BayesPI-BAR2: A New Python Package for Predicting Functional Non-coding Mutations in Cancer Patient Cohorts.

Introduction: 98% of mutations are non-coding mutations. Thus far, most non-coding mutations are ignored or merely classified as variant of unknown significance with a few exceptions, despite the fact that non-coding region plays significant role in regulations and other important biological processes. Although they would not be directly druggable, these non-coding mutations might be important for cancer etiology, response to treatments and prognosis assessments. Method: Mutations in non-coding genes, intron, upstream and downstream, and intergenic regions are collected from COSMIC and TCGA as defined in Ensemble GRCh37.75 gene definition and hg19 RefSeq. Over 15 million non-coding mutations are analyzed for recurrence over 10 samples. Mutations in neighborhood of IG genes and dbSNP150 polymorphisms are removed from further investigation. We analyzed remaining mutations for possible super enhancer and transcription factor binding. Result: 911 distinct recurrent mutations are identified. Promoter mutations in protein coding genes like MPRS31, NUDFB9, SZT2, WASF3 in addition to TERT are discovered. Intronic mutations of well-known cancer genes such as HIF1A, NF1, KMT2C, and ATR are discovered with their associated cancer type specificity. As for non-coding RNA's, lincRNA CROCCP2, LINC00969 and miRNA MIR4477 appear frequently. Only 182 mutations are reluctantly classified as intergenic; intergenic loci RP11-96F8.1-KSR1P1, MLLT10P1-DEFB115, and bP-21201H5.1-IGHV1OR21-1 contain unusually high recurrent mutations. Conclusion: Many non-coding mutations worth further investigation are found. Still, it is quite possible that many of these mutations might be artifacts resulting from paralogous alignments, gene definition compatibility and other technical issues. Importantly, we discovered novel intronic mutations in genes already associated with treatments. Mutations in intergenic regions might be involved in regulatory mechanisms and chromosomal structures. Understanding these non-coding mutations will further refine our understanding of biology as well as cancer. Citation Format: Takahiko Koyama, Laxmi Parida. Analysis of noncoding mutations in cancer revealed novel potentially pathogenic recurrent mutations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2353.

COSMIC, the Catalogue Of Somatic Mutations In Cancer (http://cancer.sanger.ac.uk) is the world's largest and most comprehensive online resource for exploring the impact of somatic mutations in human cancer. Now running for over 10 years, the 67th release (Oct 2013) describes 1592109 mutations in 947213 tumour samples across 25606 genes. This information is curated manually from the scientific literature, and automatically from genome resequencing consortium data portals. Full curation of the scientific literature provides in-depth understanding of the impact that each gene has in human cancer, and this has been achieved for 127 point-mutated cancer genes, and 185 fusion gene pairs. Curated genes are selected from the Cancer Gene Census (http://cancer.sanger.ac.uk/census), a listing of all genes with substantial evidence implicating them in cancer promotion, currently numbering 513 and updated frequently. The mutations discovered in the re-sequencing of over 8000 tumour genomes are now present in COSMIC (viewable in isolation from the genic curations, http://cancer.sanger.ac.uk/wgs). In addition, the Sanger has now fully exome sequenced 1015 common cancer cell lines, identifying 1146874 coding mutations annotated for functional significance, and this is available exclusively in COSMIC at (http://cancer.sanger.ac.uk/cell_lines). While COSMIC has focused on point mutations and gene fusions, many other mutation mechanisms cause oncogenesis and these are now being integrated. The 67th COSMIC release includes copy number mutations integrated into the database and major web page views. To allow easy graphical examination of this data, copy number information was reduced to ‘gain’ and ‘loss’ annotations for inclusion in histograms and tables, with much more precise detail available with a further click. Copy number data is available in detail for every gene in COSMIC, and also for every tissue. Exploring cancer via COSMIC’s Cancer Browser (http://cancer.sanger.ac.uk/cosmic/browse/tissue), results not just in a plot of the most mutated genes, but now also a circular genome plot summarizing the copy number gains and losses across all the samples from that tumour type, all explorable in more detail via clicks on the pictures. As the genomic data increases in COSMIC, it is becoming more important to qualitatively annotate the information, indicating which is more important or significant to oncogenesis. We are now building systems to better highlight known or putative functional mutations, improving the signal-to-noise ratio of cancer genome resequencing. Citation Format: C Boutselakis, S A. Forbes, P Gunasekaran, M Jia, D Beare, N Bindal, C Y. Kok, K Leung, D Minjie, R Shepherd, S Bamford, S Ward, C Cole, J W. Teague, M Stratton, P Campbell, U McDermott. COSMIC: Enhancing the world's knowledge of somatic mutations in human cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5326. doi:10.1158/1538-7445.AM2014-5326

COSMIC, the Catalogue Of Somatic Mutations In Cancer (http://www.sanger.ac.uk/cosmic), is one of the most comprehensive web systems describing the impact of somatic mutations in human cancer. The information is sourced directly from the published scientific literature and the Cancer Genome Project's high throughput sequencing laboratories at the Wellcome Trust Sanger Institute UK (CGP), allowing a high data quality to be maintained. The v44 release (Nov. 2009) contained the curation of 8336 papers covering 71 known cancer genes by point mutation and 30 by gene fusion. The mutation data for all these genes is being maintained up-to-date and new genes are regularly added to this list. Additionally, 4871 genes and 2764 samples have been scrutinized by the CGP laboratories. As cancer genetics moves toward the analysis of whole genomes, COSMIC is being updated to handle and display these data and increase the functionality of the website. With the rapid development of sequencing technologies, the quantity of data that can be put into COSMIC is increasing fast. We have extended our curation process to include large exome-wide candidate gene screens, recently including the first such screen from Sjoblom et al (2006), and the first TCGA screen (2008) with other large scale screening datasets being curated. In addition, whole-genome screens are becoming available with many non-coding variations. From the CGP laboratories, we already have two low-coverage genome rearrangement screens in COSMIC and this will rapidly expand, beginning with the imminent release of rearrangement scans in a set of 24 breast tumours. High-coverage genome analyses are also beginning to become available; with full genome coverage, the majority of somatic mutations in a tumour can be identified and COSMIC is being prepared to receive and display this data. With the inclusion of these new data, the COSMIC website has evolved to allow more easy access to required data. New specialization filters are in place to provide methods to search for subsets of COSMIC data in the usual user-friendly web pages. More extensive investigations can be performed using the new COSMIC Biomart, an industry-standard data mining system allowing access through web pages or programmatic interfaces. As the inclusion of new genomic data accelerates and the systems through which it can be accessed evolve, COSMIC is well placed to remain a central resource in human cancer genetics. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 93.

Non-coding mutations can create splice sites, however the true extent of how such somatic non-coding mutations affect RNA splicing are largely unexplored. Here we use the MiSplice pipeline to analyze 783 cancer cases with WGS data and 9494 cases with WES data, discovering 562 non-coding mutations that lead to splicing alterations. Notably, most of these mutations create new exons. Introns associated with new exon creation are significantly larger than the genome-wide average intron size. We find that some mutation-induced splicing alterations are located in genes important in tumorigenesis (ATRX, BCOR, CDKN2B, MAP3K1, MAP3K4, MDM2, SMAD4, STK11, TP53 etc.), often leading to truncated proteins and affecting gene expression. The pattern emerging from these exon-creating mutations suggests that splice sites created by non-coding mutations interact with pre-existing potential splice sites that originally lacked a suitable splicing pair to induce new exon formation. Our study suggests the importance of investigating biological and clinical consequences of noncoding splice-inducing mutations that were previously neglected by conventional annotation pipelines. MiSplice will be useful for automatically annotating the splicing impact of coding and non-coding mutations in future large-scale analyses.

COSMIC, the Catalogue Of Somatic Mutations In Cancer (http://cancer.sanger.ac.uk/cosmic) is a long-term sustainable effort to collect, standardise and integrate information on somatic mutations and other molecular alterations that cause human cancer. Being the world's largest and most comprehensive database of somatic mutations in cancer, it also provides web-based tools for exploration and interpretation of collected data. The content of the database is obtained primarily by careful curation of the scientific literature by a team of experienced post-doctoral curators, allowing mutations to be described across every form of human cancer at high resolution (covering 1,706 cancer phenotypes). This is then combined with data from a number of on-line sources, including the TCGA and ICGC web portals. During exhaustive manual curation, all the available information about mutations and samples (e.g. disease type, demographic data, treatments) are collected, standardised and integrated to allow for both creation of wide virtual cohorts and large-scale studies, as well as precise analysis at the level of a single sample, gene or mutation. To support investigations into how genes dysfunction to drive cancer, the 83rd release of COSMIC (Nov 2017) encompasses 5,366,273 coding mutations and 18,845 gene fusions in 1,343,214 cancer samples, hand-curated from 25,501 scientific publications, including 32,514 whole cancer genomes. Further curations support examination of gene dysregulation in cancer, including 16,961,605 non-coding variants, 1,180,789 Copy Number Variants, 9,176,464 gene expression variants, and 7,879,142 differentially methylated CpGs. Specialised COSMIC projects highlight specific knowledge of cancer in order to characterise events with a higher impact in disease aetiology. The Cancer Gene Census (http://cancer.sanger.ac.uk/census), currently defines and describes 699 genes, how their dysfunctions drive oncogenesis, and characterises their impact on hallmarks of cancer. COSMIC-3D (http://cancer.sanger.ac.uk/cosmic3d) provides an interactive view of cancer mutations in the context of 3D protein structures, and predicts potential drug-binding sites. Mutations causing drug resistance are now described as a new resource (http://cancer.sanger.ac.uk/cosmic/drug_resistance). To allow for the incorporation of new data exploration tools, the COSMIC web page layouts have been updated and their usability has been improved, giving more options to filter webpage content. COSMIC is significantly updated 4 times a year, and is available free-of-charge for academic and non-profit users via COSMIC webpage (http://cancer.sanger.ac.uk/cosmic) or to download through COSMIC downloads (http://cancer.sanger.ac.uk/cosmic/download) Citation Format: Zbyslaw Sondka, Sally Bamford, Charlotte G. Cole, Elisabeth Dawson, Laura Ponting, Raymund Stefancsik, Sari Ward, Harry C. Jubb, Sam Thompson, Dave Beare, Nidhi Bindal, Charambulos Boutselakis, Peter Fish, Bhavana Harsha, Chai Yin Kok, Chris Ramshaw, Claire Rye, John Tate, Shicai Wang, Peter J. Campbell, Simon A. Forbes. COSMIC: Integrating and interpreting the world's knowledge of somatic mutations in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3284.

COSMIC, the Catalogue Of Somatic Mutations In Cancer (http://cancer.sanger.ac.uk/cosmic) is a continual effort to integrate all available information on somatic mutations and other molecular alterations causing every form of human cancer. Being the world’s largest and most comprehensive database of somatic mutations in human cancer, it also provides web-based tools for exploration and interpretation of collected data. The content of the database is primarily obtained from the scientific literature by the team of experienced post-doctoral curators and combined with information from online sources, including the TCGA and ICGC. During thorough & exhaustive manual curation, all the available information about mutations and tumor samples (e.g. disease type, demographic data, treatments) are collected, standardized and integrated to allow for both creation of wide virtual cohorts and large-scale studies, as well as precise analysis at the level of a single sample, gene or mutation. The 87th release of COSMIC (Nov 2018) encompasses 5,992,260 coding mutations and 19,574 gene fusions, curated from 1,403,267 cancer samples, including 35,490 whole cancer exomes/genomes, primarily hand-curated data from 26,494 scientific publications. Additionally, COSMIC describes 1,179,545 Copy Number Variants, 9,147,833 gene expression variants, and 7,879,142 differentially methylated CPGs. In addition to this broad database, COSMIC includes a range of specialized projects highlighting specific aspects of cancer in order to emphasize events with a higher impact in disease etiology. This includes the Cancer Gene Census (http://cancer.sanger.ac.uk/census), which defines and describes genes (currently 719) and their dysfunctions driving oncogenesis, and characterizes their impact on hallmarks of cancer. COSMIC3D (http://cancer.sanger.ac.uk/cosmic3d) provides an interactive view of cancer mutations in the context of 3D protein structures, and predicts potential drug-binding sites. Significantly updated 4 times a year, COSMIC is available free-of-charge for academic and non-profit users via COSMIC webpage (http://cancer.sanger.ac.uk/cosmic) or to download through COSMIC downloads (http://cancer.sanger.ac.uk/cosmic/download). Citation Format: Simon Andrew Forbes, david beare, charalampos boutselakis, sally bamford, kate noble, claire rye, john tate, chai yin kok, charlie hathaway, laura ponting, christopher ramshaw, raymund stefancsik, samantha thompson, bhavana harsha, nidhi bindal, shicai wang, steven jupe, helen speedy, celestino creatore, peter fish, sari ward, charlotte cole, elisabeth dawson, zbyslaw sondka. COSMIC: Describing the world’s knowledge of somatic mutations in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 906.

Background: Liver cancer accounts for more than 700,000 deaths each year making it the third leading cause of cancer-related deaths worldwide. Late diagnosis of the disease is the reason behind most deaths. Driver mutations are genetic alterations in tumor cells, which are responsible for the development of liver cancer; therefore, the identification of genetic biomarkers is necessary for the prediction and early diagnosis of liver cancer. Objectives: The main objective of this study is to identify pathogenic alleles that may act as potential biomarkers for the prediction of liver cancer. It also identifies the role of novel genes in liver cancer that are not known to cause the disease. Methods: The mutation data of non-coding variants were downloaded from the catalogue of somatic mutations in cancer (COSMIC) databases. Different bioinformatics tools were, then, used to retrieve mutations in liver cancer. The genetic alterations in hepatocellular carcinoma (HCC) were analyzed. Results: The present study successfully identified pathogenic alleles (consistent mutations) along with a set of novel genes that might be involved in the development of liver cancer. It identified non-coding mutations near human genes and transcription factor binding sites of HepG2 cells. This study also identified mutations near the genes that are involved in the Ras/MAFK signaling pathway of the Hepatitis B virus. Conclusions: The pathogenic alleles identified in this study may provide targeted therapy for the treatment of liver cancer. The identification of novel genes may help to understand the progression of liver cancer at the molecular level. The identified driver mutations may act as potential biomarkers and therapeutic targets for early prediction and treatment of liver cancer.

I come from a family in which there have been no scientists or doctors. I was interested, however, in biology at school and started my scientific career by training in medicine at Oxford University and Guys Hospital, London. Practising as a doctor reinforced my curiosity about the biological processes underlying human disease. As a consequence, I pursued a clinical vocation in histopathology, a discipline that couples exposure to the sights and smells of the autopsy room with a daily journey into the often beautiful, sometimes ugly world of healthy and diseased human tissues under the microscope. After an introduction to general histopathology in Nick Wright's department at the Hammersmith Hospital, London, I completed my postgraduate medical training in neuropathology with Peter Lantos at the Maudsley Hospital, London.Peering at the nuclei of cancer cells under the microscope, for me it was a matter of fascination that hidden within them were the key events converting normal cells into cancer cells, and frustration because they were out of reach. Many of the tissue samples examined by pathologists are from cancers. The clonal theory of cancer development and the general role of DNA mutations in generating cancer cell clones had been established by 1986 when I was working as a junior pathologist. Indeed, the first mutated cancer gene, HRAS , had recently been identified through application of the, then new, technologies of recombinant DNA technology. Peering at the nuclei of cancer cells under the microscope, for me it was a matter of fascination that hidden within them were the key events converting normal cells into cancer cells, and frustration because they were out of reach. So, I took 3 years break from medicine to study for a PhD, learning the methods and thinking of molecular oncology in Colin Cooper's laboratory at the Institute …

Somatic mutations in coding regions of the genome may result in non-functional proteins that can lead to cancer or other diseases, however cancer mutations in the non-coding regions have rarely been studied and the interpretation of their effects is difficult. Non-coding mutations might act by breaking or creating transcription factor binding motifs in promoters, enhancers or silencers resulting in altered expression of target gene(s). A high number of mutations have been reported in coding and non-coding regions in cells of liver cancer. Hepatocyte nuclear factor 4α is a transcription factor that regulates the expression of several genes in liver cells, while the motifs it binds are frequently mutated in promoters and enhancers in liver cancer. The aim of the study is to evaluate the genetic effects of a non-coding somatic mutation frequently observed in liver cancer. We evaluated experimentally the effects of a somatic mutation frequently reported in liver cancer as a motif-breaker for the binding of hepatocyte nuclear factor 4α. The effects of the mutation on protein binding and enhancer activity were studied in HepG2cells via electrophoresis mobility shift assay and dual luciferase reporter assays. We also studied genome-wide promoter-enhancer interactions performing targeted chromosome conformation capture in liver tissue to identify putative target genes whose expression could be altered by the mutation. We found that the mutation leads to reduced protein binding and a decrease in enhancer activity. The enhancer harboring the mutation interacts with the promoters of ANAPC13, MAP6D1and MUC13, which have been implicated in liver cancer. The study highlights the importance of non-coding somatic mutations, vastly understudied, but likely to contribute to cancer development and progression.

BackgroundAn important step towards personalizing cancer treatment is to integrate heterogeneous evidences to catalog mutational hotspots that are biologically and therapeutically relevant and thus represent where targeted therapy would likely be beneficial. However, existing methods do not sufficiently delineate varying functionality of individual mutations within the same genes.ResultsWe observed a large discordancy of mutation rates across different mutation subtypes and tumor types, and nominated 702 hotspot mutations in 549 genes in the Catalog of Somatic Mutations in Cancer (COSMIC) by considering context specific mutation characteristics such as genes, cancer types, mutation rates, mutation subtypes and sequence contexts. We observed that hotspot mutations were highly prevalent in Non CpG-island C/G transition and transversion sequence contexts in 10 tumor types, and specific insertion hotspot mutations were enriched in breast cancer and deletion hotspot mutations in colorectal cancer. We found that the hotspot mutations nominated by our approach were significantly more conserved than non-hotspot mutations in the corresponding cancer genes. We also examined the biological significance and pharmacogenomics properties of these hotspot mutations using data in the Cancer Genome Atlas (TCGA) and the Cancer Cell-Line Encyclopedia (CCLE), and found that 53 hotspot mutations are independently associated with diverse functional evidences in 1) mRNA and protein expression, 2) pathway activity, or 3) drug sensitivity and 82 were highly enriched in specific tumor types. We highlighted the distinct functional indications of hotspot mutations under different contexts and nominated novel hotspot mutations such as MAP3K4 A1199 deletion, NR1H2 Q175 insertion, and GATA3 P409 insertion as potential biomarkers or drug targets.ConclusionWe identified a set of hotspot mutations across 17 tumor types by considering the background mutation rate variations among genes, tumor subtypes, mutation subtypes, and sequence contexts. We illustrated the common and distinct mutational signatures of hotspot mutations among different tumor types and investigated their variable functional relevance under different contexts, which could potentially serve as a resource for explicitly selecting targets for diagnosis, drug development, and patient management.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2727-x) contains supplementary material, which is available to authorized users.

Unlike most tumor suppressor genes, the most common genetic alterations in TP53 are missense mutations1,2. Mutant p53 protein is often abundantly expressed in cancers, and specific allelic variants exhibit dominant-negative or gain-of-function activities in experimental models3–8. To gain a systematic view of p53 function, we interrogated loss-of-function screens conducted in hundreds of human cancer cell lines and performed TP53 saturation mutagenesis screens in an isogenic pair of TP53-wild-type and -null cell lines. We found that loss or dominant-negative inhibition of p53 function reliably enhanced cellular fitness. By integrating these data with the COSMIC mutational signatures database9,10, we developed a statistical model that describes the TP53 mutational spectrum as a function of the baseline probability of acquiring each mutation and the fitness advantage conferred by attenuation of p53 activity. Collectively, these observations show that widely-acting and tissue-specific mutational processes combine with phenotypic selection to dictate the frequencies of recurrent TP53 mutations.

<b>Abstract ID 24526</b> <b>Poster Board 232</b> The cytochrome P450 (CYP) enzymes are responsible for the metabolism of many drugs. Single nucleotide polymorphisms (SNPs) have large impacts on the activity of these enzymes and result in altered metabolism for a variety of drugs. Cancer patients frequently suffer from both chronic pain and depression, leading to co-administration of selective serotonin reuptake inhibitors (ssRIs) and opioids. CYP2D6 is necessary to convert ssRIs into their inactive metabolites and to activate several prodrug opioids. Ideally, to avoid drug-drug interactions in cancer patients when prescribing ssRI-opioid combinations, careful analysis of relevant SNPs through PCR in individual patients would be employed. However, this approach remains technically and economically inaccessible to many cancer demographics and clinical settings. To address these limitations and provide genomic-based alternatives, we analyzed the frequency of CYP2D6 mutations in sequenced specimens from a varied pool of cancer patients. The primary goal of this study is to use cancer data trends linked to CYP2D6 DNA sequence disruptions to improve informed selection of ssRI-opioid treatments based on specific patient characteristics. We mined the Catalogue of Somatic Mutations in Cancer (COSMIC) from the Sanger Institute to identify CYP2D6 mutations occurring in patients with various cancers types. We also used the CHASM-3.1 and VEST-4 artificial intelligence algorithms from the Cancer-Related Analysis of Variants Toolkit (CRAVAT) to evaluate the functional significance of missense mutations. Results from CHASM reveal the probability of mutations either being drivers or passengers in cancer, while VEST indicates the likelihood of mutations being pathogenic. We found that 188 of 409 patients showed CYP2D6 mutations localized to the coding region. A majority of these (126) were missense mutations, 18 showed high pathogenicity, and no cancer drivers were identified. The three tissue types most frequently affected by CYP2D6 mutations were the large intestine (60), lung (58), and upper aerodigestive tract (49). The mean age at time of sequencing (MATS) for all tissues was 58.2 years. Three tissues showed significant age differences relative to the MATS of all tissue types: the urinary tract (mean=69.5, p=0.0080), hematopoietic and lymphoid tissue (mean=49.1, p=0.0039), and the stomach (mean=66.0, p=0.0027). The primary histology associated with CYP2D6 mutations was carcinoma (327 of 409 patients). Intriguingly, cancers linked to lymphoid neoplasm histology had a significantly different MATS (mean=41.4 years, p=0.0002) relative to all histology types (mean=58.2). The observed missense mutations in the conserved P450 domain of CYP2D6 suggest structural changes that likely affect protein function, thereby precluding effective metabolism of this enzyme’s drug substrates. Therefore, these results indicate that cautious, personalized, and deliberate prescription should be used when co-administering opioids and ssRIs to patients who meet criteria associated with decreased CYP2D6 metabolism in the cancer contexts herein reported.

Genomic sequence mutations can be pathogenic in both germline and somatic cells. Several authors have observed that often the same genes are involved in cancer when mutated in somatic cells and in genetic diseases when mutated in the germline. Recent advances in high-throughput sequencing techniques have provided us with large databases of both types of mutations, allowing us to investigate this issue in a systematic way. Hence, we applied a machine learning based framework to this problem, comparing multiple models. The models achieved significant predictive power as shown by both cross-validation and their application to recently discovered gene/phenotype associations not used for training. We found that genes characterized by high frequency of somatic mutations in the most common cancers and ancient evolutionary age are most likely to be involved in abnormal phenotypes and diseases. These results suggest that the combination of tolerance for mutations at the cell viability level (measured by the frequency of somatic mutations in cancer) and functional relevance (demonstrated by evolutionary conservation) are the main predictors of disease genes. Our results thus confirm the deep relationship between pathogenic mutations in somatic and germline cells, provide new insight into the common origin of cancer and genetic diseases, and can be used to improve the identification of new disease genes.

BackgroundOncometabolites, often generated as a result of a gene mutation, show pro-oncogenic function when abnormally accumulated in cancer cells. Identification of such mutation-associated metabolites will facilitate developing treatment strategies for cancers, but is challenging due to the large number of metabolites in a cell and the presence of multiple genes associated with cancer development.ResultsHere we report the development of a computational workflow that predicts metabolite-gene-pathway sets. Metabolite-gene-pathway sets present metabolites and metabolic pathways significantly associated with specific somatic mutations in cancers. The computational workflow uses both cancer patient-specific genome-scale metabolic models (GEMs) and mutation data to generate metabolite-gene-pathway sets. A GEM is a computational model that predicts reaction fluxes at a genome scale and can be constructed in a cell-specific manner by using omics data. The computational workflow is first validated by comparing the resulting metabolite-gene pairs with multi-omics data (i.e., mutation data, RNA-seq data, and metabolome data) from acute myeloid leukemia and renal cell carcinoma samples collected in this study. The computational workflow is further validated by evaluating the metabolite-gene-pathway sets predicted for 18 cancer types, by using RNA-seq data publicly available, in comparison with the reported studies. Therapeutic potential of the resulting metabolite-gene-pathway sets is also discussed.ConclusionsValidation of the metabolite-gene-pathway set-predicting computational workflow indicates that a decent number of metabolites and metabolic pathways appear to be significantly associated with specific somatic mutations. The computational workflow and the resulting metabolite-gene-pathway sets will help identify novel oncometabolites and also suggest cancer treatment strategies.

Over the recent years, enormous progress has been made in mapping complex traits in humans. These studies have demonstrated that there is a crucial need to generate appropriate gene-disease annotation repositories accessed through modern resources. Our focus here is to create a comprehensive database with mobile access to authentic somatic mutations in cancer and classified diseases worldwide, considered as the foundation for clinical and genomics research, epidemiology and precision medicine. In this manuscript, we present a non-commercial, academic and publically available iOS app i.e., PAS-MUT, which invites global users to freely download it on iPhone & iPad devices, quickly adopt its easy to use interface and search for somatic mutations, genes and related diseases. PAS-MUT is developed with Swift multi-paradigm programming language, XCODE integrated development environment for MacOS, and PHP scripting that uses MySQL database and web servers. PAS-MUT database includes over a million authentic somatic mutations released global, including available at the COSMIC, CNVD, SwissVar, and ClinVar etc. PAS-MUT is founded on the clinical and scientific premise to support and promote healthcare and genomics data sharing with technological advancements.