SUITOR: Selecting the Number of Mutational Signatures Through Cross-Validation

• Activity Number: 384 - Next-Generation Sequencing and High-Dimensional Data
• Type: Contributed
• Date/Time: Thursday, August 12, 2021 : 12:00 PM to 1:50 PM
• Sponsor: Biometrics Section
• Author(s): DongHyuk Lee* and Difei Wang and Xiaohong R. Yang and Jianxin Shi and Maria T. Landi and Bin Zhu
• Companies: National Cancer Institute and National Cancer Institute and National Cancer Institute and National Cancer Institute/National Institutes of Health and National Cancer Institute and National Cancer Institute
• Keywords: Cross-Validation; ECM algorithm; Non negative matrix factorization

Abstract

For de novo mutational signature analysis, the critical first step is to decide how many signatures should be expected in a cancer genomics study. An incorrect number could mislead downstream analyses. Here we present SUITOR (Selecting the nUmber of mutatIonal signaTures thrOugh cRoss-validation), an unsupervised cross-validation method that requires little assumptions and no numerical approximations to select the optimal number of signatures without overfitting the data. In vitro studies and in silico simulations demonstrated that SUITOR can correctly identify signatures, some of which were missed by other widely used methods. Applied to 2,540 whole-genome sequenced tumors across 22 cancer types, SUITOR selected signatures with the smallest prediction errors and almost all signatures of breast cancer selected by SUITOR were validated in an independent breast cancer study. SUITOR is a powerful tool to select the optimal number of mutational signatures, facilitating downstream analyses with etiological or therapeutic importance.