Genome sequencing has catalogued somatic alterations in human cancers and identified many putative tumor suppressor genes. However, simply identifying genomic alterations does not reveal their functional importance to cancer growth. Genetically engineered mouse models uniquely enable the introduction of defined genetic alterations into normal adult cells, which results in the initiation and growth of tumors entirely within their natural in vivo setting. However, conventional Cre/Lox-based systems are not readily scalable or sufficiently quantitative to investigate the deluge of candidate genes being uncovered by genome sequencing. To increase the scope and precision of in vivo cancer modeling, we have developed methods to integrate conventional genetically engineered mouse models, CRISPR/Cas9-based somatic genome engineering, and quantitative genomics with mathematical approaches. Tumor barcoding coupled with CRISPR/Cas9-mediated gene inactivation and high-throughput barcode sequencing (Tuba-seq) enables the investigation of multiple tumor genotypes in parallel, as well as the quantification of cancer initiation and growth. We have applied these methods to analyze the impact of inactivating many diverse genes in parallel, uncover novel functional tumor suppressors, and map the genetic interactions that drive cancer cell fitness. We are developing and optimizing other multiplexed techniques to generate additional dimensions of information across all aspects of cancer initiation, growth, and therapy responses.