Plasma-derived extracellular vesicle analysis and deconvolution enable prediction and tracking of melanoma checkpoint blockade outcome
Alvin Shi, Gyulnara G. Kasumova, William A. Michaud, Jessica Cintolo-Gonzalez, Marta Díaz‐Martínez, Jacqueline Ohmura, Arnav Mehta, Isabel Chien, Dennie T. Frederick, Sonia Cohen, Deborah Plana, Douglas B. Johnson, Keith T. Flaherty, Ryan J. Sullivan, Manolis Kellis, Genevieve M. Boland
Abstract
Immune checkpoint inhibitors (ICIs) show promise, but most patients do not respond. We identify and validate biomarkers from extracellular vesicles (EVs), allowing non-invasive monitoring of tumor- intrinsic and host immune status, as well as a prediction of ICI response. We undertook transcriptomic profiling of plasma-derived EVs and tumors from 50 patients with metastatic melanoma receiving ICI, and validated with an independent EV-only cohort of 30 patients. Plasma-derived EV and tumor transcriptomes correlate. EV profiles reveal drivers of ICI resistance and melanoma progression, exhibit differentially expressed genes/pathways, and correlate with clinical response to ICI. We created a Bayesian probabilistic deconvolution model to estimate contributions from tumor and non-tumor sources, enabling interpretation of differentially expressed genes/pathways. EV RNA-seq mutations also segregated ICI response. EVs serve as a non-invasive biomarker to jointly probe tumor-intrinsic and immune changes to ICI, function as predictive markers of ICI responsiveness, and monitor tumor persistence and immune activation.