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

Chromosomal instability (CIN) — persistent chromosome gain or loss through abnormal karyokinesis — is a hallmark of cancer that drives aneuploidy. Intrinsic chromosome mis- segregation rates, a measure of CIN, can inform prognosis and are a likely biomarker for response to anti-microtubule agents. However, existing methodologies to measure this rate are labor intensive, indirect, and confounded by karyotype selection reducing observable diversity. We developed a framework to simulate and measure CIN, accounting for karyotype selection, and recapitulated karyotype-level clonality in simulated populations. We leveraged approximate Bayesian computation using phylogenetic topology and diversity to infer mis-segregation rates and karyotype selection from single-cell DNA sequencing data. Experimental validation of this approach revealed extensive chromosome mis-segregation rates caused by the chemotherapy paclitaxel (17.5±0.14/division). Extending this approach to clinical samples revealed the 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.

染色质不稳定性（CIN）——通过异常的核质分裂导致的染色体持续获得或丢失——是癌症的一个显著特征，它推动了非整倍体的形成。内源性染色体非分离率，作为CIN的衡量指标，可以预测预后，并且很可能是对微管蛋白抑制剂反应的生物标志物。然而，现有的测量这种比率的方法劳动密集、间接，且受核型选择的干扰，降低了可观察的多样性。我们开发了一个框架来模拟和测量CIN，考虑了核型选择，并在模拟群体中重现了核型级别的克隆性。我们利用基于系统发育拓扑和多样性的近似贝叶斯计算，从单细胞DNA测序数据中推断非分离率和核型选择。该方法的实验验证揭示了由化疗药物紫杉醇（17.5±0.14/分裂）引起的广泛的染色体非分离率。将此方法扩展到临床样本中，发现推断的比率在癌症细胞系的直接观察范围内。这项工作为量化人类肿瘤中的CIN并开发其为预测生物标志物提供了必要的框架。