Candidate driver genes in microsatellite‐unstable colorectal cancer

Abstract

Defects in the mismatch repair system lead to microsatellite instability (MSI), a feature observed in~15% of all colorectal cancers (CRCs). Microsatellite mutations that drive tumourigenesis, typically inactivation of tumour suppressors, are selected for and are frequently detected in MSI cancers. Here, we evaluated somatic mutations in microsatellite repeats of 790 genes chosen based on reduced expression in MSI CRC and existence of a coding mononucleotide repeat of 6-10 bp in length. All the repeats were initially sequenced in 30 primary MSI CRC samples and whenever frameshift mutations were identified in >20%, additional 70 samples were sequenced. To distinguish driver mutations from passengers, we similarly analyzed the occurrence of frameshift mutations in 121 intronic control repeats and utilized a statistical regression model to determine cutoff mutation frequencies for repeats of all types (A/T and C/G, 6-10 bp). Along with several know target genes, including TGFBR2, ACVR2, and MSH3, six novel candidate driver genes emerged that harbored significantly more mutations than identical control repeats. The mutation frequencies in 100 MSI CRC samples were 51% in G8 of GLYR1, 47% in T9 of ABCC5, 43% in G8 of WDTC1, 33% in A8 of ROCK1, 30% in T8 of OR51E2, and 28% in A8 of TCEB3. Immunohistochemical staining of GLYR1 revealed defective protein expression in tumors carrying biallelic mutations, supporting a loss of function hypothesis. This is a large scale, unbiased effort to identify genes that when mutated are likely to contribute to MSI CRC development.DNA mismatch repair (MMR) system recognizes and removes misincorporations and slippage errors occurring in normal DNA replication. Defects in the MMR system lead to genetic instability referred to as microsatellite instability (MSI). MSI occurs as a consequence of a germline defect and a subsequent somatic inactivation of the wild-type allele of one of the key genes involved in this system, MLH1, MSH2, MSH6, and PMS2. 1 In sporadic CRC, MSI is typically caused

Optimization of a pentaplex panel for MSI analysis without control DNA in a Brazilian population: correlation with ancestry markers

“…[13][14][15] Several MSI-target genes have been identified in MSI-positive tumors, which are involved in important diverse cellular functions and pathways such as DNA repair (eg, MRE11, MSH6, BRCA1, and BRCA2), cell growth (eg, TGFbRII and EGFR), and apoptosis (eg, IGFR and BAX). 14,16,17 In sporadic tumors, the MSI is strongly associated with the presence of BRAF oncogene mutations, 18 a lack of KRAS mutation, and the inactivation of PTEN tumor suppressor gene. 19,20 Thus, the mutation profile in MSI tumors is fundamentally different from other CRCs.…”

Rho‐kinase Gene Polymorphisms in Related Disease States

“…In a colorectal cancer study, the ROCK1 mutation (typically c.3921delA leading to V1309*) is also predicted to partially delete the autoinhibitory domain [43]. ROCK1 nonsense mutation (G285*) introduces a premature stop codon at the 285th amino acid of ROCK1, leading to the truncation of ∼79% of ROCK1 [12].…”

“…Breast cancer Y405*, S1126* [10] Lung cancer P1193S [10] rs11874761, rs8085504, rs2127958, rs17202375, rs288980 [11] Gastric cancer G285* [12] Colorectal cancer rs73963110, rs35996865 [13] rs35768389, rs73963110, rs2127958, rs288980 [14] rs35996865 (in male patients) [14] V1309* [43] Clear cell renal cell carcinoma rs35996865 [16] rs8089974, rs11874761 [16] Tetralogy of Fallot rs288979, rs56085230 (Tyr269Tyr) [27] rs2292296 (Leu1097Phe), rs7237677, rs7227454, rs288989, rs45449301 (Ile432Val), rs288979, rs17202368, rs17202375, rs2271255 (Lys222Glu), rs1481280, rs8085504, rs398528, rs112165707 (Ser595Ser), rs45562542 (Thr773Ser) [27] Disease…”

Minimizing Redundancy Among Genes Selected Based on the Overlapping Analysis

“…For each experimental repetition, the split seed was changed while the same folds and Golub et al (1999) Breast 4948 78 34/44 0.369 Michiels et al (2005) Srbct 2308 54 29/25 0.0008 Statnikov et al (2005) Lung 12533 181 150/31 0.003 Gordon et al (2002) GSE24514 22215 49 34/15 0.0406 Alhopuro et al (2012) GSE4045 22215 37 29/8 0.2045 Laiho et al (2007) GSE14333 54675 229 138/91 0.4141 Jorissen et al (2009) training datasets were kept for all feature selection methods. To avoid bias, gene selection algorithms have been performed only on the training sets.…”