Machine Learning for Precision Oncology
Cancer treatment is unique as no single cancer is the same and the way it affects varies from person to person. Cancer has more than 20,000 known pathways driven by gene mutations. These must be identified and targeted with focused therapies rather than ad hoc generalized treatments. Standard cancer treatment is ineffective in >75% of the patients. Precision medicine is an effective alternative to standard chemotherapy for many patients. Prescribing a personalized combination of treatments for cancer patients using aggregated information from the patient’s molecular profile (-omics data), radiomics data, tumor tissue samples, and the patient’s medical history (EHR) is a challenging problem. Machine Learning holds the key to addressing this problem efficiently and accurately. With the advent of Deep Neural Networks, Machine Learning algorithms are now capable of dealing with the challenges of scalability and high dimensionality of data. Multi-modal Learning in Deep Neural Networks can integrate different types of data (spatial, sequential, time-series, tabular, etc,) from multiple sources to perform a prediction. These features indicate deep learning’s ability to transform big data from genomics, radiomics, histopathology, and EHR, into clinically actionable knowledge. Translation of AI to clinical environments could usher in a new era of Precision Oncology in which all the available treatments for cancer, including molecularly-targeted, immunotherapy, and cytotoxic chemotherapies, will be utilized effectively in a patient-specific manner. Once deployed, the oncologists will be able to prescribe treatments to patients using insights provided by a computer-based decision support system that selects a combination of treatments by maximizing potential therapeutic efficacy while minimizing the side effects associated with ineffective treatments. One of the major hurdles for successful translation of deep learning algorithms from research to practice in precision medicine is their interpretability to physicians. The current state-of-the-art techniques in drug-sensitivity prediction, cancer patient stratification, etc, face non-trivial challenges in terms of interpretability. The interpretation and understanding of how a complex model arrives at its decisions, referred to as a “black box” problem, is a significant roadblock preventing widespread adoption of machine learning-powered applications in the healthcare sector.