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

Alsaleem, MA, Ball, G ORCID: https://orcid.org/0000-0001-5828-7129, Toss, MS, Raafat, S, Aleskandarany, M, Joseph, C, Ogden, A, Bhattarai, S, Rida, PCG, Khani, F, Davis, M, Elemento, O, Aneja, R, Ellis, IO, Green, A, Mongan, NP and Rakha, E, 2020. A novel prognostic two-gene signature for triple negative breast cancer. Modern Pathology. ISSN 0893-3952

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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

Item Type:	Journal article
Publication Title:	Modern Pathology
Creators:	Alsaleem, M.A., Ball, G., Toss, M.S., Raafat, S., Aleskandarany, M., Joseph, C., Ogden, A., Bhattarai, S., Rida, P.C.G., Khani, F., Davis, M., Elemento, O., Aneja, R., Ellis, I.O., Green, A., Mongan, N.P. and Rakha, E.
Publisher:	Springer Nature
Date:	13 May 2020
ISSN:	0893-3952
Identifiers:	Number Type 10.1038/s41379-020-0563-7 DOI 1347756 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Jill Tomkinson
Date Added:	07 Sep 2020 15:45
Last Modified:	31 May 2021 15:13
URI:	https://irep.ntu.ac.uk/id/eprint/40634

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