The impact of the spatial heterogeneity of resistant cells and fibroblasts on treatment response

A long-standing practice in the treatment of cancer is that of hitting hard with the maximum tolerated dose to eradicate tumors. This continuous therapy, however, selects for resistant cells, leading to the failure of the treatment. A different type of treatment strategy, adaptive therapy, has recently been shown to have a degree of success in both preclinical xenograft experiments and clinical trials. Adaptive therapy is used to maintain a tumor's volume by exploiting the competition between drug-sensitive and drug-resistant cells with minimum effective drug doses or timed drug holidays. To further understand the role of competition in the outcomes of adaptive therapy, we developed a 2D on-lattice agent-based model. Our simulations show that the superiority of the adaptive strategy over continuous therapy depends on the local competition shaped by the spatial distribution of resistant cells. Cancer cell migration and increased carrying capacity accelerate the progression of the tumor under both types of treatments by reducing the spatial competition. Intratumor competition can also be affected by fibroblasts, which produce microenvironmental factors that promote cancer cell growth. Our simulations show that the spatial architecture of fibroblasts modulates the benefits of adaptive therapy. Finally, as a proof of concept, we simulated the outcomes of adaptive therapy in multiple metastatic sites composed of different spatial distributions of fibroblasts and drug-resistant cell populations.

Emerging Insights into Targeted Therapy-Tolerant Persister Cells in Cancer

Drug resistance is perhaps the greatest challenge in improving outcomes for cancer patients undergoing treatment with targeted therapies. It is becoming clear that “persisters,” a subpopulation of drug-tolerant cells found in cancer populations, play a critical role in the development of drug resistance. Persisters are able to maintain viability under therapy but are typically slow cycling or dormant. These cells do not harbor classic drug resistance driver alterations, and their partial resistance phenotype is transient and reversible upon removal of the drug. In the clinic, the persister state most closely corresponds to minimal residual disease from which relapse can occur if treatment is discontinued or if acquired drug resistance develops in response to continuous therapy. Thus, eliminating persister cells will be crucial to improve outcomes for cancer patients. Using lung cancer targeted therapies as a primary paradigm, this review will give an overview of the characteristics of drug-tolerant persister cells, mechanisms associated with drug tolerance, and potential therapeutic opportunities to target this persister cell population in tumors.

Optimal Chemotherapy Scheduling for Non-Genetic Drug Resistance

One of the most difficult challenges in cancer therapy is the emergence of drug resistance within tumors. Sometimes drug resistance can emerge as the result of mutations and Darwinian selection. However, recently another phenomenon has been discovered, in which tumor cells switch back and forth between drug-sensitive and pre-resistant states. Upon exposure to the drug, sensitive cells die off, and pre-resistant cells become locked in to a state of permanent drug resistance. In this paper, we explore the implications of this transient state switching for therapy scheduling. We propose a model to describe the phenomenon and estimate parameters from experimental melanoma data. We then compare the performance of continuous and alternating drug schedules, and use sensitivity analysis to explore how different conditions affect the efficacy of each schedule. We find that for our estimated parameters, a continuous therapy schedule is optimal. However we also find that an alternating schedule can be optimal for other, hypothetical parameter sets, depending on the difference in growth rate between pre- drug and post-drug cells, the delay between exposure to the drug and emergence of resistance, and the rate at which pre-resistant cells become resistant relative to the rate at which they switch back to the sensitive state.

Propensity-Score Matched Analysis of the Efficacy of Maintenance/Continuous Therapy in Newly Diagnosed Patients With Multiple Myeloma: A Multicenter Retrospective Collaborative Study of the Japanese Society of Myeloma

Maintenance/continuous therapy is considered a standard of care for both transplant-eligible and -ineligible patients with multiple myeloma (MM). However, long-term benefits of such therapy have not yet been clarified in the context of clinical practice. We retrospectively analyzed the efficacy of maintenance/continuous therapy in newly diagnosed MM patients using the cohort data by propensity-score matching based on age, gender, revised International Staging System (R-ISS) stage, and implementation of transplantation to reduce the bias due to confounding variables. Among 720 patients, 161 were identified for each of the maintenance and no maintenance groups. Maintenance/continuous therapy employed immunomodulatory drugs (n = 83), proteasome inhibitors (n = 48), combination of both (n = 29), or dexamethasone alone (n = 1). Progression-free survival (PFS) was significantly prolonged in the maintenance group compared with the no maintenance group (median, 37.7 and 21.9 months, p = 0.0002, respectively). Prolongation of PFS was observed in both transplanted and non-transplanted patients (p = 0.017 and p = 0.0008, respectively), with standard risk (p < 0.00001), R-ISS stage I (p = 0.037) and stage II (p = 0.00094), and those without obtaining complete response (p = 0.0018). There was no significant difference in overall survival (p = 0.19), but it appeared to be better in non-transplanted patients by continuous therapy. These results support the usefulness of maintenance/continuous therapy in the management of MM.

Adaptive Therapy for Metastatic Melanoma: Predictions from Patient Calibrated Mathematical Models

Adaptive therapy is an evolution-based treatment approach that aims to maintain tumor volume by employing minimum effective drug doses or timed drug holidays. For successful adaptive therapy outcomes, it is critical to find the optimal timing of treatment switch points in a patient-specific manner. Here we develop a combination of mathematical models that examine interactions between drug-sensitive and resistant cells to facilitate melanoma adaptive therapy dosing and switch time points. The first model assumes genetically fixed drug-sensitive and -resistant popul tions that compete for limited resources. The second model considers phenotypic switching between drug-sensitive and -resistant cells. We calibrated each model to fit melanoma patient biomarker changes over time and predicted patient-specific adaptive therapy schedules. Overall, the models predict that adaptive therapy would have delayed time to progression by 6–25 months compared to continuous therapy with dose rates of 6–74% relative to continuous therapy. We identified predictive factors driving the clinical time gained by adaptive therapy, such as the number of initial sensitive cells, competitive effect, switching rate from resistant to sensitive cells, and sensitive cell growth rate. This study highlights that there is a range of potential patient-specific benefits of adaptive therapy and identifies parameters that modulate this benefit.