Abstract
e14048 Background: Cancer associated macrophage like cells (CAMLs), a circulating stromal cell subtype, has been shown as an independent prognostic indicator of survival in late stage metastatic breast cancer when cells enlarge to ≥50µm. However, no study has evaluated the clinical relationship between changes in CAML size and their ability to predict treatment response. While CAMLs are prognostic for progression, we hypothesized that monitoring changes in CAMLs after initiation of therapy may be predictive for responsiveness of new treatment regimens. Methods: A prospective 12 months blind multi-institutional pilot study was undertaken to evaluate CAMLs before, and after, induction of a new line of investigational therapy based on CT scans. Patients with progressive metastatic breast cancer (n = 29) who had failed at least 2 prior therapies were recruited. A baseline (BL) blood sample was taken prior to induction of a new therapy and a 2nd sample (T1) taken after initiation (~30 days). Blood was filtered by CellSieve filtration.The quantities and sizes of CAMLs were analyzed based on PFS hazard ratios (HRs) by censored univariate analysis. Results: CAMLs were found in 97% of BL samples and 93% of T1 samples. At first CT scan, after the assigned dose of investigational treatment, 17 of 29 patients had clinical progression with 14/17 (82%) patients having an increased CAML size and 3/17 (18%) having a decreased CAML size. The remaining 12 of 29 patients saw clinically stable, or regression, of disease with 10/12 (83%) having decreased CAML size and 2/12 (17%) having increased CAML size. Overall CAML size change after therapy induction was 83% accurate at predicting response or progression based on CT scans. Further, patients with increasing CAML size at T1 had a 4 month mPFS vs 10 month mPFS for decreasing CAMLs, with a lower 12 month PFS HR = 3.7 (95%CI = 1.5-10.1, p = 0.020). Conclusions: Our data suggests that in metastatic breast cancer, monitoring CAMLs changes over the first 30 days of treatment accurately predicts responsiveness of disease to new treatments. Further, we suggests using blood sampling may increase the clinical value of blood based diagnostics by rapidly predicting the benefit of subsequent therapies.
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