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Arafa, A. T.

Publications and source records attributed to Arafa, A. T..

2 recordsLinked to original sources
bioRxiv2026

Dynamics of circulating tumor cell subsets defined by PSMA and EpCAM predict survival in metastatic castration-resistant prostate cancer patients treated with 177Lu-PSMA-617

Background: 177Lu-PSMA-617 radioligand treatment represents a major advancement in the management of metastatic castration-resistant prostate cancer (mCRPC), but key questions remain about tumor-cell dynamics and evolution in patients who have received this drug. Here, we used a circulating tumor cell (CTC) capture assay to assess the prognostic capacity of CTC subset dynamics following 177Lu-PSMA-617 treatment in a cohort of mCRPC patients. Methods: Using an AI-empowered holographic imaging platform combined with in-flow protein marker analysis, we prospectively analyzed serial CTC samples from patients receiving 177Lu-PSMA-617 therapy at pre-treatment and on-treatment time points (n=39 of 100 enrolled patients). PSMA and EpCAM proteins in CTCs were determined by immunofluorescence staining. Proportions and absolute counts of CTC subsets delineated by PSMA and EpCAM proteins were assessed in PSA50 responders (n=19) and non-responders (n=20). Within-patient changes in CTC subsets were compared using Wilcoxon signed-rank tests with Benjamini-Hochberg correction. Associations of CTC subset abundance and dynamics with overall survival were assessed using Kaplan-Meier estimates with log-rank tests and univariable Cox proportional hazards models. Results: PSMA and EpCAM stratified CTCs into subsets, some of which were associated with survival outcomes following 177Lu-PSMA-617. We observed an increase in the proportion of PSMA-/EpCAM+ CTCs following 177Lu-PSMA-617, with a larger increase occurring in PSA50 responders (6.2% to 18.7%; p=0.041) than non-responders (7.9% to 15.4%; p=0.023). This corresponded to a decrease in the proportion of PSMA+/EpCAM- CTCs, whereas the proportion of PSMA+/EpCAM+ CTCs did not decline. On-treatment counts of >5 CTCs, relative to [≤]5 CTCs, were associated with shorter overall survival in the PSMA+/EpCAM+ (HR 3.56, 95%CI 1.32-9.60, p=0.012) and PSMA+/EpCAM- (HR 7.50, 95%CI 0.96-52.99, p=0.054) subsets. Finally, on-treatment declines in PSMA+/EpCAM+ (HR 0.74, p=0.49) and PSMA+/EpCAM- (HR 0.61, p=0.215) CTC subsets were not significantly associated with overall survival relative to sustained high levels of these cells. Conclusions: In mCRPC patients receiving 177Lu-PSMA-617, dynamic changes in CTC subsets delineated by PSMA and EpCAM expression may have prognostic value, justifying additional evaluation.

cancer biology↗
bioRxiv2025

Proteomic profiling of extracellular vesicles distinguishes prostate cancer molecular subtypes

Prostate cancer is the most common non-cutaneous cancer among men in the United States. Most prostate cancers are driven by androgen receptor (AR) signaling, but there are an increasing number of cases that lose AR and gain neuroendocrine (NE) features (AR-/NE+) or lack both (AR-/NE-). These latter subtypes are particularly aggressive and lethal. Extracellular vesicles (EVs) have shown great potential as biomarkers for noninvasive liquid biopsy assays, as EVs contain biomolecules from their cells of origin. Here, we used a shotgun proteomics approach with mass spectrometry to interrogate the global proteome of EVs isolated from prostate cancer cell lines reflecting diverse clinical subtypes, including AR-/NE+ and AR-/NE-models. We identified 3,952 EV proteins, which clustered largely by tumor subtype and provided enough proteomic coverage to derive classic gene signatures of AR or NE identity that are of high relevance for prostate cancer prognostication. EVs isolated from AR+ cells displayed high levels of proteins regulated by AR and mTOR signaling. EVs isolated from AR-/NE+ cells contained known NE markers such as SYP and CHGA, whereas EVs from AR-/NE-models were enriched in basal cell markers and proteins that regulate epithelial-to-mesenchymal transition (EMT). We integrated our cell line data with recently published EV proteomics data from 27 advanced prostate cancer patients and found 2,733 overlapping proteins including cell surface markers relevant to prostate cancer, AR activity indicators, and proteins enriched in specific subtypes (AR+, AR-/NE-, AR-/NE+). This approach is especially promising for rare cancer subtypes, such as prostate cancers that lose AR- related features and gain NE features, so as to optimize the use of these liquid biopsy samples for clinical decision making.

cancer biology↗