A novel prognostic two-gene signature for triple negative breast cancer

• Published in: Modern pathology Vol. 33; no. 11; pp. 2208 - 2220
• Main Authors: Alsaleem, Mansour A., Ball, Graham, Toss, Michael S., Raafat, Sara, Aleskandarany, Mohammed, Joseph, Chitra, Ogden, Angela, Bhattarai, Shristi, Rida, Padmashree C.G., Khani, Francesca, Davis, Melissa, Elemento, Olivier, Aneja, Ritu, Ellis, Ian O., Green, Andrew, Mongan, Nigel P., Rakha, Emad
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01-11-2020; Nature Publishing Group US; Elsevier Limited
• Subjects: 38; 38/1; 38/91; 631/208/514/2254; 631/67/1347; 692/53/2422; 692/53/2423; 82/80; Biomarkers, Tumor; Breast cancer; Chemotherapy; Cytotoxicity; Databases, Genetic; Female; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Genomes; Genotypes; Humans; Immunohistochemistry; Laboratory Medicine; Medical prognosis; Medicine; Medicine & Public Health; Metastases; Metastasis; Neural networks; Next-generation sequencing; Pathology; Patients; Prognosis; Protein expression; Regression analysis; Retrospective Studies; Ribonucleic acid; RNA; Survival; Survival Rate; Transcriptome; Triple Negative Breast Neoplasms - genetics; Triple Negative Breast Neoplasms - mortality; Triple Negative Breast Neoplasms - pathology
• ISSN: 0893-3952; 1530-0285
• DOI: 10.1038/s41379-020-0563-7

The absence of a robust risk stratification tool for triple negative breast cancer (TNBC) underlies imprecise and nonselective treatment of these patients with cytotoxic chemotherapy. This study aimed to interrogate transcriptomes of TNBC resected samples using next generation sequencing to identify novel biomarkers associated with disease outcomes. A subset of cases (n = 112) from a large, well-characterized cohort of primary TNBC (n = 333) were subjected to RNA-sequencing. Reads were aligned to the human reference genome (GRCH38.83) using the STAR aligner and gene expression quantified using HTSEQ. We identified genes associated with distant metastasis-free survival and breast cancer-specific survival by applying supervised artificial neural network analysis with gene selection to the RNA-sequencing data. The prognostic ability of these genes was validated using the Breast Cancer Gene-Expression Miner v4. 0 and Genotype 2 outcome datasets. Multivariate Cox regression analysis identified a prognostic gene signature that was independently associated with poor prognosis. Finally, we corroborated our results from the two-gene prognostic signature by their protein expression using immunohistochemistry. Artificial neural network identified two gene panels that strongly predicted distant metastasis-free survival and breast cancer-specific survival. Univariate Cox regression analysis of 21 genes common to both panels revealed that the expression level of eight genes was independently associated with poor prognosis (p < 0.05). Adjusting for clinicopathological factors including patient's age, grade, nodal stage, tumor size, and lymphovascular invasion using multivariate Cox regression analysis yielded a two-gene prognostic signature (ACSM4 and SPDYC), which was associated with poor prognosis (p < 0.05) independent of other prognostic variables. We validated the protein expression of these two genes, and it was significantly associated with patient outcome in both independent and combined manner (p < 0.05). Our study identifies a prognostic gene signature that can predict prognosis in TNBC patients and could potentially be used to guide the clinical management of TNBC patients.