American Association for Cancer Research
Browse

Supplementary Figure 7 from Diffsig: Associating Risk Factors with Mutational Signatures

Download (410.45 kB)
journal contribution
posted on 2024-05-01, 07:43 authored by Ji-Eun Park, Markia A. Smith, Sarah C. Van Alsten, Andrea Walens, Di Wu, Katherine A. Hoadley, Melissa A. Troester, Michael I. Love

This figure shows TCGA results with a different set of breast cancer signatures

Funding

National Institute of Environmental Health Sciences (NIEHS)

United States Department of Health and Human Services

Find out more...

National Cancer Institute (NCI)

United States Department of Health and Human Services

Find out more...

History

ARTICLE ABSTRACT

Somatic mutational signatures elucidate molecular vulnerabilities to therapy, and therefore detecting signatures and classifying tumors with respect to signatures has clinical value. However, identifying the etiology of the mutational signatures remains a statistical challenge, with both small sample sizes and high variability in classification algorithms posing barriers. As a result, few signatures have been strongly linked to particular risk factors. Here, we develop a statistical model, Diffsig, for estimating the association of one or more continuous or categorical risk factors with DNA mutational signatures. Diffsig takes into account the uncertainty associated with assigning signatures to samples as well as multiple risk factors' simultaneous effect on observed DNA mutations. We applied Diffsig to breast cancer data to assess relationships between five established breast-relevant mutational signatures and etiologic variables, confirming known mechanisms of cancer development. In simulation, our model was capable of accurately estimating expected associations in a variety of contexts. Diffsig allows researchers to quantify and perform inference on the associations of risk factors with mutational signatures. We expect Diffsig to provide more robust associations of risk factors with signatures to lead to better understanding of the tumor development process and improved models of tumorigenesis.

Usage metrics

    Cancer Epidemiology, Biomarkers & Prevention

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC