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.

deepDriver: Predicting Cancer Driver Genes Based on Somatic Mutations Using Deep Convolutional Neural Networks

“…Four out of 10 genes have been studied as driver genes of LUAD. The tenth gene “HERC2P3” contains a microsatellite locus that can precisely discriminate LUAD samples and non-tumor samples (Velmurugan et al, 2017). As for three competing algorithms, Tables 3–5 show their prediction results.…”

CAGm: a repository of germline microsatellite variations in the 1000 genomes project

“…This discrepancy is slowly changing. So far, germline microsatellite markers have been proposed for breast cancer (26,27), ovarian cancer (27) and lung cancer (28). The Comparative Analysis of Germline Microsatellites (CAGm) database presented in this work may expedite the discovery of additional markers by serving as a catalog of germline genotypes in healthy individuals.…”

“…Trinucleotide repeats can have dramatic effects and are known to increase the risk of particular genetic diseases such as Huntington’s disease, spinocerebellar ataxia and myotonic dystrophy (25). Second, recent studies have demonstrated the diagnostic potential of microsatellites for a limited number of cancers: breast cancer (26,27), ovarian cancer (27) and lung cancer (28). Third, germline DNA is easily obtained and therefore a well-suited target for genomic based testing for somatic microsatellite polymorphisms.…”

Conserved microsatellites may contribute to stem-loop structures in 5′, 3′ terminals of Ebolavirus genomes

“…In addition, microsatellites expansion has been proved to cause more than 30 neurological and neuromuscular diseases [11], for instance, epilepsy caused by abnormal expansions of TTTCA and TTTTA repeats [12], Huntington disease cansed by CAG repeats [13], etc. Also it is related to lung cancer [14], gastric cancer [15], etc. The occurrence of microsatellites in non-coding regions are also associated with diseases by regulating gene expression, such as Friedreich Ataxia [16].…”