Abstract Cancer tissues harbor thousands of mutations, and a given oncogene may be mutated at hundreds of sites across different samples. Most of these somatic mutations are expected to be inconsequential passenger mutations that reflect the general instability of the tumors. The discovery of most of the currently known driver mutations has been facilitated by their accumulation in mutation hot-spots within their respective genes. However, a vast majority of mutations in cancer tissues are rare and their functional significance remains unknown. Several lines of in vitro and clinical evidence also indicate that there is a significant number of, as yet unidentified, activating driver mutations which could serve as predictive markers in oncology. We are developing our in vitro Screen for Activating Mutations (“iSCREAM”)[1] further and advancing it to better recapitulate natural intratumor heterogeneity by taking the system in vivo. To establish the in vivo model for screening activating mutations we utilize the SALE-Y cells which are immortalized human small airway epithelial cells harboring an activating YAP1 variant[2]. In immunocompromised mice, SALE-Y cells fail to form tumors up to 120 days, but they become tumorigenic after transduction of mutations activating the EGFR-MAPK signaling pathway[2]. We will use the SALE-Y cell background for transduction of an expression library encoding thousands of different randomly mutated variants of the kinase to be tested. EGFR will be used to set up the model because: i) EGFR mutants are known to of transforming the SALE-Y cells[2] and ii) we have previously used EGFR as a model to set up the in vitro screen[1]. The SALE-Y expressing the random collection of EGFR variants will be inoculated subcutaneously into immunocompromised mice, and the activating mutations will be given an opportunity to outcompete passenger mutations during subsequent expansion and suvival of the xenograft. Genomic DNA will be harvested from the tumor tissue and subjected to targeted next-generation sequencing allowing unbiased identification of enriched activating mutations promoting tumor growth.