Mus musculus
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA169022 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Assessing the impact of genomic alterations on protein networks is fundamental in identifying the mechanisms that shape cancer heterogeneity. We have used isobaric labelling to characterize the proteomic landscapes of 50 colorectal cancer cell lines and to decipher the functional consequences of somatic genomic variants. The robust quantification of over 9,000 proteins and 11,000 phosphopeptides on average, enabled the de novo construction of a functional protein correlation network which ultimately exposed the collateral effects of mutations on protein complexes. CRISPR-cas9 deletion of key chromatin modifiers confirmed that the consequences of genomic alterations can propagate through protein interactions in a transcript-independent manner. Lastly, we leveraged the quantified proteome to perform unsupervised classification of the cell lines and to build predictive models of drug response in colorectal cancer. Overall, we provide a deep integrative view of the functional network and the molecular structure underlying the heterogeneity of colorectal cancer cells.
| MSV000081528 | MassIVE
Project description:Assessing the impact of genomic alterations on protein networks is fundamental in identifying the mechanisms that shape cancer heterogeneity. We have used isobaric labelling to characterize the proteomic landscapes of 50 colorectal cancer cell lines and to decipher the functional consequences of somatic genomic variants. The robust quantification of over 9,000 proteins and 11,000 phosphopeptides on average, enabled the de novo construction of a functional protein correlation network which ultimately exposed the collateral effects of mutations on protein complexes. CRISPR-cas9 deletion of key chromatin modifiers confirmed that the consequences of genomic alterations can propagate through protein interactions in a transcript-independent manner. Lastly, we leveraged the quantified proteome to perform unsupervised classification of the cell lines and to build predictive models of drug response in colorectal cancer. Overall, we provide a deep integrative view of the functional network and the molecular structure underlying the heterogeneity of colorectal cancer cells.
2017-08-09 | PXD005235 | Pride
Project description:High-throughput genomic studies have identified thousands of genetic alterations in colorectal cancer (CRC). Distinguishing driver from passenger mutations is critical for developing rational therapeutic strategies. Because only a few transcriptional subtypes exist in previously studied tumor types, we hypothesize that highly heterogeneous genomic alterations may converge to a limited number of distinct mechanisms that drive unique gene expression patterns in different transcriptional subtypes. In this study, we defined transcriptional subtypes for CRC and identified driver networks/pathways for each subtype, respectively. Applying consensus clustering to a patient cohort with 1173 samples identified three transcriptional subtypes, which were validated in an independent cohort with 485 samples. The three subtypes were characterized by different transcriptional programs related to normal adult colon, early colon embryonic development, and epithelial mesenchymal transition, respectively. They also showed statistically different clinical outcomes. For each subtype, we mapped somatic mutation and copy number variation data onto an integrated signaling network and identified subtype-specific driver networks using a random walk-based strategy. We found that genomic alterations in the Wnt signaling pathway were common among all three subtypes; however, unique combinations of pathway alterations including Wnt, VEGF, Notch and TGF-beta drove distinct molecular and clinical phenotypes in different CRC subtypes. Our results provide a coherent and integrated picture of human CRC that links genomic alterations to molecular and clinical consequences, and which provides insights for the development of personalized therapeutic strategies for different CRC subtypes.
2013-12-22 | GSE38831 | GEO
Project description:High-throughput genomic studies have identified thousands of genetic alterations in colorectal cancer (CRC). Distinguishing driver from passenger mutations is critical for developing rational therapeutic strategies. Because only a few transcriptional subtypes exist in previously studied tumor types, we hypothesize that highly heterogeneous genomic alterations may converge to a limited number of distinct mechanisms that drive unique gene expression patterns in different transcriptional subtypes. In this study, we defined transcriptional subtypes for CRC and identified driver networks/pathways for each subtype, respectively. Applying consensus clustering to a patient cohort with 1173 samples identified three transcriptional subtypes, which were validated in an independent cohort with 485 samples. The three subtypes were characterized by different transcriptional programs related to normal adult colon, early colon embryonic development, and epithelial mesenchymal transition, respectively. They also showed statistically different clinical outcomes. For each subtype, we mapped somatic mutation and copy number variation data onto an integrated signaling network and identified subtype-specific driver networks using a random walk-based strategy. We found that genomic alterations in the Wnt signaling pathway were common among all three subtypes; however, unique combinations of pathway alterations including Wnt, VEGF, Notch and TGF-beta drove distinct molecular and clinical phenotypes in different CRC subtypes. Our results provide a coherent and integrated picture of human CRC that links genomic alterations to molecular and clinical consequences, and which provides insights for the development of personalized therapeutic strategies for different CRC subtypes. To characterize the embryonic development of colon, we conducted a time course microarray study using the inbred C57BL/6 (Jackson Laboratories, Bar Harbor, ME) mice. Seven samples corresponding to the mouse colonic development from E13.5 to E18.5 and adult (eight week post-natal) were collected. RNA samples were submitted to the Vanderbilt Functional Genomics Shared Resource (FSGR, http://array.mc.vanderbilt.edu), where RNA was hybridized to the Affymetrix Mouse Genome 430 2.0 GeneChip Expression Arrays (Santa Clara, CA) according to manufacturer’s instructions. The RMA algorithm was used for data normalization. Mouse gene symbols were mapped to human gene symbols by the Human and Mouse Orthology list available from the Mouse Genome Informatics (http://www.informatics.jax.org/).
2013-12-22 | E-GEOD-38831 | biostudies-arrayexpress
Project description:Colorectal Cancer Concurrent Gene Signature Based on Coherent Patterns between Genomic and Transcriptional Alterations
| PRJNA812057 | ENA
Project description:Comparison of genomic alterations of primary colorectal cancers with liver metastases of the same patient Keywords: array CGH, colorectal cancer, colon cancer, liver metastasis
2010-07-07 | GSE10179 | GEO
Project description:The well-known colorectal adenoma-carcinoma sequence suggests that a normal epithelial cell, through accumulations of genetic lesion and epigenetic disregulation can transform into a benign adenoma then further develop into a cancer. Using microarray-based comparative genomic hybridization (CGH), we reveal genome-wide copy number variations in colorectal cancer and polyp and use them to determine the tissueM-^Rs clonal relationship.
2011-06-30 | E-MEXP-3149 | biostudies-arrayexpress
Project description:Comparison of genomic alterations of primary colorectal cancers with liver metastases of the same patient Keywords: array CGH, colorectal cancer, colon cancer, liver metastasis 21 primary colorectal cancers and 21 matched liver metastases hybridized against sex-matched control pools
2010-07-07 | E-GEOD-10179 | biostudies-arrayexpress
Project description:Genomic alterations in proximal and distal colorectal cancer
| PRJNA382764 | ENA
Project description:Genomic gains and losses, particularly amplification of oncogenes and deletion of tumor suppressor genes, are critical molecular events involved in tumorigenesis and cancer progression. These genomic structural abnormalities trigger pathway alterations which activate/inactivate transcription factors along protein network, and then affect gene transcription profiles. Therefore, trace-back analysis of the pathway alteration by integrating genomic copy number, transcription profile, and known protein network data is expected to provide key information to interpret tumorigenesis and cancer progression processes. However, there are a number of pathway alteration candidates, so that it is difficult to understand overall picture. Primitive approaches such as filtering by arbitrary selection of thresholds involve a risk of overlooking important pathway alterations and their triggers. We proposed a visualization method for the trace-back analysis of pathway alterations, called a Cluster Overlap Distribution Map (CODM). We applied the CODM to trace-back analysis of pathway alterations related to subtype classifications of high grade neuroendocrine carcinoma samples; 1) small cell lung carcinoma (SCLC) vs. large cell neuroendocrine carcinoma (LCNEC), and 2) group1 vs. group2 (this is the classification based on transcription profiles and group2 has a higher survival rate than group1). By effective use of 3D and color spaces, the CODM allowed us to understand the overall picture of pathway alteration without arbitrary selection of thresholds and to extract 6 pathway alterations related to only group1 vs. groups2, 2 pathway alterations related to only SCLC vs. LCNEC, and 2 pathway alterations related to both group1 vs. group2 and SCLC vs. LCNEC. Keywords: lung cancer profile
2022-04-01 | GSE9074 | GEO