Sensitive and specific post-call filtering of genetic variants in xenograft and primary tumors.

Motivation: Tumor genome sequencing offers great promise for guiding research and therapy, but spurious variant calls can arise from multiple sources. Mouse contamination can generate many spurious calls when sequencing patient-derived xenografts. Paralogous genome sequences can also generate spurious calls when sequencing any tumor. We developed a BLAST-based algorithm, Mouse And Paralog EXterminator (MAPEX), to identify and filter out spurious calls from both these sources. Results: When calling variants from xenografts, MAPEX has similar sensitivity and specificity to more complex algorithms. When applied to any tumor, MAPEX also automatically flags calls that potentially arise from paralogous sequences. Our implementation, mapexr, runs quickly and easily on a desktop computer. MAPEX is thus a useful addition to almost any pipeline for calling genetic variants in tumors. Availability and implementation: The mapexr package for R is available at https://github.com/bmannakee/mapexr under the MIT license. Contact: mannakee@email.arizona.edu or rgutenk@email.arizona.edu or eknudsen@email.arizona.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

The impact of genome-wide association studies on biomedical research publications.

The past decade has seen major investment in genome-wide association studies (GWAS). Among the many goals of GWAS, a major one is to identify and motivate research on novel genes involved in complex human disease. To assess whether this goal is being met, we quantified the effect of GWAS on the overall distribution of biomedical research publications and on the subsequent publication history of genes newly associated with complex disease. We found that the historical skew of publications toward genes involved in Mendelian disease has not changed since the advent of GWAS. Genes newly implicated by GWAS in complex disease do experience additional publications compared to control genes, and they are more likely to become exceptionally studied. But the magnitude of both effects has declined over the past decade. Our results suggest that reforms to encourage follow-up studies may be needed for GWAS to most successfully guide biomedical research toward the molecular mechanisms underlying complex human disease.

Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution.

The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein's rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces.

Testing whether metazoan tyrosine loss was driven by selection against promiscuous phosphorylation.

Protein tyrosine phosphorylation is a key regulatory modification in metazoans, and the corresponding kinase enzymes have diversified dramatically. This diversification is correlated with a genome-wide reduction in protein tyrosine content, and it was recently suggested that this reduction was driven by selection to avoid promiscuous phosphorylation that might be deleterious. We tested three predictions of this intriguing hypothesis. 1) Selection should be stronger on residues that are more likely to be phosphorylated due to local solvent accessibility or structural disorder. 2) Selection should be stronger on proteins that are more likely to be promiscuously phosphorylated because they are abundant. We tested these predictions by comparing distributions of tyrosine within and among human and yeast orthologous proteins. 3) Selection should be stronger against mutations that create tyrosine versus remove tyrosine. We tested this prediction using human population genomic variation data. We found that all three predicted effects are modest for tyrosine when compared with the other amino acids, suggesting that selection against deleterious phosphorylation was not dominant in driving metazoan tyrosine loss.

Characterizing selective pressures on the pathway for de novo biosynthesis of pyrimidines in yeast.

BACKGROUND: Selection on proteins is typically measured with the assumption that each protein acts independently. However, selection more likely acts at higher levels of biological organization, requiring an integrative view of protein function. Here, we built a kinetic model for de novo pyrimidine biosynthesis in the yeast Saccharomyces cerevisiae to relate pathway function to selective pressures on individual protein-encoding genes. RESULTS: Gene families across yeast were constructed for each member of the pathway and the ratio of nonsynonymous to synonymous nucleotide substitution rates (dN/dS) was estimated for each enzyme from S. cerevisiae and closely related species. We found a positive relationship between the influence that each enzyme has on pathway function and its selective constraint. CONCLUSIONS: We expect this trend to be locally present for enzymes that have pathway control, but over longer evolutionary timescales we expect that mutation-selection balance may change the enzymes that have pathway control.

Genomic landscape of lymphatic malformations: a case series and response to the PI3Kα inhibitor alpelisib in an N-of-1 clinical trial.

Background: Lymphatic malformations (LMs) often pose treatment challenges due to a large size or a critical location that could lead to disfigurement, and there are no standardized treatment approaches for either refractory or unresectable cases. Methods: We examined the genomic landscape of a patient cohort of LMs (n = 30 cases) that underwent comprehensive genomic profiling using a large-panel next-generation sequencing assay. Immunohistochemical analyses were completed in parallel. Results: These LMs had low mutational burden with hotspot PIK3CA mutations (n = 20) and NRAS (n = 5) mutations being most frequent, and mutually exclusive. All LM cases with Kaposi sarcoma-like (kaposiform) histology had NRAS mutations. One index patient presented with subacute abdominal pain and was diagnosed with a large retroperitoneal LM harboring a somatic PIK3CA gain-of-function mutation (H1047R). The patient achieved a rapid and durable radiologic complete response, as defined in RECIST1.1, to the PI3Kα inhibitor alpelisib within the context of a personalized N-of-1 clinical trial (NCT03941782). In translational correlative studies, canonical PI3Kα pathway activation was confirmed by immunohistochemistry and human LM-derived lymphatic endothelial cells carrying an allele with an activating mutation at the same locus were sensitive to alpelisib treatment in vitro, which was demonstrated by a concentration-dependent drop in measurable impedance, an assessment of cell status. Conclusions: Our findings establish that LM patients with conventional or kaposiform histology have distinct, yet targetable, driver mutations. Funding: R.P. and W.A. are supported by awards from the Levy-Longenbaugh Fund. S.G. is supported by awards from the Hugs for Brady Foundation. This work has been funded in part by the NCI Cancer Center Support Grants (CCSG; P30) to the University of Arizona Cancer Center (CA023074), the University of New Mexico Comprehensive Cancer Center (CA118100), and the Rutgers Cancer Institute of New Jersey (CA072720). B.K.M. was supported by National Science Foundation via Graduate Research Fellowship DGE-1143953. Clinical trial number: NCT03941782.

BATCAVE: calling somatic mutations with a tumor- and site-specific prior.

Detecting somatic mutations withins tumors is key to understanding treatment resistance, patient prognosis and tumor evolution. Mutations at low allelic frequency, those present in only a small portion of tumor cells, are particularly difficult to detect. Many algorithms have been developed to detect such mutations, but none models a key aspect of tumor biology. Namely, every tumor has its own profile of mutation types that it tends to generate. We present BATCAVE (Bayesian Analysis Tools for Context-Aware Variant Evaluation), an algorithm that first learns the individual tumor mutational profile and mutation rate then uses them in a prior for evaluating potential mutations. We also present an R implementation of the algorithm, built on the popular caller MuTect. Using simulations, we show that adding the BATCAVE algorithm to MuTect improves variant detection. It also improves the calibration of posterior probabilities, enabling more principled tradeoff between precision and recall. We also show that BATCAVE performs well on real data. Our implementation is computationally inexpensive and straightforward to incorporate into existing MuTect pipelines. More broadly, the algorithm can be added to other variant callers, and it can be extended to include additional biological features that affect mutation generation.

Loss of amino-terminal acetylation suppresses a prion phenotype by modulating global protein folding.

Amino-terminal acetylation is among the most ubiquitous of protein modifications in eukaryotes. Although loss of N-terminal acetylation is associated with many abnormalities, the molecular basis of these effects is known for only a few cases, where acetylation of single factors has been linked to binding avidity or metabolic stability. In contrast, the impact of N-terminal acetylation for the majority of the proteome, and its combinatorial contributions to phenotypes, are unknown. Here, by studying the yeast prion [PSI(+)], an amyloid of the Sup35 protein, we show that loss of N-terminal acetylation promotes general protein misfolding, a redeployment of chaperones to these substrates, and a corresponding stress response. These proteostasis changes, combined with the decreased stability of unacetylated Sup35 amyloid, reduce the size of prion aggregates and reverse their phenotypic consequences. Thus, loss of N-terminal acetylation, and its previously unanticipated role in protein biogenesis, globally resculpts the proteome to create a unique phenotype.