Spatial structure impacts adaptive therapy by shaping intra-tumoral competition

Abstract

Background Adaptive therapy aims to tackle cancer drug resistance by leveraging resource competition between drug-sensitive and resistant cells. Here, we present a theoretical study of intra-tumoral competition during adaptive therapy, to investigate under which circumstances it will be superior to aggressive treatment. Methods We develop and analyse a simple, 2-D, on-lattice, agent-based tumour model in which cells are classified as fully drug-sensitive or resistant. Subsequently, we compare this model to its corresponding non-spatial ordinary differential equation model, and fit it to longitudinal prostate-specific antigen data from 65 prostate cancer patients undergoing intermittent androgen deprivation therapy following biochemical recurrence. Results Leveraging the individual-based nature of our model, we explicitly demonstrate competitive suppression of resistance during adaptive therapy, and examine how different factors, such as the initial resistance fraction or resistance costs, alter competition. This not only corroborates our theoretical understanding of adaptive therapy, but also reveals that competition of resistant cells with each other may play a more important role in adaptive therapy in solid tumours than was previously thought. To conclude, we present two case studies, which demonstrate the implications of our work for: (i) mathematical modelling of adaptive therapy, and (ii) the intra-tumoral dynamics in prostate cancer patients during intermittent androgen deprivation treatment, a precursor of adaptive therapy. Conclusion Our work shows that the tumour’s spatial architecture is an important factor in adaptive therapy and provides insights into how adaptive therapy leverages both inter- and intra-specific competition to control resistance.

The sum and the parts: dynamics of multiple and individual metastases during adaptive therapy

“…However, it is not the number of metastases that matters, but the largest lesions that dominate the dynamics. Larger metastases have longer cycles, while smaller metastases have quicker cycles, similar to findings where more spread out tumor seeds had faster cycles than larger patches of cells in [18]. We also found that more drug resistance slows the treatment cycles, as expected, and a faster cell turnover speeds up drug response time by increasing the surface area to aid in drug penetration but slows the regrowth time by reducing the net proliferation rate.…”

A Phase 1b Adaptive Androgen Deprivation Therapy Trial in Metastatic Castration Sensitive Prostate Cancer

“…To improve the accuracy of tumor burden assessment, we are expanding this model to incorporate clinical PSA, testosterone, imaging data, histology data on AR and Cyp17 immunohistochemistry and genomic data on ctDNA, AR amplification and mutations in ctDNA. New models to incorporate spatial heterogeneity are also being used to analyze the trial data [ 30 , 31 ]. Due to the small sample size and lack of long term follow up, we have not detected statistically significant associations between duration of adaptive therapy with Gleason score, choice of NHA (cyp17 inhibitor vs. AR antagonist), mCSPC status (de novo vs. recurrent, high-risk vs. non high-risk).…”

Delaying Emergence of Resistance to KRAS Inhibitors with Adaptive Therapy: “Treatment-to-Contain” Instead of “Treatment-to-Cure”

“…In fact, it slows down the rate at which cancer cells gain resistance with a prolonged treatment-free period owing to reduced drug exposure [39][40][41][42][43][44][45]. In contrast, the lack of prolonged treatment-free time during the continuous administration of a drug, such as sotorasib or adagrasib, results in an increased rate of cancer cells acquiring resistance mutations and prevents patients from achieving a complete response to a drug [46,47]. In the long run, this strategy has the potential for KRAS patients to be given the same treatment for a longer period, safely maintaining a safe tumour burden that is financially feasible for patients and increasing progression-free survival [48].…”