A survey of CIN measures across mechanistic models

Abstract

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.