"Novel Mouse Models for Quantitative Understanding of Baseline and Therapy-Driven Evolution of Prostate Cancer Metastasis"

Weill Medical Coll Of Cornell Univ, New York NY

Investigators

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Abstract

PROJECT SUMMARY / ABSTRACT On average, a man dies from PCa every 16 minutes, mainly due to development of secondary malignant growths outside of the primary cancer site, known as metastases. The cornerstone of PCa treatment is androgen deprivation therapy (ADT). ADT temporarily halts PCa, but leads to resistance in nearly all cases, resulting in castration-resistant PC (CRPC). CRPC then undergoes further evolution of metastatic subclones and results in incurable disease. Research techniques revealing resistance mechanisms and clonal evolution of metastatic PCa are lacking due to the limited capacity of current animal models to mimic PCa evolution in its native microenvironment as well as inefficient methods for tracing subclonal evolution. Therefore, we developed EvoCaP (!Evolution in Cancer of the Prostate”), a mouse model of endogenous metastasis that recapitulates human PCa genetically, by using PTEN/TP53 co-deletions enriched in metastatic patients, and phenotypically, by focal initiation of primary disease progressing to bones, lungs, lymph nodes and liver metastases. Our model uses a lentiviral platform - LV.CreBC10 carrying: (1) Cre (Pten/Trp53 co- deletions; activation of Cas9, fluorescence and luminescence markers); (2) Barcode with ten sites for marking by Cas9 (BC10); (3) RNA guide specifically marking BC10; and (4) guide or short hairpin RNA for testing metastatic drivers. Luminescence (FLuc) permits continuous tracking of disease progression and fluorescence (eGFP) allows for specific sorting of cancer cells. BC10 represents a synthetic array of on-target sites, in order of decreasing activity, for the RNA guide that attracts Cas9 to generate subsequently specific edits. To streamline barcode analysis, we have established an R package - EvoTraceR. This comprehensive system enables: (1) the profiling of cancer cells based on shared mutational patterns in primary and metastasis; and (2) the building of phylogenetic trees to track evolution toward metastases in a robust and flexible way. Our central hypothesis is that differences in distinct molecular and phenotypical clonal architectures will be precisely detected between primary and metastatic sites depending on therapy status, enabling the inhibition of metastasis and/or resistance promoting genes and pathways. Our analyses will establish and mechanistically validate drivers of metastatic clonal expansion caused by Pten/Tp53-loss (basal) and also investigate how evolutionary pressure from therapy (ADT), applied at different stages of PCa, leads to the emergence of resistant clones. We will then use Cas9/guide (g)RNA and inducible short hairpins to target genes altered in those expanding clones to identify drivers of both treatment-naive and treatment-induced PCa metastasis. EvoCaP can feasibly track molecular evolution and validate targets for drug development, which may lead to identification of novel metastatic driver genes and pathways. Thus, therapies could be applied in: (1) primary diseases for early detection and interruption of metastases development; and (2) already existing metastases. Importantly, technologies developed in this project can also be applied to other types of metastatic cancers.