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

Gerratana, L; Pierga, JY; Reuben, JM; Davis, AA; Wehbe, FH; Dirix, L; Fehm, T; Nole, F; Gisbert-Criado, R; Mavroudis, D; Grisanti, S; Garcia-Saenz, JA; Stebbing, J; Caldas, C; Gazzaniga, P; Manso, L; Zamarchi, R; Bonotto, M; Fernandez de Lascoiti, A; De Mattos-Arruda, L; Ignatiadis, M; Sandri, MT; Generali, D; De Angelis, C; Dawson, SJ; Janni, W; Caranana, V; Riethdorf, S; Solomayer, EF; Puglisi, F; Giuliano, M; Pantel, K; Bidard, FC; Cristofanilli, M

Author(s): Gerratana, L; Pierga, JY; Reuben, JM; Davis, AA; Wehbe, FH; Dirix, L; Fehm, T; Nole, F; Gisbert-Criado, R; Mavroudis, D; Grisanti, S; Garcia-Saenz, JA; Stebbing, J; Caldas, C; Gazzaniga, P; Manso, L; Zamarchi, R; Bonotto, M; Fernandez de Lascoiti, A; De Mattos-Arruda, L; Ignatiadis, M; Sandri, MT; Generali, D; De Angelis, C; Dawson, SJ; Janni, W; Caranana, V; Riethdorf, S; Solomayer, EF; Puglisi, F; Giuliano, M; Pantel, K; Bidard, FC; Cristofanilli, M;
Details: Publication Year 2022-07-05,Volume 27,Issue #7,Page e561-e570
Journal Title: Oncologist
Publication Type: Research article
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: Biomarkers, Tumor; *Breast Neoplasms; *Clinical Trials as Topic; Computer Simulation; Female; Humans; *Liver Neoplasms/drug therapy; *Neoplastic Cells, Circulating/pathology; Prognosis; Retrospective Studies; K-nearest neighbor; biomarker; clinical trial model; liquid biopsy; machine learning
Department(s): Laboratory Research; Medical Oncology
PubMed ID: 35278078
Publisher's Version: https://doi.org/10.1093/oncolo/oyac045
Open Access at Publisher's Site: https://doi.org/10.1093/oncolo/oyac045
Terms of Use/Rights Notice: Refer to copyright notice on published article.

Creation Date: 2024-10-31 04:54:54

Last Modified: 2024-10-31 04:56:11