Urinary extracellular vesicles for high-precision bladder cancer subtyping and prognosis

Abstract Bladder cancer exhibits molecular heterogeneity that complicates early diagnosis and prognosis, and drives confounding clinical outcomes. Non–muscle invasive and muscle-invasive subtypes, especially for intermediate to high grade, carry a 25 – 50% progression-free survival rate, underscoring the need for high precision prognostic strategy. Urinary extracellular vesicles (uEVs) are promising carriers of tumor-derived RNAs and proteins. However, significant challenges in studying uEVs arise from the diverse cellular origin of uEVs associated with the dynamic composition of urine, which presents roadblocks for developing the clinical utility of uEVs. We introduced an AI-driven EV liquid biopsy pipeline that integrates (1) standardized EV isolation via NanoPom magnetic beads, (2) transcriptomic profiling for molecular subtyping, and (3) prognostic scoring algorithm. In a discovery cohort of 16 bladder cancer patients including both MIBC and NMIBC, we compared NanoPom isolated uEVs with ExoEasy and Fujifilm MagCapture isolated uEVs, for identifying bladder cancer subtype-specific gene signatures, and externally validated them using UCSC Xena. The result outperformed currently reported bladder cancer diagnostic biomarkers from assays including Galeas, CxBladder, and Xpert. In a validation cohort of matched 7 patient plasma samples, we confirmed with plasma derived EVs for correlating with urinary EV biomarkers from NGS sequencing. The prognostic score stratified patients into low-, intermediate-, and high-grade risk groups based on Xena’s bladder cancer survival outcomes. Our AI-driven uEV liquid biopsy pipeline proves the concept for high precision bladder cancer subtyping and prognosis, which could potentially facilitate treatment decision and lead to advanced profiling of bladder tumor biology using uEV liquid biopsy. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study was funded by the National Institutes of Health grant NIGMS MIRA Award 1R35GM113794 (MH). Cystic Fibrosis Foundation, CFF HE21I0 (MH). University of Florida Health Cancer Center UFHCC GU pilot (MH). University of Florida Health Cancer Center Pilot Grant # AI-2022-02 (KG). National Institutes of Health grant NCI Award 5R01CA265907 (KG). Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRB of University of Florida gave ethical approval of this work, IRB# 20210240 I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability All raw and processed sequencing data generated by this study is available at the NCBI Gene Expression Omnibus (GEO# GSE308996). Computer code used in this manuscript is available at github.com/zfg2013/ExoMasso. All data produced in the present study are available upon reasonable request to the authors.