Mus musculus
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA169022 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Genomic determinants of protein abundance variation in colorectal cancer cells
2021-11-09 | E-PROT-6 | ExpressionAtlas
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
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2021-05-25 | E-PROT-18 | ExpressionAtlas
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2023-06-21 | E-PROT-101 | ExpressionAtlas
Project description:Colorectal Cancer Concurrent Gene Signature Based on Coherent Patterns between Genomic and Transcriptional Alterations
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Project description:Genomic characterization of ovarian cancer spheroids
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