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Runjun Kumar, S. Joshua Swamidass and Ron Bose present an unsupervised parsimony-guided method, ParsSNP, for prioritizing candidate cancer driver mutations. They apply ParsSNP to a gastric cancer data set and predict potential driver mutations not detected by other methods, including truncations in known tumor-suppressor genes and previously confirmed drivers. Methods are needed to reliably prioritize biologically active driver mutations over inactive passengers in high-throughput sequencing cancer data sets. We present ParsSNP, an unsupervised functional impact predictor that is guided by parsimony. ParsSNP uses an expectation–maximization framework to find mutations that explain tumor incidence broadly, without using predefined training labels that can introduce biases. We compare ParsSNP to five existing tools (CanDrA, CHASM, FATHMM Cancer, TransFIC, and Condel) across five distinct benchmarks. ParsSNP outperformed the existing tools in 24 of 25 comparisons. To investigate the real-world benefit of these improvements, we applied ParsSNP to an independent data set of 30 patients with diffuse-type gastric cancer. ParsSNP identified many known and likely driver mutations that other methods did not detect, including truncation mutations in known tumor suppressors and the recurrent driver substitution RHOA p.Tyr42Cys. In conclusion, ParsSNP uses an innovative, parsimony-based approach to prioritize cancer driver mutations and provides dramatic improvements over existing methods.

A database of known drug-gene interactions, with information derived from many public sources, allows the identification of genes that are currently targeted by a drug and the membership of genes in a category, such as kinase genes, that have a high potential for drug development. The Drug-Gene Interaction database (DGIdb) mines existing resources that generate hypotheses about how mutated genes might be targeted therapeutically or prioritized for drug development. It provides an interface for searching lists of genes against a compendium of drug-gene interactions and potentially 'druggable' genes. DGIdb can be accessed at http://dgidb.org/ .

In this study we use somatic cancer mutations to identify important functional residues within sets of related genes. We focus on protein kinases, a superfamily of phosphotransferases that share homologous sequences and structural motifs and have many connections to cancer. We develop several statistical tests for identifying Significantly Mutated Positions (SMPs), which are positions in an alignment with mutations that show signs of selection. We apply our methods to 21,917 mutations that map to the alignment of human kinases and identify 23 SMPs. SMPs occur throughout the alignment, with many in the important A-loop region, and others spread between the N and C lobes of the kinase domain. Since mutations are pooled across the superfamily, these positions may be important to many protein kinases. We select eleven mutations from these positions for functional validation. All eleven mutations cause a reduction or loss of function in the affected kinase. The tested mutations are from four genes, including two tumor suppressors (TGFBR1 and CHEK2) and two oncogenes (KDR and ERBB2). They also represent multiple cancer types, and include both recurrent and non-recurrent events. Many of these mutations warrant further investigation as potential cancer drivers.

Here we report targeted sequencing of 83 genes using DNA from primary breast cancer samples from 625 postmenopausal (UBC-TAM series) and 328 premenopausal (MA12 trial) hormone receptor-positive (HR+) patients to determine interactions between somatic mutation and prognosis. Independent validation of prognostic interactions was achieved using data from the METABRIC study. Previously established associations between MAP3K1 and PIK3CA mutations with luminal A status/favorable prognosis and TP53 mutations with Luminal B/non-luminal tumors/poor prognosis were observed, validating the methodological approach. In UBC-TAM, NF1 frame-shift nonsense (FS/NS) mutations were also a poor outcome driver that was validated in METABRIC. For MA12, poor outcome associated with PIK3R1 mutation was also reproducible. DDR1 mutations were strongly associated with poor prognosis in UBC-TAM despite stringent false discovery correction ( q  = 0.0003). In conclusion, uncommon recurrent somatic mutations should be further explored to create a more complete explanation of the highly variable outcomes that typifies ER+ breast cancer. Unravelling the link between somatic mutation and prognosis in estrogen positive (ER+) breast cancer requires the use of long-term follow-up data. Here, combining archival formalin-fixed paraffin embedded tissue and targeted sequencing in three cohorts of ER+ breast cancer, the authors find associations with clinical outcome for NF1 frame-shift nonsense mutations, PIK3R1 mutation, and DDR1 mutations.

Various post‐translational modifications (PTMs) fine‐tune the functions of almost all eukaryotic proteins, and co‐regulation of different types of PTMs has been shown within and between a number of proteins. Aiming at a more global view of the interplay between PTM types, we collected modifications for 13 frequent PTM types in 8 eukaryotes, compared their speed of evolution and developed a method for measuring PTM co‐evolution within proteins based on the co‐occurrence of sites across eukaryotes. As many sites are still to be discovered, this is a considerable underestimate, yet, assuming that most co‐evolving PTMs are functionally associated, we found that PTM types are vastly interconnected, forming a global network that comprise in human alone >50 000 residues in about 6000 proteins. We predict substantial PTM type interplay in secreted and membrane‐associated proteins and in the context of particular protein domains and short‐linear motifs. The global network of co‐evolving PTM types implies a complex and intertwined post‐translational regulation landscape that is likely to regulate multiple functional states of many if not all eukaryotic proteins. This study is the first large‐scale comparative analysis of multiple types of post‐translational modifications in different eukaryotic species. The resulting network of co‐evolving and functionally associated modifications reveals the global landscape of post‐translational regulation. Synopsis This study is the first large‐scale comparative analysis of multiple types of post‐translational modifications in different eukaryotic species. The resulting network of co‐evolving and functionally associated modifications reveals the global landscape of post‐translational regulation. In all, 115 149 non‐redundant post‐translational modifications (PTMs) of 13 different types were collected from 8 eukaryotes. Comparison of evolution speed reveals that carboxylation is the most conserved while SUMOylation is the fastest evolving PTM type. Co‐evolution of PTM pairs that co‐occur within proteins reveals a vastly interconnected global network of functionally associated PTM types in eukaryotes. Central to the network of functionally associated PTM types appear phosphorylation, acetylation, ubiquitination and O‐linked glycosylation that control both temporal events and processes that govern protein localization.

A major goal of cancer genome sequencing is to identify mutations or other somatic alterations that can be targeted by selective and specific drugs. dGene is an annotation tool designed to rapidly identify genes belonging to one of ten druggable classes that are frequently targeted in cancer drug development. These classes were comprehensively populated by combining and manually curating data from multiple specialized and general databases. dGene was used by The Cancer Genome Atlas squamous cell lung cancer project, and here we further demonstrate its utility using recently released breast cancer genome sequencing data. dGene is designed to be usable by any cancer researcher without the need for support from a bioinformatics specialist. A full description of dGene and options for its implementation are provided here.

Protein post‐translational modifications (PTMs) represent important regulatory states that when combined have been hypothesized to act as molecular codes and to generate a functional diversity beyond genome and transcriptome. We systematically investigate the interplay of protein phosphorylation with other post‐transcriptional regulatory mechanisms in the genome‐reduced bacterium Mycoplasma pneumoniae . Systematic perturbations by deletion of its only two protein kinases and its unique protein phosphatase identified not only the protein‐specific effect on the phosphorylation network, but also a modulation of proteome abundance and lysine acetylation patterns, mostly in the absence of transcriptional changes. Reciprocally, deletion of the two putative N ‐acetyltransferases affects protein phosphorylation, confirming cross‐talk between the two PTMs. The measured M. pneumoniae phosphoproteome and lysine acetylome revealed that both PTMs are very common, that (as in Eukaryotes) they often co‐occur within the same protein and that they are frequently observed at interaction interfaces and in multifunctional proteins. The results imply previously unreported hidden layers of post‐transcriptional regulation intertwining phosphorylation with lysine acetylation and other mechanisms that define the functional state of a cell. The effect of kinase, phosphatase and N ‐acetyltransferase deletions on proteome phosphorylation and acetylation was investigated in Mycoplasma pneumoniae . Bi‐directional cross‐talk between post‐transcriptional modifications suggests an underlying regulatory molecular code in prokaryotes. Synopsis The effect of kinase, phosphatase and N ‐acetyltransferase deletions on proteome phosphorylation and acetylation was investigated in Mycoplasma pneumoniae . Bi‐directional cross‐talk between post‐transcriptional modifications suggests an underlying regulatory molecular code in prokaryotes. Post‐translational modifications (PTMs) change the chemical properties of proteins, conferring diversity beyond the amino‐acid sequence. Proteins are often modified on multiple sites. A PTM code has been proposed, whereby modifications at specific positions influence further modifications. These regulatory circuits though have rarely been studied on a large‐scale; conservation in prokaryotes remains elusive. Here, we studied two important PTMs– phosphorylation and lysine acetylation in the small bacterium Mycoplasma pneumoniae . We combined genetics and quantitative mass spectrometry to measure the effect of systematic kinase, phosphatase and N ‐acetyltransferase deletions on proteome abundance, phosphorylation and lysine acetylation. The data set represents a comprehensive analysis of both phosphorylation and lysine acetylation in a single prokaryote. It reveals (1) proteins often carry multiple modifications and multiple types of PTMs, reminiscent of the PTM code proposed in eukaryotes, (2) phosphorylation exerts pleiotropic effect on proteins abundances, phosphorylation, but also lysine acetylation, (3) the cross‐talk between the two PTMs is bi‐directional and (4) PTMs are frequently located at interaction interfaces and in multifunctional proteins, illustrating how PTMs could modulate protein functions affecting the way they interact. The study provides an unbiased and quantitative view on cross‐talk between phosphorylation and lysine acetylation. It suggests that these regulatory circuits are a fundamental principle of regulation that might have evolved before the divergence of prokaryotes and eukaryotes.

The original version of this Article contained errors in the depiction of confidence intervals in the NF1 BCSS data illustrated in Figure 3b. These have now been corrected in both the PDF and HTML versions of the Article. The incorrect version of Figure 3b is presented in the associated Author Correction.

Genomics for rare disease diagnosis has advanced at a rapid pace due to our ability to perform in-depth analyses on individual patients with ultra-rare diseases. The increasing sizes of ultra-rare disease cohorts internationally newly enables cohort-wide analyses for new discoveries, but well-calibrated statistical genetics approaches for jointly analyzing these patients are still under development. The Undiagnosed Diseases Network (UDN) brings multiple clinical, research and experimental centers under the same umbrella across the United States to facilitate and scale case-based diagnostic analyses. Here, we present the first joint analysis of whole genome sequencing data of UDN patients across the network. We introduce new, well-calibrated statistical methods for prioritizing disease genes with de novo recurrence and compound heterozygosity. We also detect pathways enriched with candidate and known diagnostic genes. Our computational analysis, coupled with a systematic clinical review, recapitulated known diagnoses and revealed new disease associations. We further release a software package, RaMeDiES, enabling automated cross-analysis of deidentified sequenced cohorts for new diagnostic and research discoveries. Gene-level findings and variant-level information across the cohort are available in a public-facing browser ( https://dbmi-bgm.github.io/udn-browser/ ). These results show that case-level diagnostic efforts should be supplemented by a joint genomic analysis across cohorts. Using well-calibrated statistical methods the authors jointly analyze Undiagnosed Diseases Network genomes, identifying known and novel disease genes. Software is publicly available to support future cross-cohort rare disease discovery efforts.

Paired tumor-normal sequencing of thousands of patient’s exomes has revealed millions of somatic mutations, but functional characterization and clinical decision making are stymied because biologically neutral ‘passenger’ mutations greatly outnumber pathogenic ‘driver’ mutations. Since most mutations will return negative results if tested, conventional resource-intensive experiments are reserved for mutations which are observed in multiple patients or rarer mutations found in well-established cancer genes. Most mutations are therefore never tested, diminishing the potential to discover new mechanisms of cancer development and treatment opportunities. Computational methods that reliably prioritize mutations for testing would greatly increase the translation of sequencing results to clinical care. The goal of this thesis is to develop new approaches that use datasets of protein-coding somatic mutations to identify putative cancer-causing genes and mutations, and to validate these predictions in silico and experimentally. This effort will be split among several inter-related efforts, which taken together will help experimental biologists and clinicians focus on hypotheses that can yield novel insights into cancer biology, development, and treatment.