Explainable Machine Learning Identifies Factors for Dosage Compensation in Aneuploid Human Cancer Cells

ABSTRACT: Change in chromosome copy number, or aneuploidy, is a typical feature of cancer cells, which can alter the protein abundance of hundreds or thousands of proteins. Yet, evidence from aneuploid cell lines suggests that the protein dosage changes are to a great extent buffered, and thus the abundance of many proteins is similar to the abundance in euploid cells despite gene copy number changes. Despite ubiquitous occurrence of protein dosage buffering, there is limited understanding of the involved molecular mechanisms. Moreover, whether protein dosage buffering affects all proteins and cells to similar degree, and whether it brings adaptive advantage remains unclear. Here, we established a novel approach to quantify protein dosage buffering in a gene copy-number dependent manner, and show that dosage compensation is wide-spread in cancer cell lines and in-vivo tumor samples, yet the extent of buffering is variable. By devising multifactorial machine learning (ML) models that efficiently predict gene dosage buffering and the usage of explainable ML techniques, we demonstrate that mean gene dependency, protein complex participation, haploinsufficiency and mRNA decay are strongly predictive for buffering. Our analysis also indicates that proteotoxic stress and drug sensitivity is reduced in samples that exhibit high protein buffering on average.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Culture

SUBMITTER: Markus Räschle  

LAB HEAD: Markus Räschle

PROVIDER: PXD060017 | Pride | 2025-09-12

REPOSITORIES: Pride