Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes.

ABSTRACT:

Background

Aneuploidy, an abnormal number of chromosomes within a cell, is a hallmark of cancer. Patterns of aneuploidy differ across cancers, yet are similar in cancers affecting closely related tissues. The selection pressures underlying aneuploidy patterns are not fully understood, hindering our understanding of cancer development and progression.

Results

Here, we apply interpretable machine learning methods to study tissue-selective aneuploidy patterns. We define 20 types of features corresponding to genomic attributes of chromosome-arms, normal tissues, primary tumors, and cancer cell lines (CCLs), and use them to model gains and losses of chromosome arms in 24 cancer types. To reveal the factors that shape the tissue-specific cancer aneuploidy landscapes, we interpret

SUBMITTER: Jubran J 

PROVIDER: S-EPMC11020441 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature