Using sigLASSO to optimize cancer mutation signatures jointly with sampling likelihood.

Nature Communications
Shantao LiMark B Gerstein

Abstract

Multiple mutational processes drive carcinogenesis, leaving characteristic signatures in tumor genomes. Determining the active signatures from a full repertoire of potential ones helps elucidate mechanisms of cancer development. This involves optimally decomposing the counts of cancer mutations, tabulated according to their trinucleotide context, into a linear combination of known signatures. Here, we develop sigLASSO (a software tool at github.com/gersteinlab/siglasso) to carry out this optimization efficiently. sigLASSO has four key aspects: (1) It jointly optimizes the likelihood of sampling and signature fitting, by explicitly factoring multinomial sampling into the objective function. This is particularly important when mutation counts are low and sampling variance is high (e.g., in exome sequencing). (2) sigLASSO uses L1 regularization to parsimoniously assign signatures, leading to sparse and interpretable solutions. (3) It fine-tunes model complexity, informed by data scale and biological priors. (4) Consequently, sigLASSO can assess model uncertainty and abstain from making assignments in low-confidence contexts.

References

May 23, 2012·Cell·Serena Nik-ZainalUNKNOWN Breast Cancer Working Group of the International Cancer Genome Consortium
Jan 16, 2013·Cell Reports·Ludmil B AlexandrovMichael R Stratton
Aug 16, 2013·Nature·Ludmil B AlexandrovMichael R Stratton
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Citations

Sep 2, 2021·Annual Review of Biomedical Data Science·Yoo-Ah KimTeresa M Przytycka
Nov 3, 2021·Genome Medicine·Itay SasonRoded Sharan
Nov 5, 2021·NAR Genomics and Bioinformatics·Taro Matsutani, Michiaki Hamada

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Methods Mentioned

BETA
deamination
PCA

Software Mentioned

R package “ microbenchmark ”
mathop
siglasso
COSMIC
LASSO
SparseSignatures
SigProfiler
deconstructSigs
R
whichSignatures

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