Full genome expression profiling of primary colorectal cancer (CRC)
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ABSTRACT: Fresh-frozen primary tumor samples from CRC patients were analyzed from their gene expression. Full genome unsupervised clustering identified three intrinsic subtypes that are associated with tumor and clinical characteristics.
Project description:Fresh-frozen primary tumor samples from CRC patients were analyzed from their gene expression. Full genome unsupervised clustering identified three intrinsic subtypes that are associated with tumor and clinical characteristics. Primary CRC samples (188) were hybridzed against a common colon cancer reference pool (CRP).
Project description:Colorectal cancer (CRC) is one of the most prevalent cancers, with over one million new cases per year. Overall, prognosis of CRC largely depends on the disease stage and metastatic status. As precision oncology for patients with CRC continues to improve, this study aimed to integrate genomic, transcriptomic, and proteomic analyses to identify significant differences in expression during CRC progression using a unique set of paired patient samples while considering tumour heterogeneity.We analysed fresh-frozen tissue samples prepared under strict cryogenic conditions of matched healthy colon mucosa, colorectal carcinoma, and liver metastasis from the same patients. Somatic mutations of known cancer-related genes were analysed using Illumina's TruSeq Amplicon Cancer Panel; the transcriptome was assessed comprehensively using Clariom D microarrays. The global proteome was evaluated by liquid chromatography-coupled mass spectrometry (LC‒MS/MS) and validated by two-dimensional difference in-gel electrophoresis. Subsequent unsupervised principal component clustering, statistical comparisons, and gene set enrichment analyses were calculated based on differential expression results.Although panomics revealed low RNA and protein expression of CA1, CLCA1, MATN2, AHCYL2, and FCGBP in malignant tissues compared to healthy colon mucosa, no differentially expressed RNA or protein targets were detected between tumour and metastatic tissues. Subsequent intra-patient comparisons revealed highly specific expression differences (e.g., SRSF3, OLFM4, and CEACAM5) associated with patient-specific transcriptomes and proteomes.Our research results highlight the importance of inter- and intra-tumour heterogeneity as well as individual, patient-paired evaluations for clinical studies. In addition to changes among groups reflecting CRC progression, we identified significant expression differences between normal colon mucosa, primary tumour, and liver metastasis samples from individuals, which might accelerate implementation of precision oncology in the future.
Project description:Colorectal tumorigenesis proceedes through well defined clinical stages assoicated with charateristic mutations. Besides genetic alterations, epi-driver genes that are aberrantly expressed in cancers in a fashion that confers a seletive growth advantage can also contribute to tumor evolution. To gain a global view of methylation patterns in normal and maliganant colorectal epithelia, we performed genome-wide DNA methylation analysis on DNAs from 48 fresh frozen CRC samples at different stages of CRC progression. We used IlluminaHumanMethylation450 Beadchip to get a broad view of genome-wide DNA methylationdata during CRC progression and identified significantly differentially methylated genes during CRC progression
Project description:(Purpose) Biological classification of colorectal cancer (CRC) can help to understand its heterogeneous background. The purpose of this study is to classify CRC based on gene expression profiles using formalin-fixed paraffin-embedded (FFPE) samples and to correlate subgroups of CRC with biological features and clinical outcomes. (Results) CRC was clustered into four subgroups by unsupervised hierarchical clustering method. These subgroups show different biological and clinical features. (Conclusion) Gene expression profiles of CRC using FFPE samples distinguish four subgroups that had different biological features and clinical outcomes. These subgroups may explain heterogeneity of CRC and be useful biomarker for clinical. Patients and Methods: One hundred patients with unresectable and advanced or recurrent CRC who underwent the surgical resection from 1998 to 2010 were enrolled in this study. RNA extracted from FFPE samples was subjected to gene expression microarray. After comprehensive gene expression analysis, CRC were classified by an unsupervised hierarchical clustering and a principle component analysis (PCA). Mutation analysis of KRAS, BRAF, PIK3CA and TP53 genes were performed by direct DNA sequencing. Correlation between the biological information, clinicopathological factors and clinical outcomes were analyzed.
Project description:To identify novel hypermethylated genes in colorectal cancer (CRC) and to test their potential application in CRC early diagnosis, we performed a genome-wide screening of 57,723 CpG dinucleotides covering 4,010 genes in paired DNA samples extracted from 3 fresh frozen CRC tissues and their matching non-tumor adjacent tissues from a cohort of 3 CRC patients undergoing curative surgery using MIRA-based microarray. We also validated candidate hypermethylated genes screened by MIRA-based microarray in independent CRC samples using combined bisulfite restriction analysis. A total of 297 CpG dinucleotides in CRC covering 211 genes were found to be hypermethylated in CRC tissues. From these 211 candidate methylated genes, seven novel methylated genes were picked up for validation and three genes were confirmed to be methylated in cancer samples but not in non-cancer samples.We also compared the methylation levels of these three novel hypermethylated genes with those of Vimentin and SEPT9, well-known hypermethylated genes in CRC, and found that methylated PHOX2B, FGF12 and GAD2 were better than methylated Vimentin and SEPT9 in differentiating CRC cancer tissue from normal tissue. Significant enrichment analysis of GO terms of the hypermethylated genes showed that a high proportion of hypermethylated genes in tumor tissues are involved in regulation of transcription. Paired experiments, colorectal cancer tissue vs. adjacent non-cancer tissue. Biological replicates: 3 cancer replicates, 3 paired non-cancer replicates.
Project description:To identify novel hypermethylated genes in colorectal cancer (CRC) and to test their potential application in CRC early diagnosis, we performed a genome-wide screening of 57,723 CpG dinucleotides covering 4,010 genes in paired DNA samples extracted from 3 fresh frozen CRC tissues and their matching non-tumor adjacent tissues from a cohort of 3 CRC patients undergoing curative surgery using MIRA-based microarray. We also validated candidate hypermethylated genes screened by MIRA-based microarray in independent CRC samples using combined bisulfite restriction analysis. A total of 297 CpG dinucleotides in CRC covering 211 genes were found to be hypermethylated in CRC tissues. From these 211 candidate methylated genes, seven novel methylated genes were picked up for validation and three genes were confirmed to be methylated in cancer samples but not in non-cancer samples.We also compared the methylation levels of these three novel hypermethylated genes with those of Vimentin and SEPT9, well-known hypermethylated genes in CRC, and found that methylated PHOX2B, FGF12 and GAD2 were better than methylated Vimentin and SEPT9 in differentiating CRC cancer tissue from normal tissue. Significant enrichment analysis of GO terms of the hypermethylated genes showed that a high proportion of hypermethylated genes in tumor tissues are involved in regulation of transcription.
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:Colorectal tumorigenesis proceedes through well defined clinical stages assoicated with charateristic mutations. Besides genetic alterations, epi-driver genes that are aberrantly expressed in cancers in a fashion that confers a seletive growth advantage can also contribute to tumor evolution. To gain a global view of methylation patterns in normal and maliganant colorectal epithelia, we performed genome-wide DNA methylation analysis on DNAs from 48 fresh frozen CRC samples at different stages of CRC progression. We used IlluminaHumanMethylation450 Beadchip to get a broad view of genome-wide DNA methylationdata during CRC progression and identified significantly differentially methylated genes during CRC progression A total of 48 macro-dissected tissues including normal colon tissue, adenomas, carcinomas and metastases were collected by the Department of Pathology at the University of Virginia under the supervision of an experienced pathologist. DNA was extracted and run on IlluminaHumanMethylation450 Beadchip by Expression Analyisis.
Project description:We noticed that a recently identified poor prognosis stem/serrated molecular subtype of colorectal cancer (CRC) is characterized by up-regulation of transcripts known to be also expressed by stromal cells. To better define the origin of such transcripts, we analyzed RNAseq and microarray datasets from CRC mouse xenografts, where human cancer cells are supported by murine stroma. The analysis revealed that mRNA levels of stem/serrated subtype genes are mostly due to stromal expression, even when the stromal fraction is below 5%. Indeed, a classifier based on genes exclusively expressed by cancer-associated fibroblasts was significantly associated, in multiple datasets, to poor prognosis of CRC and to radioresistance of rectal cancer. Tumor Matched with Corresponding PDX
Project description:(Purpose) Biological classification of colorectal cancer (CRC) can help to understand its heterogeneous background. The purpose of this study is to classify CRC based on gene expression profiles using formalin-fixed paraffin-embedded (FFPE) samples and to correlate subgroups of CRC with biological features and clinical outcomes. (Results) CRC was clustered into four subgroups by unsupervised hierarchical clustering method. These subgroups show different biological and clinical features. (Conclusion) Gene expression profiles of CRC using FFPE samples distinguish four subgroups that had different biological features and clinical outcomes. These subgroups may explain heterogeneity of CRC and be useful biomarker for clinical.