Abstract 223: Precision medicine-based platform to guide the treatment of EML4-ALK fusion lung cancers and other NSCLC

Abstract Background: 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, in the US, 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 (TKIs) have proven extremely effective, inevitably, resistance develops with limited effective treatment options. Additionally, NSCLCs without identified molecular alterations have limited treatment options beyond radiation, immunotherapy, chemotherapy, and resection. Methods: We developed a precision medicine-based 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) and screened with drugs curated to each tumor type. 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 real time (~7 days) to integrate with drug screening results. Results: To date, we have screened 83 cases, including 8 EML4-ALK NSCLC. We have demonstrated an ability to produce high quality data from low input samples (biopsies). In one 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 reproducibility and consistency of our assay. Screening of EML4-ALK tumors which have progressed to second or higher line TKIs, demonstrate sensitivity to earlier generation ALK TKIs, a known phenomenon. Characterization of tumors with unknown clinical drivers identifies ~1/3 tumors with no prioritized variants and particularly poor response to chemotherapies. Our results recapitulate known resistance/progression in samples previously exposed to therapy, demonstrating a strong negative predictive value. Longitudinal assessment will be required to robustly assess positive predictive value (PPV). Conclusions: Our PMP captures robust and reproducible results that are consistent with known clinical pathogenesis. Moving forward, we are collecting longitudinal data from enrolled patients in parallel with clinical trials to demonstrate the PPV of our PMP. We additionally strive to demonstrate reproducibility to obtain Clinical Laboratory Improvement Amendments approval and to deliver results to patients and physicians to help guide clinical care. Citation Format: Nathan M. Merrill, Aaron Udager, Angel Qin, Kiran Lagisetty, Liwei Bao, Xu Cheng, Hamadi Madhi, Ananya Banerjee, Marziyeh Salehi Jahromi, Laura Goo, Varun Kathawate, Bryce Vandenburg, Marisa Aikins, Mark Slayton, Peter Ulintz, Zhaoping Qin, Chia-jen Liu, Habib Serhan, John Jefferies, Muhammad Sajawal Ali, Vishal Navani, Michael Monument, Johannes Kratz, Amber Smith, Andrew Chang, Gregory Kalemkerian, Sunitha Nagrath, Peggy Hsu, Matthew B. Soellner, Sofia D. Merajver. Precision medicine-based platform to guide the treatment of EML4-ALK fusion lung cancers and other NSCLC abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 223.