Project description:In colorectal cancer (CRC), chromosomal instability (CIN) is typically studied using comparative-genomic hybridization (CGH) arrays. We studied paired (tumor and surrounding healthy) fresh-frozen tissue from 86 CRC patients using Illumina’s Infinium-based SNP array. This method allowed us to study CIN in CRC, with simultaneous analysis of copy number (CN) and B-allele frequency (BAF), which is a representation of allelic composition. This data helped us to detect mono-allelic and bi-allelic amplifications/deletion, copy neutral loss of heterozygosity, and levels of mosaicism for mixed cell populations, some of which can not be assessed with other methods that do not measure BAF. We identified associations between CN abnormalities and different CRC phenotypes (MSI, histological diagnosis, location, tumor grade, stage, MSI and presence of lymph node metastasis). We showed commonalities between regions of CN change observed in CRC and the regions reported in previous studies of other solid cancers (e.g., amplifications of 20q, 13q, 8q, 5p and deletions of 18q, 17p and 8p). From the Therapeutic Target Database we found relevant drugs, targeted to the genes located in these regions with CN changes, approved or in trials for other cancers and common diseases. These drugs may be considered for future therapeutic trials in CRC, based on personalized cytogenetic diagnosis. We also found many regions harboring genes which are not currently targeted by any relevant drugs that may be considered for future drug discovery studies. Our study shows the application of high-density SNP arrays for cytogenetic study in CRC and its importance for personalized treatment. DNA was extracted from colon tissue samples provided by 86 CRC patients. Each patient provided paired CRC tumor tissue and adjacent normal colon mucosa samples, which were fresh-frozen after resection. Samples were genotyped using Illumina's Infinium-based 610 Quad and CytoSNP 12 microarray chips. The genotype data from paired tumor and normal samples was used to detect tumor-specific chromosomal instabilities in copy number, including copy-neutral LOH, which cannot be assessed by many other cytogenetic methods. Supplementary file "GSE34678_raw_GPL8887.txt" includes the raw data for Samples using GPL8887 (GSM853162-GSM853239); supplementary file "GSE34678_raw_GPL13829.txt" includes the raw data for Samples using GPL13829 (GSM853240-GSM853363).

Project description:Background & Aims. The current staging system for colorectal cancer (CRC) based on TNM classification allows prediction of potential recurrence. However, it does not necessarily make reliable personalized prediction of prognosis. In this paper we describe combination of clinicopathological data and gene signature of dissected tumor specimen with stage II and III CRC patients would improve the situation.. Methods. A total of 1978 CRC were collected over 5 years, and then 371 stage II and 322 stage III of them with more than 45.9 months records were subjected to clinicopathological feature analyses. Out of this collection, 129 stage II and III CRC cases were selected for analyses of gene expression profiles with resected specimen. The gene signatures were analyzed by repeated random divisions of the samples into training and test sets to extract discriminator genes. After testing the applicability of this discriminator set, it was subjected to validation using a newly obtained set of 69 samples. Results. The pathological factors in solo or in combinations could not make personalized recurrence prediction, except for partial success with stage II patients. The gene signature, on the other hand, was capable of producing a set of discriminator genes, though the accuracy was yet to be improved. We observed that the best result was obtained when discriminators were selected from stage II CRC samples and used for prognosis of stage II CRC. When stage III cases were included in the process of discriminator extraction or in the process to validate samples, the results were poorer. Finally, we examined 31 independent stage II samples with a set of 30 such discriminators and were able to obtain results with 78 % accuracy, 90 % negative predictive value (NPV), and 55% positive predictive value (PPV). Conclusions. Independent clinicopathological variables were not able to predict prognosis of individual patient, unless the factors are combined. On the other hand, gene signatures allowed accurate prediction of prognosis for individuals, especially with stage II CRC, suggesting its potential use for selection of best treatment option for individual patients. The accuracy of discriminator prediction will be further improved when we take the evolution of CRC into consideration. Of 198 samples, 129 represented the discovery phase and 69 represented the validation phase.

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