bioRxivCancer Biology
Circulating tumor extracellular vesicles to monitor metastatic prostate cancer genomics and transcriptomic evolution
I. Casanova-Salas, S. Cordoba-Terreros, D. Aguilar, L. Agundez, J. Brandariz, N. Herranz, M. Gonzalez, R. Morales-Barrera, A. Sierra, M. Soriano-Navarro, P. Cresta, S. Simonetti, G. Rodrigues, S. Arce-Gallego, L. Delgado, I. Agusti, E. Castellano-Sanz, R. Mast, M. de Albert, A. Celma, A. Santamaria, L. Gonzalez, N. Castro, M. d. M. Suanes, J. Hernandez-Losa, , J. Mateo
Abstract
Extracellular vesicles (EVs) secreted by tumors are abundant in plasma, but their potential for interrogating the molecular features of tumors through multi-omic profiling remains widely unexplored. Genomic and transcriptomic profiling of circulating EV-DNA and EV-RNA isolated from a range of in-vitro and in-vivo models of metastatic prostate cancer (mPC) revealed a high contribution of tumor material to EV-loaded DNA/RNA. Findings were validated in a cohort of longitudinal plasma samples collected from mPC patients during androgen receptor signaling inhibitor (ARSI) therapy. EV-DNA genomic features recapitulated matched-patient biopsies and associated with clinical progression. We developed a novel approach to enable the transcriptomic profiling of EV-RNA (RExCuE). We report how the transcriptomic profile in mPC EV-RNA is enriched for tumor-associated transcripts when compared to same patient blood RNA and healthy individuals EV-RNA, and reflect early on-therapy tumor adaptation changes. Altogether, we show that EV profiling enables longitudinal transcriptomic and genomic profiling of mPC in liquid biopsy.
- organism: human
- assay: bulk RNA-seq
- assay: DNA methylation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
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