PTEN loss appears to represent an early molecular alteration associated with adverse pathological features. Longer follow-up is needed to determine the predictive value of these biomarkers for recurrence and long-term outcomes.
When integrated with rVAR2-FETCH enrichment, our platform detected CTC in 83.67% (41/49) of non-small cell lung cancer patients, outperforming the complete CellSearch kit. Furthermore, machine learning models integrating CTC counts with hematological biomarkers achieved excellent diagnostic performance for lung cancer, with support vector machine demonstrating the best results (AUC = 0.977).
High CTC counts may potentially predict inferior outcomes after salvage lymph node dissection. As high counts are rare in early oligorecurrent PC, more sensitive CTC technologies and additional biomarkers are needed. The BioPoP study is registered on ClinicalTrials.gov as NCT04324983.
CTC-negative status predicted longer OS and PFS, while CAM-L positivity at T1 was associated with improved outcomes, particularly in ICI-treated patients. Combined assessment of both biomarkers may directly inform therapeutic decision-making, through early detection of outcomes.
These findings support the hypothesis that volatile anesthetics may reduce short-term recurrence risk and warrant larger, longer-term trials to validate oncological outcomes.
These results support the clinical utility of HER2 assessment on CTCs with both workflows and highlight the potential diagnostic value of label-free CTC enrichment combined with HER2 quantification. Further studies in larger cohorts should be conducted to validate our findings and investigate the clinical relevance of HER2-positive CTCs detected with the developed pipeline, particularly in the context of anti-HER2 therapies.