Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Deciphering genomic alterations in colorectal cancer through transcriptional subtype-based network analysis

ABSTRACT: 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/).

ORGANISM(S): Mus musculus

SUBMITTER: Jing Zhu

PROVIDER: E-GEOD-38831 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Publications

Deciphering genomic alterations in colorectal cancer through transcriptional subtype-based network analysis.

Zhu Jing J Wang Jing J Shi Zhiao Z Franklin Jeffrey L JL Deane Natasha G NG Coffey Robert J RJ Beauchamp R Daniel RD Zhang Bing B

PloS one 20131115 11

Both transcriptional subtype and signaling network analyses have proved useful in cancer genomics research. However, these two approaches are usually applied in isolation in existing studies. We reason that deciphering genomic alterations based on cancer transcriptional subtypes may help reveal subtype-specific driver networks and provide insights for the development of personalized therapeutic strategies. In this study, we defined transcriptional subtypes for colorectal cancer (CRC) and identif ...[more]

PMID: 24260186

Similar Datasets

Project description:Colorectal carcinoma is the third leading cause of cancer-related death in the United States. In order to understand the mechanism/signaling pathways responsible for invasion, migration and metastasis in colorectal cancer, we developed an integrative and comparative genetic approach to infer transcriptional regulatory mechanisms underlying colon cancer progression. Accordingly, we filtered fourteen human colorectal cancer (CRC) microarray data sets, from an immune competent mouse model of metastasis to identify known and novel transcriptional regulators in CRC. Using this approach, Nuclear Factor of Activated T cells (NFAT) family of transcription factors were identified as metastasis driver of colon cancer. NFAT family of transcription factors is known to induce gene transcription in various disease processes, including carcinogenesis. We used parental and metastatic derivatives of MC38 mouse colon cancer cells (MC38Par and MC38Met, respectively) to evaluate the role of NFATc1 in cancer cell invasiveness. We found that high NFATc1 expression correlates with significantly increased (p<0.0001) Trans-Endothelial Invasion (TEI) in MC38Met cells. Conversely, RNAi-based inhibition of NFATc1 expression and functional inhibition with calcineurin inhibitor FK506 in MC38Met cells, both resulted in significant decreased TEI (p=0.0193 & p=0.0003). Furthermore, a set of predicted NFATc1 target mRNAs identified in our original analysis were downregulated by knock-down of NFATc1 or functional inhibition with FK506 in MC38Met cells. The expression level (mRNA) of predicted gene targets were high in human CRC specimens which had higher than median NFATc1 mRNA expression (n=11 out of total 22). The tumor-associated NFATc1 co-regulated gene signature is significantly correlated with both disease-specific and disease-free survival in Stage II and III CRC patients. We have successfully demonstrated a bioinformatics approach to identify a tumor promoter driver gene NFATc1. Our studies suggest a role of NFATc1 towards invasion and its co-regulated gene signature for poor outcomes in colorectal cancer. We developed an integrative and comparative genetic approach to infer transcriptional regulatory mechanisms underlying colon cancer progression. Using this approach, the Nuclear Factor of Activated T cells (NFAT) family of transcription factors were identified as metastasis driver of colon cancer. We used parental and metastatic derivatives of MC38 mouse colon cancer cells (MC38Par and MC38Met, respectively) to evaluate the role of NFATc1 in cancer cell invasiveness [GSE19073]. We found that high NFATc1 expression correlates with significantly increased (p<0.0001) Trans-Endothelial Invasion (TEI) in MC38Met cells. Conversely, RNAi-based inhibition of NFATc1 expression and functional inhibition with calcineurin inhibitor FK506 in MC38Met cells, both resulted in significant decreased TEI (p=0.0193 & p=0.0003). Furthermore, a set of predicted NFATc1 target mRNAs identified in our original analysis were downregulated by knock-down of NFATc1 or functional inhibition with FK506 in MC38Met cells. Finally, we generated a microarray gene expression dataset based on tumor samples collected from 122 CRC patients and tested whether the tumor-associated NFATc1co-regulated gene signature is correlated with patient survival. The following clinical information can be found in the characteristics fields of each sample; AJCC_STAGE: stage of cancer classified by AJCC (American Joint Committee on Cancer) staging system DFS_EVENT: disease free survival; cancer recurrence=1, no recurrence=0 DFS_TIME: disease free survival time (months) DSS_EVENT: disease specific survival; death from cancer=1,no death=0 DSS_TIME: disease specific survival time (months)

Dataset Information

Deciphering genomic alterations in colorectal cancer through transcriptional subtype-based network analysis

Publications

Deciphering genomic alterations in colorectal cancer through transcriptional subtype-based network analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure