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

eLife, Journal Year: 2023, Volume and Issue: 12

Published: March 23, 2023

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

Language: Английский

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Turnover Modulates the Need for a Cost of Resistance in Adaptive Therapy

Cancer Research, Journal Year: 2020, Volume and Issue: 81(4), P. 1135 - 1147

Published: Nov. 10, 2020

Abstract Adaptive therapy seeks to exploit intratumoral competition avoid, or at least delay, the emergence of resistance in cancer. Motivated by promising results prostate cancer, there is growing interest extending this approach other neoplasms. As such, it urgent understand characteristics a cancer that determine whether not will respond well adaptive therapy. A plausible candidate for such selection criterion fitness cost resistance. In article, we study general, but simple, mathematical model investigate presence necessary extend time progression beyond standard-of-care continuous Tumor cells were divided into sensitive and resistant populations their using system two ordinary differential equations based on Lotka–Volterra model. For tumors close environmental carrying capacity, was required. However, far from may be required see meaningful gains. Notably, important consider cell turnover tumor, discuss its role modulating impact cost. To conclude, present evidence predicted cost–turnover interplay data 67 patients with undergoing intermittent androgen deprivation Our work helps clarify under which circumstances beneficial suggests play an unexpectedly decision-making process. Significance: modulates speed against drug amplifying effects costs; as factor management via See related commentary Strobl et al., p. 811

Language: Английский

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Spatial structure impacts adaptive therapy by shaping intra-tumoral competition

Communications Medicine, Journal Year: 2022, Volume and Issue: 2(1)

Published: April 25, 2022

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

Language: Английский

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Agent-based methods facilitate integrative science in cancer

Trends in Cell Biology, Journal Year: 2022, Volume and Issue: 33(4), P. 300 - 311

Published: Nov. 17, 2022

Language: Английский

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State-dependent evolutionary models reveal modes of solid tumour growth

Nature Ecology & Evolution, Journal Year: 2023, Volume and Issue: 7(4), P. 581 - 596

Published: March 9, 2023

Spatial properties of tumour growth have profound implications for cancer progression, therapeutic resistance and metastasis. Yet, how spatial position governs cell division remains difficult to evaluate in clinical tumours. Here, we demonstrate that faster on the periphery leaves characteristic genetic patterns, which become evident when a phylogenetic tree is reconstructed from spatially sampled cells. Namely, rapidly dividing peripheral lineages branch more extensively acquire mutations than slower-dividing centre lineages. We develop Bayesian state-dependent evolutionary phylodynamic model (SDevo) quantifies these patterns infer differential rates between central this approach accurately infers varying birth simulated tumours across range conditions sampling strategies. then show SDevo outperforms state-of-the-art, non-cancer multi-state methods ignore sequence evolution. Finally, apply single-time-point, multi-region sequencing data hepatocellular carcinomas find evidence three- six-times-higher rate edge. With increasing availability high-resolution, sequencing, anticipate will be useful interrogating restrictions could extended non-spatial factors influence progression.

Language: Английский

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Stackelberg evolutionary game theory: how to manage evolving systems

Philosophical Transactions of the Royal Society B Biological Sciences, Journal Year: 2023, Volume and Issue: 378(1876)

Published: March 20, 2023

Stackelberg evolutionary game (SEG) theory combines classical and to frame interactions between a rational leader evolving followers. In some of these interactions, the wants preserve system (e.g. fisheries management), while in others, they try drive extinction pest control). Often worst strategy for is adopt constant aggressive overfishing management or maximum tolerable dose cancer treatment). Taking into account ecological dynamics typically leads better outcomes corresponds Nash equilibria game-theoretic terms. However, leader’s most profitable anticipate steer eco-evolutionary dynamics, leading equilibrium game. We show how our results have potential help fields where humans bring an desired outcome, such as, among management, treatment. Finally, we discuss limitations opportunities applying SEGs improve biological systems. This article part theme issue ‘Half century games: synthesis theory, application future directions’.

Language: Английский

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Treatment of evolving cancers will require dynamic decision support

Annals of Oncology, Journal Year: 2023, Volume and Issue: 34(10), P. 867 - 884

Published: Sept. 28, 2023

Cancer research has traditionally focused on developing new agents, but an underexplored question is that of the dose and frequency existing drugs. Based modus operandi established in early days chemotherapies, most drugs are administered according to predetermined schedules seek deliver maximum tolerated only adjusted for toxicity. However, we believe complex, evolving nature cancer requires a more dynamic personalized approach. Chronicling milestones field, show impact schedule choice crucially depends processes driving treatment response failure. As such, heterogeneity evolution dictate one-size-fits-all solution unlikely-instead, each patient should be mapped strategy best matches their current disease characteristics objectives (i.e. 'tumorscape'). To achieve this level personalization, need mathematical modeling. In perspective, propose five-step 'Adaptive Dosing Adjusted Personalized Tumorscapes (ADAPT)' paradigm integrate data understanding across scales derive schedules. We conclude with promising examples model-guided personalization call action address key outstanding challenges surrounding collection, model development, integration.

Language: Английский

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Computational approaches to modelling and optimizing cancer treatment

Nature Reviews Bioengineering, Journal Year: 2023, Volume and Issue: 1(10), P. 695 - 711

Published: July 19, 2023

Language: Английский

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Mathematical Model-Driven Deep Learning Enables Personalized Adaptive Therapy

Cancer Research, Journal Year: 2024, Volume and Issue: 84(11), P. 1929 - 1941

Published: April 3, 2024

Standard-of-care treatment regimens have long been designed for maximal cell killing, yet these strategies often fail when applied to metastatic cancers due the emergence of drug resistance. Adaptive developed as an alternative approach, dynamically adjusting suppress growth treatment-resistant populations and thereby delay, or even prevent, tumor progression. Promising clinical results in prostate cancer indicate potential optimize adaptive protocols. Here, we deep reinforcement learning (DRL) guide scheduling demonstrated that schedules can outperform current protocols a mathematical model calibrated dynamics, more than doubling time The DRL were robust patient variability, including both dynamics monitoring schedules. framework could produce interpretable, based on single burden threshold, replicating informing optimal strategies. had no knowledge underlying model, demonstrating capability help develop novel complex settings. Finally, proposed five-step pathway, which combined mechanistic modeling with integrated conventional tools improve interpretability compared traditional "black-box" models, allow translation this approach clinic. Overall, generated personalized consistently outperformed standard-of-care

Language: Английский

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Simulations probe the role of space in the interplay between drug-sensitive and drug-resistant cancer cells

Journal of Theoretical Biology, Journal Year: 2025, Volume and Issue: unknown, P. 112048 - 112048

Published: Feb. 1, 2025

Language: Английский

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