Implementing personalized lung cancer care with spatial profiling, patient derived organoids, and rationally designed drug testing.

e20521 Background: Lung cancer (LC) remains the leading cause of cancer-related death in 2024, with most LC diagnoses with non-small cell lung cancer (NSCLC) subtype. Tyrosine kinase inhibitors (TKI) and targeted therapy against oncogenic drivers (KRAS, EGFR, or ALK fusions, others) are effective initial therapies; however, nearly all treated patients eventually develop resistance to therapy. Key reasons are the focus on limited predictive biomarkers and lack of patient derived LC models that reflect the underlying heterogeneity and the various phenotypic cell states among subclones within and between patients. Methods: We report the establishment of a novel platform for personalized LC care by refining the resolution of molecular interrogation of diagnostic and resistance biopsies to the single cell level, and with spatial profiling using single cell spatial multiomic (scSpMO) and coupling these enhanced diagnostic tools with functional validation with rational designed drug screens in patient-derived organoids (PDOs). Results: For deriving LC PDOs in epithelial and tumor microenvironment (TME) conditions to faithfully maintain the histological and genetic features of their respective LC tissues, we developed enrichment conditions for LC PDOs guided by tumor mutation variants and activated pathways. PDOs are maintained under the same treatment condition in the clinic (e.g., Lorlatinib) and drug testing is tailored to identify potentially most effective next lines of therapy. With nine patients enrolled and others in ongoing studies, single cell PDOs (at > 80% establishment rate) were used to determine activated pathways, lineage plasticity and acquired resistance. Functional assays for acquired resistance such as PI3K/AKT signaling, Src kinase, BRAF fusion and MET hyperactivity allow the identification of potentially novel lines of therapy and treatment sequence and/or combinations for each patient, through the compassionate care program and/or trial participation. Conclusions: Our platform when empowered by combining datasets from scRNA-seq and scSpMO signatures, with validated functional drug responses in PDOs, offers the ability to perform high-content assays, predict and/or act on resistance to therapy for each patient, and provides a path for precision medicine-guided impact on patient care.