Plasma-derived extracellular vesicle analysis and deconvolution enable prediction and tracking of melanoma checkpoint blockade outcome

Immune checkpoint inhibitors (ICIs) show promise, but most patients do not respond. MIT researchers 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. The researchers 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. The researchers 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.

Tumor and EV RNA concordance

Characterization of transcriptomic similarities between patient melanomas and time-matched plasma-derived EV. (A) Scatter plot displaying the relationship between expression values of tumors and plasma EV in a representative patient (patient 178) and a histogram for R2 between paired tumors and plasma EV across the cohort. If a patient had multiple samples of the same type at the same time point, then the samples were averaged before computing the R2. (B) Concordance was calculated using a low expression threshold cutoff for expressed versus non-expressed status (Materials and Methods). Genes expressed or not expressed in tissue and EV were considered concordant (gray), while a subset of transcripts were unique EV (red) or tumor (green). (C) Selected pathways from gene set enrichment comparison of EV (left) versus patient tumors (right). GO, gene ontology; MHC, major histocompatibility complex; JAK-STAT, Janus kinase–signal transducers and activators of transcription; ncRNA, noncoding RNA. (D) CIBERSORT-inferred deconvolution estimates for all pretreatment patient tumor and pretreatment patient plasma-derived EV samples using LM22 immune reference profiles. Technical replicates were averaged, and biological replicates were considered independently. The data are segregated into three categories based on the results of a Mann-Whitney U test between EV- and tumor-inferred CIBERSORT fractions for each deconvolved cell type.

Shi A, Kasumova GG, Michaud WA et al. (2020) Plasma-derived extracellular vesicle analysis and deconvolution enable prediction and tracking of melanoma checkpoint blockade outcome. Sci Adv 6(46), eabb3461. [article]

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