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

Autor: Alsaleem MA; Nottingham Breast Cancer Research Centre, Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK.; Faculty of Applied Medical Sciences, Onizah Community College, Qassim University, Qassim, Saudi Arabia., Ball G; John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham, UK., Toss MS; Nottingham Breast Cancer Research Centre, Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK., Raafat S; Nottingham Breast Cancer Research Centre, Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK., Aleskandarany M; Nottingham Breast Cancer Research Centre, Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK.; Faculty of Medicine, Menoufyia University, Shebin El Kom, Egypt., Joseph C; Nottingham Breast Cancer Research Centre, Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK., Ogden A; Department of Biology, Georgia State University, Atlanta, GA, USA., Bhattarai S; Department of Biology, Georgia State University, Atlanta, GA, USA., Rida PCG; Department of Biology, Georgia State University, Atlanta, GA, USA., Khani F; Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, NY, USA., Davis M; Department of Genetics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, USA., Elemento O; Department of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine of Cornell University, New York, NY, USA., Aneja R; Department of Biology, Georgia State University, Atlanta, GA, USA., Ellis IO; Nottingham Breast Cancer Research Centre, Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK., Green A; Nottingham Breast Cancer Research Centre, Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK., Mongan NP; Faculty of Medicine and Health Sciences, School of Veterinary Medicine and Science, University of Nottingham Biodiscovery Institute, University of Nottingham, Nottingham, UK.; Department of Pharmacology, Weill Cornell Medicine, New York, NY, USA., Rakha E; Nottingham Breast Cancer Research Centre, Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK. Emad.Rakha@nottingham.ac.uk.; Faculty of Medicine, Menoufyia University, Shebin El Kom, Egypt. Emad.Rakha@nottingham.ac.uk.; Department of Histopathology, Division of Cancer and Stem Cells, School of Medicine, The University of Nottingham and Nottingham University Hospitals NHS Trust, Nottingham City Hospital, Nottingham, UK. Emad.Rakha@nottingham.ac.uk.
Jazyk: angličtina
Zdroj: Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc [Mod Pathol] 2020 Nov; Vol. 33 (11), pp. 2208-2220. Date of Electronic Publication: 2020 May 13.
DOI: 10.1038/s41379-020-0563-7
Abstrakt: 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.
Databáze: MEDLINE
Externí odkaz: