Abstract 4282: Deconvolution of plasma-derived exosomes for tracking and prediction of immunotherapy across multiple tissues
Authors: Shi Alvin , Kasumova Gyulnara , Chien Isabel , Cintolo-Gonzalez Jessica , Frederick Dennie T. , Alpatov Roman , Michaud William A. , Plana Deborah , Corcoran Ryan , Flaherty Keith , Sullivan Ryan , Kellis Manolis , Boland Genevieve 
Published: August 2018

There is a critical need for robust and minimally invasive biomarkers for predicting and monitoring tumor progression and response to treatment. Transcriptomes of plasma-derived exosomes (PDEs) are suitable candidates to fulfill such a role, since they contain a subtranscriptome of their cell of origin, and, since nearly all cell types secrete exosomes, this allows for the potential monitoring of multiple cell types concurrently. However, a major issue preventing the widespread adoption of plasma-derived exosome as biomarkers is that observed plasma exosomes actually result from a mixture of exosomes from multiple cell types. This confounds detailed dissection and interpretation of putative plasma-derived exosomal biomarkers. To address this issue, we develop a two-component Bayesian deconvolution model to infer the tumor-derived and immune-derived exosomal contribution to the observed mixed plasma-derived exosome signal. Our model leverages transcriptomic information from 3 different sources: (1) paired patient bulk and plasma-derived exosomes, (2) paired cell-line tumor and cell-line tumor-derived exosomes, and (3) healthy control plasma-derived exosomes to learn gene-by-gene mixing fractions between tumor and immune components and the mapping from tumor to tumor-derived exosomes transcriptomic profiles. Using this information, we are able to further infer the patient-specific tumor-derived and immune-derived exosomal transcriptomic profiles for each gene. The outputs from our model enable us to derive tumor-specific and immune-specific exosomal biomarkers. We first show that our model is performant in an extensive set of in silico simulations. Next, we applied our model to transcriptomes collected prior to and during aPD1 immunotherapy treatment from a pilot cohort of N=44 patients (N=29 responders, N=15 nonresponders) with metastatic melanoma. Analysis of our deconvolved profiles yields novel and biologically informative immune-derived and tumor-derived exosomal biomarkers that predict immunotherapy success. Moreover, time-series analysis of the deconvolved profiles show that we are able to identify significantly different tumor and immune related genes whose time dynamics differ significantly between responders and nonresponders, suggesting that plasma-derived exosomes can enable longitudinal tracking of both immune and tumor components of immunotherapy response. Finally, we show that a more sophisticated extension of our deconvolution model is able to provide an estimate of global tumor fraction for each patient, potentially allowing us to infer tumor burden through plasma-derived exosomal transcriptomic signatures. Overall, our plasma-derived exosomal deconvolution model paves the way for more widespread usage of plasma-derived exosomes as a clinical monitoring prediction and monitoring tool.

Citation Format: Alvin Shi, Gyulnara Kasumova, Isabel Chien, Jessica Cintolo-Gonzalez, Dennie T. Frederick, Roman Alpatov, William A. Michaud, Deborah Plana, Ryan Corcoran, Keith Flaherty, Ryan Sullivan, Manolis Kellis, Genevieve Boland. Deconvolution of plasma-derived exosomes for tracking and prediction of immunotherapy across multiple tissues [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4282.