Cancer arises from the acquisition and concerted action of multiple mutations and genomic aberrations in discrete combinations of tumor suppressors and oncogenes, known as "drivers". Large cancer genome-scale sequencing studies such as TCGA, are now operative and the Cancer Target Discovery and Development (CTDD) Network seeks "to bridge the gap between the enormous volumes of data generated... and the ability to use these data for the development of human cancer therapeutics". A secondary goal for the CTDD Initiative is that in five years, "the entire CTDD Network is expected to identify and characterize targets for approximately 25 or more (if possible) cancer types." and for applicants "to have or build the capacity for in depth analyses and experimental approaches utilizing datasets for many cancer types. A broad "coverage" is the paradigm for this initiative." Here, the wealth of TCGA data will be directly coupled to robust in vitro functinal validation of candidate cancer driver modules using primary mouse 3D organoid cultures of diverse tissues arrayed in high-throughput format.
In Aim 1, the Hanlee Ji and Sylvia Plevritis groups will identify co-segregating mutational modules from TCGA datasets from multiple solid tumor types, using complementary methods of supervised Bayesian analysis and Unsupervised Module Network Analysis for Master Regulators.
In Aim 2, these prioritized mutational modules, stratified for clinical significance, will undergo direct functional validation in a broadly applicble, multiplexable, in vitro 3D primary organoid system developed by the Calvin Kuo group, which is amenable to combinatorial gene engineering. In collaboration with Bill Hahn, this will utilize high throughput lentiviral introduction of cDNA or shRNA to systematically interrogate the genes within amplicons and deletions, contextually modeled in the TCGA mutational background in which these copy number variations occur. Additionally, co-segregating mutational modules from diverse tissues will undergo systematic deletion in organoid cultures to define minimal module composition, and we will pursue process development to extend the range of tissues from which organoids can be modeled. Overall, these studies describe bioinformatic and in vitro modeling approaches that are robustly portable across a variety of organ systems for functional interrogation of diverse TCGA datasets, as highly responsive to RFA-CA-12-006 and the CTDD, and with attendant implications for cancer biology, diagnosis and therapy.
This project uses novel statistical methods to pre-filter the enormous amount of mutational data from human cancers, and then directly validates these mutations as relevant to cancer in organoid cultures. The applicability of the statistical and organoid culture methods across a wide variety of cancers, combined with the ability to perform high-throughput, massively parallel organoid studies, will allow very large numbers of genes to be evaluated, and therapeutic compounds to be screened.
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