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

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

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Authors

Mansour A Alsaleem

Graham Ball

Michael S Toss

Sara Raafat

Mohammed Aleskandarany

Angela Ogden

Shristi Bhattarai

Padmashree C G Rida

Francesca Khani

Melissa Davis

Olivier Elemento

Ritu Aneja

NIGEL MONGAN nigel.mongan@nottingham.ac.uk
Professor of Oncology

EMAD RAKHA Emad.Rakha@nottingham.ac.uk
Professor of Breast Cancer Pathology



Abstract

The absence of a robust risk stratification tool for triple negative breast cancer (TNBC) underlies imprecise and non-selective 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

Citation

Alsaleem, M. A., Ball, G., Toss, M. S., Raafat, S., Aleskandarany, M., Joseph, C., …Rakha, E. (2020). A novel prognostic two-gene signature for triple negative breast cancer. Modern Pathology, 33, 2208–2220. https://doi.org/10.1038/s41379-020-0563-7

Journal Article Type Article
Acceptance Date Apr 17, 2020
Online Publication Date May 13, 2020
Publication Date 2020-11
Deposit Date Apr 22, 2020
Publicly Available Date Mar 29, 2024
Journal Modern Pathology
Print ISSN 0893-3952
Electronic ISSN 1530-0285
Publisher Nature Publishing Group
Peer Reviewed Peer Reviewed
Volume 33
Pages 2208–2220
DOI https://doi.org/10.1038/s41379-020-0563-7
Keywords triple negative breast cancer; TNBC; prognostic gene signature; ANN; ACSM4; SPDYC; NGS
Public URL https://nottingham-repository.worktribe.com/output/4325602
Publisher URL https://www.nature.com/articles/s41379-020-0563-7
Additional Information Received: 21 November 2019; Revised: 17 April 2020; Accepted: 17 April 2020; First Online: 13 May 2020; : ; : The authors declare that they have no conflict of interest.; : This work obtained ethics approval by the North West – Greater Manchester Central Research Ethics Committee under the title; Nottingham Health Science Biobank (NHSB), reference number 15/NW/0685.; : Informed consent was obtained from all individuals prior to surgery to use their tissue materials in research. All samples used in this study were pseudo-anonymized and collected prior to 2006 and stored in compliance with the UK Human Tissue Act.

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