/ publications / [3] predicting cancer evolution

Activity of mutational signatures as a marker of cancer vulnerabilities:

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.

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.

Identifying driver genes and mutations:

  • The impact of rare germline variants on human somatic mutation processes. M Vali-Pour, B Lehner*, F Supek* (2022) Nature Communications.

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.

A statistical method, ALFRED, tests Knudson’s two-hit hypothesis to systematically identify inherited cancer predisposing genes // We identify novel genes, such as the chromatin modifier NSD1, which cause cancer through germline variants and somatic loss-of-heterozygosity // 1 in 50 tumors is associated with novel ALFRED genes

By quantifying the interactions between mutations and copy number alterations (CNAs) across 10,000 tumors, we show that many cancer genes actually switch between acting as one-hit or two-hit drivers. // Third order genetic interactions identify the causes of some of these switches in dominance and dosage sensitivity as mutations in other genes.

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.