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Establishment of a predictive clinicogenomic model for FEC100 regimen in node-positive breast cancer patients

ABSTRACT: Breast cancer is a hugely heterogeneous disease, and markers for disease subtypes and therapy response remain poorly defined. For that reason, we employed a retrospective study in node-positive breast cancer to identify molecular signatures of gene expression correlating with metastatic free survival. Patients were primarily included in FEC100 (fluorouracil, epirubicin and cyclophosphamide) arms of two multicentric phase III clinical trials (PACS01 and PEGASE01 - FNCLCC). Data from nylon microarrays containing 8.032 cDNA unique sequences, representing 5.776 distinct genes, have been used to develop a predictive model for treatment outcome. We obtained the gene expression profiles of 150 population-based patients, and used stringent univariate selection technique based on Cox regression combined with principal component analysis to identify signature associated with prognosis and impact of FEC100 chemotherapy. Our work identified a gene-signature of metastatic relapse. Most of the 14 selected genes have a clear role in breast cancer, neoplasia or chemotherapy resistance. Furthermore, we showed the interest of combining transcriptomic data with clinical data into a clinicogenomic model for patients subtyping. The described model adds predictive accuracy to that provided by the well established Nottingham prognostic index or by the genomic predictor alone. Keywords: Gene-expression profiling

ORGANISM(S): Homo sapiens

PROVIDER: GSE7017 | GEO | 2007/10/18

SECONDARY ACCESSION(S): PRJNA98361

REPOSITORIES: GEO

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Project description:PURPOSE: Validated biomarkers predictive of response/resistance to anthracyclines in breast cancer are currently lacking. The neoadjuvant TOP trial, in which patients with estrogen receptor (ER)-negative tumors were treated with anthracycline (epirubicin) monotherapy, was specifically designed to evaluate the predictive value of topoisomerase IIα (TOP2A) and to develop a gene expression signature to identify those patients who do not benefit from anthracyclines. METHODS: The TOP trial included 149 patients, of which 141 were evaluable for response prediction analyses. The primary endpoint was pathological complete response (pCR). TOP2A and gene expression profiles were evaluated using pre-epirubicin biopsies. Gene expression data from ER-negative samples of the EORTC 10994/BIG 00-01 and MDACC 2003-0321 neoadjuvant trials were used for validation purposes. RESULTS: A pCR was obtained in 14% of the evaluable TOP patients. TOP2A amplification, but not protein overexpression, was significantly associated with pCR (p=0.001 and 0.22). We developed an “anthracycline-based score (A-Score)” that combines three signatures: a TOP2A gene signature and two previously published signatures related to tumor invasion and immune response. The A-Score was characterized by a high negative predictive value (NPV=0.98 [95% CI: 0.90-1.00]) overall, and in the HER2-negative and HER2-positive subpopulations. Its performance was independently confirmed in the anthracycline-based (FAC/FEC) arms of the two validation trials (BIG 00-01: 0.80 [0.61-0.92] and MDACC 2003-0321: 1.00 [0.80-1.00]). CONCLUSION: Given its high NPV, the A-Score could become, if further validated, a useful clinical tool to identify those patients who do not benefit from anthracyclines and could therefore be spared the non-negligible side effects. Predicting the efficacy of anthracyclines (epirubicin) in breast cancer (BC) patients (TOP trial) 120 microarray experiments from primary ER-negative breast tumors of anthracycline-treated patients. No replicate, no reference sample.

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Establishment of a predictive clinicogenomic model for FEC100 regimen in node-positive breast cancer patients

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