Identification of a TNIK-CDK9 Axis as a Targetable Strategy for Platinum-Resistant Ovarian Cancer

Up to 90% of high grade serous ovarian cancer (HGSC) patients will become resistant to frontline platinum chemotherapy, necessitating innovative treatments for recurrent disease. Here we leveraged the Benevolent artificial intelligence (AI) PlatformTM in combination with in-silico modeling and patient-derived chemotherapy-resistant 3D models to identify dual targeting of TNIK and CDK9 as a promising therapeutic approach. Combined TNIK and CDK9 knockdown markedly diminished chemotherapy-resistant cell viability and inhibition of TNIK and CDK9 by NCB-0846 could sensitize platinum-resistant cells to cisplatin and showed efficacy against a panel of clinically validated HGSC organoids. Additionally, CDK9 was found to be a mediator of canonical Wnt activity which can drive progression and resistance across multiple cancers including HGSC. In sum, an innovative AI approach resulted in the identification of a druggable TNIK-CDK9 axis that can target chemotherapy-resistant HGSC, presenting a model for further drug development or patient stratification of CDK9 inhibitors in clinical trials. To investigate the gene expression differences between a HGSC cell line and its cisplatin resistant counterpart. This was done in three replicates. We then performed pathway analysis using the data derived from this RNAseq dataset.