/ publications / [3] predicting cancer evolution

Mutational processes as markers of cancer vulnerabilities:

We identify robust associations between certain mutational signatures and drug activity across cancer cell line panels; these are as numerous as associations with driver gene alterations. Signatures of prior exposures to DNA damaging agents associate with resistance, while deficient DNA repair tends to sensitize to therapeutics.

We propose HMCES, a protein linked to the protection of abasic sites, as a central protein for the tolerance of A3A expression. HMCES depletion results in synthetic lethality with A3A expression preferentially in a TP53-mutant background. Our results suggest that HMCES is an attractive target for selective treatment of A3A-expressing tumors.

Identifying driver genes and mutations from tumor genomes:

oncogenes may simultaneously exhibit signatures of positive selection and also negative selection in different gene segments. | We reveal a general trend of increased positive selection on oncogene mutations not only upon CNA gains but also upon CNA deletions. | Similarly, we observe a surprising positive interaction between mutations and CNA gains in tumor suppressor genes. 

Rates and types of somatic mutation vary across individuals, but few inherited influences thereon are known. We perform a gene-based rare variant association study with diverse mutational processes, using ~11k cancer genomes, to identify 42 genes causal to 15 somatic mutational phenotypes incl. HR and MMR deficiencies.

Classifying cancers and healthy somatic tissues via mutation patterns:

Density of somatic mutations across chromosomal domains is a mutational phenotype that can differentiate human tissues // Driver mutations are poor classifiers of cancer (sub)type, while passenger mutation-based phenotypes are highly predictive // Trinucleotide signatures and regional mutation density phenotypes are complementary in classifying tumors.

Multiple cell types from diverse healthy somatic tissues usually display a stereotyped mutation profile. However, the same tissue can sometimes harbor cells with distinct mutation profiles associated to different differentiation states. For example, we identify a cell type in the kidney with unusual mutation rate increase in active chromatin.

"Prediction is very difficult, especially about the future." -- Niels Bohr.