TY - JOUR
T1 - Modeling the Prognostic Impact of CirculatingTumor Cells Enumeration in Metastatic Breast Cancer for ClinicalTrial Design Simulation
AU - Gerratana, Lorenzo
AU - Pierga, Jean Yves
AU - Reuben, James M.
AU - Davis, Andrew A.
AU - Wehbe, Firas H.
AU - Dirix, Luc
AU - Fehm, Tanja
AU - Nolé, Franco
AU - Gisbert-Criado, Rafael
AU - Mavroudis, Dimitrios
AU - Grisanti, Salvatore
AU - Garcia-Saenz, Jose A.
AU - Stebbing, Justin
AU - Caldas, Carlos
AU - Gazzaniga, Paola
AU - Manso, Luis
AU - Zamarchi, Rita
AU - Bonotto, Marta
AU - de Lascoiti, Angela Fernandez
AU - De Mattos-Arruda, Leticia
AU - Ignatiadis, Michail
AU - Sandri, Maria Teresa
AU - Generali, Daniele
AU - De Angelis, Carmine
AU - Dawson, Sarah Jane
AU - Janni, Wolfgang
AU - Carañana, Vicente
AU - Riethdorf, Sabine
AU - Solomayer, Erich Franz
AU - Puglisi, Fabio
AU - Giuliano, Mario
AU - Pantel, Klaus
AU - Bidard, François Clément
AU - Cristofanilli, Massimo
N1 - Funding Information:
The study was supported by Lynn Sage Cancer Research Foundation and the the CRO Aviano 5x1000 2014 per la Ricerca Sanitaria, Cancer Specific Intramural Grant. The funding sources had no role in the study design, data collection, data analysis, interpretation, or writing of the manuscript.
Publisher Copyright:
© The Author(s) 2022. Published by Oxford University Press.
PY - 2022/6
Y1 - 2022/6
N2 - 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 Simulatedindolent P < .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).
AB - 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 Simulatedindolent P < .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).
KW - K-nearest neighbor
KW - biomarker
KW - clinical trial model
KW - liquid biopsy
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85130980801&partnerID=8YFLogxK
U2 - 10.1093/oncolo/oyac045
DO - 10.1093/oncolo/oyac045
M3 - Article
C2 - 35278078
AN - SCOPUS:85130980801
SN - 1083-7159
VL - 27
SP - E561-E570
JO - Oncologist
JF - Oncologist
IS - 7
ER -