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.
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 segmentation of the tumor (top). These models can also include the pharmacokinetics of the usual drugs prescribed in neoadjuvant therapy (top), as well as their combined effect on tumor proliferation (top), which can be assessed with the MuSyC equation (DOX: doxorubicin, CYC: cyclophosphamide). Then, the personalized models can be used to make forecasts of treatment response in terms of tumor volume and tumor cell density maps during the course of neoadjuvant therapy (bottom). Adapted from Lorenzo et al. Arxiv, arXiv:2212.04270.
