ABSTRACT: Intra-tumoral heterogeneity in metastatic potential and survival signaling between iso-clonal HCT116 and HCT116b human colon carcinoma cell lines
Project description:Characterization of the intra-tumoral heterogeneity between two iso-clonal human colon cancer sublines HCT116 and HCT116b on their ability to undergo metastatic colonization and survive under growth factor deprivation stress (GFDS). Microarray analysis revealed an upregulation of survival and metastatic genes in the highly metastatic HCT116 primary colon tumor cells compared to the poorly metastatic HCT116b primary colon tumor cells.
Project description:Characterization of the intra-tumoral heterogeneity between two iso-clonal human colon cancer sublines HCT116 and HCT116b on their ability to undergo metastatic colonization and survive under growth factor deprivation stress (GFDS). Microarray analysis revealed an upregulation of survival and metastatic genes in the highly metastatic HCT116 primary colon tumor cells compared to the poorly metastatic HCT116b primary colon tumor cells. Total RNA obtained from isolated primary colon tumors of HCT116 and HCT116b xenograft transplanted animals obtained using the orthotopic implantation of HCT116 and HCT116b human colon cancer xenografts in the cecum of male athymic BALB/c nude mice were compared at their gene expression level.
Project description:Almost half of all patients diagnosed with colorectal cancer develop liver metastases. The potential role of intra-individual metastatic heterogeneity remains poorly characterized. By high-resolution DNA copy number analyses and a novel approach based on pair-wise genetic distance, we examined the genetic heterogeneity among multiple liver metastatic deposits obtained from 45 patients subject to curative liver resection. We found large variation in intra-individual metastatic heterogeneity. A high level of heterogeneity was associated with poor patient survival. Patients with metachronous metastases who received chemotherapy had significantly more heterogeneity than chemonaïve patients.
Project description:Deciphering intra- and inter-tumoral heterogeneity is essential for understanding gastric cancer (GC) biology and identifying effective therapeutic targets. We used single-cell RNA sequencing (scRNA-seq) to reveal the transcriptional heterogeneity of diverse cells within primary and metastatic GC and their roles in disease progression.
Project description:We have assessed clonal heterogeneity within individual primary tumours and metastasis and also during the distinct stages of malignant tumour progression using the Confetti lineage reporter in the background of the MMTV-PyMT mouse model of metastatic breast cancer. Comparative gene expression analysis of the clonal populations reveals a substantial level of heterogeneity across and also within the various stages of breast carcinogenesis. This intra-stage heterogeneity is manifested by differences in cell proliferation, where the fast-proliferating subclass is further enriched in oxidative phosphorylation and cell death.
Project description:Generation of preclinical models which recapitulate at best the extreme intra-tumoral heterogeneity (using two cell culture conditions) and inter-tumor heterogeneity between children diagnosed with high-grade gliomas.
Project description:Heterogeneity of gene expression profiles in head and neck cancer Background: Results of gene expression profiling studies from different institutes often lack consistency. This could be due to the use of different microarray platforms and protocols, or to intra-tumoral heterogeneity in mRNA expression. The aim of our study was to quantify intra-tumoral heterogeneity in head and cancer. Methods: Forty-four fresh frozen biopsies were taken from 22 patients, two per tumor. RNA was extracted, tested for quality, amplified, labeled and hybridized to a 35k oligoarray. Results: Unsupervised clustering analyses using all genes, the most variable genes, or random gene sets showed that 80-90% of biopsy pairs clustered together. A within-pair-between-pair scatter ratio analysis showed that the similarity between matching pairs was significantly greater than that between random pairs (p < 0.00001). Conclusions: Two biopsies from the same tumor show far greater similarity in gene expression than biopsies from different tumors, supporting the use of one biopsy for expression profiling.
