Abstract SY38-02: Personalized adaptive therapy for metastatic cancer: An integrated approach
Authors: Kim Eunjung , Gallaher Jill , Robertson-Tessi Mark , Smalley Inna , Gatenby Robert , Smalley Keiran , Zhang Jingsong , Eroglu Zeynep , Anderson Alexander R. A. 
Published: August 2019

Introduction: Over the past decade, there has been an explosion of new, targeted therapies for cancer. However, for advanced disease, having a vast arsenal of treatment options does not always lead to sustained outcomes. Targeted treatments are too specific for heterogeneous tumors and need to be used in combination to target all cells in order to to avoid recurrence. In contrast, cytotoxic treatments such as chemotherapy can attack a wide variety of proliferating cells but are often more taxing to the patient’s health. Despite the growing acknowledgement that heterogeneity is driving treatment failure, it is not often recognized that a successful treatment must be designed with the evolutionary response of the tumor in mind. Adaptive therapy is an evolution-based strategy that exploits heterogeneity via the cost of drug resistance (e.g. molecular synthesis needed to survive treatment). For resistant cells, benefits often exceed costs during therapy; however, in the absence of therapy, drug sensitive cells are more fit due to the intrinsic cost of resistance. Therefore, treatment holidays allow sensitive cells to outcompete their resistant counterparts, potentially extending response times whilst reducing treatment time. The goal is to keep a tumor volume under control by adjusting the dose based on tumor progression over time: a shrinking tumor will receive a lower dose while a growing tumor will receive a higher dose. A central tenant of adaptive therapeutic approaches is that the response to treatment for each individual patient becomes the driver of the decision when to stop or restart treatment. This truly personalized paradigm requires systemic metrics of cancer burden that are both non-invasive and easily accessible. For certain cancers no such markers exist and for those that do, they are, at best, surrogates of burden. Therefore a major challenge is how to temporally monitor cancer dynamics through time (and ideally through space). The next challenge is how to interpret this data in order to make real time treatment decisions. We have already developed evolutionary game theoretic, ordinary differential equation (ODE) and agent based models to address this challenge.

The success of adaptive therapy has been demonstrated in numerous murine studies, leading to a recent adaptive therapy clinical trial in metastatic prostate cancer. The trial protocol, in which patients start and stop abiraterone based on PSA levels, has proven to be far more effective than standard of care in terms of progression free survival (PFS). Additionally, far less drug has been given to the patients, improving quality of life. Given the success of this pilot study, adaptive therapy trials are now being implemented in several other cancers, including melanoma, lung, thyroid, ovarian, and breast cancers.

Phase I adaptive therapy in melanoma: Our research in advanced BRAF-mutant melanomas shows that the precise timing of BRAF-inhibitor withdrawal and rechallenge is critical to the success of adaptive therapy paradigms. We developed a two-compartment ordinary differential equation (ODE) model that describes the competition between tumor cells and allows for transition between the treatment sensitive and resistant states. We first tested mathematical-model-driven adaptive therapy on xenograft BRAF-mutant melanoma mouse models. We predicted individual mouse specific adaptive treatment schedules, based on real-time model calibration of individual mouse tumor volume changes over time. The mouse-specific adaptive schedules were far more effective than continuous treatment, showing a reduction in burden of ~50%. The resulting treatment on-off schedules were quite diverse across the animals. Next, we considered metastatic melanoma patient clinical data. Here, the patients were already treated with continuous BRAF-MEK inhibitors and their treatment responses were monitored by plasma Lactic Acid Dehydrogenase (LDH) changes. We estimated model parameters from patient plasma LDH by minimizing the difference between model predicted LDH and actual individual patient LDH. Model calibration resulted in a suite of parameter sets that fit the data equally well, defining a virtual cohort of patients. Using this virtual cohort, we predicted responses to adaptive therapy and showed that adaptive therapy might delay tumor progression up to several months if the patients were treated with adaptive therapy instead of continuous therapy. Motivated by our successful xenograft results and mathematical model simulations, our group has recently initiated a phase 1 feasibility trial of adaptive intermittent BRAF-MEK inhibitor therapy to test adaptive therapy in patients with advanced BRAF-mutant melanoma (NCT03543969).

Spatial considerations of adaptive therapy in prostate cancer: While population dynamics models can offer insight into when adaptive therapy may be a good option for treatments, there are spatial interactions to consider as well. Dysregulated vasculature and spatial heterogeneity of tumor phenotypes can play a big role in the response of a tumor mass to a given application of therapy, particularly when treatment doses are being varied or cycled irregularly. Here, we used an off-lattice agent-based model to simulate the outcomes of different population mixes exposed to two general treatment strategies with an anti-proliferative drug: a continuous application given at the maximum tolerated dose, or an adaptive strategy that incorporates dose-modulation and treatment vacations to sustain control of the tumor’s sensitive and resistant cell populations. We assume that there is a trade-off between proliferation and drug sensitivity, so that the slower growing resistant cells get trapped in the interior of the tumor during growth and can hide from the drug during treatment. The more homogeneous, sensitive tumors are cured with continuous treatment, but even a few resistant cells will cause eventual recurrence. We find that we can maintain a steady tumor size with adaptive therapy, as long as there are sufficient sensitive cells to suppress resistant cell growth. Advanced, metastatic disease is often not visible via imaging modalities, so instead tumor burden in the patient is based on a blood biomarker. For example, in prostate cancer, prostate specific antigen (PSA) gives a measure of the systemic tumor burden. PSA is easily measured at multiple time points with little burden on the patient. Though PSA doesn’t provide any information about disease resistance and it doesn’t distinguish between different dynamics from multiple metastatic lesions, we can use mathematical models to narrow down this information from the PSA dynamics during drug application and during treatment vacations. We calibrated the off-lattice agent-based model to the timescales of the PSA cycles in the prostate cancer adaptive therapy trial. We investigated how number, size, composition, and heterogeneity of multiple metastatic lesions treated with adaptive therapy affects the systemic dynamics of total tumor burden.

Conclusions: In summary, it is clear that many cancer therapies, while initially successful, eventually lead to resistance and failure because of the evolution of tumor heterogeneity in response to the strong selection pressures imposed by drug administration. We have shown theoretically, experimentally, and clinically that using evolutionary-based approaches to apply treatments can lead to improved results compared to current standards of care.

Citation Format: Eunjung Kim, Jill Gallaher, Mark Robertson-Tessi, Inna Smalley, Robert Gatenby, Keiran Smalley, Jingsong Zhang, Zeynep Eroglu, Alexander R. A. Anderson. Personalized adaptive therapy for metastatic cancer: An integrated approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr SY38-02.