Project description:mRNA profiling data for 67 colorectal cancer at baseline Supporting data for PMID: 21251323 mRNA profiles at baseline for 67 colorectal cancer cell lines
Project description:This SuperSeries is composed of the following subset Series: GSE28567: EMT is the dominant program in human colon cancer (Affymetrix) GSE28709: EMT is the dominant program in human colon cancer (lung) GSE28722: EMT is the dominant program in human colon cancer (Agilent) Refer to individual Series
Project description:BackgroundColon cancer has been classically described by clinicopathologic features that permit the prediction of outcome only after surgical resection and staging.MethodsWe performed an unsupervised analysis of microarray data from 326 colon cancers to identify the first principal component (PC1) of the most variable set of genes. PC1 deciphered two primary, intrinsic molecular subtypes of colon cancer that predicted disease progression and recurrence.ResultsHere we report that the most dominant pattern of intrinsic gene expression in colon cancer (PC1) was tightly correlated (Pearson R = 0.92, P < 10(-135)) with the EMT signature-- both in gene identity and directionality. In a global micro-RNA screen, we further identified the most anti-correlated microRNA with PC1 as MiR200, known to regulate EMT.ConclusionsThese data demonstrate that the biology underpinning the native, molecular classification of human colon cancer--previously thought to be highly heterogeneous-- was clarified through the lens of comprehensive transcriptome analysis.
Project description:mRNA profiles on 129 colorectal tumors 129 colorectal tumors profiled on Agilent array using pool of individual tumors as common reference
Project description:Lung cancer is a highly heterogeneous disease in terms of both underlying genetic lesions and response to therapeutic treatments. We performed deep whole genome sequencing and transcriptome sequencing on 19 lung cancer cell lines and 3 lung tumor/normal pairs (provisional dbGaP accession number; phs000299.v2.p1). Overall, our data show that cell line models exhibit similar mutation spectra to human tumor samples. Taken together, these data present a comprehensive genomic landscape of a large number of lung cancer samples and further demonstrate that cancer specific alternative splicing is a widespread phenomenon that has potential utility as therapeutic biomarkers. Nineteen non-small cell lung cancer cell lines were assayed for genotype, copy number and LOH using Illumina Omni2.5-4 arrays, GenomeStudio V2011.1, and a modified version of the PICNIC (PMID 19837654) algorithm.