Project description:DNA methylation in colorectal cancer diagnosis. The Illumina GoldenGate Methylation Cancer Panel I was used to select a set of candidates markers informative of colorectal cancer diagnosis from 807 cancer-related genes. In the discovery phase, tumor tissue and paired adjacent normal mucosa from 92 colorectal patients were analyzed.
Project description:DNA methylation in colorectal cancer diagnosis. The Illumina GoldenGate Methylation Cancer Panel I was used to select a set of candidates markers informative of colorectal cancer diagnosis from 807 cancer-related genes. In the discovery phase, tumor tissue and paired adjacent normal mucosa from 92 colorectal patients were analyzed. Bisulphite converted DNA from 92 colorectal tumor samples and paired adjacent normal mucosa were hybridised to the Illumina GoldenGate Methylation Cancer Panel I. Additionally, replicates were hybridised for five tumor tissue and their corresponding normal mucosa for reproducibility purposes, totalling 194 samples. Three samples (SAMPLEs 49, 51, and 162) and 50 loci did not reach the quality criteria required regarding the signal-to-noise ratio and were therefore excluded from further analysis. One additional non-tumoral sample (SAMPLE 15) was removed because it exhibited a methylation pattern quiet different from that shown by the rest of normal specimens, which could be indicative of hybridization errors. These Samples and loci are included in the raw data matrix to allow other investigators to use them if different criteria are applied. They have been also included in the Sample tables with missing values in order to preserve the structure of the data across records/files (See 'data processing' section for more details).
Project description:Mouse models have been developed to investigate colorectal cancer etiology and evaluate new anti-cancer therapies. While genetically engineered and carcinogen-induced mouse models have provided important information with regard to the mechanisms underlying the oncogenic process, xenograft models remain the standard for the evaluation of new chemotherapy and targeted drug treatments for clinical use. However, it remains unclear if drug efficacy data obtained from xenograft models translate into clinically-relevant treatment modalities. In this study, we have generated a panel of 28 patient-derived colorectal cancer explants (PDCCEs), an extension of our previous work, by direct transplantation of human colorectal cancer (CRC) tissues into NOD-SCID mice. A comprehensive histological and molecular evaluation of PDCCEs and their corresponding patient tumor demonstrates that PDCCEs maintain histological features and global biology through multiple passages. Furthermore, we demonstrate that in vivo sensitivity of PDCCEs to oxaliplatin can predict patient outcomes. Our findings suggest that PDCCEs maintain similarity to the patient tumor from which they are derived and can serve as a reliable preclinical model that can be incorporated into future strategies to optimize individual therapy for patients with CRC. 28 human primary colorectal and 37 mouse derived colorectal explant tumors
Project description:Background: Colorectal cancer (CRC) remains a major concern with high morbidity and mortality worldwide. DNA methylation alteration plays a pivotal role in cancer development. We aimed to screen novel biomarkers for CRC diagnosis and chemotherapy-related adverse event (CRAE) prediction using the advanced Illumina Infinium MethylationEPIC (850K) BeadChip. Methods: We analyzed the methylation profiles of paired tumor and normal tissues from 21 Chinese CRC patients. After normalization by potential confounders, three types of methylation profiles (differentially methylated probes, differentially methylated regions, and gene-function-differentially methylated regions) were further studied by functional annotation and pathway enrichment analysis. At last, integrated-methylation-marker systems for CRC diagnosis and CRAE prediction were developed based on genes within the mostly related pathways and LASSO regression. Findings: Tumor-related methylation was characterized with hypermethylated promoter islands and hypomethylated intragenic openseas. CRAE-related methylation was characterized with hyper- (or hypo-) methylated intragenic (or intergenic) regions. The two most important susceptible factors for various types of CARE were inactive regeneration functions and active immune response. Differentially methylated genes were significantly enriched in neuronal system, metabolism of RNA, and extracellular matrix organization. All of the integrated-methylation-marker systems demonstrated high discriminative accuracy in both CRC diagnosis (AUROC = 1) and CRAE prediction (AUROC = 0.817-1). Interpretation: In this study, we provided new insights on the formation of CRC diagnosis and CRAE based on three types of methylation profile. The integrated-methylation-marker systems combining multiple DMPs were found to have potentially diagnostic and predictive values. Hence, our findings have important clinical implications, and further validation is warranted.
Project description:Colorectal cancer (CRC) is currently the third leading cause of cancer related mortality in the world. U.S. Food and Drug Administration-approval circulating tumor markers, including carcinoembryonic antigen, carbohydrate antigen (CA) 19-9 and CA125, were used as prognostic biomarker of CRC that attributed to low sensitivity in diagnosis of CRC. Therefore, our purpose is to develop a novel strategy for novel clinical biomarker for early CRC diagnosis. We used mass spectrometry (MS) methods such as nanoLC-MS/MS, targeted LC-MS/MS, and stable iso-tope-labeled multiple reaction monitoring (MRM) MS coupled to machine learning algorithms and logistic regression to analyze plasma samples from patients with early-stage CRC, late-stage CRC, and healthy controls (HCs).
Project description:Clinical O-glycoproteomics for the development of diagnostic marker for colorectal cancer based on changes in O-glycosylation of serum protein.
Project description:Mouse models have been developed to investigate colorectal cancer etiology and evaluate new anti-cancer therapies. While genetically engineered and carcinogen-induced mouse models have provided important information with regard to the mechanisms underlying the oncogenic process, xenograft models remain the standard for the evaluation of new chemotherapy and targeted drug treatments for clinical use. However, it remains unclear if drug efficacy data obtained from xenograft models translate into clinically-relevant treatment modalities. In this study, we have generated a panel of 28 patient-derived colorectal cancer explants (PDCCEs), an extension of our previous work, by direct transplantation of human colorectal cancer (CRC) tissues into NOD-SCID mice. A comprehensive histological and molecular evaluation of PDCCEs and their corresponding patient tumor demonstrates that PDCCEs maintain histological features and global biology through multiple passages. Furthermore, we demonstrate that in vivo sensitivity of PDCCEs to oxaliplatin can predict patient outcomes. Our findings suggest that PDCCEs maintain similarity to the patient tumor from which they are derived and can serve as a reliable preclinical model that can be incorporated into future strategies to optimize individual therapy for patients with CRC.