Dataset Information

Large-scale profiling of microRNAs for The Cancer Genome Atlas.

ABSTRACT: The comprehensive multiplatform genomics data generated by The Cancer Genome Atlas (TCGA) Research Network is an enabling resource for cancer research. It includes an unprecedented amount of microRNA sequence data: ~11 000 libraries across 33 cancer types. Combined with initiatives like the National Cancer Institute Genomics Cloud Pilots, such data resources will make intensive analysis of large-scale cancer genomics data widely accessible. To support such initiatives, and to enable comparison of TCGA microRNA data to data from other projects, we describe the process that we developed and used to generate the microRNA sequence data, from library construction through to submission of data to repositories. In the context of this process, we describe the computational pipeline that we used to characterize microRNA expression across large patient cohorts.

SUBMITTER: Chu A

PROVIDER: S-EPMC4705681 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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Large-scale profiling of microRNAs for The Cancer Genome Atlas.

Chu Andy A Robertson Gordon G Brooks Denise D Mungall Andrew J AJ Birol Inanc I Coope Robin R Ma Yussanne Y Jones Steven S Marra Marco A MA

Nucleic acids research 20150813 1

The comprehensive multiplatform genomics data generated by The Cancer Genome Atlas (TCGA) Research Network is an enabling resource for cancer research. It includes an unprecedented amount of microRNA sequence data: ~11 000 libraries across 33 cancer types. Combined with initiatives like the National Cancer Institute Genomics Cloud Pilots, such data resources will make intensive analysis of large-scale cancer genomics data widely accessible. To support such initiatives, and to enable comparison o ...[more]

PMID: 26271990

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Project description:The discovery of chemotherapeutic agents for the treatment of cancer commonly uses cell proliferation assays in which cells grow as two-dimensional (2D) monolayers. Compounds identified using 2D monolayer assays often fail to advance during clinical development, most likely because these assays do not reproduce the cellular complexity of tumors and their microenvironment in vivo. The use of three-dimensional (3D) cellular systems have been explored as enabling more predictive in vitro tumor models for drug discovery. To date, small-scale screens have demonstrated that pharmacological responses tend to differ between 2D and 3D cancer cell growth models. However, the limited scope of screens using 3D models has not provided a clear delineation of the cellular pathways and processes that differentially regulate cell survival and death in the different in vitro tumor models. Here we sought to further understand the differences in pharmacological responses between cancer tumor cells grown in different conditions by profiling a large collection of 1912 chemotherapeutic agents. We compared pharmacological responses obtained from cells cultured in traditional 2D monolayer conditions with those responses obtained from cells forming spheres versus cells already in 3D spheres. The target annotation of the compound library screened enabled the identification of those key cellular pathways and processes that when modulated by drugs induced cell death in all growth conditions or selectively in the different cell growth models. In addition, we also show that many of the compounds targeting these key cellular functions can be combined to produce synergistic cytotoxic effects, which in many cases differ in the magnitude of their synergism depending on the cellular model and cell type. The results from this work provide a high-throughput screening framework to profile the responses of drugs both as single agents and in pairwise combinations in 3D sphere models of cancer cells.

Dataset Information

Large-scale profiling of microRNAs for The Cancer Genome Atlas.

Publications

Large-scale profiling of microRNAs for The Cancer Genome Atlas.

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