Pan-Cancer and Single-Cell Modeling of Genomic Alterations Through Gene Expression

Frontiers in Genetics
Daniele MercatelliFederico Manuel Giorgi

Abstract

Cancer is a disease often characterized by the presence of multiple genomic alterations, which trigger altered transcriptional patterns and gene expression, which in turn sustain the processes of tumorigenesis, tumor progression, and tumor maintenance. The links between genomic alterations and gene expression profiles can be utilized as the basis to build specific molecular tumorigenic relationships. In this study, we perform pan-cancer predictions of the presence of single somatic mutations and copy number variations using machine learning approaches on gene expression profiles. We show that gene expression can be used to predict genomic alterations in every tumor type, where some alterations are more predictable than others. We propose gene aggregation as a tool to improve the accuracy of alteration prediction models from gene expression profiles. Ultimately, we show how this principle can be beneficial in intrinsically noisy datasets, such as those based on single-cell sequencing.

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Citations

Apr 5, 2020·Journal of Clinical Medicine·Pietro H GuzziFederico M Giorgi
Apr 2, 2020·Bioinformatics·Daniele MercatelliFederico M Giorgi

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Datasets Mentioned

BETA
GSE92872
GSM3375483

Methods Mentioned

BETA
single-cell sequencing
RNA-Seq
CROP-Seq
scRNA-Seq
PLATE-Seq

Software Mentioned

MutSig
R packages
GenomicRanges R package
R affy package
Firehose
AP
VIPER
gbm
WGCNA
ARACNe

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