The deluge of multi-scale ?omics? data from The Cancer Genome Atlas Project (TCGA) and other cancer profiling projects has revealed remarkable genetic and epigenetic complexity and tremendous intrapatient variation. Accordingly, a particularly acute need exists for accurate, scalable human cancer models that can functionally interrogate these extensive datasets, identify driver oncogenic events from benign passengers and characterize their relevance to treatment response. For the last four years the Stanford Cancer Target Discovery and Development (CTD2) Center has pursued human ?organoid? culture methods for cancer modeling and driver oncogene discovery. Primary 3D organoid cultures afford the unusual opportunity to initiate cancer de novo within the epi/genetic ?tabula rasa? of cultured primary human wild-type tissue, versus the corresponding and often poorly-defined complexity of long- passaged 2D cancer cell lines. This creates a highly defined baseline for cancer modeling and functional driver oncogene validation that is leveraged throughout. Our overall approach applies state-of-the-art systems biology and robust computational resources to large-scale cancer profiling datasets, thus nominating candidate drivers that undergo direct functional evaluation in human organoid culture. This experimental scope leverages a highly synergistic team of Calvin Kuo (reporting PI, organoids), Hanlee Ji (multi-PI, cancer ITH, genomics), Christina Curtis (multi-PI, tumor evolution, cancer systems biology), Olivier Gevaert (cancer systems biology, epigenetics) and Michael Bassik (high-throughput functional genomics). Accordingly, Aims 1 and 2 couple bioinformatic prioritization of TCGA copy number alteration (CNA) and methylation data for driver discovery via organoid-based barcoded lentiviral screens and orthogonal cDNA, shRNA and CRISPR approaches.
Aim 3 exploits the ability to longitudinally observe de novo genomic and epigenomic evolution in oncogene- engineered wild-type organoids to nominate networks of cooperating oncogenes that undergo iterative organoid functional validation. Lastly, Aim 4 explores the utility of organoids to model de novo treatment resistance, using archetypal targeted and chemotherapy perturbagens as proof-of-principle and employing single cell RNA- seq/intratumoral heterogeneity and exome sequencing endpoints.
Cancer has extremely complex origins with a multitude of mutations. We utilize computational approaches to scan large-scale cancer DNA sequencing datasets and nominate candidate mutations that are directly tested for cancer-causing activity in three- dimensional ?organoid? cultures of human tissues, thus discovering new cancer therapeutic targets.
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