A survey of open questions in adaptive therapy: Bridging mathematics and clinical translation

Abstract

Adaptive therapy is a dynamic cancer treatment protocol that updates (or 'adapts') treatment decisions in anticipation of evolving tumor dynamics. This broad term encompasses many possible dynamic treatment protocols of patient-specific dose modulation or dose timing. Adaptive therapy maintains high levels of tumor burden to benefit from the competitive suppression of treatment-sensitive subpopulations on treatment-resistant subpopulations. This evolution-based approach to cancer treatment has been integrated into several ongoing or planned clinical trials, including treatment of metastatic castrate resistant prostate cancer, ovarian cancer, and BRAF-mutant melanoma. In the previous few decades, experimental and clinical investigation of adaptive therapy has progressed synergistically with mathematical and computational modeling. In this work, we discuss 11 open questions in cancer adaptive therapy mathematical modeling. The questions are split into three sections: (1) integrating the appropriate components into mathematical models (2) design and validation of dosing protocols, and (3) challenges and opportunities in clinical translation.

Keywords: adaptive therapy; cancer biology; cancer evolution & evolution; drug resistance; mathematical modeling; medicine; predictive modeling.

Figure 1.. Open questions in adaptive cancer therapy modeling: schematic of tumor burden under maximum tolerable dose (blue) and adaptive dosing (purple), with corresponding biopsies.

Adaptive therapy is designed to exploit competition between treatment-sensitive (green) and resistant (red) cells to prolong the emergence of resistance. 11 questions representing future challenges in the field of adaptive therapy are shown, and answered within the text. Questions are color-coded by section: integrating the appropriate components into mathematical models (blue), design and validation of dosing protocols (red), and challenges and opportunities in clinical translation (yellow).

Figure 2.. Disruption and restoration of tissue homeostasis.

Left: bone tissue homeostasis, including bone resorption by osteoclasts and osteoblasts. Middle: tumor cells cause disruption of homeostasis, leading to altered microenvironment factors. Conventional therapy leads to increasing tumor resistance. Right: evolution-based treatment strategies aim to restore some degree of homeostasis while allowing the tumor to remain sensitive to future treatment.

Figure 3.. Model schematic, calibration, validation, and prediction.

Adapted from Figure 4 of Brady-Nicholls et al., 2021. (A) Model schematic of treatment-resistant stem cells, sensitive non-stem cells, and prostate-specific antigen interactions. (B) Model calibration (patient 1014) and validation (patient 1016). Nested optimization was used to determine the cohort uniform parameters $\rho$ and $\phi$ and the patient-specific parameters $p_s$ and $\alpha$ for the training cohort. The uniform values were fixed in the testing cohort, and optimization was used to find the patient-specific parameters $p_s$ and $\alpha$. (C) Model predictions for patient 1016. The model predicted resistance in 39% of cycle 2 simulations and response in 100% of cycle 3 simulations. Cycle 4 predictions showed resistance in 63% of model simulations. Using cycle-specific cutoffs $k_2,k_3$, and $k_4$, the model correctly predicted that patient 1016 would continue to respond in cycles 2 and 3 but become resistant in cycle 4.

Figure 4.. Timeline of advancements in adaptive therapy: a selection of influential papers and key clinical trials leading to advancements in the field of adaptive therapy.

This selection includes papers with experimental or clinical adaptive data in addition to well-cited theoretical publications.