Diagnostic leukapheresis reveals distinct phenotypes of NSCLC circulating tumor cells.

BACKGROUND: Circulating tumor cells (CTCs) hold immense promise for unraveling tumor heterogeneity and understanding treatment resistance. However, conventional methods, especially in cancers like non-small cell lung cancer (NSCLC), often yield low CTC numbers, hindering comprehensive analyses. This study addresses this limitation by employing diagnostic leukapheresis (DLA) to cancer patients, enabling the screening of larger blood volumes. To leverage DLA's full potential, this study introduces a novel approach for CTC enrichment from DLAs. METHODS: DLA was applied to six advanced stage NSCLC patients. For an unbiased CTC enrichment, a two-step approach based on negative depletion of hematopoietic cells was used. Single-cell (sc) whole-transcriptome sequencing was performed, and CTCs were identified based on gene signatures and inferred copy number variations. RESULTS: Remarkably, this innovative approach led to the identification of unprecedented 3,363 CTC transcriptomes. The extensive heterogeneity among CTCs was unveiled, highlighting distinct phenotypes related to the epithelial-mesenchymal transition (EMT) axis, stemness, immune responsiveness, and metabolism. Comparison with sc transcriptomes from primary NSCLC cells revealed that CTCs encapsulate the heterogeneity of their primary counterparts while maintaining unique CTC-specific phenotypes. CONCLUSIONS: In conclusion, this study pioneers a transformative method for enriching CTCs from DLA, resulting in a substantial increase in CTC numbers. This allowed the creation of the first-ever single-cell whole transcriptome in-depth characterization of the heterogeneity of over 3,300 NSCLC-CTCs. The findings not only confirm the diagnostic value of CTCs in monitoring tumor heterogeneity but also propose a CTC-specific signature that can be exploited for targeted CTC-directed therapies in the future. This comprehensive approach signifies a major leap forward, positioning CTCs as a key player in advancing our understanding of cancer dynamics and paving the way for tailored therapeutic interventions.

Extracellular vesicles as potential biomarkers for diagnosis and recurrence detection of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common primary malignant liver tumor and a leading cause of cancer-related deaths worldwide. However, current diagnostic tools are often invasive and technically limited. In the last decade, non-invasive liquid biopsies have transformed the field of clinical oncology, showcasing the potential of various liquid-biopsy derived analytes, including extracellular vesicles (EVs), to diagnose and monitor HCC progression and metastatic spreading, serving as promising novel biomarkers. A prospective single-center cohort study including 37 HCC patients and 20 patients with non-malignant liver disease (NMLD), as a control group, was conducted. Serum EVs of both groups were analyzed before and after liver surgery. The study utilized microbead-based magnetic particle sorting and flow cytometry to detect 37 characteristic surface proteins of EVs. Furthermore, HCC patients who experienced tumor recurrence (R-HCC) within 12 months after surgery were compared to HCC patients without recurrence (NR-HCC). EVs of R-HCC patients (n = 12/20) showed significantly lower levels of CD31 compared to EVs of NR-HCC patients (p = 0.0033). EVs of NMLD-group showed significantly higher expressions of CD41b than EVs of HCC group (p = 0.0286). The study determined significant short-term changes in CD19 dynamics in EVs of the NMLD-group, with preoperative values being significantly higher than postoperative values (p = 0.0065). This finding of our pilot study suggests EVs could play a role as potential targets for the development of diagnostic and therapeutic approaches for the early and non-invasive detection of HCC recurrence. Further, more in-depth analysis of the specific EV markers are needed to corroborate their potential role as diagnostic and therapeutic targets for HCC.

Training an automated circulating tumor cell classifier when the true classification is uncertain.

Circulating tumor cell (CTC) and tumor-derived extracellular vesicle (tdEV) loads are prognostic factors of survival in patients with carcinoma. The current method of CTC enumeration relies on operator review and, unfortunately, has moderate interoperator agreement (Fleiss' kappa 0.60) due to difficulties in classifying CTC-like events. We compared operator review, ACCEPT automated image processing, and refined the output of a deep-learning algorithm to identify CTC and tdEV for the prediction of survival in patients with metastatic and nonmetastatic cancers. Operator review is only defined for CTC. Refinement was performed using automatic contrast maximization CM-CTC of events detected in cancer and in benign samples (CM-CTC). We used 418 samples from benign diseases, 6,293 from nonmetastatic breast, 2,408 from metastatic breast, and 698 from metastatic prostate cancer to train, test, optimize, and evaluate CTC and tdEV enumeration. For CTC identification, the CM-CTC performed best on metastatic/nonmetastatic breast cancer, respectively, with a hazard ratio (HR) for overall survival of 2.6/2.1 vs. 2.4/1.4 for operator CTC and 1.2/0.8 for ACCEPT-CTC. For tdEV identification, CM-tdEV performed best with an HR of 1.6/2.9 vs. 1.5/1.0 with ACCEPT-tdEV. In conclusion, contrast maximization is effective even though it does not utilize domain knowledge.

Clinical application of circulating tumor cells.

This narrative review aims to provide a comprehensive overview of the current state of circulating tumor cell (CTC) analysis and its clinical significance in patients with epithelial cancers. The review explores the advancements in CTC detection methods, their clinical applications, and the challenges that lie ahead. By examining the important research findings in this field, this review offers the reader a solid foundation to understand the evolving landscape of CTC analysis and its potential implications for clinical practice. The comprehensive analysis of CTCs provides valuable insights into tumor biology, treatment response, minimal residual disease detection, and prognostic evaluation. Furthermore, the review highlights the potential of CTCs as a non-invasive biomarker for personalized medicine and the monitoring of treatment efficacy. Despite the progress made in CTC research, several challenges such as standardization, validation, and integration into routine clinical practice remain. The review concludes by discussing future directions and the potential impact of CTC analysis on improving patient outcomes and guiding therapeutic decision-making in epithelial cancers.