Tracing cancer evolution and heterogeneity using Hi-C

Chromosomal rearrangements can initiate and drive cancer progression, yet it has been challenging to evaluate their impact, especially in genetically heterogeneous solid cancers. To address this problem we developed HiDENSEC, a new computational framework for analyzing chromatin conformation capture in heterogeneous samples that can infer somatic copy number alterations, characterize large-scale chromosomal rearrangements, and estimate cancer cell fractions. After validating HiDENSEC with in silico and in vitro controls, we used it to characterize chromosome-scale evolution during melanoma progression in formalin-fixed tumor samples from three patients. The resulting comprehensive annotation of the genomic events includes copy number neutral translocations that disrupt tumor suppressor genes such as NF1 , whole chromosome arm exchanges that result in loss of CDKN2A , and whole-arm copy-number neutral loss of homozygosity involving PTEN . These findings show that large-scale chromosomal rearrangements occur throughout cancer evolution and that characterizing these events yields insights into drivers of melanoma progression. It is challenging to analyse chromosomal rearrangements in heterogeneous solid cancers. Here the authors present HiDENSEC, a method to jointly infer absolute copy number, ploidy, tumor purity and large-scale rearrangements from Hi-C data. The increased statistical power afforded by joint inference enables novel insights into cancer genome evolution.