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Open AccessArticle

Defining Signatures of Arm-Wise Copy Number Change and Their Associated Drivers in Kidney Cancers

1
Bioinformatics Lab, School of Life Sciences, University of Sussex, Falmer, Brighton BN1 9QG, UK
2
Division of Cancer Biology, Institute of Cancer Research, Chester Beatty Laboratories, 237 Fulham Road, London SW3 6JB, UK
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2019, 20(22), 5762; https://doi.org/10.3390/ijms20225762
Received: 19 September 2019 / Revised: 13 November 2019 / Accepted: 14 November 2019 / Published: 16 November 2019
(This article belongs to the Special Issue In Silico Analyses: Translating and Making Sense of Omics Data)
Using pan-cancer data from The Cancer Genome Atlas (TCGA), we investigated how patterns in copy number alterations in cancer cells vary both by tissue type and as a function of genetic alteration. We find that patterns in both chromosomal ploidy and individual arm copy number are dependent on tumour type. We highlight for example, the significant losses in chromosome arm 3p and the gain of ploidy in 5q in kidney clear cell renal cell carcinoma tissue samples. We find that specific gene mutations are associated with genome-wide copy number changes. Using signatures derived from non-negative factorisation, we also find gene mutations that are associated with particular patterns of ploidy change. Finally, utilising a set of machine learning classifiers, we successfully predicted the presence of mutated genes in a sample using arm-wise copy number patterns as features. This demonstrates that mutations in specific genes are correlated and may lead to specific patterns of ploidy loss and gain across chromosome arms. Using these same classifiers, we highlight which arms are most predictive of commonly mutated genes in kidney renal clear cell carcinoma (KIRC). View Full-Text
Keywords: aneuploidy; copy number; non-negative matrix factorisation; mutational signature; machine learning aneuploidy; copy number; non-negative matrix factorisation; mutational signature; machine learning
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Benstead-Hume, G.; Wooller, S.K.; Downs, J.A.; Pearl, F.M.G. Defining Signatures of Arm-Wise Copy Number Change and Their Associated Drivers in Kidney Cancers. Int. J. Mol. Sci. 2019, 20, 5762.

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