1. Computational reactive–diffusive modeling for stratification and prognosis determination of patients with breast cancer receiving Olaparib
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Francesco Schettini, Maria Valeria De Bonis, Carla Strina, Manuela Milani, Nicoletta Ziglioli, Sergio Aguggini, Ignazio Ciliberto, Carlo Azzini, Giuseppina Barbieri, Valeria Cervoni, Maria Rosa Cappelletti, Giuseppina Ferrero, Marco Ungari, Mariavittoria Locci, Ida Paris, Giovanni Scambia, Gianpaolo Ruocco, and Daniele Generali
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Medicine ,Science - Abstract
Abstract Mathematical models based on partial differential equations (PDEs) can be exploited to handle clinical data with space/time dimensions, e.g. tumor growth challenged by neoadjuvant therapy. A model based on simplified assessment of tumor malignancy and pharmacodynamics efficiency was exercised to discover new metrics of patient prognosis in the OLTRE trial. We tested in a 17-patients cohort affected by early-stage triple negative breast cancer (TNBC) treated with 3 weeks of olaparib, the capability of a PDEs-based reactive–diffusive model of tumor growth to efficiently predict the response to olaparib in terms of SUVmax detected at 18FDG-PET/CT scan, by using specific terms to characterize tumor diffusion and proliferation. Computations were performed with COMSOL Multiphysics. Driving parameters governing the mathematical model were selected with Pearson's correlations. Discrepancies between actual and computed SUVmax values were assessed with Student’s t test and Wilcoxon rank sum test. The correlation between post-olaparib true and computed SUVmax was assessed with Pearson’s r and Spearman’s rho. After defining the proper mathematical assumptions, the nominal drug efficiency (εPD) and tumor malignancy (r c) were computationally evaluated. The former parameter reflected the activity of olaparib on the tumor, while the latter represented the growth rate of metabolic activity as detected by SUVmax. εPD was found to be directly dependent on basal tumor-infiltrating lymphocytes (TILs) and Ki67% and was detectable through proper linear regression functions according to TILs values, while r c was represented by the baseline Ki67-to-TILs ratio. Predicted post-olaparib SUV*max did not significantly differ from original post-olaparib SUVmax in the overall, gBRCA-mutant and gBRCA-wild-type subpopulations (p > 0.05 in all cases), showing strong positive correlation (r = 0.9 and rho = 0.9, p
- Published
- 2023
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