Abstract

Simple SummaryMetastatic castration-resistant prostate cancer (mCRPC) is the most aggressive and deadly form of prostate cancer. As a bone-predominant metastatic disease, liquid biopsy-based biomarkers have advantages in monitoring cancer dynamics. Previous studies have demonstrated the associations between circulating tumor cells (CTCs) and mCRPC outcomes, but little is known about the prognostic value of CTC-clusters. In this study, we investigated the associations of CTCs and CTC-clusters with mCRPC prognosis, individually and jointly, using longitudinal samples. We confirmed the associations of CTC counts with mCRPC outcomes in both baseline and longitudinal analyses. Our results also showed that the presence of CTC-clusters alone had prognostic value and that CTC-clusters may further improve CTC-based prognostic stratification in mCRPC. Our findings suggest the potential of combing CTC and CTC-clusters as non-invasive means to monitor progression and predict survival in mCRPC and build a premise for in-depth genomic and molecular analyses of CTCs and CTC-clusters.Liquid biopsy-based biomarkers have advantages in monitoring the dynamics of metastatic castration-resistant prostate cancer (mCRPC), a bone-predominant metastatic disease. Previous studies have demonstrated associations between circulating tumor cells (CTCs) and clinical outcomes of mCRPC patients, but little is known about the prognostic value of CTC-clusters. In 227 longitudinally collected blood samples from 64 mCRPC patients, CTCs and CTC-clusters were enumerated using the CellSearch platform. The associations of CTC and CTC-cluster counts with progression-free survival (PFS) and overall survival (OS), individually and jointly, were evaluated by Cox models. CTCs and CTC-clusters were detected in 24 (37.5%) and 8 (12.5%) of 64 baseline samples, and in 119 (52.4%) and 27 (11.9%) of 227 longitudinal samples, respectively. CTC counts were associated with both PFS and OS, but CTC-clusters were only independently associated with an increased risk of death. Among patients with unfavorable CTCs (≥5), the presence of CTC-clusters signified a worse survival (log-rank p = 0.0185). mCRPC patients with both unfavorable CTCs and CTC-clusters had the highest risk for death (adjusted hazard ratio 19.84, p = 0.0072), as compared to those with <5 CTCs. Analyses using longitudinal data yielded similar results. In conclusion, CTC-clusters provided additional prognostic information for further stratifying death risk among patients with unfavorable CTCs.

Highlights

  • Prostate cancer (PCa) is the most commonly diagnosed cancer and the second leading cause of cancer-related death among men in the United States [1]

  • A total of 64 metastatic castration-resistant prostate cancer (mCRPC) patients were included in this analysis (Table 1)

  • There were 44 (68.8%) patients treated by androgen receptor signaling inhibitors (ARSi) and 16 (25%) patients treated by cytotoxic reagents after the baseline blood draw

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Summary

Introduction

Prostate cancer (PCa) is the most commonly diagnosed cancer and the second leading cause of cancer-related death among men in the United States [1]. Remarkable progress has been made in the use of tissue-based molecular analyses (i.e., precision genomics) to guide treatment decisions of many cancers. Non-invasive biomarkers based on liquid biopsy samples, such as circulating tumor cells (CTCs), need to be developed to guide mCRPC treatment decisions and monitor treatment response or resistance. Clustered CTCs in peripheral blood have been reported in patients with different cancer types, but at lower frequencies than single CTCs, whereas within primary lesions of epithelial tumors, collective migration is more prominent than single-cell motility [21]. Based on an ongoing mCRPC cohort with longitudinal samples, we conducted, to our best knowledge, the first study that evaluated the prognostic value CTC-clusters in high-risk mCRPC patients with high CTC levels

Patient Characteristics
Associations between Baseline CTCs and Clinical Outcomes
Prognostic Stratification Using Longitudinal CTCs and CTC-Clusters
Study Population
CTC and CTC-Cluster Enumeration
Conclusions
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