Transcriptomic Profiling of Plasma Extracellular Vesicles Enables Reliable Annotation of the Cancer-specific Transcriptome and Molecular Subtype

Longitudinal monitoring of patients with advanced cancers is crucial to evaluate both disease burden and treatment response. Current liquid biopsy approaches mostly rely on the detection of DNA-based biomarkers. However, plasma RNA analysis can unleash tremendous opportunity for tumor state interrogation and molecular subtyping. Through the application of deep learning algorithms to the deconvolved transcriptomes of plasma extracellular vesicles (EVs) RNA, we successfully predict consensus molecular subtypes in metastatic colorectal cancer (mCRC) patients and analogous RNA-based subtypes in patients with pancreatic ductal adenocarcinoma (PDAC). We further demonstrate the ability monitor changes in transcriptomic subtype under treatment selection pressure. Our approach also identified gene fusions and neoepitopes from plasma EVs, which were validated in matched tissue samples. These results demonstrate the feasibility of transcriptomic-based liquid biopsy platforms for precision oncology approaches, spanning from the longitudinal monitoring of tumor subtype changes to identification of expressed fusions and neoantigens as potential therapeutic targets, sans the need for tissue-based sampling. Statement of significance Direct analysis of tumor-encoded transcriptomes in liquid biopsies can unveil new opportunities for cancer detection, classification, and identification of therapeutic targets. We developed an approach to interrogate tumor molecular subtypes and their changes, as well as gene fusions and neoepitopes through the analysis of bulk RNA transcriptomic data from plasma EVs.