DriverDB: an exome sequencing database for cancer driver gene identification

Cheng, Wei‐Chung; Chung, I‐Fang; Chen, Chen-Yang; Sun, Hsin‐Yun; Fen, Jun-Jeng; Tang, Wei-Chun; Chang, Ting‐Yu; Wong, Tai-Tong; Wang, Hsei‐Wei

doi:10.1093/nar/gkt1025

Cited by 78 publications

(73 citation statements)

References 66 publications

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“…Mutations in breast cancer-driver genes listed by DriverDB9 were found in 65 of the 77 sample pairs with a median of two driver gene mutations per sample (Supplementary Table 4). In 25 pairs all driver mutations were identified in both samples.…”

Section: Resultsmentioning

confidence: 99%

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Impact of mutational profiles on response of primary oestrogen receptor-positive breast cancers to oestrogen deprivation

et al. 2016

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Pre-surgical studies allow study of the relationship between mutations and response of oestrogen receptor-positive (ER+) breast cancer to aromatase inhibitors (AIs) but have been limited to small biopsies. Here in phase I of this study, we perform exome sequencing on baseline, surgical core-cuts and blood from 60 patients (40 AI treated, 20 controls). In poor responders (based on Ki67 change), we find significantly more somatic mutations than good responders. Subclones exclusive to baseline or surgical cores occur in ∼30% of tumours. In phase II, we combine targeted sequencing on another 28 treated patients with phase I. We find six genes frequently mutated: PIK3CA, TP53, CDH1, MLL3, ABCA13 and FLG with 71% concordance between paired cores. TP53 mutations are associated with poor response. We conclude that multiple biopsies are essential for confident mutational profiling of ER+ breast cancer and TP53 mutations are associated with resistance to oestrogen deprivation therapy.

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Section: Resultsmentioning

confidence: 99%

“…Validated mutations in the baseline and surgery samples were compared with known driver genes in the DriverDB9 database. Therefore, ‘breast' tissue was selected as cancer type and all genes identified by at least two tools were downloaded from the website.…”

Section: Methodsmentioning

confidence: 99%

Impact of mutational profiles on response of primary oestrogen receptor-positive breast cancers to oestrogen deprivation

et al. 2016

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“…datasets from TCGA, International Cancer Genome Consortium (ICGC) and published papers [40, 41]. In DriverDB, there are 420 primary tumors and 37 adjacent normal tissues (including 34 normal-tumor pairs) in the gastric cancer dataset of TCGA.…”

Section: Methodsmentioning

confidence: 99%

Cholesterol import and steroidogenesis are biosignatures for gastric cancer patient survival

et al. 2016

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Androgens, estrogens, progesterone and related signals are reported to be involved in the pathology of gastric cancer. However, varied conclusions exist based on serum hormone levels, receptor expressions, and in vitro or in vivo studies. This report used a web-based gene survival analyzer to evaluate biochemical processes, including cholesterol importing via lipoprotein/receptors (L/R route), steroidogenic enzymes, and steroid receptors, in gastric cancer patients prognosis. The sex hormone receptors (androgen receptor, progesterone receptor, and estrogen receptor ESR1 or ESR2), L/R route (low/high-density lipoprotein receptors, LDLR/LRP6/SR-B1 and lipoprotein lipase, LPL) and steroidogenic enzymes (CYP11A1, HSD3B1, CYP17, HSD17B1, HSD3B1, CYP19A1 and SRD5A1) were associated with 5-year survival of gastric cancer patients. The AR, PR, ESR1 and ESR2 are progression promoters, as are the L/R route LDLR, LRP6, SR-B1 and LPL. It was found that CYP11A1, HSD3B1, CYP17, HSD17B1 and CYP19A1 promote progression, but dihydrotestosterone (DHT) converting enzyme SRD5A1 suppresses progression. Analyzing steroidogenic lipidome with a hazard ratio score algorithm found that CYP19A1 is the progression confounder in surgery, HER2 positive or negative patients. Finally, in the other patient cohort from TCGA, CYP19A1 was expressed higher in the tumor compared to that in normal counterparts, and also promoted progression. Lastly, exemestrane (type II aromatase inhibitor) dramatically suppress GCa cell growth in pharmacological tolerable doses in vitro. This work depicts a route-specific outside-in delivery of cholesterol to promote disease progression, implicating a host-to-tumor macroenvironmental regulation. The result indicating lipoprotein-mediated cholesterol entry and steroidogenesis are GCa progression biosignatures. And the exemestrane clinical trial in GCa patients of unmet medical needs is suggested.

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“…18 DriverDB incorporates annotation databases and algorithms with sequencing data to help identify and visualize driver genes related to particular tumor type or mutation profile across a cancer type. 19 The MutSigCV algorithm ranks mutations that occur more often than based on chance and background mutation processes. 3 Together, all these resources can be used to investigate and provide a clinical interpretation for a given somatic mutation.…”

Section: Discussionmentioning

confidence: 99%

Cancer Driver Log (CanDL)

Damodaran

Miya

Kautto

et al. 2015

The Journal of Molecular Diagnostics

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Massively parallel sequencing technologies have enabled characterization of genomic alterations across multiple tumor types. Efforts have focused on identifying driver mutations because they represent potential targets for therapy. However, because of the presence of driver and passenger mutations, it is often challenging to assign the clinical relevance of specific mutations observed in patients. Currently, there are multiple databases and tools that provide in silico assessment for potential drivers; however, there is no comprehensive resource for mutations with functional characterization. Therefore, we created an expert-curated database of potentially actionable driver mutations for molecular pathologists to facilitate annotation of cancer genomic testing. We reviewed scientific literature to identify variants that have been functionally characterized in vitro or in vivo as driver mutations. We obtained the chromosome location and all possible nucleotide positions for each amino acid change and uploaded them to the Cancer Driver Log (CanDL) database with associated literature reference indicating functional driver evidence. In addition to a simple interface, the database allows users to download all or selected genes as a comma-separated values file for incorporation into their own analysis pipeline. Furthermore, the database includes a mechanism for third-party contributions to support updates for novel driver mutations. Overall, this freely available database will facilitate rapid annotation of cancer genomic testing in molecular pathology laboratories for mutations. (J Mol Diagn 2015, 17: 554e559; http://dx

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DriverDB: an exome sequencing database for cancer driver gene identification

Cited by 78 publications

References 66 publications

Impact of mutational profiles on response of primary oestrogen receptor-positive breast cancers to oestrogen deprivation

Impact of mutational profiles on response of primary oestrogen receptor-positive breast cancers to oestrogen deprivation

Cholesterol import and steroidogenesis are biosignatures for gastric cancer patient survival

Cancer Driver Log (CanDL)

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