In “A pan-cancer compendium of chromosomal instability” Markowetz et al., present a robust analysis framework - copy-number signatures - showing how to characterize CIN in various cancer types and associate it with clinical phenotypes. The work is relevant both for stratification of cancer patients, and for the discovery of novel drug targets.
Using SNP-array derived copy number profiles for 6,335 cancer samples, the authors measured the distributions of five fundamental copy number features. A mixture model and subsequently non-negative matrix factorization were used to identify copy-number signatures - 10 pan-cancer, and additional 7 in cancer-type specific analyses. The signatures, termed CX1-17, were then studied in terms of activity in all 33 tumor types. The signatures were divided into groups based on the underlying putative etiology, such as defective mitosis and impaired homologous recombination. The putative etiology of the signatures included canonical cancer pathways known to be the major drivers of CIN. Several of these pathways have been explored for the development of targeted therapy, where the signatures could serve as biomarkers. A good example could be the signature CX4 (associated with PI3K-AKT activation) which was correlated with response to inhibition of CCND1 via arcyriaflavin-A and may indicate a therapeutic strategy for reversing tolerance to whole-genome duplication (WGD) events.
At MNM, we have been working with diverse mutation patterns in cancer genomes, including copy-number signatures, and share the viewpoint of the authors that such frameworks form a fantastic basis for companion diagnostics tools, for cohort stratification in clinical trials, and for drug target discovery. As indicated by Markowetz et al. the whole-genome sequencing data that we use in MNM allows for identification of copy-number changes with even higher resolution than the array-based data. At the same time, it provides an opportunity to include single-base and short-indel signatures, as well as structural variant signatures in the models. Coupling this with a powerful AI technology and MNM’s proprietary AI-driven drug target discovery platform, we aim to introduce effective targeted therapies for all cancer patients.
