2026-04-14T02:16:04Zhttps://recercat.cat/oai/request

oai:recercat.cat:11351/83712024-12-13T10:30:22Zcom_2072_378070com_2072_378040col_2072_378092

Modeling the Prognostic Impact of Circulating Tumor Cells Enumeration in Metastatic Breast Cancer for Clinical Trial Design Simulation Gerratana, Lorenzo Pierga, Jean-Yves Reuben, James Davis, Andrew Wehbe, Firas H. Dirix, Luc Mattos Arruda, Leticia de Mama - Càncer - Prognosi Cèl·lules canceroses Simulació per ordinador DISEASES::Neoplasms::Neoplasms by Site::Breast Neoplasms DISEASES::Neoplasms::Neoplastic Processes::Neoplasm Metastasis::Neoplastic Cells, Circulating INFORMATION SCIENCE::Information Science::Computing Methodologies::Computer Simulation ENFERMEDADES::neoplasias::neoplasias por localización::neoplasias de la mama ENFERMEDADES::neoplasias::procesos neoplásicos::metástasis neoplásica::células neoplásicas circulantes CIENCIA DE LA INFORMACIÓN::Ciencias de la información::metodologías computacionales::simulación por ordenador Biomarker; Liquid biopsy; Machine learning Biomarcadores; Biopsia líquida; Aprendizaje automático Biomarcadors; Biòpsia líquida; Aprenentatge automàtic 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). 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. 2023-11-08T10:18:39Z 2023-11-08T10:18:39Z 2023-11-08T10:18:39Z 2022-10-31T08:57:27Z 2022-10-31T08:57:27Z 2022-07 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/11351/8371 The Oncologist;27(7) https://doi.org/10.1093/oncolo/oyac045 Attribution-NonCommercial 4.0 International http://creativecommons.org/licenses/by-nc/4.0/ info:eu-repo/semantics/openAccess Oxford University Press Scientia