Abstract B013: Precision medicine platform to guide the treatment of NSCLC

Abstract Lung cancer (LC) remains the top cause of cancer-associated mortality worldwide, with a 10-year overall survival rate of only 5%. While most LCs are smoking related, 25% of non-small cell LC (NSCLC) are diagnosed in patients with little or no smoking history. Fusions involving anaplastic lymphoma kinase (ALK) are the oncogenic driver in ∼3–7% of NSCLC. While inhibitors targeting the kinase domain of ALK have proven effective, inevitably, resistance develops with limited subsequent efficacious options. We aimed to integrate multiomic characterization and drug sensitivity testing of minimally cultured NSCLC samples to provide a ranked list of most effective drugs for each patient. We developed a precision medicine platform (PMP) to screen patient-derived material (PDM) directly from the operating room with curated panels of drugs. PDM collected during clinically indicated procedures is plated in 3D-culture to generate patient-derived organoids (PDOs). PDOs are screened at therapeutically relevant doses, drawing from pharmacokinetic data for each drug. We have optimized an assay to rapidly screen for EML4-ALK fusions and can perform next-generation sequencing in ∼7 days to integrate with drug screening results. To date, we have screened 80+ NSCLC tissue/fluid collections and molecularly characterized 77 of these PDMs. Our cohort prioritized collection of tissue from patients with EML4-ALK LC, resulting in the collection of 14 distinct cases from patients, with 13 patients having progressed to 2nd line therapy or beyond. While our dataset is enriched in non-smokers (32/80 screened models), we observed only 4 cases of LC with mutations in EGFR. Our cohort included 12 models with mutations in KRAS, with ¼ of those occurring in never-smokers. We have demonstrated an ability to produce high quality drug screening data from low input samples (biopsies). In one case of EML4-ALK NSCLC, we were able to collect PDM from two distinct anatomic spaces (pleural effusion and peritoneal fluid) and screen with the same panel of drugs, with nearly identical results, highlighting the consistency of our assay. Our results recapitulate known resistance in samples previously exposed to therapy, demonstrating a strong negative predictive value. Sequencing identified diverse driver mutations in populations of both smokers and non-smokers. Our PMP captures robust results that are consistent with known clinical pathogenesis. Prioritization of drug screening using compounds with diverse inhibition mechanisms is critical when driver mutations are unknown to ensure we screened the most clinically relevant drugs for each individual tumor. We are currently collecting longitudinal data from enrolled patients in parallel with clinical trials to demonstrate the positive predictive value of our PMP. We additionally strive to fully demonstrate reproducibility to obtain Clinical Laboratory Improvement Amendments approval. Citation Format: Nathan M Merrill, Aaron M Udager, Angel Qin, Kiran Lagisetty, Liwei Bao, Xu Cheng, Hamadi Madhi, Ananya Banerjee, Marziyeh Salehi Jahromi, Laura Goo, Varun Kathawate, Bryce Vandenburg, Mary Horn, Derek Nancarrow, Tusharika Rastogi, Albert Liu, Ning Gu, Zhaoping Qin, Habib Serhan, Marisa Aikins, Vishal Navani, John Jefferies, Muhammad Sajawal Ali, Michael Monument, Johannes Kratz, Amber Smith, Andrew Chang, Gregory Kalemkerian, Stacy Fry, Peter Ulintz, Sunitha Nagrath, Peggy Hsu, Matthew B Soellner, Sofia D Merajver. Precision medicine platform to guide the treatment of NSCLC [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr B013.