bioRxivCancer BiologyPreprint
Pan-cancer organoid validation of tumor outlier chromosomal amplification events
A. A. Salahudeen, K. Yuki, J. Seoane, A. T. Mah, A. R. Smith, K. Kolahi, S. M. De la O, D. J. Hart, J. Ding, Z. Ma, S. A. Barkal, N. D. Shukla, H. C. Zhang, M. A. Cantrell, A. Batish, T. Usui, D. E. Root, W. Hahn, C. Curtis, C. J. Kuo
Abstract
Somatic copy number gains are pervasive in many cancer types, yet their roles in oncogenesis are often poorly explored. This lack of understanding is in part due to broad extensions of copy gains across cancer genomes spanning large chromosomal regions, obscuring causal driver loci. Here we employed a multi-tissue pan-organoid modeling approach to validate candidate oncogenic loci identified within pan-cancer TCGA data by the overlap of extreme copy number amplifications with extreme expression dysregulation for each gene. The candidate outlier loci nominated by this integrative computational analysis were functionally validated by infecting cancer type-specific barcoded full length cDNA lentiviral libraries into cognate minimally transformed human and mouse organoids bearing initial oncogenic mutations from esophagus, oral cavity, colon, stomach, pancreas and lung. Presumptive amplification oncogenes were identified by barcode enrichment as a proxy for increased proliferation. Iterative analysis validated DYRK2 at 12q15, encoding a serine-threonine kinase, as an amplified head and neck squamous carcinoma oncogene in p53-/- oral mucosal organoids. Similarly, FGF3, amplified at 11q13 in 41% of esophageal squamous carcinomas, was validated in p53-/- esophageal organoids in vitro and in vivo with pharmacologic inhibition by small molecule and soluble receptor FGFR antagonists. Our studies establish the feasibility of pan-organoid contextual modeling of pan-cancer candidate genomic drivers, enabling oncogene discovery and preclinical therapeutic modeling.
- organism: human
- organism: mouse
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
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