CRAVAT: cancer-related analysis of variants toolkit

Comprehensive Characterization of Cancer Driver Genes and Mutations

Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified >3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60%-85% of predicted mutations as likely drivers. We found that >300 MSI tumors are associated with high PD-1/PD-L1, and 57% of tumors analyzed harbor putative clinically actionable events. Our study represents the most comprehensive discovery of cancer genes and mutations to date and will serve as a blueprint for future biological and clinical endeavors.

Novel putative drivers revealed by targeted exome sequencing of advanced solid tumors

Next generation sequencing (NGS) is becoming increasingly integrated into oncological practice and clinical research. NGS methods have also provided evidence for clonal evolution of cancers during disease progression and treatment. The number of variants associated with response to specific therapeutic agents keeps increasing. However, the identification of novel driver mutations as opposed to passenger (phenotypically silent or clinically irrelevant) mutations remains a major challenge. We conducted targeted exome sequencing of advanced solid tumors from 44 pre-treated patients with solid tumors including breast, colorectal and lung carcinomas, neuroendocrine tumors, sarcomas and others. We catalogued established driver mutations and putative new drivers as predicted by two distinct algorithms. The established drivers we detected were consistent with published observations. However, we also detected a significant number of mutations with driver potential never described before in each tumor type we studied. These putative drivers belong to key cell fate regulatory networks, including potentially druggable pathways. Should our observations be confirmed, they would support the hypothesis that new driver mutations are selected by treatment in clinically aggressive tumors, and indicate a need for longitudinal genomic testing of solid tumors to inform second line cancer treatment.

Systematic Functional Annotation of Somatic Mutations in Cancer

The functional impact of the vast majority of cancer somatic mutations remains unknown, representing a critical knowledge gap for implementing precision oncology. Here, we report the development of a moderate-throughput functional genomic platform consisting of efficient mutant generation, sensitive viability assays using two growth factor-dependent cell models, and functional proteomic profiling of signaling effects for select aberrations. We apply the platform to annotate >1,000 genomic aberrations, including gene amplifications, point mutations, indels, and gene fusions, potentially doubling the number of driver mutations characterized in clinically actionable genes. Further, the platform is sufficiently sensitive to identify weak drivers. Our data are accessible through a user-friendly, public data portal. Our study will facilitate biomarker discovery, prediction algorithm improvement, and drug development.

Practical aspects of NGS-based pathways analysis for personalized cancer science and medicine

Nowadays, the personalized approach to health care and cancer care in particular is becoming more and more popular and is taking an important place in the translational medicine paradigm. In some cases, detection of the patient-specific individual mutations that point to a targeted therapy has already become a routine practice for clinical oncologists. Wider panels of genetic markers are also on the market which cover a greater number of possible oncogenes including those with lower reliability of resulting medical conclusions. In light of the large availability of high-throughput technologies, it is very tempting to use complete patient-specific New Generation Sequencing (NGS) or other "omics" data for cancer treatment guidance. However, there are still no gold standard methods and protocols to evaluate them. Here we will discuss the clinical utility of each of the data types and describe a systems biology approach adapted for single patient measurements. We will try to summarize the current state of the field focusing on the clinically relevant case-studies and practical aspects of data processing.

Transcription Factor and lncRNA Regulatory Networks Identify Key Elements in Lung Adenocarcinoma

Lung cancer is the second most commonly diagnosed carcinoma and is the leading cause of cancer death. Although significant progress has been made towards its understanding and treatment, unraveling the complexities of lung cancer is still hampered by a lack of comprehensive knowledge on the mechanisms underlying the disease. High-throughput and multidimensional genomic data have shed new light on cancer biology. In this study, we developed a network-based approach integrating somatic mutations, the transcriptome, DNA methylation, and protein-DNA interactions to reveal the key regulators in lung adenocarcinoma (LUAD). By combining Bayesian network analysis with tissue-specific transcription factor (TF) and targeted gene interactions, we inferred 15 disease-related core regulatory networks in co-expression gene modules associated with LUAD. Through target gene set enrichment analysis, we identified a set of key TFs, including known cancer genes that potentially regulate the disease networks. These TFs were significantly enriched in multiple cancer-related pathways. Specifically, our results suggest that hepatitis viruses may contribute to lung carcinogenesis, highlighting the need for further investigations into the roles that viruses play in treating lung cancer. Additionally, 13 putative regulatory long non-coding RNAs (lncRNAs), including three that are known to be associated with lung cancer, and nine novel lncRNAs were revealed by our study. These lncRNAs and their target genes exhibited high interaction potentials and demonstrated significant expression correlations between normal lung and LUAD tissues. We further extended our study to include 16 solid-tissue tumor types and determined that the majority of these lncRNAs have putative regulatory roles in multiple cancers, with a few showing lung-cancer specific regulations. Our study provides a comprehensive investigation of transcription factor and lncRNA regulation in the context of LUAD regulatory networks and yields new insights into the regulatory mechanisms underlying LUAD. The novel key regulatory elements discovered by our research offer new targets for rational drug design and accompanying therapeutic strategies.

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.

NDEx 2.0: A Clearinghouse for Research on Cancer Pathways

We present NDEx 2.0, the latest release of the Network Data Exchange (NDEx) online data commons (www.ndexbio.org) and the ways in which it can be used to (i) improve the quality and abundance of biological networks relevant to the cancer research community; (ii) provide a medium for collaboration involving networks; and (iii) facilitate the review and dissemination of networks. We describe innovations addressing the challenges of an online data commons: scalability, data integration, data standardization, control of content and format by authors, and decentralized mechanisms for review. The practical use of NDEx is presented in the context of a novel strategy to foster network-oriented communities of interest in cancer research by adapting methods from academic publishing and social media. Cancer Res; 77(21); e58-61. ©2017 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.

Identification and analysis of mutational hotspots in oncogenes and tumour suppressors

Background

The key to interpreting the contribution of a disease-associated mutation in the development and progression of cancer is an understanding of the consequences of that mutation both on the function of the affected protein and on the pathways in which that protein is involved. Protein domains encapsulate function and position-specific domain based analysis of mutations have been shown to help elucidate their phenotypes.

Results

In this paper we examine the domain biases in oncogenes and tumour suppressors, and find that their domain compositions substantially differ. Using data from over 30 different cancers from whole-exome sequencing cancer genomic projects we mapped over one million mutations to their respective Pfam domains to identify which domains are enriched in any of three different classes of mutation; missense, indels or truncations. Next, we identified the mutational hotspots within domain families by mapping small mutations to equivalent positions in multiple sequence alignments of protein domains We find that gain of function mutations from oncogenes and loss of function mutations from tumour suppressors are normally found in different domain families and when observed in the same domain families, hotspot mutations are located at different positions within the multiple sequence alignment of the domain.

Conclusions

By considering hotspots in tumour suppressors and oncogenes independently, we find that there are different specific positions within domain families that are particularly suited to accommodate either a loss or a gain of function mutation. The position is also dependent on the class of mutation. We find rare mutations co-located with well-known functional mutation hotspots, in members of homologous domain superfamilies, and we detect novel mutation hotspots in domain families previously unconnected with cancer. The results of this analysis can be accessed through the MOKCa database (http://strubiol.icr.ac.uk/extra/MOKCa).

Hotspots of missense mutation identify neurodevelopmental disorder genes and functional domains

Although de novo missense mutations have been predicted to account for more cases of autism than gene-truncating mutations, most research has focused on the latter. We identified the properties of de novo missense mutations in patients with neurodevelopmental disorders (NDDs) and highlight 35 genes with excess missense mutations. Additionally, 40 amino acid sites were recurrently mutated in 36 genes, and targeted sequencing of 20 sites in 17,688 patients with NDD identified 21 new patients with identical missense mutations. One recurrent site substitution (p.A636T) occurs in a glutamate receptor subunit, GRIA1. This same amino acid substitution in the homologous but distinct mouse glutamate receptor subunit Grid2 is associated with Lurcher ataxia. Phenotypic follow-up in five individuals with GRIA1 mutations shows evidence of specific learning disabilities and autism. Overall, we find significant clustering of de novo mutations in 200 genes, highlighting specific functional domains and synaptic candidate genes important in NDD pathology.

High-confidence assessment of functional impact of human mitochondrial non-synonymous genome variations by APOGEE

24,189 are all the possible non-synonymous amino acid changes potentially affecting the human mitochondrial DNA. Only a tiny subset was functionally evaluated with certainty so far, while the pathogenicity of the vast majority was only assessed in-silico by software predictors. Since these tools proved to be rather incongruent, we have designed and implemented APOGEE, a machine-learning algorithm that outperforms all existing prediction methods in estimating the harmfulness of mitochondrial non-synonymous genome variations. We provide a detailed description of the underlying algorithm, of the selected and manually curated training and test sets of variants, as well as of its classification ability.

Targeted sequencing-based analyses of candidate gene variants in ulcerative colitis-associated colorectal neoplasia

BACKGROUND

Long-standing ulcerative colitis (UC) leading to colorectal cancer (CRC) is one of the most serious and life-threatening consequences acknowledged globally. Ulcerative colitis-associated colorectal carcinogenesis showed distinct molecular alterations when compared with sporadic colorectal carcinoma.

METHODS

Targeted sequencing of 409 genes in tissue samples of 18 long-standing UC subjects at high risk of colorectal carcinoma (UCHR) was performed to identify somatic driver mutations, which may be involved in the molecular changes during the transformation of non-dysplastic mucosa to high-grade dysplasia. Findings from the study are also compared with previously published genome wide and exome sequencing data in inflammatory bowel disease-associated and sporadic colorectal carcinoma.

RESULTS

Next-generation sequencing analysis identified 1107 mutations in 275 genes in UCHR subjects. In addition to TP53 (17%) and KRAS (22%) mutations, recurrent mutations in APC (33%), ACVR2A (61%), ARID1A (44%), RAF1 (39%) and MTOR (61%) were observed in UCHR subjects. In addition, APC, FGFR3, FGFR2 and PIK3CA driver mutations were identified in UCHR subjects. Recurrent mutations in ARID1A (44%), SMARCA4 (17%), MLL2 (44%), MLL3 (67%), SETD2 (17%) and TET2 (50%) genes involved in histone modification and chromatin remodelling were identified in UCHR subjects.

CONCLUSIONS

Our study identifies new oncogenic driver mutations which may be involved in the transition of non-dysplastic cells to dysplastic phenotype in the subjects with long-standing UC with high risk of progression into colorectal neoplasia.

Comprehensive strategy for the design of precision drugs and identification of genetic signature behind proneness of the disease-a pharmacogenomic approach

The proneness of diseases and susceptibility towards drugs vary from person to person. At present, there is a strong demand for the personalization of drugs. The genetic signature behind proneness of the disease has been studied through a comprehensive 'octopodial approach'. All the genetic variants included in the approach have been introduced. The breast cancer associated with BRCA1 mutation has been taken as the illustrative example to introduce all these factors. The genetic variants associated with the drug action of tamoxifen have been fully illustrated in the manuscript. The design of a new personalized anti-breast cancer drug has been explained in the third phase. For the design of new personalized drugs, a metabolite of anti-cancer drug chlorambucil has been taken as the template. The design of drug has been made with respect to the protein 1T15 of BRCA1 gene corresponding to the genetic signature of rs28897696.

The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies

The Human Gene Mutation Database (HGMD^®) constitutes a comprehensive collection of published germline mutations in nuclear genes that underlie, or are closely associated with human inherited disease. At the time of writing (March 2017), the database contained in excess of 203,000 different gene lesions identified in over 8000 genes manually curated from over 2600 journals. With new mutation entries currently accumulating at a rate exceeding 17,000 per annum, HGMD represents de facto the central unified gene/disease-oriented repository of heritable mutations causing human genetic disease used worldwide by researchers, clinicians, diagnostic laboratories and genetic counsellors, and is an essential tool for the annotation of next-generation sequencing data. The public version of HGMD ( http://www.hgmd.org ) is freely available to registered users from academic institutions and non-profit organisations whilst the subscription version (HGMD Professional) is available to academic, clinical and commercial users under license via QIAGEN Inc.

The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies

The Human Gene Mutation Database (HGMD^®) constitutes a comprehensive collection of published germline mutations in nuclear genes that underlie, or are closely associated with human inherited disease. At the time of writing (March 2017), the database contained in excess of 203,000 different gene lesions identified in over 8000 genes manually curated from over 2600 journals. With new mutation entries currently accumulating at a rate exceeding 17,000 per annum, HGMD represents de facto the central unified gene/disease-oriented repository of heritable mutations causing human genetic disease used worldwide by researchers, clinicians, diagnostic laboratories and genetic counsellors, and is an essential tool for the annotation of next-generation sequencing data. The public version of HGMD (http://www.hgmd.org) is freely available to registered users from academic institutions and non-profit organisations whilst the subscription version (HGMD Professional) is available to academic, clinical and commercial users under license via QIAGEN Inc.

Limited heterogeneity of known driver gene mutations among the metastases of individual patients with pancreatic cancer

The extent of heterogeneity among driver gene mutations present in naturally occurring metastases-that is, treatment-naive metastatic disease-is largely unknown. To address this issue, we carried out 60× whole-genome sequencing of 26 metastases from four patients with pancreatic cancer. We found that identical mutations in known driver genes were present in every metastatic lesion for each patient studied. Passenger gene mutations, which do not have known or predicted functional consequences, accounted for all intratumoral heterogeneity. Even with respect to these passenger mutations, our analysis suggests that the genetic similarity among the founding cells of metastases was higher than that expected for any two cells randomly taken from a normal tissue. The uniformity of known driver gene mutations among metastases in the same patient has critical and encouraging implications for the success of future targeted therapies in advanced-stage disease.

NDEx: A Community Resource for Sharing and Publishing of Biological Networks

Networks are a powerful and flexible paradigm that facilitate communication and computation about interactions of any type, whether social, economic, or biological. NDEx, the Network Data Exchange, is an online commons to enable new modes of collaboration and publication using biological networks. NDEx creates an access point and interface to a broad range of networks, whether they express molecular interactions, curated relationships from literature, or the outputs of systematic analysis of big data. Research organizations can use NDEx as a distribution channel for networks they generate or curate. Developers of bioinformatic applications can store and query NDEx networks via a common programmatic interface. NDEx can also facilitate the integration of networks as data in electronic publications, thus making a step toward an ecosystem in which networks bearing data, hypotheses, and findings flow seamlessly between scientists.

Evaluating the evaluation of cancer driver genes

Sequencing has identified millions of somatic mutations in human cancers, but distinguishing cancer driver genes remains a major challenge. Numerous methods have been developed to identify driver genes, but evaluation of the performance of these methods is hindered by the lack of a gold standard, that is, bona fide driver gene mutations. Here, we establish an evaluation framework that can be applied to driver gene prediction methods. We used this framework to compare the performance of eight such methods. One of these methods, described here, incorporated a machine-learning-based ratiometric approach. We show that the driver genes predicted by each of the eight methods vary widely. Moreover, the P values reported by several of the methods were inconsistent with the uniform values expected, thus calling into question the assumptions that were used to generate them. Finally, we evaluated the potential effects of unexplained variability in mutation rates on false-positive driver gene predictions. Our analysis points to the strengths and weaknesses of each of the currently available methods and offers guidance for improving them in the future.

The Impact of Environmental and Endogenous Damage on Somatic Mutation Load in Human Skin Fibroblasts

Accumulation of somatic changes, due to environmental and endogenous lesions, in the human genome is associated with aging and cancer. Understanding the impacts of these processes on mutagenesis is fundamental to understanding the etiology, and improving the prognosis and prevention of cancers and other genetic diseases. Previous methods relying on either the generation of induced pluripotent stem cells, or sequencing of single-cell genomes were inherently error-prone and did not allow independent validation of the mutations. In the current study we eliminated these potential sources of error by high coverage genome sequencing of single-cell derived clonal fibroblast lineages, obtained after minimal propagation in culture, prepared from skin biopsies of two healthy adult humans. We report here accurate measurement of genome-wide magnitude and spectra of mutations accrued in skin fibroblasts of healthy adult humans. We found that every cell contains at least one chromosomal rearrangement and 600–13,000 base substitutions. The spectra and correlation of base substitutions with epigenomic features resemble many cancers. Moreover, because biopsies were taken from body parts differing by sun exposure, we can delineate the precise contributions of environmental and endogenous factors to the accrual of genetic changes within the same individual. We show here that UV-induced and endogenous DNA damage can have a comparable impact on the somatic mutation loads in skin fibroblasts.

Somatic genomes are constantly accumulating changes caused by endogenous lesions, errors in DNA replication and repair, as well as environmental insults. Despite the importance of somatic genome instability in aging and age-related pathologies, including cancers, accurate measurements of mutation loads in healthy cells is still missing. In this study, we developed an experimental approach to accurately determine the somatic genome changes accrued in cell lineages over the lifetime of healthy humans. We show that the amounts and types of mutations in skin cells resemble many cancers, thus indicating that the mechanisms that lead to carcinogenesis are also functional in healthy cells. Moreover, sun-exposed skin cells have a higher mutation load attributable to ultraviolet radiation (UV) unlike cells from hips that were protected by clothing. Our work provides precise measurements of the mutation loads in single cells in human skin. Furthermore our data allowed defining the mutagenic impacts of environmental and endogenous processes within the same individual and led to conclusion that these processes have a comparable impact on the somatic mutation load.

Mutational patterns in oncogenes and tumour suppressors

All cancers depend upon mutations in critical genes, which confer a selective advantage to the tumour cell. Knowledge of these mutations is crucial to understanding the biology of cancer initiation and progression, and to the development of targeted therapeutic strategies. The key to understanding the contribution of a disease-associated mutation to the development and progression of cancer, comes from an understanding of the consequences of that mutation on the function of the affected protein, and the impact on the pathways in which that protein is involved. In this paper we examine the mutation patterns observed in oncogenes and tumour suppressors, and discuss different approaches that have been developed to identify driver mutations within cancers that contribute to the disease progress. We also discuss the MOKCa database where we have developed an automatic pipeline that structurally and functionally annotates all proteins from the human proteome that are mutated in cancer.

Computational approaches in target identification and drug discovery

In the big data era, voluminous datasets are routinely acquired, stored and analyzed with the aim to inform biomedical discoveries and validate hypotheses. No doubt, data volume and diversity have dramatically increased by the advent of new technologies and open data initiatives. Big data are used across the whole drug discovery pipeline from target identification and mechanism of action to identification of novel leads and drug candidates. Such methods are depicted and discussed, with the aim to provide a general view of computational tools and databases available. We feel that big data leveraging needs to be cost-effective and focus on personalized medicine. For this, we propose the interplay of information technologies and (chemo)informatic tools on the basis of their synergy.

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.

The total synthesis of K-252c (staurosporinone) via a sequential C-H functionalisation strategy

A synthesis of the bioactive indolocarbazole alkaloid K-252c (staurosporinone) via a sequential C-H functionalisation strategy is reported. The route exploits direct functionalisation reactions around a simple arene core and comprises of two highly-selective copper-catalysed C-H arylations, a copper-catalysed C-H amination and a palladium-catalysed C-H carbonylation, which build up the structural complexity of the natural product framework.

Pharmacometabolomics-aided Pharmacogenomics in Autoimmune Disease

Inter-individual variability has been a major hurdle to optimize disease management. Precision medicine holds promise for improving health and healthcare via tailor-made therapeutic strategies. Herein, we outline the paradigm of "pharmacometabolomics-aided pharmacogenomics" in autoimmune diseases. We envisage merging pharmacometabolomic and pharmacogenomic data (to address the interplay of genomic and environmental influences) with information technologies to facilitate data analysis as well as sense- and decision-making on the basis of synergy between artificial and human intelligence. Humans can detect patterns, which computer algorithms may fail to do so, whereas data-intensive and cognitively complex settings and processes limit human ability. We propose that better-informed, rapid and cost-effective omics studies need the implementation of holistic and multidisciplinary approaches.

A Broad Overview of Computational Methods for Predicting the Pathophysiological Effects of Non-synonymous Variants

Next-generation sequencing has provided extraordinary opportunities to investigate the massive human genetic variability. It helped identifying several kinds of genomic mismatches from the wild-type reference genome sequences and to explain the onset of several pathogenic phenotypes and diseases susceptibility. In this context, distinguishing pathogenic from functionally neutral amino acid changes turns out to be a task as useful as complex, expensive, and time-consuming.Here, we present an exhaustive and up-to-dated survey of the algorithms and software packages conceived for the estimation of the putative pathogenicity of mutations, along with a description of the most popular mutation datasets that these tools used as training sets. Finally, we present and describe software for the prediction of cancer-related mutations.

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.

Recurrent Mutations of Chromatin-Remodeling Genes and Kinase Receptors in Pheochromocytomas and Paragangliomas

PURPOSE

Pheochromocytomas and paragangliomas (PPGL) are genetically heterogeneous tumors of neural crest origin, but the molecular basis of most PPGLs is unknown.

EXPERIMENTAL DESIGN

We performed exome or transcriptome sequencing of 43 samples from 41 patients. A validation set of 136 PPGLs was used for amplicon-specific resequencing. In addition, a subset of these tumors was subjected to microarray-based transcription, protein expression, and histone methylation analysis by Western blotting or immunohistochemistry. In vitro analysis of mutants was performed in cell lines.

RESULTS

We detected mutations in chromatin-remodeling genes, including histone-methyltransferases, histone-demethylases, and histones in 11 samples from 8 patients (20%). In particular, we characterized a new cancer syndrome involving PPGLs and giant cell tumors of bone (GCT) caused by a postzygotic G34W mutation of the histone 3.3 gene, H3F3A Furthermore, mutations in kinase genes were detected in samples from 15 patients (37%). Among those, a novel germline kinase domain mutation of MERTK detected in a patient with PPGL and medullary thyroid carcinoma was found to activate signaling downstream of this receptor. Recurrent germline and somatic mutations were also detected in MET, including a familial case and sporadic PPGLs. Importantly, in each of these three genes, mutations were also detected in the validation group. In addition, a somatic oncogenic hotspot FGFR1 mutation was found in a sporadic tumor.

CONCLUSIONS

This study implicates chromatin-remodeling and kinase variants as frequent genetic events in PPGLs, many of which have no other known germline driver mutation. MERTK, MET, and H3F3A emerge as novel PPGL susceptibility genes. Clin Cancer Res; 22(9); 2301-10. ©2015 AACR.

Whole Genome Sequencing Defines the Genetic Heterogeneity of Familial Pancreatic Cancer

Pancreatic cancer is projected to become the second leading cause of cancer-related death in the United States by 2020. A familial aggregation of pancreatic cancer has been established, but the cause of this aggregation in most families is unknown. To determine the genetic basis of susceptibility in these families, we sequenced the germline genomes of 638 patients with familial pancreatic cancer and the tumor exomes of 39 familial pancreatic adenocarcinomas. Our analyses support the role of previously identified familial pancreatic cancer susceptibility genes such as BRCA2, CDKN2A, and ATM, and identify novel candidate genes harboring rare, deleterious germline variants for further characterization. We also show how somatic point mutations that occur during hematopoiesis can affect the interpretation of genome-wide studies of hereditary traits. Our observations have important implications for the etiology of pancreatic cancer and for the identification of susceptibility genes in other common cancer types.

SIGNIFICANCE

The genetic basis of disease susceptibility in the majority of patients with familial pancreatic cancer is unknown. We whole genome sequenced 638 patients with familial pancreatic cancer and demonstrate that the genetic underpinning of inherited pancreatic cancer is highly heterogeneous. This has significant implications for the management of patients with familial pancreatic cancer.

Integrated analysis of oral tongue squamous cell carcinoma identifies key variants and pathways linked to risk habits, HPV, clinical parameters and tumor recurrence

Oral tongue squamous cell carcinomas (OTSCC) are a homogeneous group of tumors characterized by aggressive behavior, early spread to lymph nodes and a higher rate of regional failure. Additionally, the incidence of OTSCC among younger population (<50yrs) is on the rise; many of whom lack the typical associated risk factors of alcohol and/or tobacco exposure. We present data on single nucleotide variations (SNVs), indels, regions with loss of heterozygosity (LOH), and copy number variations (CNVs) from fifty-paired oral tongue primary tumors and link the significant somatic variants with clinical parameters, epidemiological factors including human papilloma virus (HPV) infection and tumor recurrence. Apart from the frequent somatic variants harbored in TP53, CASP8, RASA1, NOTCH and CDKN2A genes, significant amplifications and/or deletions were detected in chromosomes 6-9, and 11 in the tumors. Variants in CASP8 and CDKN2A were mutually exclusive. CDKN2A, PIK3CA, RASA1 and DMD variants were exclusively linked to smoking, chewing, HPV infection and tumor stage. We also performed a whole-genome gene expression study that identified matrix metalloproteases to be highly expressed in tumors and linked pathways involving arachidonic acid and NF-k-B to habits and distant metastasis, respectively. Functional knockdown studies in cell lines demonstrated the role of CASP8 in a HPV-negative OTSCC cell line. Finally, we identified a 38-gene minimal signature that predicts tumor recurrence using an ensemble machine-learning method. Taken together, this study links molecular signatures to various clinical and epidemiological factors in a homogeneous tumor population with a relatively high HPV prevalence.

Vanno: a visualization-aided variant annotation tool

Next-generation sequencing (NGS) technologies have revolutionized the field of genetics and are trending toward clinical diagnostics. Exome and targeted sequencing in a disease context represent a major NGS clinical application, considering its utility and cost-effectiveness. With the ongoing discovery of disease-associated genes, various gene panels have been launched for both basic research and diagnostic tests. However, the fundamental inconsistencies among the diverse annotation sources, software packages, and data formats have complicated the subsequent analysis. To manage disease-associated NGS data, we developed Vanno, a Web-based application for in-depth analysis and rapid evaluation of disease-causative genome sequence alterations. Vanno integrates information from biomedical databases, functional predictions from available evaluation models, and mutation landscapes from TCGA cancer types. A highly integrated framework that incorporates filtering, sorting, clustering, and visual analytic modules is provided to facilitate exploration of oncogenomics datasets at different levels, such as gene, variant, protein domain, or three-dimensional structure. Such design is crucial for the extraction of knowledge from sequence alterations and translating biological insights into clinical applications. Taken together, Vanno supports almost all disease-associated gene tests and exome sequencing panels designed for NGS, providing a complete solution for targeted and exome sequencing analysis. Vanno is freely available at http://cgts.cgu.edu.tw/vanno.

ProKinO: a unified resource for mining the cancer kinome

Protein kinases represent a large and diverse family of evolutionarily related proteins that are abnormally regulated in human cancers. Although genome sequencing studies have revealed thousands of variants in protein kinases, translating "big" genomic data into biological knowledge remains a challenge. Here, we describe an ontological framework for integrating and conceptualizing diverse forms of information related to kinase activation and regulatory mechanisms in a machine readable, human understandable form. We demonstrate the utility of this framework in analyzing the cancer kinome, and in generating testable hypotheses for experimental studies. Through the iterative process of aggregate ontology querying, hypothesis generation and experimental validation, we identify a novel mutational hotspot in the αC-β4 loop of the kinase domain and demonstrate the functional impact of the identified variants in epidermal growth factor receptor (EGFR) constitutive activity and inhibitor sensitivity. We provide a unified resource for the kinase and cancer community, ProKinO, housed at http://vulcan.cs.uga.edu/prokino.

SNP2Structure: A Public and Versatile Resource for Mapping and Three-Dimensional Modeling of Missense SNPs on Human Protein Structures

One of the long-standing challenges in biology is to understand how non-synonymous single nucleotide polymorphisms (nsSNPs) change protein structure and further affect their function. While it is impractical to solve all the mutated protein structures experimentally, it is quite feasible to model the mutated structures in silico. Toward this goal, we built a publicly available structure database resource (SNP2Structure, https://apps.icbi.georgetown.edu/snp2structure) focusing on missense mutations, msSNP. Compared with web portals with similar aims, SNP2Structure has the following major advantages. First, our portal offers direct comparison of two related 3D structures. Second, the protein models include all interacting molecules in the original PDB structures, so users are able to determine regions of potential interaction changes when a protein mutation occurs. Third, the mutated structures are available to download locally for further structural and functional analysis. Fourth, we used Jsmol package to display the protein structure that has no system compatibility issue. SNP2Structure provides reliable, high quality mapping of nsSNPs to 3D protein structures enabling researchers to explore the likely functional impact of human disease-causing mutations.

Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes

Cancer is often driven by the accumulation of genetic alterations, including single nucleotide variants, small insertions or deletions, gene fusions, copy-number variations, and large chromosomal rearrangements. Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data and catalog somatic mutations in both common and rare cancer types. So far, the somatic mutation landscapes and signatures of >10 major cancer types have been reported; however, pinpointing driver mutations and cancer genes from millions of available cancer somatic mutations remains a monumental challenge. To tackle this important task, many methods and computational tools have been developed during the past several years and, thus, a review of its advances is urgently needed. Here, we first summarize the main features of these methods and tools for whole-exome, whole-genome and whole-transcriptome sequencing data. Then, we discuss major challenges like tumor intra-heterogeneity, tumor sample saturation and functionality of synonymous mutations in cancer, all of which may result in false-positive discoveries. Finally, we highlight new directions in studying regulatory roles of noncoding somatic mutations and quantitatively measuring circulating tumor DNA in cancer. This review may help investigators find an appropriate tool for detecting potential driver or actionable mutations in rapidly emerging precision cancer medicine.

Next-Generation Sequencing of Pulmonary Sarcomatoid Carcinoma Reveals High Frequency of Actionable MET Gene Mutations

PURPOSE

To further understand the molecular pathogenesis of pulmonary sarcomatoid carcinoma (PSC) and develop new therapeutic strategies in this treatment-refractory disease.

MATERIALS AND METHODS

Whole-exome sequencing in a discovery set (n = 10) as well as targeted MET mutation screening in an independent validation set (n = 26) of PSC were performed. Reverse transcriptase polymerase chain reaction and Western blotting were performed to validate MET exon 14 skipping. Functional studies for validation of the oncogenic roles of MET exon 14 skipping were conducted in lung adenosquamous cell line H596 (MET exon 14 skipped and PIK3CA mutated) and gastric adenocarcinoma cell line Hs746T (MET exon 14 skipped). Response to MET inhibitor therapy with crizotinib in a patient with advanced PSC and MET exon 14 skipping was evaluated to assess clinical translatability.

RESULTS

In addition to confirming mutations in known cancer-associated genes (TP53, KRAS, PIK3CA, MET, NOTCH, STK11, and RB1), several novel mutations in additional genes, including RASA1, CDH4, CDH7, LAMB4, SCAF1, and LMTK2, were identified and validated. MET mutations leading to exon 14 skipping were identified in eight (22%) of 36 patient cases; one of these tumors also harbored a concurrent PIK3CA mutation. Short interfering RNA silencing of MET and MET inhibition with crizotinib showed marked effects on cell viability and decrease in downstream AKT and mitogen-activated protein kinase activation in Hs746T and H596 cells. Concurrent PIK3CA mutation required addition of a second agent for successful pathway suppression and cell viability effect. Dramatic response to crizotinib was noted in a patient with advanced chemotherapy-refractory PSC carrying a MET exon 14 skipping mutation.

CONCLUSION

Mutational events of MET leading to exon 14 skipping are frequent and potentially targetable events in PSC.

Identification of cancer predisposition variants in apparently healthy individuals using a next-generation sequencing-based family genomics approach

Cancer, like many common disorders, has a complex etiology, often with a strong genetic component and with multiple environmental factors contributing to susceptibility. A considerable number of genomic variants have been previously reported to be causative of, or associated with, an increased risk for various types of cancer. Here, we adopted a next-generation sequencing approach in 11 members of two families of Greek descent to identify all genomic variants with the potential to predispose family members to cancer. Cross-comparison with data from the Human Gene Mutation Database identified a total of 571 variants, from which 47 % were disease-associated polymorphisms, 26 % disease-associated polymorphisms with additional supporting functional evidence, 19 % functional polymorphisms with in vitro/laboratory or in vivo supporting evidence but no known disease association, 4 % putative disease-causing mutations but with some residual doubt as to their pathological significance, and 3 % disease-causing mutations. Subsequent analysis, focused on the latter variant class most likely to be involved in cancer predisposition, revealed two variants of prime interest, namely MSH2 c.2732T>A (p.L911R) and BRCA1 c.2955delC, the first of which is novel. KMT2D c.13895delC and c.1940C>A variants are additionally reported as incidental findings. The next-generation sequencing-based family genomics approach described herein has the potential to be applied to other types of complex genetic disorder in order to identify variants of potential pathological significance.

MitImpact: an exhaustive collection of pre-computed pathogenicity predictions of human mitochondrial non-synonymous variants

Mitochondrial DNA carries a tiny, but fundamental portion of the eukaryotic genetic code. As its nuclear counterpart, it is susceptible to point mutations. Their level of pathogenicity has been assessed for the newly discovered mutations only, leaving some degree of uncertainty on the potential impact of the unknown mutations. Here we present Mitochondrial mutation Impact (MitImpact), a queryable lightweight web interface to a reasoned collection of structurally and evolutionary annotated pathogenicity predictions, obtained by assembling pre-computed with on-the-fly-computed sets of pathogenicity estimations, for all the possible mitochondrial missense variants. It presents itself as a resource for fast and reliable evaluation of gene-specific susceptibility of unknown and verified amino acid changes. MitImpact is freely available at http://bioinformatics.css-mendel.it/ (tools section). ©2014 Wiley Periodicals, Inc.

Hypermutation in human cancer genomes: footprints and mechanisms

A role for somatic mutations in carcinogenesis is well accepted, but the degree to which mutation rates influence cancer initiation and development is under continuous debate. Recently accumulated genomic data have revealed that thousands of tumour samples are riddled by hypermutation, broadening support for the idea that many cancers acquire a mutator phenotype. This major expansion of cancer mutation data sets has provided unprecedented statistical power for the analysis of mutation spectra, which has confirmed several classical sources of mutation in cancer, highlighted new prominent mutation sources (such as apolipoprotein B mRNA editing enzyme catalytic polypeptide-like (APOBEC) enzymes) and empowered the search for cancer drivers. The confluence of cancer mutation genomics and mechanistic insight provides great promise for understanding the basic development of cancer through mutations.

Preliminary whole-exome sequencing reveals mutations that imply common tumorigenicity pathways in multiple endocrine neoplasia type 1 patients

BACKGROUND

Whole-exome sequencing studies have not established definitive somatic mutation patterns among patients with sporadic hyperparathyroidism (HPT). No sequencing has evaluated multiple endocrine neoplasia type 1 (MEN1)-related HPT. We sought to perform whole-exome sequencing in HPT patients to identify somatic mutations and associated biological pathways and tumorigenic networks.

METHODS

Whole-exome sequencing was performed on blood and tissue from HPT patients (MEN1 and sporadic) and somatic single nucleotide variants (SNVs) were identified. Stop-gain and stop-loss SNVs were analyzed with Ingenuity Pathways Analysis (IPA). Loss of heterozygosity (LOH) was also assessed.

RESULTS

Sequencing was performed on 4 MEN1 and 10 sporadic cases. Eighteen stop-gain/stop-loss SNV mutations were identified in 3 MEN1 patients. One complex network was identified on IPA: Cellular function and maintenance, tumor morphology, and cardiovascular disease (IPA score = 49). A nonsynonymous SNV of TP53 (lysine-to-glutamic acid change at codon 81) identified in a MEN1 patient was suggested to be a driver mutation (Cancer-specific High-throughput Annotation of Somatic Mutations; P = .002). All MEN1 and 3/10 sporadic specimens demonstrated LOH of chromosome 11.

CONCLUSION

Whole-exome sequencing revealed somatic mutations in MEN1 associated with a single tumorigenic network, whereas sporadic pathogenesis seemed to be more diverse. A somatic TP53 mutation was also identified. LOH of chromosome 11 was seen in all MEN1 and 3 of 10 sporadic patients.

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.

DawnRank: discovering personalized driver genes in cancer

Large-scale cancer genomic studies have revealed that the genetic heterogeneity of the same type of cancer is greater than previously thought. A key question in cancer genomics is the identification of driver genes. Although existing methods have identified many common drivers, it remains challenging to predict personalized drivers to assess rare and even patient-specific mutations. We developed a new algorithm called DawnRank to directly prioritize altered genes on a single patient level. Applications to TCGA datasets demonstrated the effectiveness of our method. We believe DawnRank complements existing driver identification methods and will help us discover personalized causal mutations that would otherwise be obscured by tumor heterogeneity. Source code can be accessed at http://bioen-compbio.bioen.illinois.edu/DawnRank/.

The genomic landscape of nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) has extremely skewed ethnic and geographic distributions, is poorly understood at the genetic level and is in need of effective therapeutic approaches. Here we determined the mutational landscape of 128 cases with NPC using whole-exome and targeted deep sequencing, as well as SNP array analysis. These approaches revealed a distinct mutational signature and nine significantly mutated genes, many of which have not been implicated previously in NPC. Notably, integrated analysis showed enrichment of genetic lesions affecting several important cellular processes and pathways, including chromatin modification, ERBB-PI3K signaling and autophagy machinery. Further functional studies suggested the biological relevance of these lesions to the NPC malignant phenotype. In addition, we uncovered a number of new druggable candidates because of their genomic alterations. Together our study provides a molecular basis for a comprehensive understanding of, and exploring new therapies for, NPC.

Identification of recurrent SMO and BRAF mutations in ameloblastomas

Here we report the discovery of oncogenic mutations in the Hedgehog and mitogen-activated protein kinase (MAPK) pathways in over 80% of ameloblastomas, locally destructive odontogenic tumors of the jaw, by genomic analysis of archival material. Mutations in SMO (encoding Smoothened, SMO) are common in ameloblastomas of the maxilla, whereas BRAF mutations are predominant in tumors of the mandible. We show that a frequently occurring SMO alteration encoding p.Leu412Phe is an activating mutation and that its effect on Hedgehog-pathway activity can be inhibited by arsenic trioxide (ATO), an anti-leukemia drug approved by the US Food and Drug Administration (FDA) that is currently in clinical trials for its Hedgehog-inhibitory activity. In a similar manner, ameloblastoma cells harboring an activating BRAF mutation encoding p.Val600Glu are sensitive to the BRAF inhibitor vemurafenib. Our findings establish a new paradigm for the diagnostic classification and treatment of ameloblastomas.

AVIA: an interactive web-server for annotation, visualization and impact analysis of genomic variations

MOTIVATION

The plethora of information that emerges from large-scale genome characterization studies has triggered the development of computational frameworks and tools for efficient analysis, interpretation and visualization of genomic data. Functional annotation of genomic variations and the ability to visualize the data in the context of whole genome and/or multiple genomes has remained a challenging task. We have developed an interactive web-based tool, AVIA (Annotation, Visualization and Impact Analysis), to explore and interpret large sets of genomic variations (single nucleotide variations and insertion/deletions) and to help guide and summarize genomic experiments. The annotation, summary plots and tables are packaged and can be downloaded by the user from the email link provided.

AVAILABILITY AND IMPLEMENTATION

http://avia.abcc.ncifcrf.gov.

A graph theoretic approach to utilizing protein structure to identify non-random somatic mutations

Background

It is well known that the development of cancer is caused by the accumulation of somatic mutations within the genome. For oncogenes specifically, current research suggests that there is a small set of "driver" mutations that are primarily responsible for tumorigenesis. Further, due to recent pharmacological successes in treating these driver mutations and their resulting tumors, a variety of approaches have been developed to identify potential driver mutations using methods such as machine learning and mutational clustering. We propose a novel methodology that increases our power to identify mutational clusters by taking into account protein tertiary structure via a graph theoretical approach.

Results

We have designed and implemented GraphPAC (Graph Protein Amino acid Clustering) to identify mutational clustering while considering protein spatial structure. Using GraphPAC, we are able to detect novel clusters in proteins that are known to exhibit mutation clustering as well as identify clusters in proteins without evidence of prior clustering based on current methods. Specifically, by utilizing the spatial information available in the Protein Data Bank (PDB) along with the mutational data in the Catalogue of Somatic Mutations in Cancer (COSMIC), GraphPAC identifies new mutational clusters in well known oncogenes such as EGFR and KRAS. Further, by utilizing graph theory to account for the tertiary structure, GraphPAC discovers clusters in DPP4, NRP1 and other proteins not identified by existing methods. The R package is available at: http://bioconductor.org/packages/release/bioc/html/GraphPAC.html.

Conclusion

GraphPAC provides an alternative to iPAC and an extension to current methodology when identifying potential activating driver mutations by utilizing a graph theoretic approach when considering protein tertiary structure.

Predicting the functional consequences of somatic missense mutations found in tumors

Cancer-specific High-throughput Annotation of Somatic Mutations (CHASM) is a computational method that uses supervised machine learning to prioritize somatic missense mutations detected in tumor sequencing studies. Missense mutations are a key mechanism by which important cellular behaviors, such as cell growth, proliferation, and survival, are disrupted in cancer. However, only a fraction of the missense mutations observed in tumor genomes are expected to be cancer causing. Distinguishing tumorigenic "driver" mutations from their neutral "passenger" counterparts is currently a pressing problem in cancer research.CHASM trains a Random Forest classifier on driver mutations from the COSMIC databases and uses background nucleotide substitution rates observed in tumor sequencing data to model tumor type-specific passenger mutations. Each missense mutation is represented by quantitative features that fall into five major categories: physiochemical properties of amino acid residues; scores derived from multiple sequence alignments of protein or DNA; region-based amino acid sequence composition; predicted properties of local protein structure; and annotations from the UniProt feature tables. Both a software package and a Web server implementation of CHASM are available to facilitate high-throughput prioritization of somatic missense mutations from large, multi-tumor exome sequencing studies. After ranking candidate driver mutations with CHASM, the vector of features describing each mutation can be used to suggest possible mechanism by which mutations alter protein activity in tumorigenesis. This chapter details the application of both implementations of CHASM to tumor sequencing data.

The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine

The Human Gene Mutation Database (HGMD®) is a comprehensive collection of germline mutations in nuclear genes that underlie, or are associated with, human inherited disease. By June 2013, the database contained over 141,000 different lesions detected in over 5,700 different genes, with new mutation entries currently accumulating at a rate exceeding 10,000 per annum. HGMD was originally established in 1996 for the scientific study of mutational mechanisms in human genes. However, it has since acquired a much broader utility as a central unified disease-oriented mutation repository utilized by human molecular geneticists, genome scientists, molecular biologists, clinicians and genetic counsellors as well as by those specializing in biopharmaceuticals, bioinformatics and personalized genomics. The public version of HGMD (http://www.hgmd.org) is freely available to registered users from academic institutions/non-profit organizations whilst the subscription version (HGMD Professional) is available to academic, clinical and commercial users under license via BIOBASE GmbH.

Computational approaches to identify functional genetic variants in cancer genomes

The International Cancer Genome Consortium (ICGC) aims to catalog genomic abnormalities in tumors from 50 different cancer types. Genome sequencing reveals hundreds to thousands of somatic mutations in each tumor but only a minority of these drive tumor progression. We present the result of discussions within the ICGC on how to address the challenge of identifying mutations that contribute to oncogenesis, tumor maintenance or response to therapy, and recommend computational techniques to annotate somatic variants and predict their impact on cancer phenotype.

MuPIT interactive: webserver for mapping variant positions to annotated, interactive 3D structures

Mutation position imaging toolbox (MuPIT) interactive is a browser-based application for single-nucleotide variants (SNVs), which automatically maps the genomic coordinates of SNVs onto the coordinates of available three-dimensional (3D) protein structures. The application is designed for interactive browser-based visualization of the putative functional relevance of SNVs by biologists who are not necessarily experts either in bioinformatics or protein structure. Users may submit batches of several thousand SNVs and review all protein structures that cover the SNVs, including available functional annotations such as binding sites, mutagenesis experiments, and common polymorphisms. Multiple SNVs may be mapped onto each structure, enabling 3D visualization of SNV clusters and their relationship to functionally annotated positions. We illustrate the utility of MuPIT interactive in rationalizing the impact of selected polymorphisms in the PharmGKB database, somatic mutations identified in the Cancer Genome Atlas study of invasive breast carcinomas, and rare variants identified in the exome sequencing project. MuPIT interactive is freely available for non-profit use at http://mupit.icm.jhu.edu .