Computational reactive–diffusive modeling for stratification and prognosis determination of patients with breast cancer receiving Olaparib

• Published in: Scientific reports Vol. 13; no. 1; p. 11951
• Main Authors: Schettini, Francesco, De Bonis, Maria Valeria, Strina, Carla, Milani, Manuela, Ziglioli, Nicoletta, Aguggini, Sergio, Ciliberto, Ignazio, Azzini, Carlo, Barbieri, Giuseppina, Cervoni, Valeria, Cappelletti, Maria Rosa, Ferrero, Giuseppina, Ungari, Marco, Locci, Mariavittoria, Paris, Ida, Scambia, Giovanni, Ruocco, Gianpaolo, Generali, Daniele
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 24-07-2023; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705; 692/308; 692/4028; 692/53; Breast cancer; Computed tomography; Differential equations; Humanities and Social Sciences; Humans; Ki-67 Antigen; Lymphocytes; Malignancy; Mathematical models; multidisciplinary; Partial differential equations; Pharmacodynamics; Positron Emission Tomography Computed Tomography; Prognosis; Science; Science (multidisciplinary); Subpopulations; Triple Negative Breast Neoplasms; Tumor-infiltrating lymphocytes; Tumors
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-38760-z

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 SUV
max
detected at
18
FDG-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 SUV
max
values were assessed with Student’s t test and Wilcoxon rank sum test. The correlation between post-olaparib true and computed SUV
max
was assessed with Pearson’s r and Spearman’s rho. After defining the proper mathematical assumptions, the nominal drug efficiency (ε
PD
) 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 SUV
max
. ε
PD
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 SUV
max
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
 < 0.0001 both). A model of simplified tumor dynamics was exercised to effectively produce an upfront prediction of efficacy of 3-week neoadjuvant olaparib in terms of SUV
max
. Prospective evaluation in independent cohorts and correlation of these outcomes with more recognized efficacy endpoints is now warranted for model confirmation and tailoring of escalated/de-escalated therapeutic strategies for early-TNBC patients.