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 in vitro and in vivo models of metastatic prostate cancer (mPC) reveal a high contribution of tumor material to EV-loaded DNA/RNA, validating the findings in two cohorts of longitudinal plasma samples collected from patients during androgen receptor signaling inhibitor (ARSI) or taxane-based therapy. EV-DNA genomic features recapitulate matched-patient biopsies and circulating tumor DNA (ctDNA) and associate with clinical progression. We develop a novel approach to enable transcriptomic profiling of EV-RNA (RExCuE). We report how the transcriptome of circulating EVs is enriched for tumor-associated transcripts, captures certain patient and tumor features, and reflects on-therapy tumor adaptation changes. Altogether, we show that EV profiling enables longitudinal transcriptomic and genomic profiling of mPC in liquid biopsy.
Subject(s)
Extracellular Vesicles , Genomics , Prostatic Neoplasms , Transcriptome , Male , Humans , Prostatic Neoplasms/genetics , Prostatic Neoplasms/pathology , Prostatic Neoplasms/blood , Extracellular Vesicles/genetics , Extracellular Vesicles/metabolism , Genomics/methods , Animals , Gene Expression Profiling/methods , Neoplasm Metastasis , Mice , Biomarkers, Tumor/genetics , Biomarkers, Tumor/blood , Liquid Biopsy/methods , Circulating Tumor DNA/genetics , Circulating Tumor DNA/blood , Gene Expression Regulation, Neoplastic , Cell Line, TumorABSTRACT
Breast cancer is the cancer with the most incidence and mortality in women. microRNAs are emerging as novel prognosis/diagnostic tools. Our aim was to identify a serum microRNA signature useful to predict cancer development. We focused on studying the expression levels of 30 microRNAs in the serum of 96 breast cancer patients vs. 92 control individuals. Bioinformatic studies provide a microRNA signature, designated as a predictor, based on the expression levels of five microRNAs. Then, we tested the predictor in a group of 60 randomly chosen women. Lastly, a proteomic study unveiled the overexpression and downregulation of proteins differently expressed in the serum of breast cancer patients vs. that of control individuals. Twenty-six microRNAs differentiate cancer tissue from healthy tissue, and 16 microRNAs differentiate the serum of cancer patients from that of the control group. The tissue expression of miR-99a, miR-497, miR-362, and miR-1274, and the serum levels of miR-141 correlated with patient survival. Moreover, the predictor consisting of miR-125b, miR-29c, miR-16, miR-1260, and miR-451 was able to differentiate breast cancer patients from controls. The predictor was validated in 20 new cases of breast cancer patients and tested in 60 volunteer women, assigning 11 out of 60 women to the cancer group. An association of low levels of miR-16 with a high content of CD44 protein in serum was found. Circulating microRNAs in serum can represent biomarkers for cancer prediction. Their clinical relevance and the potential use of the predictor here described are discussed.
