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Issue 272: In our recent work, we investigate a new spatiotemporal biology-based model to forecast breast cancer growth and response to neoadjuvant therapy. This model accounts for tumor cell proliferation and invasion, mechanical constraints to tumor growth, as well as drug pharmacokinetics and pharmacodynamics, including drug-drug synergistic effects. We focus on two standard neoadjuvant chemotherapeutic regimens for locally advanced triple negative breast cancer: doxorubicin plus cyclophosphamide (DC), and paclitaxel plus carboplatin (PC). In this context, we constructed the parameter space for our model by combining patient-specific, MRI-informed values from our previous studies on breast cancer forecasting and in vitro measurements of drug pharmacodynamics and synergistic effects obtained via high-throughput, time-resolved, automated microscopy. The sensitivity analysis is run in two MRI-based scenarios corresponding to a well-perfused and a poorly perfused tumor. Out of the 15 parameters considered in this study, only 3 (DC) to 5 (PC) parameters, which represent drug-induced changes to tumor cell net proliferation, exhibit a relevant impact on model forecasts. Thus, these results dramatically limit the number of parameters that require in vivo MRI-constrained calibration, thereby facilitating the clinical application of our model.