Combination of the proliferation platform ProLiFiler and the bioinformatic analysis platform Cancer Data Miner for preclinical investigation of inhibitors of the KRAS, PI3K and MDM2 signaling pathways

Background: Testing novel anti-cancer agents across large panel of tumor models covering genetic diversity of cancers is increasingly considered as a cornerstone of preclinical development. For this purpose, Reaction Biology developed “ProLiFiler” a standard proliferation panel of 140 cell lines (CLs) covering most common cancer types to evaluate anti-proliferative activity of novel drugs. Partnering has been made with 4HF Biotec and their in-silico platform, named “Cancer Data Miner”, to investigate and to understand molecular basis of drug sensitivity. Here we report the use of our platforms to realize integrative pharmacogenomic studies for three recent small molecules targeting major altered pathways in cancers. It includes SOS1::KRAS interaction inhibitor BI-3406, MDM2 inhibitor Nutlin-3a, and PI3K inhibitor Taselisib. Main goal of the study is to provide meaningful information for these three drugs regarding their efficacy and potency, the validation of their mechanism of actions (MOA), the suitable clinical indications, possible drug combinations and the predictive biomarkers of sensitivity or resistance.

Material and methods: The three compounds are tested for anti-proliferative activity in vitro in a 2D monolayer assay using the “ProLiFiler” CLs panel. For data analytics, the resulting in vitro data are loaded on the “Cancer Data Miner” platform and connected to CL annotations including whole exome mutations, chromosomal aberrations, gene expression profiles or drug sensitivity profiles.

Results: The drug response profiles will be reported for the three compounds individually and compared between them, showing respective efficacy, potency, and CL/cancer entity selectivity. Using the MOA Finder tool, we will correlate BI-3406, Nutlin-3a, and Taselisib individual IC50 profiles to those of more than 800 compounds with known MOA that are integrated on the platform. The analyses will show the drugs most closely related to the 3 compounds and that are expected to have similar MOA. With the biomarker discovery tools, we will run high throughput statistical analyses to reveal whole exome mutations, copy number variations and expression significantly associated with drug sensitivity/resistance. For interpretation, pathway and enrichment analysis will be presented. A focus will be made on key alterations like genes related to TP53-MDM2 and PIK3CA-PTEN pathways to evaluate their predictivity.

Conclusion: The present work will show the whole panel of analyses proposed by 140 CL-ProLiFiler and Cancer Data Miner complementary platforms, allowing to acquire key information at an early stage of drug development and helping to setup next steps such as selection of models for in vivo testing.