Computational modeling of lesion dynamics in HER2+ breast cancer: integrating gut microbiota diversity into therapy response prediction

Mathematical models based on partial differential equations (PDEs) can be exploited to integrate heterogeneous clinical and biological data for the interpretation of tumor dynamics during systemic therapy. In this study, a PDE-based model of tumor volume evolution was exercised to investigate the predictive role of clinical and microbiota-derived biomarkers in patients with HER2-positive breast cancer undergoing neoadjuvant chemotherapy. Within a retrospective cohort of 15 patients, a training subset of eight was used to identify and optimize a set of virtual parameters describing tumor proliferation and treatment efficacy. Tumor growth rate ( r ) and drug efficiency for the epirubicin–cyclophosphamide branch (ϵ _PD1 ) were modeled as functions of baseline Ki67 expression, while a Spearman correlation analysis identified key microbiota features (Firmicutes/Bacteroidetes ratio and the Simpson Diversity Index) associated with treatment response, or drug efficiency of the taxane–trastuzumab branch (ϵ _PD2 ). Model robustness was subsequently assessed in an independent testing subset of seven patients. Simulated tumor volume dynamics did not significantly differ from clinical observations and showed strong predictive capability in discriminating therapeutic response (p = 0.0070), correctly identifying all partial responses and 80% of pathological complete responses. After defining appropriate mathematical assumptions, microbiota-informed drug efficiency parameters were shown to effectively capture inter-patient variability in treatment sensitivity. A simplified model of tumor dynamics integrating microbiota-derived variables was thus demonstrated to provide an upfront prediction of neoadjuvant chemotherapy efficacy. Prospective validation in larger cohorts and correlation with established clinical endpoints are now warranted to confirm the model and support patient-specific optimization of therapeutic strategies in HER2-positive breast cancer.