Serial Analysis of Circulating Tumor Cells in Metastatic Breast Cancer Receiving First-Line Chemotherapy.

Area of Science:

• Oncology
• Translational Research
• Biostatistics

Background:

• Assessing the prognostic value of circulating tumor cell (CTC) dynamics during treatment is crucial for metastatic breast cancer (MBC) management.
• Current prognostic models may not fully capture the predictive power of CTC changes over time.

Purpose of the Study:

• To investigate the prognostic significance of CTC dynamics during first-line chemotherapy in MBC patients.
• To develop and validate a novel model for classifying patients based on CTC trajectories (tCTC).

Main Methods:

• A latent mixture model was developed using serial CTC data from 469 MBC patients to identify distinct tCTC patterns.
• Cox regression analysis was employed to evaluate the association between tCTC patterns and progression-free survival (PFS) and overall survival (OS).
• Model performance was compared using Akaike Information Criterion and validated in an independent cohort of 1856 patients.

Main Results:

• Four distinct tCTC patterns were identified: undetectable, low, intermediate, and high.
• Patients with low, intermediate, and high tCTC patterns showed significantly worse PFS and OS compared to those with undetectable CTCs (P < .001).
• The tCTC model demonstrated superior predictive performance for PFS and OS compared to baseline CTCs or early post-treatment CTC status, confirmed by validation studies.

Conclusions:

• Four novel prognostic groups in MBC were identified based on tCTC patterns during chemotherapy.
• These groups, particularly the 'very poor outcome' group (intermediate + high CTCs), can guide treatment intensification.
• This tCTC-based classification refines risk stratification strategies for future clinical trials in MBC.