Precision Immunotherapy Organoid Modeling in Melanoma

Mamatha Serasanambati, Sherry Hsu, David Jung Han Lee, Saurabh Sharma, Allison Betof-Warner, Amanda R. Kirane

Introduction: Melanoma exhibits unpredictable responses to immune checkpoint inhibitors (ICI), often accompanied by high risks of adverse events, with biomarkers of therapeutic response remaining poorly defined. Leveraging patient-derived organoids (PDO), we investigate novel predictive strategies employing secondary cellular co-culture models, a complex challenge due to need to propagate patient-faithful immune cells and tumor cells. Furthermore, we examine technical variation by Fine Needle Aspiration (FNA), 2-dimensional culture (Fig. 1A), and air-liquid interface for optimization. 

Methods: Melanoma PDO, derived from tumor biopsies or surgical specimens, were characterized on day 0 and after development in vitro using immunofluorescence. Impact of treatment with anti-PD-1, LAG3, and combinations of ICI was measured by cell viability, confluency, and size and compared to clinical behavior of patients. 

Results: Organoids recapitulated parental melanoma tissues. Preliminary findings indicated significant reductions in organoid viability and size following treatment with ICI in known responders (Fig. 1B). Anti-tumor cytotoxicity was markedly increased with combination of anti-PD-1 with LAG3, matching clinical evidence of complete response for combination therapy for following PD-1 resistance (Fig. 1C). We confirm persistence of key immune players at days 14, 30, and 45 to validate expansion of our method as a clinically predictive tool and interrogation of patient-specific mechanisms of resistance. 

Conclusion: PDOs represent a promising tool for identifying therapeutic vulnerabilities within the tumor microenvironment and uncovering new markers associated with melanoma therapy resistance. Further investigations will expand a larger cohort of neoadjuvantly treated patients to determine prospective utility of our assay to provide high through-put drug response prediction.