Modeling the Prognostic Impact of Circulating Tumor Cells Enumeration in Metastatic Breast Cancer for Clinical Trial Design Simulation

Abstract

Despite the strong prognostic stratification of circulating tumor cells (CTCs) enumeration in metastatic breast cancer (MBC), current clinical trials usually do not include a baseline CTCs in their design. This study aimed to generate a classifier for CTCs prognostic simulation in existing datasets for hypothesis generation in patients with MBC. A K-nearest neighbor machine learning algorithm was trained on a pooled dataset comprising 2436 individual MBC patients from the European Pooled Analysis Consortium and the MD Anderson Cancer Center to identify patients likely to have CTCs ≥ 5/7 mL blood (StageIVaggressive vs StageIVindolent). The model had a 65.1% accuracy and its prognostic impact resulted in a hazard ratio (HR) of 1.89 (Simulatedaggressive vs SimulatedindolentP < .001), similar to patients with actual CTCs enumeration (HR 2.76; P < .001). The classifier's performance was then tested on an independent retrospective database comprising 446 consecutive hormone receptor (HR)-positive HER2-negative MBC patients. The model further stratified clinical subgroups usually considered prognostically homogeneous such as patients with bone-only or liver metastases. Bone-only disease classified as Simulatedaggressive had a significantly worse overall survival (OS; P < .0001), while patients with liver metastases classified as Simulatedindolent had a significantly better prognosis (P < .0001). Consistent results were observed for patients who had undergone CTCs enumeration in the pooled population. The differential prognostic impact of endocrine- (ET) and chemotherapy (CT) was explored across the simulated subgroups. No significant differences were observed between ET and CT in the overall population, both in terms of progression-free survival (PFS) and OS. In contrast, a statistically significant difference, favoring CT over ET was observed among Simulatedaggressive patients (HR: 0.62; P = .030 and HR: 0.60; P = .037, respectively, for PFS and OS).

Keywords: K-nearest neighbor; biomarker; clinical trial model; liquid biopsy; machine learning.

Figure 1.

Comparison between the CTC-based risk stratification (Stage IV^indolent vs Stage IV^aggressive) and the KNN simulation (Simulated^indolent vs Simulated^aggressive). The model was capable to simulate a comparable risk stratification with respect to CTCs enumeration both for StageIV^indolent (CTCs vs Simulation HR 1.18, 95%CI 0.93-1.51, P = .177) and StageIV^aggressive (CTCs vs Simulation HR 0.88, 95%CI 0.70-1.09, P = .242).

Figure 2.

CTCs enumeration simulation across MBC subtypes. Patients classified as Simulated^aggressive (Agg) had a significantly higher CTCs enumeration with respect to Simulated IV^indolent (Ind) in HR-positive MBC (A), HER2-positive MBC (B) and TNBC (C). Patients classified as Simulated^aggressive experienced a significantly worse prognosis (D).

Figure 3.

CTCs enumeration simulation of on an independent cohort comprising HR-positive, HER2-negative first-line MBC patients. The classifier was capable to stratify patients both in terms of OS (A) and PFS (B).

Figure 4.

CTCs stratification and simulation among patients with bone-only (BO) disease (A, C) and liver (Liv) involvement (B, D). In the pooled population, patients with bone only metastases classified as StageIV^aggressive had a significantly worse prognosis with respect to StageIV^indolent (P < .0001) (A). Similar results were observed in the Simulated counterpart (P < .0001) (C). Consistently, patients with liver metastases classified as StageIV^indolent had a significantly better prognosis than the StageIV^aggressive counterpart (P < .0001) (B) Similar results were observed in Simulated^indolent patients with liver metastases (P < .0001) (D).

Figure 5.

Impact of first-line ET and CT in terms of OS and PFS according to the classifier’s stratification. No significant impact was observed in the total population (A, D) nor in the Simulated^indolent subgroup (B, E) in terms of OS and PFS, while a significantly different outcome was observed in the Simulated^aggressive subgroup (C, F).