Biology subjects

Lynch, A. R.

Publications and source records attributed to Lynch, A. R..

3 recordsLinked to original sources

bioRxiv2023

The history of chromosomal instability in genome doubled tumors

Tumors frequently display high chromosomal instability (CIN) and contain multiple copies of genomic regions. Here, we describe GRITIC, a generic method for timing genomic gains leading to complex copy number states, using single-sample bulk whole-genome sequencing data. By applying GRITIC to 5,656 tumors, we found that non-parsimonious evolution is frequent in the formation of complex copy number states in genome-duplicated tumors. We measured CIN before and after genome duplication in human tumors and found that late genome doubling was followed by an increase in the rate of copy number gain. Copy number gains often accumulate as punctuated bursts, commonly after genome duplication. We infer that genome duplications typically affect the selection landscape of copy number losses, while only minimally impacting copy number gains. In summary, GRITIC is a novel copy number gain timing framework that permits the analysis of copy number evolution in chromosomally unstable tumors. Statement of significanceComplex genomic gains are associated with whole-genome duplications, which are frequent across tumors, span a large fraction of their genomes, and are linked to poorer outcomes. GRITIC infers when these gains occur during tumor development, which will help to identify the genetic events that drive tumor evolution.

genomics↗

bioRxiv2023

A survey of CIN measures across mechanistic models

Chromosomal instability (CIN) is the persistent reshuffling of cancer karyotypes via chromosome mis-segregation during cell division. In cancer, CIN exists at varying levels that have differential effects on tumor progression. However, mis-segregation rates remain challenging to assess in human cancer despite an array of available measures. We evaluated measures of CIN by comparing quantitative methods using specific, inducible phenotypic CIN models of chromosome bridges, pseudobipolar spindles, multipolar spindles, and polar chromosomes. For each, we measured CIN fixed and timelapse fluorescence microscopy, chromosome spreads, 6-centromere FISH, bulk transcriptomics, and single cell DNA sequencing (scDNAseq). As expected, microscopy of tumor cells in live and fixed samples correlated well (R=0.77; p<0.01) and sensitively detect CIN. Cytogenetics approaches include chromosome spreads and 6-centromere FISH, which also correlate well (R=0.77; p<0.01) but had limited sensitivity for lower rates of CIN. Bulk genomic DNA signatures and bulk transcriptomic scores, CIN70 and HET70, did not detect CIN. By contrast, single-cell DNA sequencing (scDNAseq) detects CIN with high sensitivity, and correlates very well with imaging methods (R=0.83; p<0.01). In summary, single-cell methods such as imaging, cytogenetics, and scDNAseq can measure CIN, with the latter being the most comprehensive method accessible to clinical samples. To facilitate comparison of CIN rates between phenotypes and methods, we propose a standardized unit of CIN: Mis-segregations per Diploid Division (MDD). This systematic analysis of common CIN measures highlights the superiority of single-cell methods and provides guidance for measuring CIN in the clinical setting. SignificanceCancer relies on genomic changes to drive evolution. One type of change, Chromosomal INstability (CIN), promotes plasticity and heterogeneity of chromosome sets via ongoing errors in mitosis. The rate of these errors informs patient prognosis, drug response, and risk of metastasis. However, measuring CIN in patient tissues is challenging, hindering the emergence of CIN rate as a prognostic and predictive clinical biomarker. To advance clinical measures of CIN, we quantitatively tested the relative performance of several CIN measures in tandem using four well-defined, inducible CIN models. This survey revealed poor sensitivity in several common CIN assays and highlights the primacy of single-cell approaches. Further, we propose a standard, normalized unit of CIN, permitting comparison across methods and studies.

cancer biology↗

bioRxiv2021

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.

cancer biology↗