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.
This study assesses the cerebrospinal fluid (CSF) levels of the viral receptor angiotensin-converting enzyme 2 (ACE2) and of the serine protease TMPRSS2 fragments in patients with SARS-CoV-2 infection presenting encephalitis (CoV-Enceph). The study included biobanked CSF from 18 CoV-Enceph, 4 subjects with COVID-19 without encephalitis (CoV), 21 non-COVID-related encephalitis (Enceph), and 21 neurologically healthy controls. Participants underwent a standardized assessment for encephalitis. A large subset of samples underwent an extended panel of CSF neuronal, glial and inflammatory biomarkers. ACE2 and TMPRSS2 species were determined in the CSF by western blotting. ACE2 was present in CSF as several species, full-length forms, and two cleaved fragments of 80 and 85 kDa. CoV-Enceph patients displayed increased CSF levels of full-length species, as well as the 80 kDa fragment, but not the alternative 85 kDa fragment, compared with controls and Enceph patients, characterized by increases of both fragments. Furthermore, TMPRSS2 was increased in the CSF of Enceph patients compared with controls, but not in CoV-Enceph patients. The CoV patients without encephalitis displayed unaltered CSF levels of ACE2 and TMPRSS2 species. Patients suffering from encephalitis displayed an overall increase in CSF ACE2 probably as a consequence of brain inflammation. The increase of the shortest ACE2 fragment only in CoV-Enceph patients may reflect the enhanced cleavage of the receptor triggered by SARS-CoV-2, thus serving to monitor brain penetrance of the virus associated with the rare encephalitis complication. TMPRSS2 changes in the CSF appeared related with inflammation, but not with SARS-CoV-2 infection.
