Chromosomal instability drives non-random chromosome mis-segregation
Session type: Poster / e-Poster / Silent Theatre session
A major hallmark of cancer is chromosomal instability (CIN). CIN generates aneuploidy - an abnormal number of chromosomes - and is associated with increased metastasis and poor patient prognosis. Because recurrent patterns of aneuploidy are found across multiple cancer types, key questions remain: are human chromosomes affected equally by CIN? And do distinct drivers of CIN affect different chromosomes? However these have remained unanswered due to technical limitations.
To resolve this we analysed individual chromosome aneuploidy rates in a high throughput manner using single cell cytometer- and sequencing-based technologies and in the absence of fitness effects and selection. We then derived mechanisms underpinning bias by examining mitosis and DNA replication using microscopy, cell biological assays and cytogenetics.
We show that human chromosome mis-segregation is non-random, and furthermore that the nature of resulting aneuploidy landscapes depend on the cause of CIN (Worrall and Tamura et al, Cell Reports, 2018). We observe that a small subset of chromosomes is highly prone to lagging at anaphase following pharmacological induction of merotelic attachments and CIN and that chromosomes 1 and 2 are particularly to prone to cohesion fatigue resulting in a failure to correct their improper attachment to the mitotic spindle. Replication stress also generates a distinct aneuploidy landscape affecting a subset of chromosomes more than others.
Determining chromosome-specific aneuploidy rates following distinct drivers of CIN has provided key insights into hitherto unappreciated differences in chromosome biology. It also provides a framework for determining chromosome-level alteration rates that could improve the interpretation of recurrent aneuploidy patterns in tumours. Moreover, we show that distinct drivers of CIN generate unique landscapes of aneuploidy. This leads to the possibility that analysis of non-random mis-segregation in cancer could allow us to decipher driver mechanisms.