Guillermo Lorenzo


Integrating multiscale data and mechanistic models to predict breast cancer response to neoadjuvant therapies


Neoadjuvant therapy is a standard treatment for locally advanced breast cancer before surgery. In this therapeutic option, pathological complete response is defined as the absence of tumor after completion of the prescribed drug regimen and it has been linked to superior cancer control and survival.

The early assessment of a patient's breast tumor response to  neoadjuvant therapy would enable the treating oncologist to adapt the treatment plan of a non-responding patient (e.g., by adapting regimen schedule, dosage, and drugs).  As a result, this early adjustment would contribute to improve therapeutic outcomes and limit the treatment toxicities. However, the current methods to assess the tumor response to neoadjuvant therapy are unfit for this purpose because they either rely on changes in tumor size (which are only measurable after several drug cycles) or tissue biomarkers (which require an invasive biopsy and are subject to sampling errors due to tumor heterogeneity).

To address this challenge, this work aims at leveraging patient-specific in silico forecasts of breast cancer response to neoadjuvant therapeutic regimens, which are obtained via computer simulation of mathematical models describing the action of the usual drug combinations on the patient's tumor growth. To personalize these predictions, patient-specific longitudinal anatomic and quantitative magnetic resonance data acquired early in the course of the treatment are used to calibrate the model. Experimental data on combined drug effects and synergies further inform about expected parameter values and ideal model formulations to describe these key phenomena underlying treatment outcomes. 
Figure. The mechanistic models of breast cancer response in this research are personalized by using longitudinal anatomic and quantitative imaging data from each patient, which enable the estimation of tumor cell density, perfusion maps, and the segmentations of breast tissues (left). These models can also include the pharmacokinetics of the usual drugs prescribed in neoadjuvant therapy (right, top plots), as well as their combined effect on tumor proliferation (right bottom plots), which can be assessed with the MuSyC equation.  DOX: doxorubicin, CYC: cyclophosphamide, PTX: paclitaxel, CPT: carboplatin .

Publications


Quantitative in vivo imaging to enable tumor forecasting and treatment optimization


Guillermo Lorenzo, David A. Hormuth, Angela M. Jarrett, Ernesto A. B. F. Lima, Shashank Subramanian, George Biros, J. Tinsley Oden, Thomas J. R. Hughes, Thomas E. Yankeelov


arXiv: Tissues and Organs, 2021


Math, magnets, and medicine: enabling personalized oncology


David A. Hormuth, Angela M. Jarrett, Guillermo Lorenzo, Ernesto A. B. F. Lima, Chengyue Wu, Caroline Chung, Debra Patt, Thomas E. Yankeelov


Expert Review of Precision Medicine and Drug Development, vol. 6(2), 2021, pp. 79-81


Patient-specific characterization of breast cancer hemodynamics using image-guided computational fluid dynamics


Chengyue Wu, David A. Hormuth, Todd A. Oliver, Federico Pineda, Guillermo Lorenzo, Gregory S. Karczmar, Robert D. Moser, Thomas E. Yankeelov


IEEE Transactions on Medical Imaging, vol. 39(9), 2020, pp. 2760-2771