We have developed several GEM and GEM-derived allograft (GDA) cancer models for preclinical biomarker and therapeutic development. Our major accomplishments include: Generation of 6 driver-specific GEM and GDA metastatic human-relevant SEOC models along with multiple cell cultures for each Inducible models were shown to develop SEOC that resembles the human disease in both molecular and biological properties. Models that retain wild type BRCA1 and BRCA2 and models that are deficient in BRCA1 or 2 were established. Models were validated to respond to PARP inhibition with outcomes similar to those in humans; i.e. tumors deficient in BRCA1 were responsive to PARP inhibition, whereas tumors expressing wild type BRCA1 did not. Generation of human-relevant GEM and GDA primary GBM models and cell lines Tractable intracranial transplant GBM models were developed and therapeutic evaluation workflows were established. The combination of MEK and PI3 kinase inhibition were shown to inhibit tumor growth and extend survival, whereas inhibition of either alone proved less effective. Development of a human EGFR-driven erlotinib resistant focally evolving lung adenocarcinoma (LA) model All previously established models of this type develop diffuse adenocarcinoma lesions, making it difficult to follow disease for enrollment into drug studies and to monitor and quantify efficacy. We developed a model wherein the erlotinib resistant human EGFRL858R;T790M drives focal development of LA. The model has been validated for increased accuracy of tumor development and efficacy evaluation. Generation/characterization of GEM-Lung adenocarcinoma models driven by 3 distinct human EML4/ALK fusion variants. Transgenic mice harboring human EMK4/Alk fusion genes representing the most frequent human translocations and crisotinib-resistant forms were generated. Mice were shown to express these proteins specifically in lung leading to the development of LA. Generation/characterization of metastatic GDA-PDAC model and cell cultures. Cell cultures were established from PDAC that developed in the KPC GEM model (Tuveson and Hingorani). Cells transplanted orthotopically into the pancreas of recipient immunocompetent mice develop into PDAC with characteristics of the parental tumor. This model has the highest metastatic rate of any GEM PDAC model reported thus far. Generation/characterization of 2 missense mutant p53 knock-in models that induce from wildtype to mutant alleles. In previously existing inducible p53 missense mutant GEMs, the engineered allele is a p53 null allele that is induced to express the mutant protein upon Cre activation. However, during spontaneous cancer development in humans, wild type p53 converts directly to mutant p53. We constructed GEM strains for three of the most common p53 missense mutations in human cancers R172H, R270H, and R270C). Generation/characterization of GEM and GDA PDAC models harboring the above inducible p53 mutant alleles PDAC models harboring these p53 alleles were generated by crossing the alleles into mice harboring the inducible RasG12D and PDX-Cre alleles. Mice harboring the common p53-172 mutant have been characterized and shown to develop PDAC with properties similar to the KPC model. Generation of a tool mouse harboring an inducible imaging probe A strain that harbors a Cre-inducible luciferase allele at the Rosa26 locus has been generated and validated to express imagable levels of luciferase in a tissue specific manner that depends on the specificity of Cre expression/ Establishment of ES technology to generate GEM cohorts ES cells generated from blastocysts out of the penultimate cross of the 4-allele GEM-GBM model were used to generate chimeric mice that developed GBM identical to the parental GEM model. Adaptation of CRISPR technology for high efficiency in vivo allele modification CRISPR technology was optimized and has been used to develop a melanocyte-specific CreER driver mouse strain. Development of mouse colony and preclinical evaluation management databases Two integrated databases were developed tor managing (1) efficient GEM breeding and maintenance, and (2) preclinical therapeutic evaluation, workflow execution, and data collection.
