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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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“Understanding Cancer in its Full Complexity through Mining Cancer Tissue Omic Data”
Ying Xu, PhD
Regents' Professor and Chair
GRA Eminent Scholar
Department of Biochemistry and Molecular Biology
Institute of Bioinformatics
University of Georgia
A vast majority of the published cancer studies in the past few decades was conducted on cancer cells rather than cancer tissues. Knowing that the microenvironment plays key roles in cancer initiation, development and metastasis, we must reassess the true relevance of many of these published results to cancer. We have recently developed a new framework for cancer studies by treating cancer as a survival process under increasingly more challenging stresses, which evolve as a cancer evolves. Our main hypothesis is that cell proliferation is a sustained and common pathway to survival under all major cancer-associated stresses. The availability of large-scale cancer tissue omic data enables us to systematically identify various stress types present in each tissue and how each cancer tumor responds to the encountered stresses, ultimately validating, refining or rejecting this fundamentally novel hypothesis. In this presentation, I will discuss (1) how data mining can be used to identify such stresses and their responses, leading to substantially improved understanding about cancer evolution from its onset; and (2) how data mining-based discoveries can be integrated with cell-based experimental findings, leading to more comprehensive understanding about the key drivers and facilitators of cancer evolution, hence potentially leading to much improved treatment paradigms for challenging cancer cases.
Reference: Ying Xu, Juan Cui, Dave Puett, Cancer Bioinformatics, Springer 2014.
