Dataset Information

High-depth, high-accuracy microsatellite genotyping enables precision lung cancer risk classification.

ABSTRACT: There remains a large discrepancy between the known genetic contributions to cancer and that which can be explained by genomic variants, both inherited and somatic. Recently, understudied repetitive DNA regions called microsatellites have been identified as genetic risk markers for a number of diseases including various cancers (breast, ovarian and brain). In this study, we demonstrate an integrated process for identifying and further evaluating microsatellite-based risk markers for lung cancer using data from the cancer genome atlas and the 1000 genomes project. Comparing whole-exome germline sequencing data from 488 TCGA lung cancer samples to germline exome data from 390 control samples from the 1000 genomes project, we identified 119 potentially informative microsatellite loci. These loci were found to be able to distinguish between cancer and control samples with sensitivity and specificity ratios over 0.8. Then these loci, supplemented with additional loci from other cancers and controls, were evaluated using a target enrichment kit and sample-multiplexed nextgen sequencing. Thirteen of the 119 risk markers were found to be informative in a well powered study (>0.99 for a 0.95 confidence interval) using high-depth (579x±315) nextgen sequencing of 30 lung cancer and 89 control samples, resulting in sensitivity and specificity ratios of 0.90 and 0.94, respectively. When 8 loci harvested from the bioinformatic analysis of other cancers are added to the classifier, then the sensitivity and specificity rise to 0.93 and 0.97, respectively. Analysis of the genes harboring these loci revealed two genes (ARID1B and REL) and two significantly enriched pathways (chromatin organization and cellular stress response) suggesting that the process of lung carcinogenesis is linked to chromatin remodeling, inflammation, and tumor microenvironment restructuring. We illustrate that high-depth sequencing enables a high-precision microsatellite-based risk classifier analysis approach. This microsatellite-based platform confirms the potential to create clinically actionable diagnostics for lung cancer.

SUBMITTER: Velmurugan KR

PROVIDER: S-EPMC5701090 | biostudies-other | 2017 Nov

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

High-depth, high-accuracy microsatellite genotyping enables precision lung cancer risk classification.

Velmurugan K R KR Varghese R T RT Fonville N C NC Garner H R HR

Oncogene 20170731 46

There remains a large discrepancy between the known genetic contributions to cancer and that which can be explained by genomic variants, both inherited and somatic. Recently, understudied repetitive DNA regions called microsatellites have been identified as genetic risk markers for a number of diseases including various cancers (breast, ovarian and brain). In this study, we demonstrate an integrated process for identifying and further evaluating microsatellite-based risk markers for lung cancer ...[more]

PMID: 28759038

Similar Datasets

Project description:BackgroundThe organic matrix contained in biominerals plays an important role in regulating mineralization and in determining biomineral properties. However, most components of biomineral matrices remain unknown at present. In sea urchin tooth, which is an important model for developmental biology and biomineralization, only few matrix components have been identified. The recent publication of the Strongylocentrotus purpuratus genome sequence rendered possible not only the identification of genes potentially coding for matrix proteins, but also the direct identification of proteins contained in matrices of skeletal elements by in-depth, high-accuracy proteomic analysis.ResultsWe identified 138 proteins in the matrix of tooth powder. Only 56 of these proteins were previously identified in the matrices of test (shell) and spine. Among the novel components was an interesting group of five proteins containing alanine- and proline-rich neutral or basic motifs separated by acidic glycine-rich motifs. In addition, four of the five proteins contained either one or two predicted Kazal protease inhibitor domains. The major components of tooth matrix were however largely identical to the set of spicule matrix proteins and MSP130-related proteins identified in test (shell) and spine matrix. Comparison of the matrices of crushed teeth to intact teeth revealed a marked dilution of known intracrystalline matrix proteins and a concomitant increase in some intracellular proteins.ConclusionThis report presents the most comprehensive list of sea urchin tooth matrix proteins available at present. The complex mixture of proteins identified may reflect many different aspects of the mineralization process. A comparison between intact tooth matrix, presumably containing odontoblast remnants, and crushed tooth matrix served to differentiate between matrix components and possible contributions of cellular remnants. Because LC-MS/MS-based methods directly measures peptides our results validate many predicted genes and confirm the existence of the corresponding proteins. Knowledge of the components of this model system may stimulate further experiments aiming at the elucidation of structure, function, and interaction of biomineral matrix components.

Dataset Information

High-depth, high-accuracy microsatellite genotyping enables precision lung cancer risk classification.

Publications

High-depth, high-accuracy microsatellite genotyping enables precision lung cancer risk classification.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure