Project description:Response to drug therapy in individual colorectal cancer (CRC) patients is associated with tumor biology. Here we describe the genomic landscape of tumor samples of a homogeneous well-annotated series of patients with metastatic CRC of two phase III clinical trials, CAIRO and CAIRO2. DNA copy number aberrations of 349 patients are determined. Within three treatment arms, 194 chromosomal sub-regions are associated with progression free survival PFS (uncorrected single-test p-values < 0.005). These sub-regions are filtered for effect on mRNA expression, using an independent data set from The Cancer Genome Atlas (TCGA) which returned 171 genes. Three chromosomal regions are associated with a significant difference in PFS between treatment arms with or without irinotecan. One of these regions, 6q16.1-q21, correlates in vitro with sensitivity to SN-38, the active metabolite of irinotecan. This genomic landscape of metastatic CRC reveals a number of DNA copy number aberrations associated with response to drug therapy. aCGH data of colorectal cancers of patients from 2 clinical trials (CAIRO, CAIRO2). 105 patients were treated with capecitabine first line (CAIRO arm A), 111 patients were treated with capecitabine and irinotecan first line (CAIRO arm B), and 133 patients were treated with capecitabine, oxaliplatin and bevacizumab (CAIRO2 arm A).
Project description:For the understanding intrinsic cancer cell signatures and the surrounding microenviroment, we provide single-cell 3' RNA sequencing dataon 63,689 cells from 23 CRC patients with 23 primary colorectal cancer and 10 matched normal mucosa samples. Analysis of primary colorectal cancer and normal mucosa samples depicts a comprehensive cellular landscape of colorectal cancer and potential cellular interaction, which would be a valuable resource for the development of therapeutic strategies.
Project description:For the understanding of intrinsic cancer cell signatures and the surrounding microenvironment, we provide single-cell 3’ RNA sequencing data on 27,414 cells from 6 CRC patients in core and border tumor regions, as well as in matched normal mucosa. Analysis of primary colorectal cancer and normal mucosa samples depicts a comprehensive cellular landscape of colorectal cancer and potential cellular interactions, which would be a valuable resource for the development of therapeutic strategies.
Project description:We characterized the epigenetic landscape of human colorectal cancer (CRC). To this extent, we performed gene expression profiling using high throughput sequencing (RNA-seq) and genome wide binding/occupancy profiling (ChIP-seq) for histone modifications correlated to transcriptional activity, enhancers, elongation and repression (H3K4me3, H3K4me1, H3K27Ac, H3K36me3, H3K27me3) in patient-derived organoids (PDOs), and in normal and tumoral primary colon tissues. We also generated ChIP-seq data for transcription factors YAP/TAZ in human CRC PDOs.
Project description:We characterized the epigenetic landscape of human colorectal cancer (CRC). To this extent, we performed gene expression profiling using high throughput sequencing (RNA-seq) and genome wide binding/occupancy profiling (ChIP-seq) for histone modifications correlated to transcriptional activity, enhancers, elongation and repression (H3K4me3, H3K4me1, H3K27Ac, H3K36me3, H3K27me3) in patient-derived organoids (PDOs), and in normal and tumoral primary colon tissues. We also generated ChIP-seq data for transcription factors YAP/TAZ in human CRC PDOs.
Project description:One tumor tissue specimen pathologically diagnosed as adenocarcinoma undergoing radical colorectal surgery without any preoperative treatment at our center was collected, and the single cell transcriptome sequencing was performed, which was consistent with the inclusion and exclusion criteria for the prospective observational clinical study.The detailed procedures of pre-processing and quality control were as follows: (1) Raw data quality control; (2) Data comparison and mapping; (3) Cell Barcode Correction; (4) UMI disaggregation; (5) Count matrix generation; (6) Quality control indicators; (7) Data standardization and normalization; (8) Identification of highly variable genes (HVGs); (9) Filtering condition settings: nCount_RNA > 1000 & nFeature_RNA < 3000 & percent.mt < 10 & nFeature_RNA > 200. The t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm was used for downscaling and cell clustering, and the "SingleR" R package was used for cell annotation. The "CellChat" and "NicheNet" R packages were used for cell communication. The "monocle2" was used for cell trajectory analysis.