Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Four subgroups by gene expression profile correlate with biological and clinical features in colorectal cancer.

ABSTRACT: (Purpose) Biological classification of colorectal cancer (CRC) can help to understand its heterogeneous background. The purpose of this study is to classify CRC based on gene expression profiles using formalin-fixed paraffin-embedded (FFPE) samples and to correlate subgroups of CRC with biological features and clinical outcomes. (Results) CRC was clustered into four subgroups by unsupervised hierarchical clustering method. These subgroups show different biological and clinical features. (Conclusion) Gene expression profiles of CRC using FFPE samples distinguish four subgroups that had different biological features and clinical outcomes. These subgroups may explain heterogeneity of CRC and be useful biomarker for clinical. Patients and Methods: One hundred patients with unresectable and advanced or recurrent CRC who underwent the surgical resection from 1998 to 2010 were enrolled in this study. RNA extracted from FFPE samples was subjected to gene expression microarray. After comprehensive gene expression analysis, CRC were classified by an unsupervised hierarchical clustering and a principle component analysis (PCA). Mutation analysis of KRAS, BRAF, PIK3CA and TP53 genes were performed by direct DNA sequencing. Correlation between the biological information, clinicopathological factors and clinical outcomes were analyzed.

ORGANISM(S): Homo sapiens

SUBMITTER: masahiro inoue

PROVIDER: E-GEOD-33193 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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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/).

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Four subgroups by gene expression profile correlate with biological and clinical features in colorectal cancer.

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