Quantifying chromosomal instability from intratumoral karyotype diversity using agent-based modeling and Bayesian inference
- University of Wisconsin-Madison, United States
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±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.
Data availability
Single-cell DNA sequencing data from this study has been deposited in NCBI SRA (PRJNA725515). All data and scripts used for modeling and analysis have been deposited in OSF at https://osf.io/snrg3/.
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Quantifying chromosomal instability from intratumoral karyotype diversity using agent- based modeling and Bayesian inferenceOpen-Science Framework, DOI: 10.17605/OSF.IO/SNRG3.
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Ongoing chromosomal instability and karyotype evolution in human colorectal cancer organoidsNature Genetics, 10.1038/s41588-019-0399-6.
Article and author information
Author details
Funding
National Cancer Institute (R01CA234904)
- Mark E Burkard
National Institutes of Health (R01GM141068)
- Mark E Burkard
National Cancer Institute (P30CA014520)
- Mark E Burkard
National Cancer Institute (F31CA254247)
- Andrew R Lynch
National Institutes of Health (T32HG002760)
- Andrew R Lynch
National Institutes of Health (T32GM81061)
- Andrew R Lynch
National Institutes of Health (T32GM008692)
- Nicholas L. Arp
National Institutes of Health (T32GM008688)
- Amber S Zhou
National Institutes of Health (T32GM140935)
- Nicholas L. Arp
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2022, Lynch et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Quantifying chromosomal instability from intratumoral karyotype diversity using agent-based modeling and Bayesian inference
eLife 11:e69799.
https://doi.org/10.7554/eLife.69799
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