Evolutionary Action Score of TP53 Identifies High-Risk Mutations Associated with Decreased Survival and Increased Distant Metastases in Head and Neck Cancer

TP53 is the most frequently altered gene in head and neck squamous cell carcinoma, with mutations occurring in over two-thirds of cases, but the prognostic significance of these mutations remains elusive. In the current study, we evaluated a novel computational approach termed evolutionary action (EAp53) to stratify patients with tumors harboring TP53 mutations as high or low risk, and validated this system in both in vivo and in vitro models. Patients with high-risk TP53 mutations had the poorest survival outcomes and the shortest time to the development of distant metastases. Tumor cells expressing high-risk TP53 mutations were more invasive and tumorigenic and they exhibited a higher incidence of lung metastases. We also documented an association between the presence of high-risk mutations and decreased expression of TP53 target genes, highlighting key cellular pathways that are likely to be dysregulated by this subset of p53 mutations that confer particularly aggressive tumor behavior. Overall, our work validated EAp53 as a novel computational tool that may be useful in clinical prognosis of tumors harboring p53 mutations

Separation of Recombination and SOS Response in Escherichia coli RecA Suggests LexA Interaction Sites

RecA plays a key role in homologous recombination, the induction of the DNA damage response through LexA cleavage and the activity of error-prone polymerase in Escherichia coli. RecA interacts with multiple partners to achieve this pleiotropic role, but the structural location and sequence determinants involved in these multiple interactions remain mostly unknown. Here, in a first application to prokaryotes, Evolutionary Trace (ET) analysis identifies clusters of evolutionarily important surface amino acids involved in RecA functions. Some of these clusters match the known ATP binding, DNA binding, and RecA-RecA homo-dimerization sites, but others are novel. Mutation analysis at these sites disrupted either recombination or LexA cleavage. This highlights distinct functional sites specific for recombination and DNA damage response induction. Finally, our analysis reveals a composite site for LexA binding and cleavage, which is formed only on the active RecA filament. These new sites can provide new drug targets to modulate one or more RecA functions, with the potential to address the problem of evolution of antibiotic resistance at its root

Negative Feedback in Genetic Circuits Confers Evolutionary Resilience and Capacitance

Natural selection for specific functions places limits upon the amino acid substitutions a protein can accept. Mechanisms that expand the range of tolerable amino acid substitutions include chaperones that can rescue destabilized proteins and additional stability-enhancing substitutions. Here, we present an alternative mechanism that is simple and uses a frequently encountered network motif. Computational and experimental evidence shows that the self-correcting, negative-feedback gene regulation motif increases repressor expression in response to deleterious mutations and thereby precisely restores repression of a target gene. Furthermore, this ability to rescue repressor function is observable across the Eubacteria kingdom through the greater accumulation of amino acid substitutions in negative-feedback transcription factors compared to genes they control. We propose that negative feedback represents a self-contained genetic canalization mechanism that preserves phenotype while permitting access to a wider range of functional genotypes

Codon-level co-occurrences of germline variants and somatic mutations in cancer are rare but often lead to incorrect variant annotation and underestimated impact prediction

<div><p>Cancer cells explore a broad mutational landscape, bringing the possibility that tumor-specific somatic mutations could fall in the same codons as germline SNVs and leverage their presence to produce substitutions with a larger impact on protein function. While multiple, temporally consecutive mutations to the same codon have in the past been detected in the germline, this phenomenon has not yet been explored in the context of germline-somatic variant co-occurrences during cancer development. We examined germline context at somatic mutation sites for 1395 patients across four cancer cohorts (breast, skin, colon, and head and neck) and found 392 codon-level co-occurrences between germline and somatic variants, including over a dozen in well-known cancer genes. We found that for the majority of these co-occurrence events, traditional somatic calling led to an inaccurate representation of the protein site and a significantly lower predicted impact on protein fitness. We conclude that these events often lead to imprecise annotation of somatic variants but do not appear to be a frequent source of driver events during cancer development.</p></div

Decoding Cancer Variants of Unknown Significance for Helicase-Nuclease-RPA Complexes Orchestrating DNA Repair During Transcription and Replication.

All tumors have DNA mutations, and a predictive understanding of those mutations could inform clinical treatments. However, 40% of the mutations are variants of unknown significance (VUS), with the challenge being to objectively predict whether a VUS is pathogenic and supports the tumor or whether it is benign. To objectively decode VUS, we mapped cancer sequence data and evolutionary trace (ET) scores onto crystallography and cryo-electron microscopy structures with variant impacts quantitated by evolutionary action (EA) measures. As tumors depend on helicases and nucleases to deal with transcription/replication stress, we targeted helicase-nuclease-RPA complexes: (1) XPB-XPD (within TFIIH), XPF-ERCC1, XPG, and RPA for transcription and nucleotide excision repair pathways and (2) BLM, EXO5, and RPA plus DNA2 for stalled replication fork restart. As validation, EA scoring predicts severe effects for most disease mutations, but disease mutants with low ET scores not only are likely destabilizing but also disrupt sophisticated allosteric mechanisms. For sites of disease mutations and VUS predicted to be severe, we found strong co-localization to ordered regions. Rare discrepancies highlighted the different survival requirements between disease and tumor mutations, as well as the value of examining proteins within complexes. In a genome-wide analysis of 33 cancer types, we found correlation between the number of mutations in each tumor and which pathways or functional processes in which the mutations occur, revealing different mutagenic routes to tumorigenesis. We also found upregulation of ancient genes including BLM, which supports a non-random and concerted cancer process: reversion to a unicellular, proliferation-uncontrolled, status by breaking multicellular constraints on cell division. Together, these genes and global analyses challenge the binary "driver" and "passenger" mutation paradigm, support a gradient impact as revealed by EA scoring from moderate to severe at a single gene level, and indicate reduced regulation as well as activity. The objective quantitative assessment of VUS scoring and gene overexpression in the context of functional interactions and pathways provides insights for biology, oncology, and precision medicine

Cancer genes are affected by co-occurrence events but co-occurrence events do not exhibit positive selection signals.

<p>(A) COSMIC cancer genes affected by co-occurrence events and their affected network partners. Interactions were defined as having medium confidence or higher in STRING v.10.0, and were visualized in Cytoscape. (B) Variant allele fractions of all somatic variants involved in co-occurrence events with the germline. (C) Ratio of somatic variants found to be <i>in cis</i> versus <i>in trans</i> with germline variants. The expected ratio was derived from the allelic fractions of all germline variants in each cohort. Observed ratios were calculated for somatic variants that were candidates for affecting the same codon as a germline variant (<i>same codon</i>), and for somatic variants that were 1–2 nts away from a germline variant but predicted to affect a different codon (<i>adj</i>. <i>codon</i>).</p

Impact of codon-level germline-somatic variant co-occurrences on prediction accuracy.

<p>(A) Representation of different types of incorrect variant predictions. If the amino acid substitution given by somatic calling was not changed by considering the germline context, the interaction was considered to be ‘no change’. If there was a change, data was split by whether it changed to a nonsense (<i>n</i>), silent (<i>s</i>), or different missense (<i>m</i>) substitution. (B) Paired EA impact of all co-occurrence events. Blue lines indicate pairs in which the reported substitution overestimated impact, red lines indicate pairs which the reported substitution underestimated impact, and grey indicates no change. Adjacent box plots show distributions of overall groups. (C) Paired EA impact of all co-occurrence events in which one missense substitution was incorrectly called as another. Blue lines indicate pairs in which the reported substitution overestimated impact and red lines indicate pairs which the reported substitution underestimated impact. Adjacent box plots show distributions of overall groups.</p