EDP-317

Protein kinases play a vital role in a wide range of cellular processes, and compounds that inhibit kinase activity emerging as a primary focus for targeted therapy development, especially in cancer. Consequently, efforts to characterize the behavior of kinases in response to inhibitor treatment, as well as downstream cellular responses, have been performed at increasingly large scales. Previous work with smaller datasets have used baseline profiling of cell lines and limited kinome profiling data to attempt to predict small molecule effects on cell viability, but these efforts did not use multi-dose kinase profiles and achieved low accuracy with very limited external validation. This work focuses on two large-scale primary data types, kinase inhibitor profiles and gene expression, to predict the results of cell viability screening. We describe the process by which we combined these data sets, examined their properties in relation to cell viability and finally developed a set of computational models that achieve a reasonably high prediction accuracy (R2 of 0.78 and RMSE of 0.154). Using these models, we identified a set of kinases, several of which are understudied, that are strongly influential in the cell viability prediction models. In addition, we also tested to see if a wider range of multiomics data sets could improve the model results and found that proteomic kinase inhibitor profiles were the single most informative data type. Finally, we validated a small subset of the model predictions in several triple-negative and HER2 positive breast cancer cell lines demonstrating that the model performs well with compounds and cell lines that were not included in the training data set. Overall, this result demonstrates that generic knowledge of the kinome is predictive of very specific cell phenotypes, and has the potential to be integrated into targeted therapy development pipelines.

The hippo signaling pathway is a central tumor suppressor cascade frequently inactivated in selected human cancers, leading to the aberrant activation of TEAD transcription factors. Whereas several TEAD auto-palmitoylation inhibitors are currently in development, a comprehensive assessment of this novel drug-modality is missing. Here, we report a comparative analysis among six TEADi(s) using cell-based and biochemical assays in Hippo pathway deficient mesothelioma. Our analysis revealed varying potency and selectivity across TEADi, also highlighting their limited efficacy. To overcome this limitation, we performed an unbiased, quantitative high-throughput drug screening by combining the TEADi VT-103 with a library of approximately 3000 oncology-focused drugs. By exploiting this library’s mechanistic redundancy, we identified several drug-classes robustly synergized with TEADi. These included glucocorticoid-receptor (GR) agonists, Mek1/2 inhibitors, mTOR inhibitors, and PI3K inhibitors, among others. Altogether, we report a coherent single-agent dataset informing on potency and selectivity of TEAD-palmitoylation inhibitors as single-agents. We also describe a rational pipeline enabling the systematic identification of TEAD druggable co-dependencies. This data should support the pre-clinical development of drug combination strategies for the treatment of Hippo-deficient mesothelioma, and more broadly, for other cancers dependent on the oncogenic activity of YAP/TEAD.