PERFECTO: Prediction of Extended Response and Growth Functions for Estimating Chemotherapy Outcomes in Breast Cancer

Abstract

Understanding tumors evolution under chemotherapy is central in the design of cancer therapy regimens. Drug resistance poses a major obstacle in the battle against most types of cancer and therapy design. Personalized treatments have the potential to offer greater effectiveness and the ability to prevent and circumvent drug resistance. In this study we introduce PERFECTO (Prediction of Extended Response and Growth Functions for Estimating ChemoTherapy Outcomes), a machine learning system capable of extracting the tumor growth function and response under chemotherapy. Exploiting the underlying correlations in the clinical data, the system captures the statistical peculiarities of tumor growth in-vivo without an explicit modeling of tumor microenvironment and expensive clinical investigations. We demonstrate the learning capabilities of PERFECTO in predicting unperturbed tumor growth and chemotherapy tumor growth from multiple clinical breast cancer datasets. We postulate that predictability is the key. Using PERFECTO clinicians will be able to improve treatment plans for patient-specific parameters from individual tumors. Our preliminary experiments on in-vitro, animal and in-vivo datasets, shown that, with a high degree of confidence, PERFECTO is able to estimate treatment effectiveness through an accurate tumor growth response prediction, independent of the breast cancer cell line. This in turn can alleviate the need of ordering extra clinical tests or any extra wait time before treatment initiation.