Assessment of the Clinical Relevance of BRCA2 Missense Variants by Functional and Computational Approaches.

Many variants of uncertain significance (VUS) have been identified in BRCA2 through clinical genetic testing. VUS pose a significant clinical challenge because the contribution of these variants to cancer risk has not been determined. We conducted a comprehensive assessment of VUS in the BRCA2 C-terminal DNA binding domain (DBD) by using a validated functional assay of BRCA2 homologous recombination (HR) DNA-repair activity and defined a classifier of variant pathogenicity. Among 139 variants evaluated, 54 had ?99% probability of pathogenicity, and 73 had ?95% probability of neutrality. Functional assay results were compared with predictions of variant pathogenicity from the Align-GVGD protein-sequence-based prediction algorithm, which has been used for variant classification. Relative to the HR assay, Align-GVGD significantly (p < 0.05) over-predicted pathogenic variants. We subsequently combined functional and Align-GVGD prediction results in a Bayesian hierarchical model (VarCall) to estimate the overall probability of pathogenicity for each VUS. In addition, to predict the effects of all other BRCA2 DBD variants and to prioritize variants for functional studies, we used the endoPhenotype-Optimized Sequence Ensemble (ePOSE) algorithm to train classifiers for BRCA2 variants by using data from the HR functional assay. Together, the results show that systematic functional assays in combination with in silico predictors of pathogenicity provide robust tools for clinical annotation of BRCA2 VUS.

Network Analysis of Protein Adaptation: Modeling the Functional Impact of Multiple Mutations.

The evolution of new biochemical activities frequently involves complex dependencies between mutations and rapid evolutionary radiation. Mutation co-occurrence and covariation have previously been used to identify compensating mutations that are the result of physical contacts and preserve protein function and fold. Here, we model pairwise functional dependencies and higher order interactions that enable evolution of new protein functions. We use a network model to find complex dependencies between mutations resulting from evolutionary trade-offs and pleiotropic effects. We present a method to construct these networks and to identify functionally interacting mutations in both extant and reconstructed ancestral sequences (Network Analysis of Protein Adaptation). The time ordering of mutations can be incorporated into the networks through phylogenetic reconstruction. We apply NAPA to three distantly homologous ß-lactamase protein clusters (TEM, CTX-M-3, and OXA-51), each of which has experienced recent evolutionary radiation under substantially different selective pressures. By analyzing the network properties of each protein cluster, we identify key adaptive mutations, positive pairwise interactions, different adaptive solutions to the same selective pressure, and complex evolutionary trajectories likely to increase protein fitness. We also present evidence that incorporating information from phylogenetic reconstruction and ancestral sequence inference can reduce the number of spurious links in the network, whereas preserving overall network community structure. The analysis does not require structural or biochemical data. In contrast to function-preserving mutation dependencies, which are frequently from structural contacts, gain-of-function mutation dependencies are most commonly between residues distal in protein structure.

IPMNs with co-occurring invasive cancers: neighbours but not always relatives.

OBJECTIVE: Intraductal papillary mucinous neoplasms (IPMNs) are precursor lesions that can give rise to invasive pancreatic carcinoma. Although approximately 8% of patients with resected pancreatic ductal adenocarcinoma have a co-occurring IPMN, the precise genetic relationship between these two lesions has not been systematically investigated. DESIGN: We analysed all available patients with co-occurring IPMN and invasive intrapancreatic carcinoma over a 10-year period at a single institution. For each patient, we separately isolated DNA from the carcinoma, adjacent IPMN and distant IPMN and performed targeted next generation sequencing of a panel of pancreatic cancer driver genes. We then used the identified mutations to infer the relatedness of the IPMN and co-occurring invasive carcinoma in each patient. RESULTS: We analysed co-occurring IPMN and invasive carcinoma from 61 patients with IPMN/ductal adenocarcinoma as well as 13 patients with IPMN/colloid carcinoma and 7 patients with IPMN/carcinoma of the ampullary region. Of the patients with co-occurring IPMN and ductal adenocarcinoma, 51% were likely related. Surprisingly, 18% of co-occurring IPMN and ductal adenocarcinomas were likely independent, suggesting that the carcinoma arose from an independent precursor. By contrast, all colloid carcinomas were likely related to their associated IPMNs. In addition, these analyses showed striking genetic heterogeneity in IPMNs, even with respect to well-characterised driver genes. CONCLUSION: This study demonstrates a higher prevalence of likely independent co-occurring IPMN and ductal adenocarcinoma than previously appreciated. These findings have important implications for molecular risk stratification of patients with IPMN.

Missense variants in CFTR nucleotide-binding domains predict quantitative phenotypes associated with cystic fibrosis disease severity.

Predicting the impact of genetic variation on human health remains an important and difficult challenge. Often, algorithmic classifiers are tasked with predicting binary traits (e.g. positive or negative for a disease) from missense variation. Though useful, this arrangement is limiting and contrived, because human diseases often comprise a spectrum of severities, rather than a discrete partitioning of patient populations. Furthermore, labeling variants as causal or benign can be error prone, which is problematic for training supervised learning algorithms (the so-called garbage in, garbage out phenomenon). We explore the potential value of training classifiers using continuous-valued quantitative measurements, rather than binary traits. Using 20 variants from cystic fibrosis transmembrane conductance regulator (CFTR) nucleotide-binding domains and six quantitative measures of cystic fibrosis (CF) severity, we trained classifiers to predict CF severity from CFTR variants. Employing cross validation, classifier prediction and measured clinical/functional values were significantly correlated for four of six quantitative traits (correlation P-values from 1.35 × 10(-4) to 4.15 × 10(-3)). Classifiers were also able to stratify variants by three clinically relevant risk categories with 85-100% accuracy, depending on which of the six quantitative traits was used for training. Finally, we characterized 11 additional CFTR variants using clinical sweat chloride testing, two functional assays, or all three diagnostics, and validated our classifier using blind prediction. Predictions were within the measured sweat chloride range for seven of eight variants, and captured the differential impact of specific variants on the two functional assays. This work demonstrates a promising and novel framework for assessing the impact of genetic variation.

Assessing the Pathogenicity of Insertion and Deletion Variants with the Variant Effect Scoring Tool (VEST-Indel).

Insertion/deletion variants (indels) alter protein sequence and length, yet are highly prevalent in healthy populations, presenting a challenge to bioinformatics classifiers. Commonly used features--DNA and protein sequence conservation, indel length, and occurrence in repeat regions--are useful for inference of protein damage. However, these features can cause false positives when predicting the impact of indels on disease. Existing methods for indel classification suffer from low specificities, severely limiting clinical utility. Here, we further develop our variant effect scoring tool (VEST) to include the classification of in-frame and frameshift indels (VEST-indel) as pathogenic or benign. We apply 24 features, including a new "PubMed" feature, to estimate a gene's importance in human disease. When compared with four existing indel classifiers, our method achieves a drastically reduced false-positive rate, improving specificity by as much as 90%. This approach of estimating gene importance might be generally applicable to missense and other bioinformatics pathogenicity predictors, which often fail to achieve high specificity. Finally, we tested all possible meta-predictors that can be obtained from combining the four different indel classifiers using Boolean conjunctions and disjunctions, and derived a meta-predictor with improved performance over any individual method.

A combination of molecular markers and clinical features improve the classification of pancreatic cysts.

BACKGROUND & AIMS: The management of pancreatic cysts poses challenges to both patients and their physicians. We investigated whether a combination of molecular markers and clinical information could improve the classification of pancreatic cysts and management of patients. METHODS: We performed a multi-center, retrospective study of 130 patients with resected pancreatic cystic neoplasms (12 serous cystadenomas, 10 solid pseudopapillary neoplasms, 12 mucinous cystic neoplasms, and 96 intraductal papillary mucinous neoplasms). Cyst fluid was analyzed to identify subtle mutations in genes known to be mutated in pancreatic cysts (BRAF, CDKN2A, CTNNB1, GNAS, KRAS, NRAS, PIK3CA, RNF43, SMAD4, TP53, and VHL); to identify loss of heterozygozity at CDKN2A, RNF43, SMAD4, TP53, and VHL tumor suppressor loci; and to identify aneuploidy. The analyses were performed using specialized technologies for implementing and interpreting massively parallel sequencing data acquisition. An algorithm was used to select markers that could classify cyst type and grade. The accuracy of the molecular markers was compared with that of clinical markers and a combination of molecular and clinical markers. RESULTS: We identified molecular markers and clinical features that classified cyst type with 90%-100% sensitivity and 92%-98% specificity. The molecular marker panel correctly identified 67 of the 74 patients who did not require surgery and could, therefore, reduce the number of unnecessary operations by 91%. CONCLUSIONS: We identified a panel of molecular markers and clinical features that show promise for the accurate classification of cystic neoplasms of the pancreas and identification of cysts that require surgery.

Predicting survival in head and neck squamous cell carcinoma from TP53 mutation.

For TP53-mutated head and neck squamous cell carcinomas (HNSCCs), the codon and specific amino acid sequence change resulting from a patient's mutation can be prognostic. Thus, developing a framework to predict patient survival for specific mutations in TP53 would be valuable. There are many bioinformatics and functional methods for predicting the phenotypic impact of genetic variation, but their overall clinical value remains unclear. Here, we assess the ability of 15 different methods to predict HNSCC patient survival from TP53 mutation, using TP53 mutation and clinical data from patients enrolled in E4393 by the Eastern Cooperative Oncology Group (ECOG), which investigated whether TP53 mutations in surgical margins were predictive of disease recurrence. These methods include: server-based computational tools SIFT, PolyPhen-2, and Align-GVGD; our in-house POSE and VEST algorithms; the rules devised in Poeta et al. with and without considerations for splice-site mutations; location of mutation in the DNA-bound TP53 protein structure; and a functional assay measuring WAF1 transactivation in TP53-mutated yeast. We assessed method performance using overall survival (OS) and progression-free survival (PFS) from 420 HNSCC patients, of whom 224 had TP53 mutations. Each mutation was categorized as "disruptive" or "non-disruptive". For each method, we compared the outcome between the disruptive group vs. the non-disruptive group. The rules devised by Poeta et al. with or without our splice-site modification were observed to be superior to others. While the differences in OS (disruptive vs. non-disruptive) appear to be marginally significant (Poeta rules + splice rules, P = 0.089; Poeta rules, P = 0.053), both algorithms identified the disruptive group as having significantly worse PFS outcome (Poeta rules + splice rules, P = 0.011; Poeta rules, P = 0.027). In general, prognostic performance was low among assessed methods. Further studies are required to develop and validate methods that can predict functional and clinical significance of TP53 mutations in HNSCC patients.

Fast, accurate, and racially unbiased pan-cancer tumor-only variant calling with tabular machine learning.

Accurately identifying somatic mutations is essential for precision oncology and crucial for calculating tumor-mutational burden (TMB), an important predictor of response to immunotherapy. For tumor-only variant calling (i.e., when the cancer biopsy but not the patient's normal tissue sample is sequenced), accurately distinguishing somatic mutations from germline variants is a challenging problem that, when unaddressed, results in unreliable, biased, and inflated TMB estimates. Here, we apply machine learning to the task of somatic vs germline classification in tumor-only solid tumor samples using TabNet, XGBoost, and LightGBM, three machine-learning models for tabular data. We constructed a training set for supervised classification using features derived exclusively from tumor-only variant calling and drawing somatic and germline truth labels from an independent pipeline using the patient-matched normal samples. All three trained models achieved state-of-the-art performance on two holdout test datasets: a TCGA dataset including sarcoma, breast adenocarcinoma, and endometrial carcinoma samples (AUC > 94%), and a metastatic melanoma dataset (AUC > 85%). Concordance between matched-normal and tumor-only TMB improves from R2 = 0.006 to 0.71-0.76 with the addition of a machine-learning classifier, with LightGBM performing best. Notably, these machine-learning models generalize across cancer subtypes and capture kits with a call rate of 100%. We reproduce the recent finding that tumor-only TMB estimates for Black patients are extremely inflated relative to that of white patients due to the racial biases of germline databases. We show that our approach with XGBoost and LightGBM eliminates this significant racial bias in tumor-only variant calling.

Phenotype-optimized sequence ensembles substantially improve prediction of disease-causing mutation in cystic fibrosis.

Cystic fibrosis transmembrane conductance regulator (CFTR) mutation is associated with a phenotypic spectrum that includes cystic fibrosis (CF). The disease liability of some common CFTR mutations is known, but rare mutations are seen in too few patients to categorize unequivocally, making genetic diagnosis difficult. Computational methods can predict the impact of mutation, but prediction specificity is often below that required for clinical utility. Here, we present a novel supervised learning approach for predicting CF from CFTR missense mutation. The algorithm begins by constructing custom multiple sequence alignments called phenotype-optimized sequence ensembles (POSEs). POSEs are constructed iteratively, by selecting sequences that optimize predictive performance on a training set of CFTR mutations of known clinical significance. Next, we predict CF disease liability from a different set of CFTR mutations (test-set mutations). This approach achieves improved prediction performance relative to popular methods recently assessed using the same test-set mutations. Of clinical significance, our method achieves 94% prediction specificity. Because databases such as HGMD and locus-specific mutation databases are growing rapidly, methods that automatically tailor their predictions for a specific phenotype may be of immediate utility. If the performance achieved here generalizes to other systems, the approach could be an excellent tool to help establish genetic diagnoses.

Association of Baseline and Pharmacodynamic Biomarkers With Outcomes in Patients Treated With the PD-1 Inhibitor Budigalimab.

Budigalimab, a novel anti-PD-1 monoclonal antibody, demonstrated efficacy and biomarker pharmacodynamics in patients with head and neck squamous cell carcinoma (HNSCC) or non-small cell lung cancer (NSCLC) consistent with those reported by other PD-1 inhibitors. Herein are presented additional outcomes of biomarker analyses from the phase 1 study of budigalimab monotherapy in patients with HNSCC and NSCLC (NCT03000257). PD-1 inhibitor naive patients with advanced HNSCC (n=41) or NSCLC (n=40) received budigalimab intravenously at 250 mg every 2 weeks (Q2W) or 500 mg Q4W until progression. Archival tumor specimens were evaluated by immunohistochemistry for CD8 and tumor PD-1 ligand 1 (PD-L1) expression, RNA, and whole-exome sequencing. Serum and whole blood samples were acquired at baseline and at select on-treatment time points. As of October 2019, best overall response of 15% in HNSCC and 18% in NSCLC was observed in all treated patients; both cohorts reported responses in PD-L1+ and PD-L1- tumors. Treatment with budigalimab was associated with increases in multiple soluble biomarkers including interferon gamma-induced chemokines. Expanded overall T-cell counts, total CD8 T-cell counts, and percentages of CD8+CD45RA-CD62L- effector memory T cells were observed at cycle 1, day 15 in responders. Univariate analysis demonstrated an association between prolonged progression-free survival and higher tumor mutational burden/neoantigen load, smaller tumor size, lower platelet-lymphocyte ratios, lower CCL23, lower colony-stimulating factor 1, and lower interleukin-6 levels at baseline. The biomarker analysis presented herein identified additional early pharmacodynamic biomarkers associated with anti-PD-1 activity and improved clinical responses to budigalimab in patients with advanced HNSCC and NSCLC.

De novo design of peptide-calcite biomineralization systems.

Many organisms produce complex, hierarchically structured, inorganic materials via protein-influenced crystal growth--a process known as biomineralization. Understanding this process would shed light on hard-tissue formation and guide efforts to develop biomaterials. We created and tested a computational method to design protein-biomineralization systems. The algorithm folds a protein from a fully extended structure and simultaneously optimizes the fold, orientation, and sequence of the protein adsorbed to a crystal surface. We used the algorithm to design peptides (16 residues) to modify calcite (CaCO(3)) crystallization. We chemically synthesized six peptides that were predicted to bind different states of a calcite growth plane. All six peptides dramatically affected calcite crystal growth (as observed by scanning electron microscopy), and the effects were dependent on the targeted state of the {001} growth plane. Additionally, we synthesized and assayed scrambled variants of all six designed peptides to distinguish cases where sequence composition determines the interactions versus cases where sequence order (and presumably structure) plays a role. Scrambled variants of negatively charged peptides also had dramatic effects on calcite crystallization; in contrast, scrambled variants of positively charged peptides had a variable effect on crystallization, ranging from dramatic to mild. Special emphasis is often placed on acidic protein residues in calcified tissue mineralization; the work presented here suggests an important role for basic residues as well. In particular, this work implicates a potential role for basic residues in sequence-order specificity for peptide-mineral interactions.

Solution- and adsorbed-state structural ensembles predicted for the statherin-hydroxyapatite system.

We have developed a multiscale structure prediction technique to study solution- and adsorbed-state ensembles of biomineralization proteins. The algorithm employs a Metropolis Monte Carlo-plus-minimization strategy that varies all torsional and rigid-body protein degrees of freedom. We applied the technique to fold statherin, starting from a fully extended peptide chain in solution, in the presence of hydroxyapatite (HAp) (001), (010), and (100) monoclinic crystals. Blind (unbiased) predictions capture experimentally observed macroscopic and high-resolution structural features and show minimal statherin structural change upon adsorption. The dominant structural difference between solution and adsorbed states is an experimentally observed folding event in statherin's helical binding domain. Whereas predicted statherin conformers vary slightly at three different HAp crystal faces, geometric and chemical similarities of the surfaces allow structurally promiscuous binding. Finally, we compare blind predictions with those obtained from simulation biased to satisfy all previously published solid-state NMR (ssNMR) distance and angle measurements (acquired from HAp-adsorbed statherin). Atomic clashes in these structures suggest a plausible, alternative interpretation of some ssNMR measurements as intermolecular rather than intramolecular. This work demonstrates that a combination of ssNMR and structure prediction could effectively determine high-resolution protein structures at biomineral interfaces.

A multimodality test to guide the management of patients with a pancreatic cyst.

Pancreatic cysts are common and often pose a management dilemma, because some cysts are precancerous, whereas others have little risk of developing into invasive cancers. We used supervised machine learning techniques to develop a comprehensive test, CompCyst, to guide the management of patients with pancreatic cysts. The test is based on selected clinical features, imaging characteristics, and cyst fluid genetic and biochemical markers. Using data from 436 patients with pancreatic cysts, we trained CompCyst to classify patients as those who required surgery, those who should be routinely monitored, and those who did not require further surveillance. We then tested CompCyst in an independent cohort of 426 patients, with histopathology used as the gold standard. We found that clinical management informed by the CompCyst test was more accurate than the management dictated by conventional clinical and imaging criteria alone. Application of the CompCyst test would have spared surgery in more than half of the patients who underwent unnecessary resection of their cysts. CompCyst therefore has the potential to reduce the patient morbidity and economic costs associated with current standard-of-care pancreatic cyst management practices.

Caspase-1 from Human Myeloid-Derived Suppressor Cells Can Promote T Cell-Independent Tumor Proliferation.

Immunosuppressive myeloid-derived suppressive cells (MDSCs) are characterized by their phenotypic and functional heterogeneity. To better define their T cell-independent functions within the tumor, sorted monocytic CD14+CD11b+HLA-DRlow/- MDSCs (mMDSC) from squamous cell carcinoma patients showed upregulated caspase-1 activity, which was associated with increased IL1ß and IL18 expression. In vitro studies demonstrated that mMDSCs promoted caspase-1-dependent proliferation of multiple squamous carcinoma cell lines in both human and murine systems. In vivo, growth rates of B16, MOC1, and Panc02 were significantly blunted in chimeric mice adoptively transferred with caspase-1 null bone marrow cells under T cell-depleted conditions. Adoptive transfer of wild-type Gr-1+CD11b+ MDSCs from tumor-bearing mice reversed this antitumor response, whereas caspase-1 inhibiting thalidomide-treated MDSCs phenocopied the antitumor response found in caspase-1 null mice. We further hypothesized that MDSC caspase-1 activity could promote tumor-intrinsic MyD88-dependent carcinogenesis. In mice with wild-type caspase-1, MyD88-silenced tumors displayed reduced growth rate, but in chimeric mice with caspase-1 null bone marrow cells, MyD88-silenced tumors did not display differential tumor growth rate. When we queried the TCGA database, we found that caspase-1 expression is correlated with overall survival in squamous cell carcinoma patients. Taken together, our findings demonstrated that caspase-1 in MDSCs is a direct T cell-independent mediator of tumor proliferation. Cancer Immunol Res; 6(5); 566-77. ©2018 AACR.

CRAVAT 4: Cancer-Related Analysis of Variants Toolkit.

Cancer sequencing studies are increasingly comprehensive and well powered, returning long lists of somatic mutations that can be difficult to sort and interpret. Diligent analysis and quality control can require multiple computational tools of distinct utility and producing disparate output, creating additional challenges for the investigator. The Cancer-Related Analysis of Variants Toolkit (CRAVAT) is an evolving suite of informatics tools for mutation interpretation that includes mutation mapping and quality control, impact prediction and extensive annotation, gene- and mutation-level interpretation, including joint prioritization of all nonsilent mutation consequence types, and structural and mechanistic visualization. Results from CRAVAT submissions are explored in an interactive, user-friendly web environment with dynamic filtering and sorting designed to highlight the most informative mutations, even in the context of very large studies. CRAVAT can be run on a public web portal, in the cloud, or downloaded for local use, and is easily integrated with other methods for cancer omics analysis. Cancer Res; 77(21); e35-38. ©2017 AACR.

Autologous reconstitution of human cancer and immune system in vivo.

Correlative studies from checkpoint inhibitor trials have indicated that better understanding of human leukocytic trafficking into the human tumor microenvironment can expedite the translation of future immune-oncologic agents. In order to directly characterize signaling pathways that can regulate human leukocytic trafficking into the tumor, we have developed a completely autologous xenotransplantation method to reconstitute the human tumor immune microenvironment in vivo. We were able to genetically mark the engrafted CD34+ bone marrow cells as well as the tumor cells, and follow the endogenous leukocytic infiltration into the autologous tumor. To investigate human tumor intrinsic factors that can potentially regulate the immune cells in our system, we silenced STAT3 signaling in the tumor compartment. As expected, STAT3 signaling suppression in the tumor compartment in these autologously reconstituted humanized mice showed increased tumor infiltrating lymphocytes and reduction of arginase-1 in the stroma, which were associated with slower tumor growth rate. We also used this novel system to characterize human myeloid suppressor cells as well as to screen novel agents that can alter endogenous leukocytic infiltration into the tumor. Taken together, we present a valuable method to study individualized human tumor microenvironments in vivo without confounding allogeneic responses.

A novel approach for selecting combination clinical markers of pathology applied to a large retrospective cohort of surgically resected pancreatic cysts.

OBJECTIVE: Our objective was to develop an approach for selecting combinatorial markers of pathology from diverse clinical data types. We demonstrate this approach on the problem of pancreatic cyst classification. MATERIALS AND METHODS: We analyzed 1026 patients with surgically resected pancreatic cysts, comprising 584 intraductal papillary mucinous neoplasms, 332 serous cystadenomas, 78 mucinous cystic neoplasms, and 42 solid-pseudopapillary neoplasms. To derive optimal markers for cyst classification from the preoperative clinical and radiological data, we developed a statistical approach for combining any number of categorical, dichotomous, or continuous-valued clinical parameters into individual predictors of pathology. The approach is unbiased and statistically rigorous. Millions of feature combinations were tested using 10-fold cross-validation, and the most informative features were validated in an independent cohort of 130 patients with surgically resected pancreatic cysts. RESULTS: We identified combinatorial clinical markers that classified serous cystadenomas with 95% sensitivity and 83% specificity; solid-pseudopapillary neoplasms with 89% sensitivity and 86% specificity; mucinous cystic neoplasms with 91% sensitivity and 83% specificity; and intraductal papillary mucinous neoplasms with 94% sensitivity and 90% specificity. No individual features were as accurate as the combination markers. We further validated these combinatorial markers on an independent cohort of 130 pancreatic cysts, and achieved high and well-balanced accuracies. Overall sensitivity and specificity for identifying patients requiring surgical resection was 84% and 81%, respectively. CONCLUSIONS: Our approach identified combinatorial markers for pancreatic cyst classification that had improved performance relative to the individual features they comprise. In principle, this approach can be applied to any clinical dataset comprising dichotomous, categorical, and continuous-valued parameters.

Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework.

The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are caused by genetic variants affecting important protein features, such as enzyme active sites or interaction interfaces. The scientific community has catalogued millions of genetic variants in genomic databases and thousands of protein structures in the Protein Data Bank. Mapping mutations onto three-dimensional (3D) structures enables atomic-level analyses of protein positions that may be important for the stability or formation of interactions; these may explain the effect of mutations and in some cases even open a path for targeted drug development. To accelerate progress in the integration of these data types, we held a two-day Gene Variation to 3D (GVto3D) workshop to report on the latest advances and to discuss unmet needs. The overarching goal of the workshop was to address the question: what can be done together as a community to advance the integration of genetic variants and 3D protein structures that could not be done by a single investigator or laboratory? Here we describe the workshop outcomes, review the state of the field, and propose the development of a framework with which to promote progress in this arena. The framework will include a set of standard formats, common ontologies, a common application programming interface to enable interoperation of the resources, and a Tool Registry to make it easy to find and apply the tools to specific analysis problems. Interoperability will enable integration of diverse data sources and tools and collaborative development of variant effect prediction methods.

Exome-Scale Discovery of Hotspot Mutation Regions in Human Cancer Using 3D Protein Structure.

The impact of somatic missense mutation on cancer etiology and progression is often difficult to interpret. One common approach for assessing the contribution of missense mutations in carcinogenesis is to identify genes mutated with statistically nonrandom frequencies. Even given the large number of sequenced cancer samples currently available, this approach remains underpowered to detect drivers, particularly in less studied cancer types. Alternative statistical and bioinformatic approaches are needed. One approach to increase power is to focus on localized regions of increased missense mutation density or hotspot regions, rather than a whole gene or protein domain. Detecting missense mutation hotspot regions in three-dimensional (3D) protein structure may also be beneficial because linear sequence alone does not fully describe the biologically relevant organization of codons. Here, we present a novel and statistically rigorous algorithm for detecting missense mutation hotspot regions in 3D protein structures. We analyzed approximately 3 × 10(5) mutations from The Cancer Genome Atlas (TCGA) and identified 216 tumor-type-specific hotspot regions. In addition to experimentally determined protein structures, we considered high-quality structural models, which increase genomic coverage from approximately 5,000 to more than 15,000 genes. We provide new evidence that 3D mutation analysis has unique advantages. It enables discovery of hotspot regions in many more genes than previously shown and increases sensitivity to hotspot regions in tumor suppressor genes (TSG). Although hotspot regions have long been known to exist in both TSGs and oncogenes, we provide the first report that they have different characteristic properties in the two types of driver genes. We show how cancer researchers can use our results to link 3D protein structure and the biologic functions of missense mutations in cancer, and to generate testable hypotheses about driver mechanisms. Our results are included in a new interactive website for visualizing protein structures with TCGA mutations and associated hotspot regions. Users can submit new sequence data, facilitating the visualization of mutations in a biologically relevant context. Cancer Res; 76(13); 3719-31. ©2016 AACR.