Resolving genetic heterogeneity in cancer

Abstract

To a large extent cancer conforms to evolutionary rules defined by the rates at which clones mutate, adapt and grow. Compared to species evolution however, cancer is a particular case, due to the vast population size, chromosomal instability, and the potential for phenotypic plasticity. Nevertheless, an evolutionary framework is a powerful aid in our understanding of cancer progression and therapy failure, and could be applied to predict individual tumour behaviour and aid treatment strategies. Introduction Tumours are composed of subpopulations of cells (subclones) that may be distinguished by a variety of features impacting their phenotype, including genetic alterations. Genetic intratumour heterogeneity (ITH) has been documented across most cancers (reviewed in 1) and acts as a substrate for clonal evolution. The fundamental biological mechanisms underlying clonal evolution [G] in cancer are similar to those that underpin the evolution of asexually-reproducing species: replication, heritable variation, genetic drift [G], selection [G] and environmental changes. Central to the neo-Darwinian synthesis of evolutionary biology is the paradigm of molecular evolution [G], which links Mendelian genetics to Darwinian adaptation. Molecular evolution is relevant to cancer because the use of genomic sequencing is a key technology to understand temporal and spatial patterns of somatic evolution [G]. At the core of molecular evolution, in turn, is theoretical population genetics, which has been the fundamental mathematical formalism to describe evolution for the past 90 years 2,3 The same theoretical framework has been used to understand clonal evolution in cancer 4 5 6 7 8 9 10 11. The study of the evolutionary dynamics of cancer clones is fundamentally concerned with the relative frequencies of cancer subpopulations over space

Correcting reference bias from the Illumina Isaac aligner enables analysis of cancer genomes

“…Genomic analyses of tumors are becoming increasingly sophisticated, allowing investigation of their histories and the mutations and processes that drive their development (Jolly and Van Loo, 2018;Turajlic et al, 2019). However, these analyses place many assumptions on the data they use (Dentro et al, 2017).…”

Degree and site of chromosomal instability define its oncogenic potential

“…kb band (when targeted with CiMKi allele). 10 Confirmed targeted ES cell clones for both CiMKi mutations were injected into C57BL/6 blastocyst, which were then transplanted into pseudo-pregnant females (standard techniques, performed under the license of the GDL Utrecht). Chimeric mice were bred with C57BL/6 mice to obtain germline transmission.…”

“…Aneuploidy is the result of chromosomal instability (CIN): the occasional gain or loss of whole chromosomes during mitosis. CIN can lead to genome destabilization [6][7][8][9] , and is associated with high intra-tumoral genomic heterogeneity, immune evasion, and promotion of metastases (reviewed in 10 ). Furthermore, CIN and aneuploidy are correlated with tumor aggressiveness, therapy resistance and poor patient prognosis [11][12][13][14][15][16][17][18][19] .…”

Stochastic dynamics of extra-chromosomal DNA

“…Circular ecDNA lacks centromeres, causing random segregation of ecDNA into daughter cells after division [5] ( Figure 1). Compared to somatic changes of chromosomal DNA, this adds additional stochasticity to the evolutionary dynamics of ecDNA [10,11,12]. Consequently, patterns of inter-and intra-patient ecDNA heterogeneity differ compared to chromosomal point mutations [13,14,15].…”