Dataset Information

Copy Number Variation Pattern for Discriminating MACROD2 States of Colorectal Cancer Subtypes.

ABSTRACT: Copy number variation (CNV) is a common structural variation pattern of DNA, and it features a higher mutation rate than single-nucleotide polymorphisms (SNPs) and affects a larger fragment of genomes. CNV is related with the genesis of complex diseases and can thus be used as a strategy to identify novel cancer-predisposing markers or mechanisms. In particular, the frequent deletions of mono-ADP-ribosylhydrolase 2 (MACROD2) locus in human colorectal cancer (CRC) alters DNA repair and the sensitivity to DNA damage and results in chromosomal instability. The relationship between CNV and cancer has not been explained. In this study, on the basis of the genome variation profiling by the SNP array from 651 CRC primary tumors, we computationally analyzed the CNV data to select crucial SNP sites with the most relevance to three different states of MACROD2 (heterozygous deletion, homozygous deletion, and normal state), suggesting that these CNVs may play functional roles in CRC tumorigenesis. Our study can shed new insights into the genesis of cancer based on CNV, providing reference for clinical diagnosis, and treatment prognosis of CRC.

SUBMITTER: Zhang S

PROVIDER: S-EPMC6930883 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Copy Number Variation Pattern for Discriminating MACROD2 States of Colorectal Cancer Subtypes.

Zhang ShiQi S Pan XiaoYong X Zeng Tao T Guo Wei W Gan Zijun Z Zhang Yu-Hang YH Chen Lei L Zhang YunHua Y Huang Tao T Cai Yu-Dong YD

Frontiers in bioengineering and biotechnology 20191219

Copy number variation (CNV) is a common structural variation pattern of DNA, and it features a higher mutation rate than single-nucleotide polymorphisms (SNPs) and affects a larger fragment of genomes. CNV is related with the genesis of complex diseases and can thus be used as a strategy to identify novel cancer-predisposing markers or mechanisms. In particular, the frequent deletions of mono-ADP-ribosylhydrolase 2 (<i>MACROD2</i>) locus in human colorectal cancer (CRC) alters DNA repair and the ...[more]

PMID: 31921812

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Project description:Genetic markers for distant metastatic disease in patients with colorectal cancer (CRC) are not well defined. Identification of genetic alterations associated with metastatic CRC could help to guide systemic and local treatment strategies. We evaluated the association of tumor necrosis factor receptor superfamily member 10C (TNFRSF10C) copy number variation (CNV) with distant metastatic disease in patients with CRC using The Cancer Genome Atlas (TCGA).Genetic sequencing data and clinical characteristics were obtained from TCGA for all available patients with CRC. There were 515 CRC patient samples with CNV and clinical outcome data, including a subset of 144 rectal adenocarcinoma patient samples. Using the TCGA CRC dataset, CNV of TNFRSF10C was evaluated for association with distant metastatic disease (M1 vs. M0). Multivariate logistic regression analysis with odds ratio (OR) using a 95% confidence interval (CI) was performed adjusting for age, T stage, N stage, adjuvant chemotherapy, gender, microsatellite instability (MSI), location, and surgical margin status.TNFRSF10C CNV in patients with CRC was associated with distant metastatic disease [OR 4.81 (95% CI, 2.13-10.85) P<0.001] and positive lymph nodes [OR 18.83 (95% CI, 8.42-42.09)]; P<0.001) but not MSI (OR P=0.799). On multivariate analysis, after adjusting for pathologic T stage, N stage, adjuvant chemotherapy, gender, and MSI, TNFRSF10C CNV remained significantly associated with distant metastatic disease (OR P=0.018). Subset analysis revealed that TNFRSF10C CNV was also significantly associated with distant metastatic disease in patients with rectal adenocarcinoma (OR P=0.016).TNFRSF10C CNV in patients with CRC is associated with distant metastatic disease. With further validation, such genetic profiles could be used clinically to support optimal systemic treatment strategies versus more aggressive local therapies in patients with CRC, including radiation therapy for rectal adenocarcinoma.

Project description:DNA copy number aberrations (CNAs) are of biological and medical interest because they help identify regulatory mechanisms underlying tumor initiation and evolution. Identification of tumor-driving CNAs (driver CNAs) however remains a challenging task, because they are frequently hidden by CNAs that are the product of random events that take place during tumor evolution. Experimental detection of CNAs is commonly accomplished through array comparative genomic hybridization (aCGH) assays followed by supervised and/or unsupervised statistical methods that combine the segmented profiles of all patients to identify driver CNAs. Here, we extend a previously-presented supervised algorithm for the identification of CNAs that is based on a topological representation of the data. Our method associates a two-dimensional (2D) point cloud with each aCGH profile and generates a sequence of simplicial complexes, mathematical objects that generalize the concept of a graph. This representation of the data permits segmenting the data at different resolutions and identifying CNAs by interrogating the topological properties of these simplicial complexes. We tested our approach on a published dataset with the goal of identifying specific breast cancer CNAs associated with specific molecular subtypes. Identification of CNAs associated with each subtype was performed by analyzing each subtype separately from the others and by taking the rest of the subtypes as the control. Our results found a new amplification in 11q at the location of the progesterone receptor in the Luminal A subtype. Aberrations in the Luminal B subtype were found only upon removal of the basal-like subtype from the control set. Under those conditions, all regions found in the original publication, except for 17q, were confirmed; all aberrations, except those in chromosome arms 8q and 12q were confirmed in the basal-like subtype. These two chromosome arms, however, were detected only upon removal of three patients with exceedingly large copy number values. More importantly, we detected 10 and 21 additional regions in the Luminal B and basal-like subtypes, respectively. Most of the additional regions were either validated on an independent dataset and/or using GISTIC. Furthermore, we found three new CNAs in the basal-like subtype: a combination of gains and losses in 1p, a gain in 2p and a loss in 14q. Based on these results, we suggest that topological approaches that incorporate multiresolution analyses and that interrogate topological properties of the data can help in the identification of copy number changes in cancer.

Dataset Information

Copy Number Variation Pattern for Discriminating MACROD2 States of Colorectal Cancer Subtypes.

Publications

Copy Number Variation Pattern for Discriminating MACROD2 States of Colorectal Cancer Subtypes.

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