Publications

Mutational signatures are increasingly used to understand the mechanisms causing cancer, predict prognosis and stratify patients for therapy. However, inference of mutational signatures can be error-prone, particularly in the case of featureless, low-sparsity signatures, which often get confounded. One of them is the homologous recombination deficiency-associated signature SBS3, relevant because of its association with prognosis in ovarian and breast cancer and because of its potential use as a biomarker for synthetic lethality therapies. Here, we present the multimodal method for mutational signature extraction, operating on single-base substitutions (SBS) and indels jointly, and highlight its accuracy signature identification and patient survival prediction. Across four different cohorts of whole-genome sequenced ovarian cancers, the multimodal SBS/indel approach correctly distinguished the commonly confused signatures SBS3, SBS8, SBS39, SBS40 and SBS5. Moreover, we identified two different multimodal m-SBS3 signatures, m-SBS3a and m-SBS3b, with distinct patterns in the indel spectrum. Specifically, the m-SBS3b signature was strongly predictive of better survival in high-grade serous ovarian cancer patients, replicating across the four cohorts, with effect sizes greatly exceeding other genetic markers of survival. m-SBS3 further predicted survival in platinum-treated patients with various cancer types, supporting a general utility of the multimodal mutational signatures for generating biologically and clinically meaningful readouts.

Premature termination codons (PTCs) cause ~10–20% of inherited diseases and are a major mechanism of tumor suppressor gene inactivation in cancer. A general strategy to alleviate the effects of PTCs would be to promote translational readthrough. Nonsense suppression by small molecules has proven effective in diverse disease models, but translation into the clinic is hampered by ineffective readthrough of many PTCs. Here we directly tackle the challenge of defining drug efficacy by quantifying the readthrough of ~5,800 human pathogenic stop codons by eight drugs. We find that different drugs promote the readthrough of complementary subsets of PTCs defined by local sequence context. This allows us to build interpretable models that accurately predict drug-induced readthrough genome-wide, and we validate these models by quantifying endogenous stop codon readthrough. Accurate readthrough quantification and prediction will empower clinical trial design and the development of personalized nonsense suppression therapies.

Cancer driver genes can undergo positive selection for various types of genetic alterations, including gain-of-function or loss-of-function mutations and copy number alterations (CNA). We investigated the landscape of different types of alterations affecting driver genes in 17,644 cancer exomes and genomes. We find that oncogenes may simultaneously exhibit signatures of positive selection and also negative selection in different gene segments, suggesting a method to identify additional tumor types where an oncogene is a driver or a vulnerability. Next, we characterize the landscape of CNA-dependent selection effects, revealing a general trend of increased positive selection on oncogene mutations not only upon CNA gains but also upon CNA deletions. Similarly, we observe a positive interaction between mutations and CNA gains in tumor suppressor genes. Thus, two-hit events involving point mutations and CNA are universally observed regardless of the type of CNA and may signal new therapeutic opportunities. An analysis with focus on the somatic CNA two-hit events can help identify additional driver genes relevant to a tumor type. By a global inference of point mutation and CNA selection signatures and interactions thereof across genes and tissues, we identify 9 evolutionary archetypes of driver genes, representing different mechanisms of (in)activation by genetic alterations.