Quantifying chromosomal instability from intratumoral karyotype diversity using agent-based modeling and Bayesian inference

Andrew R. Lynch; Nicholas L. Arp; Amber S. Zhou; Beth A. Weaver; Mark E. Burkard

doi:10.7554/eLife.69799

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Quantifying chromosomal instability from intratumoral karyotype diversity using agent-based modeling and Bayesian inference

Journal article

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Quantifying chromosomal instability from intratumoral karyotype diversity using agent-based modeling and Bayesian inference

Andrew R. Lynch, Nicholas L. Arp, Amber S. Zhou, Beth A. Weaver and Mark E. Burkard

eLife, Vol.11, 69799

04/05/2022

DOI: 10.7554/eLife.69799

PMCID: PMC9054132

PMID: 35380536

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Abstract

Chromosomal instability (CIN)-persistent chromosome gain or loss through abnormal mitotic segregation-is a hallmark of cancer that drives aneuploidy. Intrinsic chromosome mis-segregation rate, a measure of CIN, can inform prognosis and is a promising biomarker for response to anti-microtubule agents. However, existing methodologies to measure this rate are labor intensive, indirect, and confounded by selection against aneuploid cells, which reduces observable diversity. We developed a framework to measure CIN, accounting for karyotype selection, using simulations with various levels of CIN and models of selection. To identify the model parameters that best fit karyotype data from single-cell sequencing, we used approximate Bayesian computation to infer mis-segregation rates and karyotype selection. Experimental validation confirmed the extensive chromosome mis-segregation rates caused by the chemotherapy paclitaxel (18.5 & PLUSMN; 0.5/division). Extending this approach to clinical samples revealed that inferred rates fell within direct observations of cancer cell lines. This work provides the necessary framework to quantify CIN in human tumors and develop it as a predictive biomarker.

Biology

Life Sciences & Biomedicine

Life Sciences & Biomedicine - Other Topics

Science & Technology

Details

Title: Subtitle: Quantifying chromosomal instability from intratumoral karyotype diversity using agent-based modeling and Bayesian inference
Creators: Andrew R. Lynch - University of Wisconsin–Madison
Nicholas L. Arp - University of Wisconsin–Madison
Amber S. Zhou - University of Wisconsin–Madison
Beth A. Weaver - University of Wisconsin–Madison
Mark E. Burkard - University of Wisconsin–Madison
Resource Type: Journal article
Publication Details: eLife, Vol.11, 69799
DOI: 10.7554/eLife.69799
PMID: 35380536
PMCID: PMC9054132
NLM abbreviation: Elife
ISSN: 2050-084X
eISSN: 2050-084X
Publisher: eLIFE SCIENCES PUBL LTD
Number of pages: 43
Grant note: T32GM008688. / Flow Cytometry Core Facility 5 T32GM081061 / UW Cellular and Molecular Pathology 5R01CA234904 / MEB and BAW from the NCI NIH; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA 5T32HG002760 / UW Genomic Sciences Training Program T32GM008692 / University of Wisconsin Carbone Cancer Center (UWCCC)
Language: English
Date published: 04/05/2022
Academic Unit: Internal Medicine
Record Identifier: 9984701257102771

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