Dynamics of Tumor Heterogeneity Derived from Clonal Karyotypic Evolution

Abstract

Numerical chromosomal instability is a ubiquitous feature of human neoplasms. Due to experimental limitations, fundamental characteristics of karyotypic changes in cancer are poorly understood. Using an experimentally inspired stochastic model, based on the potency and chromosomal distribution of oncogenes and tumor suppressor genes, we show that cancer cells have evolved to exist within a narrow range of chromosome missegregation rates that optimizes phenotypic heterogeneity and clonal survival. Departure from this range reduces clonal fitness and limits subclonal diversity. Mapping of the aneuploid fitness landscape reveals a highly favorable, commonly observed, near-triploid state onto which evolving diploid- and tetraploid-derived populations spontaneously converge, albeit at a much lower fitness cost for the latter. Finally, by analyzing 1,368 chromosomal translocation events in five human cancers, we find that karyotypic evolution also shapes chromosomal translocation patterns by selecting for more oncogenic derivative chromosomes. Thus, chromosomal instability can generate the heterogeneity required for Darwinian tumor evolution.

Chromosomal Instability as a Driver of Tumor Heterogeneity and Evolution

“…for a near-triploid state (Storchova and Kuffer 2008;Laughney et al 2015). Interestingly, Storchova and Kuffer found that intratumor karyotypic heterogeneity was maximal in tumors with near-triploid karyotypes, which may signify that higher rates of CIN allow a more rapid convergence onto this favorable state, or that the near-triploid state allows more facile clonal diversification (Storchova and Kuffer 2008).…”

“…Our understanding of how CIN enables tumor subclones to sample this vast aneuploid fitness landscape is only beginning to emerge. Laughney et al (2015) explored this by allowing clonal populations to sample the aneuploid fitness landscape in silico, and found that they strikingly converged onto a favorable near-triploid state that maximized the numbers of chromosomes that are more likely to harbor oncogenes and minimize the copies of chromosomes that are more likely to harbor tumor suppressor genes (Fig. 2).…”

“…To quantitatively model the evolutionary impact of CIN, Laughney et al (2015) simulated the process of clonal evolution under chromosomally unstable conditions. Cell viability was determined by the net dosage of pro-and antiproliferative genes they contained, dictated by the respective chromosome copy numbers (Fig.…”

“…Numerical CIN is primarily defined by the "rate" at which chromosomes missegregate throughout consecutive cell divisions (Lengauer et al 1998). The frequency of chromosome missegregation rates, directly measured in cancer-derived cell lines, are on the order of 1.3 Â 10 23 per chromosome copy per cell division, which is 50 -100-fold higher than in normal nontransformed cells (Thompson and Compton 2008;Bakhoum et al 2009b;Laughney et al 2015). This translates into a chromosome missegregation event every 3-10 cell divisions after accounting for cellular ploidy.…”

“…This translates into a chromosome missegregation event every 3-10 cell divisions after accounting for cellular ploidy. In fullfledged malignancies, chromosome missegregation events may have a positive or negative effect on cellular fitness depending on the altered dosage of hundreds to thousands of genes carried by the missegregated chromosomes (Davoli et al 2013;Laughney et al 2015); making CIN a powerful tool to rapidly shape the phenotypic landscape of tumor cell populations.…”