Related Experiment Video
Updated: Sep 28, 2025
10:33
Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation
Published on: September 4, 2017
15.9K
Quantifying chromosomal instability from intratumoral karyotype diversity using agent-based modeling and Bayesian
Andrew R Lynch1,2, Nicholas L Arp1, Amber S Zhou1,2
1Carbone Cancer Center, University of Wisconsin-Madison, Madison, United States.
Elife
|April 5, 2022
Summary
We developed a new method to accurately measure chromosomal instability (CIN), a key cancer driver. This approach accounts for cell selection, enabling better cancer prognosis and treatment prediction.
Area of Science:
- Oncology
- Genetics
- Computational Biology
Background:
- Chromosomal instability (CIN) drives cancer by causing aneuploidy through abnormal chromosome segregation.
- The intrinsic chromosome mis-segregation rate is a measure of CIN, valuable for prognosis and predicting response to anti-microtubule agents.
- Current methods for measuring CIN are laborious, indirect, and limited by selection against aneuploid cells, reducing observable diversity.
Purpose of the Study:
- To develop a novel computational framework for accurately quantifying CIN, accounting for karyotype selection.
- To infer chromosome mis-segregation rates and karyotype selection using approximate Bayesian computation.
- To validate the framework experimentally and apply it to clinical samples for biomarker development.
Main Methods:
- Developed a computational framework integrating simulations of CIN and selection models.
- Utilized approximate Bayesian computation to infer model parameters from single-cell sequencing karyotype data.
- Performed experimental validation using paclitaxel treatment and applied the framework to clinical cancer samples.
Main Results:
- The developed framework accurately measures CIN, accounting for karyotype selection.
- Experimental validation confirmed significant chromosome mis-segregation rates induced by paclitaxel (18.5 ± 0.5/division).
- Inferred CIN rates from clinical samples aligned with direct observations from cancer cell lines.
Conclusions:
- This study presents a robust framework for quantifying CIN in human tumors.
- The method provides a reliable way to measure chromosome mis-segregation rates, overcoming limitations of previous approaches.
- This work paves the way for developing CIN as a predictive biomarker for cancer prognosis and treatment response.
