Re-searching anthracycline therapy

A multifactorial 'Consensus Signature' by in silico analysis to predict response to neoadjuvant anthracycline-based chemotherapy in triple-negative breast cancer

BACKGROUND

Owing to the complex processes required for anthracycline-induced cytotoxicity, a prospectively defined multifactorial Consensus Signature (ConSig) might improve prediction of anthracycline response in triple-negative breast cancer (TNBC) patients, whose only standard systemic treatment option is chemotherapy.

AIMS

We aimed to construct and evaluate a multifactorial signature, comprising measures of each function required for anthracycline sensitivity in TNBC.

METHODS

ConSigs were constructed based on five steps required for anthracycline function: drug penetration, nuclear topoisomerase IIα (topoIIα) protein location, increased topoIIα messenger RNA (mRNA) expression, apoptosis induction, and immune activation measured by, respectively, HIF1α or SHARP1 signature, LAPTM4B mRNA, topoIIα mRNA, Minimal Gene signature or YWHAZ mRNA, and STAT1 signature. TNBC patients treated with neoadjuvant anthracycline-based chemotherapy without taxane were identified from publicly available gene expression data derived with Affymetrix HG-U133 arrays (training set). In silico analyses of correlation between gene expression data and pathological complete response (pCR) were performed using receiver-operating characteristic curves. To determine anthracycline specificity, ConSigs were assessed in patients treated with anthracycline plus taxane. Specificity, sensitivity, positive and negative predictive value, and odds ratio (OR) were calculated for ConSigs. Analyses were repeated in two validation gene expression data sets derived using different microarray platforms.

RESULTS

In the training set, 29 of 147 patients had pCR after anthracycline-based chemotherapy. Various combinations of components were evaluated, with the most powerful anthracycline response predictors being ConSig1: (STAT1+topoIIα mRNA+LAPTM4B) and ConSig2: (STAT1+topoIIα mRNA+HIF1α). ConSig1 demonstrated high negative predictive value (85%) and high OR for no pCR (3.18) and outperformed ConSig2 in validation sets for anthracycline specificity.

CONCLUSIONS

With further validation, ConSig1 may help refine selection of TNBC patients for anthracycline chemotherapy.

BCL2 is an independent predictor of outcome in basal-like triple-negative breast cancers treated with adjuvant anthracycline-based chemotherapy

Neither targeted therapies nor predictors for chemotherapy sensitivity are available for triple-negative breast cancer (TNBC). Our study included 187 patients with TNBC, 164 of whom were treated with anthracycline-based adjuvant chemotherapy. Eleven molecular biomarkers were analyzed. BCL2, epidermal growth factor receptor (EGFR), MYC, TOP2A, and Ki-67 protein expression was evaluated by immunohistochemistry. The status of the EGFR, MYC, and TOP2A genes and chromosomes 7, 8, and 17 was assessed using fluorescence in situ hybridization. High BCL2 expression predicted poor relapse-free survival (RFS) in patients treated with anthracycline-based adjuvant chemotherapy (p = 0.035), poor breast cancer-specific survival (BCSS) (p = 0.048), and a trend to poor overall survival (OS) (p = 0.085). High levels of BCL2 expression predicted poor OS in basal-like (BL) TNBC patients treated with adjuvant anthracycline-based regimens (log-rank p = 0.033, hazard ratio (HR) 3.04, 95 % confidence interval (CI) 1.04-8.91) and a trend to poor RFS (log-rank p = 0.079) and poor BCSS (log-rank p = 0.056). Multivariate analysis showed that BCL2 status, tumor size, and nodal status all had independent predictive significance for RFS (p = 0.005, p = 0.091, p = 0.003, respectively; likelihood ratio test for the whole model, p = 0.003), BCSS (p = 0.012, p = 0.077, p = 0.01, respectively; likelihood ratio test for the whole model, p = 0.016), and OS (p = 0.008, p = 0.004, p = 0.004, respectively; likelihood ratio test for the whole model, p = 0.0006). Similarly, multivariate analysis for BL TNBC showed BCL2, tumor size, and nodal status all had independent predictive significance for RFS (likelihood ratio test for the whole model, p = 0.00125), BCSS (p = 0.00035), and OS (p = 0.00063). High EGFR expression was associated with poor BCSS (p = 0.039) in patients treated with anthracycline-based adjuvant chemotherapy. Patients who underwent anthracycline-based adjuvant chemotherapy and exhibited CMYC amplification had a trend to worse BCSS (p = 0.066). In conclusion, high BCL2 expression is a significant independent predictor of poor outcome in TNBC patients treated with anthracycline-based adjuvant chemotherapy, and this is the first study showing the BCL2 prediction in BL TNBC. BCL2 expression analysis could facilitate decision making on adjuvant treatment in TNBC patients.

Integrating Breast Cancer Genetics into Clinical Practice

Breast cancer prognosis and treatment is guided by traditional clinicopathological parameters and individual molecular markers. Despite the remarkable advances in our scientific understanding of breast cancer genetics, the impact of such information on medical care has, to date, been modest. Although the use of simple genetics is already in vogue in clinical practice, the concept of molecular profiling and multiparameter gene classifiers was raised after the introduction of the high-throughput gene expression microarrays. This technology, in addition to highlighting the molecular heterogeneity of breast cancer, has led to the development of prognostic and predictive gene signatures. Studies are underway to assess the clinical validity and clinical utility of these multigene assays and their incorporation into clinical practice. This article reviews the current status and projected future use of genetics and genomics in breast cancer management and their impact on the refinement of risk stratification to permit individualized and patient-tailored therapy. Limitations based on our current scientific understanding and realistic expectations are also explored.