  Neoadjuvant chemotherapy (NAC) is a standard-of-care treatment for locally
advanced triple negative breast cancer (TNBC) before surgery. The early
assessment of TNBC response to NAC would enable an oncologist to adapt the
therapeutic plan of a non-responding patient, thereby improving treatment
outcomes while preventing unnecessary toxicities. To this end, a promising
approach consists of obtaining \textsl{in silico} personalized forecasts of
tumor response to NAC \textsl{via} computer simulation of mechanistic models
constrained with patient-specific magnetic resonance imaging (MRI) data
acquired early during NAC. Here, we present a model featuring the essential
mechanisms of TNBC growth and response to NAC, including an explicit
description of drug pharmacodynamics and pharmacokinetics. As longitudinal
\textsl{in vivo} MRI data for model calibration is limited, we perform a
sensitivity analysis to identify the model mechanisms driving the response to
two NAC drug combinations: doxorubicin with cyclophosphamide, and paclitaxel
with carboplatin. The model parameter space is constructed by combining
patient-specific MRI-based \textsl{in silico} parameter estimates and
\textit{in vitro} measurements of pharmacodynamic parameters obtained using
time-resolved microscopy assays of several TNBC lines. 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 herein, only the
baseline tumor cell net proliferation rate along with the maximum
concentrations and effects of doxorubicin, carboplatin, and paclitaxel exhibit
a relevant impact on model forecasts (total effect index, $S_T>$0.1). These
results dramatically limit the number of parameters that require \textsl{in
vivo} MRI-constrained calibration, thereby facilitating the clinical
application of our model.
