1. Cancer Biology
  2. Computational and Systems Biology

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

  1. Andrew R Lynch
  2. Nicholas L. Arp
  3. Amber S Zhou
  4. Beth A Weaver
  5. Mark E Burkard  Is a corresponding author
  1. University of Wisconsin-Madison, United States
https://doi.org/10.7554/eLife.69799
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  1. Andrew R Lynch
  2. Nicholas L. Arp
  3. Amber S Zhou
  4. Beth A Weaver
  5. Mark E Burkard
(2022)
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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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/.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Andrew R Lynch

    Carbone Cancer Center, University of Wisconsin-Madison, Madison, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0238-682X

  2. Nicholas L. Arp

    Carbone Cancer Center, University of Wisconsin-Madison, Madison, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8709-0667

  3. Amber S Zhou

    Carbone Cancer Center, University of Wisconsin-Madison, Madison, United States
    Competing interests
    No competing interests declared.

  4. Beth A Weaver

    McArdle Laboratory for Cancer Research, University of Wisconsin-Madison, Madison, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7830-3816

  5. Mark E Burkard

    Carbone Cancer Center, University of Wisconsin-Madison, Madison, United States
    For correspondence
    mburkard@wisc.edu
    Competing interests
    Mark E Burkard, declares the following: Medical advisory board of Strata Oncology; Research funding from Abbvie, Genentech, Puma, Arcus, Apollomics, Loxo Oncology/Lilly, and Elevation Oncology. I hold patents on microfluidic device for drug testing, and for homologous recombination and super-resolution microscopy technologies.I declare all interests without adjudicating relationship to the published work..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4215-7722

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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  1. Andrew R Lynch
  2. Nicholas L. Arp
  3. Amber S Zhou
  4. Beth A Weaver
  5. Mark E Burkard
(2022)
Quantifying chromosomal instability from intratumoral karyotype diversity using agent-based modeling and Bayesian inference
eLife 11:e69799.
https://doi.org/10.7554/eLife.69799

Share this article

https://doi.org/10.7554/eLife.69799

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