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Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

Purpose We characterize the clinical and molecular phenotypes of six unrelated individuals with intellectual disability and autism spectrum disorder who carry heterozygous missense variants of the PRKAR1B gene, which encodes the R1β subunit of the cyclic AMP-dependent protein kinase A (PKA). Methods Variants of PRKAR1B were identified by single- or trio-exome analysis. We contacted the families and physicians of the six individuals to collect phenotypic information, performed in vitro analyses of the identified PRKAR1B-variants, and investigated PRKAR1B expression during embryonic development. Results Recent studies of large patient cohorts with neurodevelopmental disorders found significant enrichment of de novo missense variants in PRKAR1B. In our cohort, de novo origin of the PRKAR1B variants could be confirmed in five of six individuals, and four carried the same heterozygous de novo variant c.1003C>T (p.Arg335Trp; NM_001164760). Global developmental delay, autism spectrum disorder, and apraxia/dyspraxia have been reported in all six, and reduced pain sensitivity was found in three individuals carrying the c.1003C>T variant. PRKAR1B expression in the brain was demonstrated during human embryonal development. Additionally, in vitro analyses revealed altered basal PKA activity in cells transfected with variant-harboring PRKAR1B expression constructs. Conclusion Our study provides strong evidence for a PRKAR1B-related neurodevelopmental disorder.

Discovering rare cancer driver genes is difficult because their mutational frequency is too low for statistical detection by computational methods. EPIMUTESTR is an integrative nearest-neighbor machine learning algorithm that identifies such marginal genes by modeling the fitness of their mutations with the phylogenetic Evolutionary Action (EA) score. Over cohorts of sequenced patients from The Cancer Genome Atlas representing 33 tumor types, EPIMUTESTR detected 214 previously inferred cancer driver genes and 137 new candidates never identified computationally before of which seven genes are supported in the COSMIC Cancer Gene Census. EPIMUTESTR achieved better robustness and specificity than existing methods in a number of benchmark methods and datasets.

Genetic variants drive the evolution of traits and diseases. We previously modeled these variants as small displacements in fitness landscapes and estimated their functional impact by differentiating the evolutionary relationship between genotype and phenotype. Conversely, here we integrate these derivatives to identify genes steering specific traits. Over cancer cohorts, integration identified 460 likely tumor-driving genes. Many have literature and experimental support but had eluded prior genomic searches for positive selection in tumors. Beyond providing cancer insights, these results introduce a general calculus of evolution to quantify the genotype–phenotype relationship and discover genes associated with complex traits and diseases.

We present a Chemistry and Structure Screen Integrated Efficiently (CASSIE) approach (named for Greek prophet Cassandra) to design inhibitors for cancer biology and pathogenesis. CASSIE provides an effective path to target master keys to control the repair-replication interface for cancer cells and SARS CoV-2 pathogenesis as exemplified here by specific targeting of Poly(ADP-ribose) glycohydrolase (PARG) and ADP-ribose glycohydrolase ARH3 macrodomains plus SARS CoV-2 nonstructural protein 3 (Nsp3) Macrodomain 1 (Mac1) and Nsp15 nuclease. As opposed to the classical massive effort employing libraries with large numbers of compounds against single proteins, we make inhibitor design for multiple targets efficient. Our compact, chemically diverse, 5000 compound Goldilocks (GL) library has an intermediate number of compounds sized between fragments and drugs with predicted favorable ADME (absorption, distribution, metabolism, and excretion) and toxicological profiles. Amalgamating our core GL library with an approved drug (AD) library, we employ a combined GLAD library virtual screen, enabling an effective and efficient design cycle of ranked computer docking, top hit biophysical and cell validations, and defined bound structures using human proteins or their avatars. As new drug design is increasingly pathway directed as well as molecular and mechanism based, our CASSIE approach facilitates testing multiple related targets by efficiently turning a set of interacting drug discovery problems into a tractable medicinal chemistry engineering problem of optimizing affinity and ADME properties based upon early co-crystal structures. Optimization efforts are made efficient by a computationally-focused iterative chemistry and structure screen. Thus, we herein describe and apply CASSIE to define prototypic, specific inhibitors for PARG vs distinct inhibitors for the related macrodomains of ARH3 and SARS CoV-2 Nsp3 plus the SARS CoV-2 Nsp15 RNA nuclease.

This paper reports the evaluation of predictions for the ``CALM1'' challenge in the fifth round of the Critical Assessment of Genome Interpretation held in 2018. In the challenge, the participants were asked to predict effects on yeast growth caused by missense variants of human calmodulin, a highly conserved protein in eukaryotic cells sensing calcium concentration. The performance of predictors implementing different algorithms and methods is similar. Most predictors are able to identify the deleterious or tolerated variants with modest accuracy, with a baseline predictor based purely on sequence conservation slightly outperforming the submitted predictions. Nevertheless, we think that the accuracy of predictions remains far from satisfactory, and the field awaits substantial improvements. The most poorly predicted variants in this round surround functional CALM1 sites that bind calcium or peptide, which suggests that better incorporation of structural analysis may help improve predictions.

Whole-genome sequencing (WGS) holds great potential as a diagnostic test. However, the majority of patients currently undergoing WGS lack a molecular diagnosis, largely due to the vast number of undiscovered disease genes and our inability to assess the pathogenicity of most genomic variants. The CAGI SickKids challenges attempted to address this knowledge gap by assessing state-of-the-art methods for clinical phenotype prediction from genomes. CAGI4 and CAGI5 participants were provided with WGS data and clinical descriptions of 25 and 24 undiagnosed patients from the SickKids Genome Clinic Project, respectively. Predictors were asked to identify primary and secondary causal variants. In addition, for CAGI5, groups had to match each genome to one of three disorder categories (neurologic, ophthalmologic, and connective), and separately to each patient. The performance of matching genomes to categories was no better than random but two groups performed significantly better than chance in matching genomes to patients. Two of the ten variants proposed by two groups in CAGI4 were deemed to be diagnostic, and several proposed pathogenic variants in CAGI5 are good candidates for phenotype expansion. We discuss implications for improving in silico assessment of genomic variants and identifying new disease genes.

Many computational approaches estimate the effect of coding variants, but their predictions often disagree with each other. These contradictions confound users and raise questions regarding reliability. Performance assessments can indicate the expected accuracy for each method and highlight advantages and limitations. The Critical Assessment of Genome Interpretation (CAGI) community aims to organize objective and systematic assessments: They challenge predictors on unpublished experimental and clinical data and assign independent assessors to evaluate the submissions. We participated in CAGI experiments as predictors, using the Evolutionary Action (EA) method to estimate the fitness effect of coding mutations. EA is untrained, uses homology information, and relies on a formal equation: The fitness effect equals the functional sensitivity to residue changes multiplied by the magnitude of the substitution. In previous CAGI experiments (between 2011 and 2016), our submissions aimed to predict the protein activity of single mutants. In 2018 (CAGI5), we also submitted predictions regarding clinical associations, folding stability, and matching genomic data with phenotype. For all these diverse challenges, we used EA to predict the fitness effect of variants, adjusted to specifically address each question. Our submissions had consistently good performance, suggesting that EA predicts reliably the effects of genetic variants.

Genotype-phenotype relationships shape health and population fitness but remain difficult to predict and interpret. Here, we apply an evolutionary action method to de novo missense variants in whole-exome sequences of individuals with autism spectrum disorder (ASD) to unravel genes and pathways connected to ASD. Evolutionary action predicts the impact of missense variants on protein function by measuring the fitness effect based on phylogenetic distances and substitution odds in homologous gene sequences. By examining de novo missense variants in 2384 individuals with ASD (probands) compared to matched siblings without ASD, we found missense variants in 398 genes representing 23 pathways that were biased toward higher evolutionary action scores than expected by random chance; these pathways were involved in axonogenesis, synaptic transmission, and neurodevelopment. The predicted fitness impact of de novo and inherited missense variants in candidate genes correlated with the IQ of individuals with ASD, even for new gene candidates. Taking an evolutionary action method, we detected those missense variants most likely to contribute to ASD pathogenesis and elucidated their phenotypic impact. This approach could be applied to integrate missense variants across a patient cohort to identify genes contributing to a shared phenotype in other complex diseases.

We present a Chemistry and Structure Screen Integrated Efficiently (CASSIE) approach (named for Greek prophet Cassandra) to design inhibitors for cancer biology and pathogenesis. CASSIE provides an effective path to target master keys to control the repair-replication interface for cancer cells and SARS CoV-2 pathogenesis as exemplified here by specific targeting of Poly(ADP-ribose) glycohydrolase (PARG) and ADP-ribose glycohydrolase ARH3 macrodomains plus SARS CoV-2 nonstructural protein 3 (Nsp3) Macrodomain 1 (Mac1) and Nsp15 nuclease. As opposed to the classical massive effort employing libraries with large numbers of compounds against single proteins, we make inhibitor design for multiple targets efficient. Our compact, chemically diverse, 5000 compound Goldilocks (GL) library has an intermediate number of compounds sized between fragments and drugs with predicted favorable ADME (absorption, distribution, metabolism, and excretion) and toxicological profiles. Amalgamating our core GL library with an approved drug (AD) library, we employ a combined GLAD library virtual screen, enabling an effective and efficient design cycle of ranked computer docking, top hit biophysical and cell validations, and defined bound structures using human proteins or their avatars. As new drug design is increasingly pathway directed as well as molecular and mechanism based, our CASSIE approach facilitates testing multiple related targets by efficiently turning a set of interacting drug discovery problems into a tractable medicinal chemistry engineering problem of optimizing affinity and ADME properties based upon early co-crystal structures. Optimization efforts are made efficient by a computationally-focused iterative chemistry and structure screen. Thus, we herein describe and apply CASSIE to define prototypic, specific inhibitors for PARG vs distinct inhibitors for the related macrodomains of ARH3 and SARS CoV-2 Nsp3 plus the SARS CoV-2 Nsp15 RNA nuclease.

Arrival of the novel SARS-CoV-2 has launched a worldwide effort to identify both pre-approved and novel therapeutics targeting the viral proteome, highlighting the urgent need for efficient drug discovery strategies. Even with effective vaccines, infection is possible, and at-risk populations would benefit from effective drug compounds that reduce the lethality and lasting damage of COVID-19 infection. The CoV-2 MacroD-like macrodomain (Mac1) is implicated in viral pathogenicity by disrupting host innate immunity through its mono (ADP-ribosyl) hydrolase activity, making it a prime target for antiviral therapy. We therefore solved the structure of CoV-2 Mac1 from non-structural protein 3 (Nsp3) and applied structural and sequence-based genetic tracing, including newly determined A. pompejana MacroD2 and GDAP2 amino acid sequences, to compare and contrast CoV-2 Mac1 with the functionally related human DNA-damage signaling factor poly (ADP-ribose) glycohydrolase (PARG). Previously, identified targetable features of the PARG active site allowed us to develop a pharmacologically useful PARG inhibitor (PARGi). Here, we developed a focused chemical library and determined 6 novel PARGi X-ray crystal structures for comparative analysis. We applied this knowledge to discovery of CoV-2 Mac1 inhibitors by combining computation and structural analysis to identify PARGi fragments with potential to bind the distal-ribose and adenosyl pockets of the CoV-2 Mac1 active site. Scaffold development of these PARGi fragments has yielded two novel compounds, PARG-345 and PARG-329, that crystallize within the Mac1 active site, providing critical structure-activity data and a pathway for inhibitor optimization. The reported structural findings demonstrate ways to harness our PARGi synthesis and characterization pipeline to develop CoV-2 Mac1 inhibitors targeting the ADP-ribose active site. Together, these structural and computational analyses reveal a path for accelerating development of antiviral therapeutics from pre-existing drug optimization pipelines.

DNA-dependent protein kinase catalytic subunit (DNA-PKcs) is a key member of the phosphatidylinositol-3 kinase-like (PIKK) family of protein kinases with critical roles in DNA-double strand break repair, transcription, metastasis, mitosis, RNA processing, and innate and adaptive immunity. The absence of DNA-PKcs from many model organisms has led to the assumption that DNA-PKcs is a vertebrate-specific PIKK. Here, we find that DNA-PKcs is widely distributed in invertebrates, fungi, plants, and protists, and that threonines 2609, 2638, and 2647 of the ABCDE cluster of phosphorylation sites are highly conserved amongst most Eukaryotes. Furthermore, we identify highly conserved amino acid sequence motifs and domains that are characteristic of DNA-PKcs relative to other PIKKs. These include residues in the Forehead domain and a novel motif we have termed YRPD, located in an α helix C-terminal to the ABCDE phosphorylation site loop. Combining sequence with biochemistry plus structural data on human DNA-PKcs unveils conserved sequence and conformational features with functional insights and implications. The defined generally progressive DNA-PKcs sequence diversification uncovers conserved functionality supported by Evolutionary Trace analysis, suggesting that for many organisms both functional sites and evolutionary pressures remain identical due to fundamental cell biology. The mining of cancer genomic data and germline mutations causing human inherited disease reveal that robust DNA-PKcs activity in tumors is detrimental to patient survival, whereas germline mutations compromising function are linked to severe immunodeficiency and neuronal degeneration. We anticipate that these collective results will enable ongoing DNA-PKcs functional analyses with biological and medical implications.

An increasing number of mitochondrial diseases are found to be caused by pathogenic variants in nuclear encoded mitochondrial aminoacyl-tRNA synthetases. FARS2 encodes mitochondrial phenylalanyl-tRNA synthetase (mtPheRS) which transfers phenylalanine to its cognate tRNA in mitochondria. Since the first case was reported in 2012, a total of 21 subjects with FARS2 deficiency have been reported to date with a spectrum of disease severity that falls between two phenotypes; early onset epileptic encephalopathy and a less severe phenotype characterized by spastic paraplegia. In this report, we present an additional 15 individuals from 12 families who are mostly Arabs homozygous for the pathogenic variant Y144C, which is associated with the more severe early onset phenotype. The total number of unique pathogenic FARS2 variants known to date is 21 including three different partial gene deletions reported in four individuals. Except for the large deletions, all variants but two (one in-frame deletion of one amino acid and one splice-site variant) are missense. All large deletions and the single splice-site variant are in trans with a missense variant. This suggests that complete loss of function may be incompatible with life. In this report, we also review structural, functional, and evolutionary significance of select FARS2 pathogenic variants reported here.

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer diagnosed in more than 200,000 women each year1 and is recalcitrant to targeted therapies(2,3). Although TNBCs harbor multiple hyperactive receptor tyrosine kinases (RTKs)(4–8), RTK inhibitors have been largely ineffective in TNBC patients thus far. We developed a broadly effective therapeutic strategy for TNBC that is based on combined inhibition of receptors that share the negative regulator PTPN12. Previously, we and others identified the tyrosine phosphatase PTPN12 as a tumor suppressor that is frequently inactivated in TNBC(9,10). PTPN12 restrains several RTKs(9,11–17), suggesting that PTPN12 deficiency leads to aberrant activation of multiple RTKs and a co-dependency on these receptors. This in turn leads to the therapeutic hypothesis that PTPN12-deficient TNBCs may be responsive to combined RTK inhibition. However, the repertoire of RTKs that are restrained by PTPN12 in human cells has not been systematically explored. By methodically identifying the suite of RTK substrates (MET, PDGFRb, EGFR, and others) inhibited by PTPN12, we rationalized a combination RTK-inhibitor therapy that induced potent tumor regression across heterogeneous models of TNBC. Orthogonal approaches revealed that PTPN12 was recruited to and inhibited these receptors after ligand stimulation, thereby serving as a feedback mechanism to limit receptor signaling. Cancer-associated mutation of PTPN12 or reduced PTPN12 protein levels diminished this feedback mechanism, leading to aberrant activity of these receptors. Restoring PTPN12 protein levels restrained signaling from RTKs, including PDGFRb and MET, and impaired TNBC survival. In contrast with single agents, combined inhibitors targeting the PDGFRb and MET receptors induced the apoptosis in TNBC cells in vitro and in vivo. This therapeutic strategy resulted in tumor regressions in chemo-refractory patient-derived TNBC models. Notably, response correlated with PTPN12 deficiency, suggesting that impaired receptor feedback may establish a combined addiction to these proto-oncogenic receptors. Taken together, our data provide a rationale for combining RTK inhibitors in TNBC and other malignancies that lack receptor-activating mutations.

Gnathodiaphyseal dysplasia (GDD; OMIM #166260) is an ultra-rare autosomal dominant disorder caused by heterozygous mutation in the anoctamin 5 (ANO5) gene and features fibro-osseous lesions of the jawbones, bone fragility with recurrent fractures, and bowing/sclerosis of tubular bones. The physiologic role of ANO5 is unknown. We report a 5-year-old boy with a seemingly atypical and especially severe presentation of GDD and unique ANO5 mutation. Severe osteopenia was associated with prenatal femoral fractures, recurrent postnatal fractures, and progressive bilateral enlargement of his maxilla and mandible beginning at ~ 2 months-of-age that interfered with feeding and speech and required four debulking operations. Histopathological analysis revealed benign fibro-osseous lesions resembling cemento-ossifying fibromas of the jaw without psammomatoid bodies. A novel, de novo, heterozygous, missense mutation was identified in exon 15 of ANO5 (c.1553G>A; p.Gly518Glu). Our findings broaden the phenotypic and molecular spectra of GDD. Fractures early in life with progressive facial swelling are key features. We assessed his response to a total of 7 pamidronate infusions commencing at age 15 months. Additional reports must further elucidate the phenotype, explore any genotype-phenotype correlation, and evaluate treatments.

A major challenge in genome interpretation is to estimate the fitness effect of coding variants of unknown significance (VUS). Labor, limited understanding of protein functions, and lack of assays generally limit direct experimental assessment of VUS, and make robust and accurate computational approaches a necessity. Often, however, algorithms that predict mutational effect disagree amongst themselves and with experimental data, slowing their adoption for clinical diagnostics. To objectively assess such methods, the Critical Assessment of Genome Interpretation (CAGI) community organizes contests to predict unpublished experimental data, available only to CAGI assessors. We review here the CAGI performance of Evolutionary Action (EA) predictions of mutational impact. EA models the fitness effect of coding mutations analytically, as a product of the gradient of the fitness landscape times the perturbation size. In practice, these terms are computed from phylogenetic considerations as the functional sensitivity of the mutated site and as the magnitude of amino acid substitution, respectively, and yield the percentage loss of wild-type activity. In five CAGI challenges, EA consistently performed on par or better than sophisticated machine learning approaches. This objective assessment suggests that a simple differential model of evolution can interpret the fitness effect of coding variations, opening diverse clinical applications.

The Critical Assessment of Genome Interpretation (CAGI) is a global community experiment to objectively assess computational methods for predicting phenotypic impacts of genomic variation. One of the 2015-2016 competitions focused on predicting the influence of mutations on the allosteric regulation of human liver pyruvate kinase. More than 30 different researchers accessed the challenge data. However, only four groups accepted the challenge. Features used for predictions ranged from evolutionary constraints, mutant site locations relative to active and effector binding sites, and computational docking outputs. Despite the range of expertise and strategies used by predictors, the best predictions were marginally greater than random for modified allostery resulting from mutations. In contrast, several groups successfully predicted which mutations severely reduced enzymatic activity. Nonetheless, poor predictions of allostery stands in stark contrast to the impression left by more than 700 PubMed entries identified using the identifiers "computational + allosteric." This contrast highlights a specialized need for new computational tools and utilization of benchmarks that focus on allosteric regulation.

Computational prediction yields efficient and scalable initial assessments of how variants of unknown significance may affect human health. However, when discrepancies between these predictions and direct experimental measurements of functional impact arise, inaccurate computational predictions are frequently assumed as the source. Here, we present a methodological analysis indicating that shortcomings in both computational and biological data can contribute to these disagreements. We demonstrate that incomplete assaying of multifunctional proteins can affect the strength of correlations between prediction and experiments; a variant's full impact on function is better quantified by considering multiple assays that probe an ensemble of protein functions. Additionally, many variants predictions are sensitive to protein alignment construction and can be customized to maximize relevance of predictions to a specific experimental question. We conclude that inconsistencies between computation and experiment can often be attributed to the fact that they do not test identical hypotheses. Aligning the design of the computational input with the design of the experimental output will require cooperation between computational and biological scientists, but will also lead to improved estimations of computational prediction accuracy and a better understanding of the genotype–phenotype relationship.

Autosomal dominant hyper-IgE syndrome (AD-HIES) is caused by dominant-negative mutations in STAT3; however, the molecular basis for mutant STAT3 allele dysfunction is unclear and treatment remains supportive. We hypothesized that AD-HIES mutations decrease STAT3 protein stability and that mutant STAT3 activity can be improved by agents that increase chaperone protein activity. We used computer modeling to characterize the effect of STAT3 mutations on protein stability. We measured STAT3 protein half-life (t1/2) and determined levels of STAT3 phosphorylated on tyrosine (Y) 705 (pY-STAT3) and mRNA levels of STAT3 gene targets in Epstein-Barr virus-transformed B (EBV) cells, human peripheral blood mononuclear cells (PBMCs), and mouse splenocytes incubated without or with chaperone protein modulators-HSF1A, a small-molecule TRiC modulator, or geranylgeranylacetone (GGA), a drug that upregulates heat shock protein (HSP) 70 and HSP90. Computer modeling predicted that 81% of AD-HIES mutations are destabilizing. STAT3 protein t1/2 in EBV cells from AD-HIES patients with destabilizing STAT3 mutations was markedly reduced. Treatment of EBV cells containing destabilizing STAT3 mutations with either HSF1A or GGA normalized STAT3 t1/2, increased pY-STAT3 levels, and increased mRNA levels of STAT3 target genes up to 79% of control. In addition, treatment of human PBMCs or mouse splenocytes containing destabilizing STAT3 mutations with either HSF1A or GGA increased levels of cytokine-activated pY-STAT3 within human CD4+ and CD8+ T cells and numbers of IL-17-producing CD4+ mouse splenocytes, respectively. Thus, most AD-HIES STAT3 mutations are destabilizing; agents that modulate chaperone protein function improve STAT3 stability and activity in T cells and may provide a specific treatment.

The availability of disease-specific genomic data is critical for developing new computational methods that predict the pathogenicity of human variants and advance the field of precision medicine. However, the lack of gold standards to properly train and benchmark such methods is one of the greatest challenges in the field. In response to this challenge, the scientific community is invited to participate in the Critical Assessment for Genome Interpretation (CAGI), where unpublished disease variants are available for classification by in silico methods. As part of the CAGI-5 challenge, we evaluated the performance of 18 submissions and three additional methods in predicting the pathogenicity of single nucleotide variants (SNVs) in checkpoint kinase 2 (CHEK2) for cases of breast cancer in Hispanic females. As part of the assessment, the efficacy of the analysis method and the setup of the challenge were also considered. The results indicated that though the challenge could benefit from additional participant data, the combined generalized linear model analysis and odds of pathogenicity analysis provided a framework to evaluate the methods submitted for SNV pathogenicity identification and for comparison to other available methods. The outcome of this challenge and the approaches used can help guide further advancements in identifying SNV-disease relationships.

The CAGI-5 pericentriolar material 1 (PCM1) challenge aimed to predict the effect of 38 transgenic human missense mutations in the PCM1 protein implicated in schizophrenia. Participants were provided with 16 benign variants (negative controls), 10 hypomorphic, and 12 loss of function variants. Six groups participated and were asked to predict the probability of effect and standard deviation associated to each mutation. Here, we present the challenge assessment. Prediction performance was evaluated using different measures to conclude in a final ranking which highlights the strengths and weaknesses of each group. The results show a great variety of predictions where some methods performed significantly better than others. Benign variants played an important role as negative controls, highlighting predictors biased to identify disease phenotypes. The best predictor, Bromberg lab, used a neural-network-based method able to discriminate between neutral and non-neutral single nucleotide polymorphisms. The CAGI-5 PCM1 challenge allowed us to evaluate the state of the art techniques for interpreting the effect of novel variants for a difficult target protein.

The Critical Assessment of Genome Interpretation-5 intellectual disability challenge asked to use computational methods to predict patient clinical phenotypes and the causal variant(s) based on an analysis of their gene panel sequence data. Sequence data for 74 genes associated with intellectual disability (ID) and/or autism spectrum disorders (ASD) from a cohort of 150 patients with a range of neurodevelopmental manifestations (i.e. ID, autism, epilepsy, microcephaly, macrocephaly, hypotonia, ataxia) have been made available for this challenge. For each patient, predictors had to report the causative variants and which of the seven phenotypes were present. Since neurodevelopmental disorders are characterized by strong comorbidity, tested individuals often present more than one pathological condition. Considering the overall clinical manifestation of each patient, the correct phenotype has been predicted by at least one group for 93 individuals (62%). ID and ASD were the best predicted among the seven phenotypic traits. Also, causative or potentially pathogenic variants were predicted correctly by at least one group. However, the prediction of the correct causative variant seems to be insufficient to predict the correct phenotype. In some cases, the correct prediction has been supported by rare or common variants in genes different from the causative one.

Thermodynamic stability is a fundamental property shared by all proteins. Changes in stability due to mutation are a widespread molecular mechanism in genetic diseases. Methods for the prediction of mutation-induced stability change have typically been developed and evaluated on incomplete and/or biased data sets. As part of the Critical Assessment of Genome Interpretation (CAGI), we explored the utility of high-throughput variant stability profiling (VSP) assay data as an alternative for the assessment of computational methods and evaluated state-of-the-art predictors against over 7,000 non-synonymous variants from two proteins. We found that predictions were modestly correlated with actual experimental values. Predictors fared better when evaluated as classifiers of extreme stability effects. While different methods emerged as top-performers depending on the metric, it is non-trivial to draw conclusions on their adoption or improvement. Our analyses revealed that only 16% of all variants in VSP assays could be confidently defined as stability-affecting. Furthermore, it is unclear to what extent VSP abundance scores were reasonable proxies for the stability-related quantities that participating methods were designed to predict. Overall, our observations underscore the need for clearly defined objectives when developing and using both computational and experimental methods in the context of measuring variant impact.

Accurate prediction of the impact of genomic variation on phenotype is a major goal of computational biology and an important contributor to personalized medicine. Computational predictions can lead to a better understanding of the mechanisms underlying genetic diseases, including cancer, but their adoption requires thorough and unbiased assessment. Cystathionine-beta-synthase (CBS) is an enzyme that catalyzes the first step of the transsulfuration pathway, from homocysteine to cystathionine, and in which variations are associated with human hyperhomocysteinemia and homocystinuria. We have created a computational challenge under the CAGI framework to evaluate how well different methods can predict the phenotypic effect(s) of CBS single amino acid substitutions using a blinded experimental data set. CAGI participants were asked to predict yeast growth based on the identity of the mutations. The performance of the methods was evaluated using several metrics. The CBS challenge highlighted the difficulty of predicting the phenotype of an ex vivo system in a model organism when classification models were trained on human disease data. We also discuss the variations in difficulty of prediction for known benign and deleterious variants, as well as identify methodological and experimental constraints with lessons to be learned for future challenges. ispartof: HUMAN MUTATION vol:40 issue:9 pages:1530-1545 ispartof: location:United States status: published

Frataxin (FXN) is a highly conserved protein found in prokaryotes and eukaryotes that is required for efficient regulation of cellular iron homeostasis. Experimental evidence associates amino acid substitutions of the FXN to Friedreich Ataxia, a neurodegenerative disorder. Recently, new thermodynamic experiments have been performed to study the impact of somatic variations identified in cancer tissues on protein stability. The Critical Assessment of Genome Interpretation (CAGI) data provider at the University of Rome measured the unfolding free energy of a set of variants (FXN challenge data set) with far-UV circular dichroism and intrinsic fluorescence spectra. These values have been used to calculate the change in unfolding free energy between the variant and wild-type proteins at zero concentration of denaturant ( Δ Δ G H 2 O ) . The FXN challenge data set, composed of eight amino acid substitutions, was used to evaluate the performance of the current computational methods for predicting the Δ Δ G H 2 O value associated with the variants and to classify them as destabilizing and not destabilizing. For the fifth edition of CAGI, six independent research groups from Asia, Australia, Europe, and North America submitted 12 sets of predictions from different approaches. In this paper, we report the results of our assessment and discuss the limitations of the tested algorithms.

TP53 is the most frequently altered gene in head and neck squamous cell carcinoma (HNSCC), with mutations occurring in over two thirds of cases; however, the predictive response of these mutations to cisplatin-based therapy remains elusive. In the current study, we evaluate the ability of the Evolutionary Action score of TP53-coding variants (EAp53) to predict the impact of TP53 mutations on response to chemotherapy. The EAp53 approach clearly identifies a subset of high-risk TP53 mutations associated with decreased sensitivity to cisplatin both in vitro and in vivo in preclinical models of HNSCC. Furthermore, EAp53 can predict response to treatment and, more importantly, a survival benefit for a subset of head and neck cancer patients treated with platinum-based therapy. Prospective evaluation of this novel scoring system should enable more precise treatment selection for patients with HNSCC. Cancer Res; 75(7); 1205–15. ©2015 AACR.

Correct phenotypic interpretation of variants of unknown significance for cancer-associated genes is a diagnostic challenge as genetic screenings gain in popularity in the next-generation sequencing era. The Critical Assessment of Genome Interpretation (CAGI) experiment aims to test and define the state of the art of genotype-phenotype interpretation. Here, we present the assessment of the CAGI p16INK4a challenge. Participants were asked to predict the effect on cellular proliferation of ten variants for the p16INK4a tumor suppressor, a cyclin-dependent kinase inhibitor encoded by the CDKN2A gene. Twenty-two pathogenicity predictors were assessed with a variety of accuracy measures for reliability in a medical context. Different assessment measures were combined in an overall ranking to provide more robust results. The R scripts used for assessment are publicly available from a GitHub repository for future use in similar assessment exercises. Despite a limited test-set size, our findings show a variety of results, with some methods performing significantly better. Methods combining different strategies frequently outperform simpler approaches. The best predictor, Yang&Zhou lab, uses a machine learning method combining an empirical energy function measuring protein stability with an evolutionary conservation term. The p16INK4a challenge highlights how subtle structural effects can neutralize otherwise deleterious variants. This article is protected by copyright. All rights reserved.

Understanding the molecular basis of protein function remains a central goal of biology, with the hope to elucidate the role of human genes in health and in disease, and to rationally design therapies through targeted molecular perturbations. We review here some of the computational techniques and resources available for characterizing a critical aspect of protein function – those mediated by protein–protein interactions (PPI). We describe several applications and recent successes of the Evolutionary Trace (ET) in identifying molecular events and shapes that underlie protein function and specificity in both eukaryotes and prokaryotes. ET is a part of analytical approaches based on the successes and failures of evolution that enable the rational control of PPI.

p53 (TP53) is the most frequently mutated gene in squamous cell carcinomas (SCCs) of the skin and head and neck. Certain p53 mutations are oncogenic and promote invasion and metastasis in SCCs. However, it is unclear how the oncogenic function of mutant p53 is modulated by other molecular alterations that co-exist in SCCs. Here, we show that deletion of the p53 gene and activation of an endogenous p53(R172H) gain-of-function mutation in the skin induce carcinomas with similar kinetics and penetrance. Deletion of p53 induced primarily well-differentiated SCCs. However, most of the tumours induced by p53(R172H) were poorly differentiated SCCs, the only metastatic tumours in this model. These tumours expressed higher levels of cyclin D1 than the well-differentiated SCCs and spindle carcinomas that developed in these mice. Unexpectedly, metastasis was not observed in mice that developed spindle carcinomas, which expressed high levels of the tumour suppressors p16(Ink4a) and p19(Arf) , encoded by Cdkn2a, a gene frequently deleted in human SCCs. Remarkably, deletion of the Cdkn2a gene in p53(R172H) -induced SCCs promoted a dramatic increase in metastasis rates and a shorter survival in mice that developed these tumours, compared with those observed in mice with tumours in which Cdkn2a was deleted in the presence of a p53 loss-of-function mutation or wild-type p53. Accordingly, the survival of patients with head and neck SCCs bearing co-occurring high-risk p53 mutations and CDKN2A homozygous deletions was much shorter than that of patients with tumours in which high-risk p53 mutations did not contain CDKN2A homozygous deletions, or that of patients with tumours in which homozygous CDKN2A deletions co-existed with either low-risk p53 mutations or potential loss-of-function mutations in p53. These findings genetically identify a population of SCC patients with worst outcomes and will help to predict outcomes according to the p53 status and alterations in CDKN2A. Copyright © 2016 Pathological Society of Great Britain and Ireland. Published by John WileySons, Ltd.

Desmosterolosis, a rare disorder of cholesterol biosynthesis, is caused by mutations in DHCR24, the gene encoding the enzyme 24-dehydrocholesterol reductase (DHCR24). To date, desmosterolosis has been described in only two patients. Here we report on a third patient with desmosterolosis who presented after delivery with relative macrocephaly, mild arthrogryposis, and dysmorphic facial features. Brain MRI revealed hydrocephalus, thickening of the tectum and massa intermedia, mildly effaced gyral pattern, underopercularization, and a thin corpus callosum. The diagnosis of desmosterolosis was established by detection of significant elevation of plasma desmosterol levels and reduced enzyme activity of DHCR24 upon expression of the patient’s DHCR24 cDNA in yeast. The patient was found to be a compound heterozygote for c.281G>A (p.R94H) and c.1438G->A (p.E480K) mutations. Structural and evolutionary analyses showed that residue R94 resides at the flavin adenine dinucleotide (FAD) binding site and is strictly conserved throughout evolution, while residue E480 is less conserved, but the charge shift substitution is accompanied by drastic changes in the local protein environment of that residue. We compare the phenotype of our patient with previously reported cases.

When seeking to reproduce results derived from whole-exome or genome sequencing data that could advance precision medicine, the time and expense required to produce a patient cohort make data repurposing an attractive option. The first step in repurposing is setting some quality baseline for the data so that conclusions are not spurious. This is difficult because there can be variations in quality from center to center, clinic to clinic and even patient to patient. Here, we assessed the quality of the whole-exome germline mutations of TCGA cancer patients using patterns of nucleotide substitution and negative selection against impactful mutations. We estimated the fraction of false positive variant calls for each exome with respect to two gold standard germline exomes, and found large variability in the quality of SNV calls between samples, cancer subtypes, and institutions. We then demonstrated how variant features, such as the average base quality for reads supporting an allele, can be used to identify sample-specific filtering parameters to optimize the removal of false positive calls. We concluded that while these germlines have many potential applications to precision medicine, users should assess the quality of the available exome data prior to use and perform additional filtering steps.

Although cisplatin has played a role in “standard-of-care” multimodality therapy for patients with advanced squamous cell carcinoma of the head and neck (HNSCC), the rate of treatment failure remains particularly high for patients receiving cisplatin whose tumors have mutations in the TP53 gene. We found that cisplatin treatment of HNSCC cells with mutant TP53 leads to arrest of cells in the G2 phase of the cell cycle, leading us to hypothesize that the wee-1 kinase inhibitor MK-1775 would abrogate the cisplatin-induced G2 block and thereby sensitize isogenic HNSCC cells with mutant TP53 or lacking p53 expression to cisplatin. We tested this hypothesis using clonogenic survival assays, flow cytometry, and in vivo tumor growth delay experiments with an orthotopic nude mouse model of oral tongue cancer. We also used a novel TP53 mutation classification scheme to identify which TP53 mutations are associated with limited tumor responses to cisplatin treatment. Clonogenic survival analyses indicate that nanomolar concentration of MK-1775 sensitizes HNSCC cells with high-risk mutant p53 to cisplatin. Consistent with its ability to chemosensitize, MK-1775 abrogated the cisplatin-induced G2 block in p53-defective cells leading to mitotic arrest associated with a senescence-like phenotype. Furthermore, MK-1775 enhanced the efficacy of cisplatin in vivo in tumors harboring TP53 mutations. These results indicate that HNSCC cells expressing high-risk p53 mutations are significantly sensitized to cisplatin therapy by the selective wee-1 kinase inhibitor, supporting the clinical evaluation of MK-1775 in combination with cisplatin for the treatment of patients with TP53 mutant HNSCC. Mol Cancer Ther; 14(2); 608–19. ©2014 AACR.

Summary Liver cancer has the second highest worldwide cancer mortality rate and has limited therapeutic options. We analyzed 363 hepatocellular carcinoma (HCC) cases by whole-exome sequencing and DNA copy number analyses, and we analyzed 196 HCC cases by DNA methylation, RNA, miRNA, and proteomic expression also. DNA sequencing and mutation analysis identified significantly mutated genes, including LZTR1 , EEF1A1 , SF3B1 , and SMARCA4 . Significant alterations by mutation or downregulation by hypermethylation in genes likely to result in HCC metabolic reprogramming ( ALB , APOB , and CPS1 ) were observed. Integrative molecular HCC subtyping incorporating unsupervised clustering of five data platforms identified three subtypes, one of which was associated with poorer prognosis in three HCC cohorts. Integrated analyses enabled development of a p53 target gene expression signature correlating with poor survival. Potential therapeutic targets for which inhibitors exist include WNT signaling, MDM4, MET, VEGFA, MCL1, IDH1, TERT, and immune checkpoint proteins CTLA-4, PD-1, and PD-L1.

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.

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. Cancer Res; 75(7); 1527–36. ©2015 AACR.

The relationship between genotype mutations and phenotype variations determines health in the short term and evolution over the long term, and it hinges on the action of mutations on fitness. A fundamental difficulty in determining this action, however, is that it depends on the unique context of each mutation, which is complex and often cryptic. As a result, the effect of most genome variations on molecular function and overall fitness remains unknown and stands apart from population genetics theories linking fitness effect to polymorphism frequency. Here, we hypothesize that evolution is a continuous and differentiable physical process coupling genotype to phenotype. This leads to a formal equation for the action of coding mutations on fitness that can be interpreted as a product of the evolutionary importance of the mutated site with the difference in amino acid similarity. Approximations for these terms are readily computable from phylogenetic sequence analysis, and we show mutational, clinical, and population genetic evidence that this action equation predicts the effect of point mutations in vivo and in vitro in diverse proteins, correlates disease-causing gene mutations with morbidity, and determines the frequency of human coding polymorphisms, respectively. Thus, elementary calculus and phylogenetics can be integrated into a perturbation analysis of the evolutionary relationship between genotype and phenotype that quantitatively links point mutations to function and fitness and that opens a new analytic framework for equations of biology. In practice, this work explicitly bridges molecular evolution with population genetics with applications from protein redesign to the clinical assessment of human genetic variations.

Protein kinase A (PKA) substrate phosphorylation is facilitated through its co-localization with its signaling partner by A-kinase anchoring proteins (AKAPs). mAKAP (muscle-selective AKAP) localizes PKA and its substrates such as phosphodiesterase-4D3 (PDE4D3), ryanodine receptor, and protein phosphatase 2A (PP2A) to the sarcoplasmic reticulum and perinuclear space. The genetic role of mAKAP, in modulating PKA/PDE4D3 molecular signaling during cardiac diseases, remains unclear. The purpose of this study was to examine the effects of naturally occurring mutations in human mAKAP on PKA and PDE4D3 signaling. We have recently identified potentially important human mAKAP coding non-synonymous polymorphisms located within or near key protein binding sites critical to β-adrenergic receptor signaling. Three mutations (P1400S, S2195F, and L717V) were cloned and transfected into a mammalian cell line for the purpose of comparing whether those substitutions disrupt mAKAP binding to PKA or PDE4D3. Immunoprecipitation study of mAKAP-P1400S, a mutation located in the mAKAP-PDE4D3 binding site, displayed a significant reduction in binding to PDE4D3, with no significant changes in PKA binding or PKA activity. Conversely, mAKAP-S2195F, a mutation located in mAKAP-PP2A binding site, showed significant increase in both binding propensity to PKA and PKA activity. Additionally, mAKAP-L717V, a mutation flanking the mAKAP-spectrin repeat domain, exhibited a significant increase in PKA binding compared to wild type, but there was no change in PKA activity. We also demonstrate specific binding of wild-type mAKAP to PDE4D3. Binding results were demonstrated using immunoprecipitation and confirmed with surface plasmon resonance (Biacore-2000); functional results were demonstrated using activity assays, Ca(2+) measurements, and Western blot. Comparative analysis of the binding responses of mutations to mAKAP could provide important information about how these mutations modulate signaling.

Deficiency of carbamoyl phosphate synthetase I (CPSI) results in hyperammonemia ranging from neonatally lethal to environmentally induced adult-onset disease. Over 24 years, analysis of tissue and DNA samples from 205 unrelated individuals diagnosed with CPSI deficiency (CPSID) detected 192 unique CPS1 gene changes, of which 130 are reported here for the first time. Pooled with the already reported mutations, they constitute a total of 222 changes, including 136 missense, 15 nonsense, 50 changes of other types resulting in enzyme truncation, and 21 other changes causing in-frame alterations. Only ∼10% of the mutations recur in unrelated families, predominantly affecting CpG dinucleotides, further complicating the diagnosis because of the "private" nature of such mutations. Missense changes are unevenly distributed along the gene, highlighting the existence of CPSI regions having greater functional importance than other regions. We exploit the crystal structure of the CPSI allosteric domain to rationalize the effects of mutations affecting it. Comparative modeling is used to create a structural model for the remainder of the enzyme. Missense changes are found to directly correlate, respectively, with the one-residue evolutionary importance and inversely correlate with solvent accessibility of the mutated residue. This is the first large-scale report of CPS1 mutations spanning a wide variety of molecular defects highlighting important regions in this protein.

Mitochondrial neurogastrointestinal encephalopathy(MNGIE) is an autosomal recessive disease characterized by gastrointestinal dysmotility, ophthalmoplegia, ptosis, cachexia, peripheral neuropathy, and leukoencephalopathy.1 It is caused by mutations in TYMP, a gene encoding thymidine phosphorylase.2 Deficiency in thymidine phosphorylase leads to marked elevation of thymidine and deoxyuridine levels, resulting in imbalance of the mitochondrial nucleotide pool. It has been hypothesized that alteration of deoxynucleotide triphosphate homeostasis results in improper mitochondrial DNA (mtDNA) replication that in turn causes mtDNA deletion, point mutations, and depletion.2 Although TYMP is the only gene known to be associated with typical MNGIE, a few cases of MNGIE-like phenotype associated with multiple mtDNA deletions3 or mutations in POLG4 have been reported. Although mtDNA depletion is one of the characteristic molecular findings in MNGIE, mtDNA depletion is also associated with mutations in nuclear genes that affect mitochondrial nucleotide metabolism and mtDNA replication. These genes exert their action by either directly affecting the mtDNA replication fork (POLG, POLG2, and PEO1) or regulating the mitochondrial deoxynucleotide triphosphate pool (TYMP, TK2, DGUOK, SUCLA2, SUCLG1, and ANT1).5 Mutations in RRM2B (GenBank {"type":"entrez-nucleotide","attrs":{"text":"AB036532","term_id":"60391448","term_text":"AB036532"}}AB036532) were recently implicated in a novel mitochondrial depletion syndrome presenting with early-onset seizures, hypotonia, diarrhea, renal tubulopathy, lactic acidosis, and early lethality.6–9 Here we describe a 42-year-old woman with clinical findings strongly suggestive for MNGIE but whose plasma thymidine level was normal. Further analysis showed severe mtDNA depletion in muscle tissue, and sequencing analysis revealed 2 pathogenic mutations in the RRM2B gene.

Evolutionary Action analyses of The Cancer Gene Atlas data sets show that many specific p53 missense and gain-of-function mutations are selectively overrepresented and functional in high-grade serous ovarian cancer (HGSC). As homozygous alleles, p53 mutants are differentially associated with specific loss of heterozygosity (R273; chromosome 17); copy number variation (R175H; chromosome 9); and up-stream, cancer-related regulatory pathways. The expression of immune-related cytokines was selectively related to p53 status, showing for the first time that specific p53 mutants impact, and are related to, the immune subtype of ovarian cancer. Although the majority (31%) of HGSCs exhibit loss of heterozygosity, a significant number (24%) maintain a wild-type (WT) allele and represent another HGSC subtype that is not well defined. Using human and mouse cell lines, we show that specific p53 mutants differentially alter endogenous WT p53 activity; target gene expression; and responses to nutlin-3a, a small molecular that activates WT p53 leading to apoptosis, providing “proof of principle” that ovarian cancer cells expressing WT and mutant alleles represent a distinct ovarian cancer subtype. We also show that siRNA knock down of endogenous p53 in cells expressing homozygous mutant alleles causes apoptosis, whereas cells expressing WT p53 (or are heterozygous for WT and mutant p53 alleles) are highly resistant. Therefore, despite different gene regulatory pathways associated with specific p53 mutants, silencing mutant p53 might be a suitable, powerful, global strategy for blocking ovarian cancer growth in those tumors that rely on mutant p53 functions for survival. Knowing p53 mutational status in HGSC should permit new strategies tailored to control this disease.