A global sensitivity analysis of a mechanistic model of neoadjuvant chemotherapy for triple negative breast cancer constrained by in vitro and in vivo imaging data

Guillermo Lorenzo; Angela M Jarrett; Christian T Meyer; Julie C DiCarlo; John Virostko; Vito Quaranta; Darren R Tyson; Thomas E Yankeelov

doi:10.1007/s00366-023-01873-0

A global sensitivity analysis of a mechanistic model of neoadjuvant chemotherapy for triple negative breast cancer constrained by in vitro and in vivo imaging data

Eng Comput. 2024;40(3):1469-1499. doi: 10.1007/s00366-023-01873-0. Epub 2023 Aug 7.

Authors

Guillermo Lorenzo^{1

2}, Angela M Jarrett^{2

3}, Christian T Meyer^{4

5}, Julie C DiCarlo^{2

3

6}, John Virostko^{2

3

7

8}, Vito Quaranta^{4

5

9

10}, Darren R Tyson^{4

9

10}, Thomas E Yankeelov^{2

3

7

8

11

12}

Affiliations

¹ Department of Civil Engineering and Architecture, University of Pavia, Via Ferrata 3, 27100 Pavia, Italy.
² Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, USA.
³ Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, USA.
⁴ Center for Cancer Systems Biology at Vanderbilt, Vanderbilt University, Nashville, USA.
⁵ Duet BioSystems, Inc., Nashville, USA.
⁶ Biomedical Imaging Center, The University of Texas at Austin, Austin, USA.
⁷ Department of Diagnostic Medicine, The University of Texas at Austin, Austin, USA.
⁸ Department of Oncology, The University of Texas at Austin, Austin, USA.
⁹ Department of Biochemistry, Vanderbilt University, Nashville, USA.
¹⁰ Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, USA.
¹¹ Department of Biomedical Engineering, The University of Texas at Austin, Austin, USA.
¹² Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, USA.

Abstract

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 in silico personalized forecasts of tumor response to NAC via computer simulation of mechanistic models constrained with patient-specific magnetic resonance imaging (MRI) data acquired early during NAC. Here, we present a new mechanistic model of TNBC growth and response to NAC, including an explicit description of drug pharmacodynamics and pharmacokinetics. As longitudinal 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 in silico parameter estimates and 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 in vivo MRI-constrained calibration, thereby facilitating the clinical application of our model.

Supplementary information: The online version contains supplementary material available at 10.1007/s00366-023-01873-0.

Keywords: Breast cancer; Computational oncology; Isogeometric analysis; Magnetic resonance imaging; Sensitivity analysis; Time-resolved microscopy.