학술논문
Modeling the Prognostic Impact of Circulating Tumor Cells Enumeration in Metastatic Breast Cancer for Clinical Trial Design Simulation
Original Article
Original Article
Document Type
Report
Author
Gerratana, Lorenzo; Pierga, Jean-Yves; Reuben, James M.; Davis, Andrew A.; Wehbe, Firas H.; Dirix, Luc; Fehm, Tanja; Nole, Franco; Gisbert-Criado, Rafael; Mavroudis, Dimitrios; Grisanti, Salvatore; Garcia-Saenz, Jose A.; Stebbing, Justin; Caldas, Carlos; Gazzaniga, Paola; Manso, Luis; Zamarchi, Rita; Bonotto, Marta; de Lascoiti, AngelaFernandez; De Mattos-Arruda, Leticia; Ignatiadis, Michail; Sandri, Maria-Teresa; Generali, Daniele; De Angelis, Carmine; Dawson, Sarah-Jane; Janni, Wolfgang; Caranana, Vicente; Riethdorf, Sabine; Solomayer, Erich-Franz; Puglisi, Fabio; Giuliano, Mario; Pantel, Klaus; Bidard, Francois-Clement; Cristofanilli, Massimo
Source
The Oncologist. July 2022, Vol. 27 Issue 7, pe561, 10 p.
Subject
Language
English
ISSN
1083-7159
Abstract
Implications for Practice Circulating tumor cells (CTC)-based risk stratification may have a role for future treatment strategies, as it enables the selection of subgroups with differential response potential. Machine learning [...]
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 [greater than or equal to] 5/7 mL blood ([StageI.sup.Vaggressive] vs [StageIV.sup.indolent]). The model had a 65.1% accuracy and its prognostic impact resulted in a hazard ratio (HR) of 1.89 ([Simulated.sup.aggressive] vs [Simulated.sup.indolent] P Key words: clinical trial model; machine learning; liquid biopsy; biomarker; K-nearest neighbor.
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 [greater than or equal to] 5/7 mL blood ([StageI.sup.Vaggressive] vs [StageIV.sup.indolent]). The model had a 65.1% accuracy and its prognostic impact resulted in a hazard ratio (HR) of 1.89 ([Simulated.sup.aggressive] vs [Simulated.sup.indolent] P Key words: clinical trial model; machine learning; liquid biopsy; biomarker; K-nearest neighbor.