with energy metabolism constructed can be utilized as a new prognostic marker for predicting survival in patients with TNBC.

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Identification of prognosis-relevant subgroups in patients with chemoresistant triple-negative breast cancer.

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Identification of prognosis-relevant subgroups in patients with chemoresistant triple-negative breast cancer.

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