How Do Tumor Cytogenetics Inform Cancer Treatments? Dynamic Risk Stratification and Precision Medicine Using Multi-armed Bandits

Abstract

Multiple myeloma is an incurable cancer of bone marrow plasma cells with a median overall survival of 5 years. With newly approved drugs to treat this disease over the last decade, physicians are afforded more opportunities to tailor treatment to individual patients and thereby improve survival outcomes and quality of life. However, since the optimal sequence of therapy is unknown, selecting a treatment that will result in the most effective outcome for each individual patient is challenging. This paper addresses this challenge, considering the problem of designing personalized treatment recommendations for patients with multiple myeloma using a data-driven analytics method. We formulate the treatment recommendation problem as a Bayesian contextual bandit, which sequentially selects treatments based on contextual information about patients and therapies, with the goal of maximizing overall survival outcomes. We developed a multilevel Bayesian linear Thompson sampling to learn patients’ heterogeneous response on treatment decisions, which allows us to flexibly account for patient and line-of-therapy level heterogeneity even in the absence of a large number of observations.

Facing the difficulty of evaluating the performance of the policy with only observational data, we propose a causal offline evaluation approach to measure the effect of the treatment in the presence of unmeasured confounders. We evaluate the performance of our policy on clinical data collected from 803 patients treated at Seattle Cancer Care Alliance. Our policy achieved an 19.75\% predicted improvement compared to the current clinical practice, and outperforms other benchmark strategies. Moreover, our policy achieves higher improvement for aging or high-risk patients with more complications by keeping the disease controlled at a relatively stable condition.

Keywords: Multiple Myeloma, Precision Medicine, Multi-Armed Bandit, Thompson Sampling, Hidden Markov Model (HMM)