Generating a focused view of disease ontology cancer terms for pan-cancer data integration and analysis

Wu, Tsung-Jung; Schriml, Lynn M.; Chen, Qingrong; Colbert, Maureen; Crichton, Daniel J.; Finney, Richard; Hu, Ying; Kibbe, Warren A.; Kincaid, Heather; Meerzaman, Daoud; Mitraka, Elvira; Pan, Yang; Smith, Krista; Srivastava, Sudhir; Ward, Sari; Cheng, Yan; Mazumder, Raja

doi:10.1093/database/bav032

Cited by 42 publications

(38 citation statements)

References 41 publications

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“…Results were manually curated before reported as validated records. The cancer terms for each reported interaction between miRNA and specific cancer types were then mapped to Disease Ontology [68] cancer slim in order to unify the integrated dataset [69]. We also incorporated information regarding miRNA expression changes in cancer types (up/down-regulation) from these databases, when available.…”

Section: Methodsmentioning

confidence: 99%

Identification of key differentially expressed MicroRNAs in cancer patients through pan-cancer analysis

Dingerdissen

Gupta

et al. 2018

Computers in Biology and Medicine

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microRNAs (miRNAs) functioning in gene silencing have been associated with cancer progression. However, common abnormal miRNA expression patterns and their potential roles in cancer have not yet been evaluated. To account for individual differences between patients, we retrieved miRNA sequencing data for 575 patients with both tumor and adjacent nontumorous tissues from 14 cancer types from The Cancer Genome Atlas (TCGA). We then performed differential expression analysis using DESeq2 and edgeR. Results showed that cancer types can be grouped based on the distribution of miRNAs with different expression patterns between tumor and non-tumor samples. We found 81 significantly differentially expressed miRNAs (SDEmiRNAs) in a single cancer. We also found 21 key SDEmiRNAs (nine over-expressed and 12 under-expressed) associated with at least eight cancers each and enriched in more than 60% of patients per cancer, including four newly identified SDEmiRNAs (hsa-mir-4746, hsa-mir-3648, hsa-mir-3687, and hsa-mir-1269a). The downstream effects of these 21 SDEmiRNAs on cellular function were evaluated through enrichment and pathway analysis of 7,186 protein-coding gene targets mined from literature reports of differential expression of miRNAs in cancer. This analysis enables identification of SDEmiRNA functional similarity in cell proliferation control across a wide range of cancers, and assembly of common regulatory networks over cancer-related pathways. These findings were validated by construction of a regulatory network in the PI3K pathway. This study provides evidence for the value of further analysis of SDEmiRNAs as potential biomarkers and therapeutic targets for cancer diagnosis and treatment.

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Section: Methodsmentioning

confidence: 99%

Identification of key differentially expressed MicroRNAs in cancer patients through pan-cancer analysis

Dingerdissen

Gupta

et al. 2018

Computers in Biology and Medicine

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“…Worldwide collaborative efforts, such as COSMIC database, International Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) project, enabled to catalogue NGS data of thousands of cancer genomes across many disease types[105,106]. Targeted NGS, involving gene panels, is a quicker and cost effective alternative to whole genome sequencing or exome sequencing.…”

Section: Ngs Methodologiesmentioning

confidence: 99%

Molecular predictive markers in tumors of the gastrointestinal tract

Papadopoulou¹,

Metaxa‐Mariatou²,

Tsaousis³

et al. 2016

WJGO

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Gastrointestinal malignancies are among the leading causes of cancer-related deaths worldwide. Like all human malignancies they are characterized by accumulation of mutations which lead to inactivation of tumor suppressor genes or activation of oncogenes. Advances in Molecular Biology techniques have allowed for more accurate analysis of tumors’ genetic profiling using new breakthrough technologies such as next generation sequencing (NGS), leading to the development of targeted therapeutical approaches based upon biomarker-selection. During the last 10 years tremendous advances in the development of targeted therapies for patients with advanced cancer have been made, thus various targeted agents, associated with predictive biomarkers, have been developed or are in development for the treatment of patients with gastrointestinal cancer patients. This review summarizes the advances in the field of molecular biomarkers in tumors of the gastrointestinal tract, with focus on the available NGS platforms that enable comprehensive tumor molecular profile analysis.

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“…Also, additional ontologies may be rapidly added. For example, Disease Ontology also maps to NCI codes and thus further iterations may offer a “local ontology” optional field alongside a more circumspect selection of required standardized terms [33]. While standardization often necessitates limitation, current tools in development to convert between ontologies and annotation sets will greatly enhance the perspective usage scope.…”

Section: Discussionmentioning

confidence: 99%

Somatic cancer variant curation and harmonization through consensus minimum variant level data

et al. 2016

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BackgroundTo truly achieve personalized medicine in oncology, it is critical to catalog and curate cancer sequence variants for their clinical relevance. The Somatic Working Group (WG) of the Clinical Genome Resource (ClinGen), in cooperation with ClinVar and multiple cancer variant curation stakeholders, has developed a consensus set of minimal variant level data (MVLD). MVLD is a framework of standardized data elements to curate cancer variants for clinical utility. With implementation of MVLD standards, and in a working partnership with ClinVar, we aim to streamline the somatic variant curation efforts in the community and reduce redundancy and time burden for the interpretation of cancer variants in clinical practice.MethodsWe developed MVLD through a consensus approach by i) reviewing clinical actionability interpretations from institutions participating in the WG, ii) conducting extensive literature search of clinical somatic interpretation schemas, and iii) survey of cancer variant web portals. A forthcoming guideline on cancer variant interpretation, from the Association of Molecular Pathology (AMP), can be incorporated into MVLD.ResultsAlong with harmonizing standardized terminology for allele interpretive and descriptive fields that are collected by many databases, the MVLD includes unique fields for cancer variants such as Biomarker Class, Therapeutic Context and Effect. In addition, MVLD includes recommendations for controlled semantics and ontologies. The Somatic WG is collaborating with ClinVar to evaluate MVLD use for somatic variant submissions. ClinVar is an open and centralized repository where sequencing laboratories can report summary-level variant data with clinical significance, and ClinVar accepts cancer variant data.ConclusionsWe expect the use of the MVLD to streamline clinical interpretation of cancer variants, enhance interoperability among multiple redundant curation efforts, and increase submission of somatic variants to ClinVar, all of which will enhance translation to clinical oncology practice.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0367-z) contains supplementary material, which is available to authorized users.

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Generating a focused view of disease ontology cancer terms for pan-cancer data integration and analysis

Cited by 42 publications

References 41 publications

Identification of key differentially expressed MicroRNAs in cancer patients through pan-cancer analysis

Identification of key differentially expressed MicroRNAs in cancer patients through pan-cancer analysis

Molecular predictive markers in tumors of the gastrointestinal tract

Somatic cancer variant curation and harmonization through consensus minimum variant level data

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