biostudies-literatureBahrambeigi VNational Cancer Institute (NCI)Break Through Cancer (BTC)Break Through CancerNational Cancer InstituteNCI NIH HHS1719-1732https://www.ebi.ac.uk/biostudies/studies/S-EPMC11096054biostudies-literatureUnknown84(10)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 opportunities for tumor state interrogation and molecular subtyping. Through the application of deep learning algorithms to the deconvolved transcriptomes of RNA within plasma extracellular vesicles (evRNA), we successfully predicted consensus molecular subtypes in patients with metastatic colorectal cancer. Analysis of plasma evRNA also enabled monitoring of changes in transcriptomic subtype under treatment selection pressure and identification of molecular pathways associated with recurrence. This approach also revealed expresseCancer researchTranscriptomic Profiling of Plasma Extracellular Vesicles Enables Reliable Annotation of the Cancer-Specific Transcriptome and Molecular Subtype.PMC11096054R01 CA239342P30CA016672R01CA268380R01CA218230R01 CA218230T32 CA009599R01 CA268380P30 CA016672R01CA2393425P50CA221707P50CA221707T32CA0095995R01CA218230P50 CA221707Kopetz SDhebat SStephens BMKun WHenry JTRajapakshe KIMaitra ASun RHurd MWYang PLee JJKui NWang WGuerrero PABranchi VXu ZRuppin EBahrambeigi VfalseTranscriptomic 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 opportunities for tumor state interrogation and molecular subtyping. Through the application of deep learning algorithms to the deconvolved transcriptomes of RNA within plasma extracellular vesicles (evRNA), we successfully predicted consensus molecular subtypes in patients with metastatic colorectal cancer. Analysis of plasma evRNA also enabled monitoring of changes in transcriptomic subtype under treatment selection pressure and identification of molecular pathways associated with recurrence. This approach also revealed expresse2024-01-01T00:00:00Z2024 May2026-06-01T17:43:17.888Z2025-04-06T18:29:36.493ZS-EPMC110960543845124910.1158/0008-5472.CAN-23-407010.1158/0008-5472.can-23-4070