DoCM: a database of curated mutations in cancer

Ainscough, Benjamin J.; Griffith, Malachi; Coffman, Adam; Wagner, Alex H.; Kunisaki, Jason; Choudhary, Mayank; McMichael, Joshua F.; Fulton, Robert S.; Wilson, Richard K.; Griffith, Obi L.; Mardis, Elaine R.

doi:10.1038/nmeth.4000

Cited by 101 publications

(79 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These include web tools that provide data and text summaries of the frequency, mechanisms, and druggable targets of known driver mutations [110]. Multiple tools now include “interpretations” or summaries of the driver mutations written by clinicians – including the Precision Medicine Knowledgebase (at Weill Cornell) and the Personalized Cancer Therapy knowledge base (at MD Anderson) – or by the “crowd” [111, 112] (see list of references in Table S2C).…”

Section: Analysis Approaches To Determine Molecular Subtypes and Cancmentioning

confidence: 99%

Precision Oncology: The Road Ahead

Senft

Leiserson

Ruppin

et al. 2017

Trends in Molecular Medicine

147

123

View full text Add to dashboard Cite

Current efforts in precision oncology largely focus on the benefit of genomics-guided therapy. Yet, advances in sequencing techniques provide an unprecedented view of the complex genetic and non-genetic heterogeneity within individual tumors. Herein, we outline the benefits of integrating genomic and transcriptomic analyses for advanced precision oncology. We summarize relevant computational approaches to detect novel drivers and genetic vulnerabilities, suitable for therapeutic exploration. Clinically relevant platforms to functionally test predicted drugs/drug combinations for individual patients are reviewed. Finally, we highlight the technological advances in single-cell analysis of tumor specimens. These may ultimately lead to the development of next generation cancer drugs, capable of tackling the hurdles imposed by genetic and phenotypic heterogeneity on current anticancer therapies.

show abstract

Section: Analysis Approaches To Determine Molecular Subtypes and Cancmentioning

confidence: 99%

Precision Oncology: The Road Ahead

Senft

Leiserson

Ruppin

et al. 2017

Trends in Molecular Medicine

147

123

View full text Add to dashboard Cite

show abstract

“…Diverse databases and annotations have been developed to help interpret identified tumor variants with their specific relevance to various aspects of tumor origin, growth, and treatment [12][13][14][15][16] . Such annotations can be essential in determining which identified tumor variants are actionable or otherwise merit further investigation from those that are less relevant to tumor progression.…”

Section: Cancer-specific Annotationsmentioning

confidence: 99%

OncoGEMINI: Software for Investigating Tumor Variants From Multiple Biopsies With Integrated Cancer Annotations

Nicholas

Cormier

Huang

et al. 2020

Preprint

View full text Add to dashboard Cite

DNA sequencing has unveiled extensive tumor heterogeneity in several different cancer types, with many exhibiting diverse subclonal populations. Identifying and tracing mutations throughout the expansion and progression of a tumor represents a significant challenge. Furthermore, prioritizing the subset of such mutations most likely to contribute to tumor evolution or that could serve as potential therapeutic targets represents an ongoing problem. Here we describe OncoGEMINI , a new tool designed for exploring the complex patterns and trajectory of somatic and inherited variation observed in heterogeneous tumors biopsied over the course of treatment. This is accomplished by creating a searchable database of variants that includes tumor sampling timepoints and allows for filtering methods that reflect specific changes in variant allele frequencies over time. Additionally, by incorporating existing annotations and resources that facilitate the interpretation of cancer mutations (e.g., CIViC, DGIdb), OncoGEMINI enables rapid searches for, and potential identification of, mutations that may be driving subclonal evolution.

show abstract

“…The available datasets have been categorized into 20 groups and subgroups as indicated in Links are available to data in some external databases, including AmyLoad [84] and WALTZ-DB [85] for protein aggregation, DBASS3 and DBASS5 [71,72] for splicing variants, SKEMPI [86], cancer datasets in KinMutBase [110], Kin-Driver [111], dbCPM [128], DoCM [130], and OncoKB [129], and tolerance predictor training set in DANN [47]. The latter has a link due to its huge size, the others since they are databases and as such easy to use directly and updated by third parties.…”

Section: Variation Datasetsmentioning

confidence: 99%

“…Although there are numerous studies of cancer variations, the functional verification of the relevance of those variants for the disease is usually missing. VariBench contains three datasets for variants in cancer, which have been experimentally tested [122][123][124], and links to three other sources, namely dbCPM [128], DoCM [130], and OncoKB [129]. In addition, there is the FASMIC dataset for variants which are largely cancer related [127].…”

Section: Disease-specific Datasetsmentioning

confidence: 99%

Variation Benchmark Datasets: Update, Criteria, Quality and Applications

Sarkar

Yang

Vihinen

2019

Preprint

View full text Add to dashboard Cite

Development of new computational methods and testing their performance has to be done on experimental data. Only in comparison to existing knowledge can method performance be assessed. For that purpose, benchmark datasets with known and verified outcome are needed.High-quality benchmark datasets are valuable and may be difficult, laborious and time consuming to generate. VariBench and VariSNP are the two existing databases for sharing

show abstract

DoCM: a database of curated mutations in cancer

Cited by 101 publications

References 6 publications

Precision Oncology: The Road Ahead

Precision Oncology: The Road Ahead

OncoGEMINI: Software for Investigating Tumor Variants From Multiple Biopsies With Integrated Cancer Annotations

Variation Benchmark Datasets: Update, Criteria, Quality and Applications

Contact Info

Product

Resources

About