Abstract

Abstract The large majority of high grade serous ovarian cancer (HGSOC) patients will eventually become resistant to platinum-based chemotherapy, highlighting the urgent need to develop novel therapeutics targeting recurrent, platinum-resistant disease. To study this, our lab generated two patient derived platinum-resistant tumor spheres from platinum-sensitive primary patient cells to better understand the biology behind recurrent disease and identify novel therapeutic targets. Utilizing an artificial intelligence (AI) powered model, we screened a library that consisted of compounds predicted to target known or postulated resistance mechanisms and involved a novel therapeutic target. From this screen, we identified a TNIK kinase inhibitor as the only compound to effectively kill HGSOC spheres while sparing non-malignant fallopian tube cells. TNIK plays a key role in Wnt pathway activation, which our lab has previously shown to be critical in driving cancer stemness and epithelial ovarian cancer platinum resistance. Additionally, patient data shows that TNIK is amplified in ovarian cancer more than any other cancer type and is highly activated in ovarian cancer patients. We found that this compound was able to decrease overall Wnt activity in our platinum-resistant cells and could resensitize our platinum-resistant cells to cisplatin. This work demonstrates the abilities of using AI to expedite the drug discovery process as well as uncover novel therapeutic targets which may lead to future therapeutic strategies for HGSOC patients who no longer respond to standard of care platinum chemotherapy. Citation Format: Noah Puleo, Michele Cusato, Dylan Carvette, Chantelle Husdon, Analisa DiFeo. Unraveling the function of TRAF2 and NCK interacting kinase (TNIK) in high-grade serous ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1679.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call