Oscillatory Modulation of Tumor Microenvironment pH Enhances Simulated Therapeutic Response in Breast Cancer

<title>Abstract</title> <p>Tumor acidity is a hallmark of cancer progression, contributing to therapeutic resistance, immune suppression, and metastatic potential. I present a hybrid computational framework — the BioOnco System — integrating Gated Attention Multiple Instance Learning (MIL) histopathological classification with a reaction-diffusion biophysical model to investigate oscillatory modulation of extracellular pH in breast cancer. The MIL classifier was trained on 550 patients from the BACH dataset (440 training, 110 test) and achieved an area under the receiver operating characteristic curve (AUC) of 0.9757, overall accuracy of 82%, and malignant-class sensitivity of 99%. Spatial tumor density maps derived from model attention outputs were used as initial conditions for proton dynamics simulations governed by: ∂H/∂t = D∇²H + P − R·H + A·sin(ωt), with parameters D = 0.4, P = 0.5, R_base = 0.6, A = 0.8, ω = 0.3. Four experimental groups were evaluated over 30 simulation replicates: control, chemotherapy (R×1.8), oscillatory modulation (A = 0.8, ω = 0.3), and combined therapy (R×4.0, A = 0.4). The combined therapy group demonstrated statistically significant superior tumor burden reduction compared to chemotherapy alone (Welch's t-test, p &lt; 0.0001), supporting the hypothesis that oscillatory disruption of pH homeostasis impairs tumor adaptive capacity. Experimental validation is required to confirm these observations.</p>