/ publications / [3] predicting cancer evolution

Hypermutation as a cancer vulnerability:

We identify 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:

Enrichments of somatic mutations indicate that ~1 in 5 synonymous mutations in oncogenes are cancer drivers. Involvement in known exonic splicing motifs and association to RNA-Seq data implicates many causal synonymous mutations to altered splicing. The 3’ UTRs of dosage-sensitive oncogenes also harbour causal mutations.

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.