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Rao S, Pitel B, Wagner AH, Boca SM, McCoy M, King I, Gupta S, Park BH, Warner JL, Chen J, Rogan PK, Chakravarty D, Griffith M, Griffith OL, Madhavan S. Collaborative, Multidisciplinary Evaluation of Cancer Variants Through Virtual Molecular Tumor Boards Informs Local Clinical Practices. JCO Clin Cancer Inform 2020; 4:602-613. [PMID: 32644817 PMCID: PMC7397775 DOI: 10.1200/cci.19.00169] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/18/2020] [Indexed: 12/17/2022] Open
Abstract
PURPOSE The cancer research community is constantly evolving to better understand tumor biology, disease etiology, risk stratification, and pathways to novel treatments. Yet the clinical cancer genomics field has been hindered by redundant efforts to meaningfully collect and interpret disparate data types from multiple high-throughput modalities and integrate into clinical care processes. Bespoke data models, knowledgebases, and one-off customized resources for data analysis often lack adequate governance and quality control needed for these resources to be clinical grade. Many informatics efforts focused on genomic interpretation resources for neoplasms are underway to support data collection, deposition, curation, harmonization, integration, and analytics to support case review and treatment planning. METHODS In this review, we evaluate and summarize the landscape of available tools, resources, and evidence used in the evaluation of somatic and germline tumor variants within the context of molecular tumor boards. RESULTS Molecular tumor boards (MTBs) are collaborative efforts of multidisciplinary cancer experts equipped with genomic interpretation resources to aid in the delivery of accurate and timely clinical interpretations of complex genomic results for each patient, within an institution or hospital network. Virtual MTBs (VMTBs) provide an online forum for collaborative governance, provenance, and information sharing between experts outside a given hospital network with the potential to enhance MTB discussions. Knowledge sharing in VMTBs and communication with guideline-developing organizations can lead to progress evidenced by data harmonization across resources, crowd-sourced and expert-curated genomic assertions, and a more informed and explainable usage of artificial intelligence. CONCLUSION Advances in cancer genomics interpretation aid in better patient and disease classification, more streamlined identification of relevant literature, and a more thorough review of available treatments and predicted patient outcomes.
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Affiliation(s)
- Shruti Rao
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC
| | - Beth Pitel
- Division of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Alex H. Wagner
- McDonnell Genome Institute and Department of Medicine, Washington University School of Medicine, St Louis, MO
| | - Simina M. Boca
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC
| | - Matthew McCoy
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC
| | - Ian King
- Laboratory Medicine Program, University Health Network and Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Samir Gupta
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC
| | - Ben Ho Park
- Division of Hematology and Oncology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN
| | - Jeremy L. Warner
- Departments of Medicine and Biomedical Informatics, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN
| | - James Chen
- Division of Medical Oncology, Department of Biomedical Informatics, The Ohio State University, Columbus, OH
| | - Peter K. Rogan
- Departments of Biochemistry and Oncology, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
| | - Debyani Chakravarty
- Kravis Center of Molecular Oncology, Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Malachi Griffith
- McDonnell Genome Institute and Department of Medicine, Washington University School of Medicine, St Louis, MO
| | - Obi L. Griffith
- McDonnell Genome Institute and Department of Medicine, Washington University School of Medicine, St Louis, MO
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC
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Mucaki EJ, Shirley BC, Rogan PK. Expression Changes Confirm Genomic Variants Predicted to Result in Allele-Specific, Alternative mRNA Splicing. Front Genet 2020; 11:109. [PMID: 32211018 PMCID: PMC7066660 DOI: 10.3389/fgene.2020.00109] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 01/30/2020] [Indexed: 12/11/2022] Open
Abstract
Splice isoform structure and abundance can be affected by either noncoding or masquerading coding variants that alter the structure or abundance of transcripts. When these variants are common in the population, these nonconstitutive transcripts are sufficiently frequent so as to resemble naturally occurring, alternative mRNA splicing. Prediction of the effects of such variants has been shown to be accurate using information theory-based methods. Single nucleotide polymorphisms (SNPs) predicted to significantly alter natural and/or cryptic splice site strength were shown to affect gene expression. Splicing changes for known SNP genotypes were confirmed in HapMap lymphoblastoid cell lines with gene expression microarrays and custom designed q-RT-PCR or TaqMan assays. The majority of these SNPs (15 of 22) as well as an independent set of 24 variants were then subjected to RNAseq analysis using the ValidSpliceMut web beacon (http://validsplicemut.cytognomix.com), which is based on data from the Cancer Genome Atlas and International Cancer Genome Consortium. SNPs from different genes analyzed with gene expression microarray and q-RT-PCR exhibited significant changes in affected splice site use. Thirteen SNPs directly affected exon inclusion and 10 altered cryptic site use. Homozygous SNP genotypes resulting in stronger splice sites exhibited higher levels of processed mRNA than alleles associated with weaker sites. Four SNPs exhibited variable expression among individuals with the same genotypes, masking statistically significant expression differences between alleles. Genome-wide information theory and expression analyses (RNAseq) in tumor exomes and genomes confirmed splicing effects for 7 of the HapMap SNP and 14 SNPs identified from tumor genomes. q-RT-PCR resolved rare splice isoforms with read abundance too low for statistical significance in ValidSpliceMut. Nevertheless, the web-beacon provides evidence of unanticipated splicing outcomes, for example, intron retention due to compromised recognition of constitutive splice sites. Thus, ValidSpliceMut and q-RT-PCR represent complementary resources for identification of allele-specific, alternative splicing.
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Affiliation(s)
- Eliseos J Mucaki
- Department of Biochemistry, University of Western Ontario, London, ON, Canada
| | | | - Peter K Rogan
- Department of Biochemistry, University of Western Ontario, London, ON, Canada.,CytoGnomix, London, ON, Canada.,Department of Oncology University of Western Ontario, London, ON, Canada.,Department of Computer Science, University of Western Ontario, London, ON, Canada
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Tanimoto K, Muramatsu T, Inazawa J. Massive computational identification of somatic variants in exonic splicing enhancers using The Cancer Genome Atlas. Cancer Med 2019; 8:7372-7384. [PMID: 31631560 PMCID: PMC6885893 DOI: 10.1002/cam4.2619] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 10/01/2019] [Accepted: 10/02/2019] [Indexed: 12/26/2022] Open
Abstract
Owing to the development of next-generation sequencing (NGS) technologies, a large number of somatic variants have been identified in various types of cancer. However, the functional significance of most somatic variants remains unknown. Somatic variants that occur in exonic splicing enhancer (ESE) regions are thought to prevent serine and arginine-rich (SR) proteins from binding to ESE sequence motifs, which leads to exon skipping. We computationally identified somatic variants in ESEs by compiling numerous open-access datasets from The Cancer Genome Atlas (TCGA). Using somatic variants and RNA-seq data from 9635 patients across 32 TCGA projects, we identified 646 ESE-disrupting variants. The false positive rate of our method, estimated using a permutation test, was approximately 1%. Of these ESE-disrupting variants, approximately 71% were located in the binding motifs of four classical SR proteins. ESE-disrupting variants occurred in proportion to the number of somatic variants, but not necessarily in the specific genes associated with the biological processes of cancer. Existing bioinformatics tools could not predict the pathogenicity of ESE-disrupting variants identified in this study, although these variants could cause exon skipping. We demonstrated that ESE-disrupting nonsense variants tended to escape nonsense-mediated decay surveillance. Using integrated analyses of open access data, we could specifically identify ESE-disrupting variants. We have generated a powerful tool, which can handle datasets without normal samples or raw data, and thus contribute to reducing variants of uncertain significance because our statistical approach only uses the exon-junction read counts from the tumor samples.
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Affiliation(s)
- Kousuke Tanimoto
- Genome Laboratory, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, Japan.,Genomics Research Support Unit, Research Core, Tokyo Medical and Dental University (TMDU), Japan, Tokyo, Japan
| | - Tomoki Muramatsu
- Department of Molecular Cytogenetics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Johji Inazawa
- Department of Molecular Cytogenetics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, Japan.,Bioresource Research Center, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
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Shirley BC, Mucaki EJ, Rogan PK. Pan-cancer repository of validated natural and cryptic mRNA splicing mutations. F1000Res 2019; 7:1908. [PMID: 31275557 DOI: 10.12688/f1000research.17204.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/30/2018] [Indexed: 12/26/2022] Open
Abstract
We present a major public resource of mRNA splicing mutations validated according to multiple lines of evidence of abnormal gene expression. Likely mutations present in all tumor types reported in the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) were identified based on the comparative strengths of splice sites in tumor versus normal genomes, and then validated by respectively comparing counts of splice junction spanning and abundance of transcript reads in RNA-Seq data from matched tissues and tumors lacking these mutations. The comprehensive resource features 341,486 of these validated mutations, the majority of which (69.9%) are not present in the Single Nucleotide Polymorphism Database (dbSNP 150). There are 131,347 unique mutations which weaken or abolish natural splice sites, and 222,071 mutations which strengthen cryptic splice sites (11,932 affect both simultaneously). 28,812 novel or rare flagged variants (with <1% population frequency in dbSNP) were observed in multiple tumor tissue types. An algorithm was developed to classify variants into splicing molecular phenotypes that integrates germline heterozygosity, degree of information change and impact on expression. The classification thresholds were calibrated against the ClinVar clinical database phenotypic assignments. Variants are partitioned into allele-specific alternative splicing, likely aberrant and aberrant splicing phenotypes. Single variants or chromosome ranges can be queried using a Global Alliance for Genomics and Health (GA4GH)-compliant, web-based Beacon "Validated Splicing Mutations" either separately or in aggregate alongside other Beacons through the public Beacon Network, as well as through our website. The website provides additional information, such as a visual representation of supporting RNAseq results, gene expression in the corresponding normal tissues, and splicing molecular phenotypes.
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Affiliation(s)
| | - Eliseos J Mucaki
- Biochemistry, University of Western Ontario, London, Ontario, N6A 2C1, Canada
| | - Peter K Rogan
- CytoGnomix Inc., London, Ontario, N5X 3X5, Canada.,Biochemistry, University of Western Ontario, London, Ontario, N6A 2C1, Canada.,Computer Science, University of Western Ontario, London, Ontario, N6A 2C1, Canada.,Oncology, University of Western Ontario, London, Ontario, N6A 2C1, Canada
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Rogan PK. Multigene signatures of responses to chemotherapy derived by biochemically-inspired machine learning. Mol Genet Metab 2019; 128:45-52. [PMID: 31451418 DOI: 10.1016/j.ymgme.2019.08.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Revised: 08/09/2019] [Accepted: 08/16/2019] [Indexed: 01/08/2023]
Abstract
Pharmacogenomic responses to chemotherapy drugs can be modeled by supervised machine learning of expression and copy number of relevant gene combinations. Such biochemical evidence can form the basis of derived gene signatures using cell line data, which can subsequently be examined in patients that have been treated with the same drugs. These gene signatures typically contain elements of multiple biochemical pathways which together comprise multiple origins of drug resistance or sensitivity. The signatures can capture variation in these responses to the same drug among different patients.
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Affiliation(s)
- Peter K Rogan
- Departments of Biochemistry, Oncology, and Computer Science, University of Western Ontario, London, ON N6A 2C1, UK.
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Shirley BC, Mucaki EJ, Rogan PK. Pan-cancer repository of validated natural and cryptic mRNA splicing mutations. F1000Res 2018; 7:1908. [PMID: 31275557 PMCID: PMC6544075 DOI: 10.12688/f1000research.17204.3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/27/2019] [Indexed: 11/20/2022] Open
Abstract
We present a major public resource of mRNA splicing mutations validated according to multiple lines of evidence of abnormal gene expression. Likely mutations present in all tumor types reported in the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) were identified based on the comparative strengths of splice sites in tumor versus normal genomes, and then validated by respectively comparing counts of splice junction spanning and abundance of transcript reads in RNA-Seq data from matched tissues and tumors lacking these mutations. The comprehensive resource features 341,486 of these validated mutations, the majority of which (69.9%) are not present in the Single Nucleotide Polymorphism Database (dbSNP 150). There are 131,347 unique mutations which weaken or abolish natural splice sites, and 222,071 mutations which strengthen cryptic splice sites (11,932 affect both simultaneously). 28,812 novel or rare flagged variants (with <1% population frequency in dbSNP) were observed in multiple tumor tissue types. An algorithm was developed to classify variants into splicing molecular phenotypes that integrates germline heterozygosity, degree of information change and impact on expression. The classification thresholds were calibrated against the ClinVar clinical database phenotypic assignments. Variants are partitioned into allele-specific alternative splicing, likely aberrant and aberrant splicing phenotypes. Single variants or chromosome ranges can be queried using a Global Alliance for Genomics and Health (GA4GH)-compliant, web-based Beacon "Validated Splicing Mutations" either separately or in aggregate alongside other Beacons through the public Beacon Network, as well as through our website. The website provides additional information, such as a visual representation of supporting RNAseq results, gene expression in the corresponding normal tissues, and splicing molecular phenotypes.
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Affiliation(s)
| | - Eliseos J Mucaki
- Biochemistry, University of Western Ontario, London, Ontario, N6A 2C1, Canada
| | - Peter K Rogan
- CytoGnomix Inc., London, Ontario, N5X 3X5, Canada.,Biochemistry, University of Western Ontario, London, Ontario, N6A 2C1, Canada.,Computer Science, University of Western Ontario, London, Ontario, N6A 2C1, Canada.,Oncology, University of Western Ontario, London, Ontario, N6A 2C1, Canada
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