Poster Session A   |   11:45am Expo - Hall A & C   |   Poster ID #190

A survey of CIN measures across mechanistic models

Program:
Academic Research
Category:
Molecular and Cellular Biology, Genetics
FDA Status:
Not Applicable
CPRIT Grant:
Cancer Site(s):
All Cancers
Authors:
Andrew Lynch
The University of Texas M.D. Anderson Cancer Center
Mark Burkard
University of Wisconsin - Madison
Shermineh Bradford
University of Wisconsin - Madison
Amber Zhou
University of Wisconsin - Madison
Kim Oxendine
University of Wisconsin - Madison
Les Henderson
University of Wisconsin - Madison
Vanessa Horner
University of Wisconsin - Madison
Beth Weaver
University of Wisconsin - Madison

Introduction

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.

Methods

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).

Results

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).

Conclusion

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.