Digital twins employ mathematical and computational models to virtually represent a physical object (e.g., planes and human organs), predict the behavior of the object, and enable decision-making to optimize the future behavior of the object. While digital twins have been widely used in engineering for decades, their applications to oncology are only just emerging. Due to advances in experimental techniques quantitatively characterizing cancer, as well as advances in the mathematical and computational sciences, the notion of building and applying digital twins to understand tumor dynamics and personalize the care of cancer patients has been increasingly appreciated.
In this project, we employed digital twins (i.e., mathematical models that provide virtual representation of individual patients and predict the changes at future time points) to systematically evaluate individual triple-negative breast cancer patient’s response to different neoadjuvant chemotherapy regimens, thereby patient-specifically optimizing treatments.
The digital twins were established by integrating the longitudinal MRIs with the mechanism-based model. With parameters personalized, we simulated
the individual patient's response to 128 clinically reasonable combinations of A/C/Taxol dosing-schedules. The predicted response (pCR or non-pCR) from each alternative schedule was compared to the patient response from the actual treatment.
The results of our investigation indicate that without changing the total dose, shortening the duration of A/C/Taxol administration increased the treatment efficacy. The effectiveness of altering the schedules varied substantially in different patients.
The ongoing effort includes:
1) predict patient-specific response to various therapy types (beyond chemotherapy)
2) integrat multi-modality data (i.e., histopathological data, genetic sequencing from biopsy, patient demographic information, etc.) to improve accuracy in response prediction and treatment selection
3) account for toxicity induced by the treatments
In this project, we employed digital twins (i.e., mathematical models that provide virtual representation of individual patients and predict the changes at future time points) to systematically evaluate individual triple-negative breast cancer patient’s response to different neoadjuvant chemotherapy regimens, thereby patient-specifically optimizing treatments.
The digital twins were established by integrating the longitudinal MRIs with the mechanism-based model. With parameters personalized, we simulated
the individual patient's response to 128 clinically reasonable combinations of A/C/Taxol dosing-schedules. The predicted response (pCR or non-pCR) from each alternative schedule was compared to the patient response from the actual treatment.
The results of our investigation indicate that without changing the total dose, shortening the duration of A/C/Taxol administration increased the treatment efficacy. The effectiveness of altering the schedules varied substantially in different patients.
The ongoing effort includes:
1) predict patient-specific response to various therapy types (beyond chemotherapy)
2) integrat multi-modality data (i.e., histopathological data, genetic sequencing from biopsy, patient demographic information, etc.) to improve accuracy in response prediction and treatment selection
3) account for toxicity induced by the treatments
