Towards Multidrug Adaptive Therapy

West, Jeffrey; Li, You; Zhang, Jingsong; Gatenby, Robert A.; Brown, Joel S.; Newton, Paul K.; Anderson, Alexander R.A.

doi:10.1158/0008-5472.can-19-2669

Cited by 167 publications

(147 citation statements)

References 58 publications

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“…Similar to how Lotka and Volterra both independently observed the dependence of prey on predator, 41 so to has the importance of interactions within the diverse tumor ecosystem become apparent to the field of cancer biology. 42,43 The outcome of these interactions shape this ecosystem, creating continuous feedback that defines the overall composition and growth characteristics. 44 As a result of this continuous feedback, increased resolution on the ecological underpinnings of the tumor environment is accompanied with the ability to manipulate and drive this dynamic system to clinically desirable endpoints.…”

Section: Discussionmentioning

confidence: 99%

Measuring competitive exclusion in non-small cell lung cancer

Farrokhian

Maltas

Dinh

et al. 2020

Preprint

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Therapeutic strategies for tumor control have traditionally assumed that maximizing reduction in tumor volume correlates with clinical efficacy. Unfortunately, this rapid decrease in tumor burden is almost invariably followed by the emergence of therapeutic resistance. Evolutionary based treatment strategies work to delay this inevitability by promoting the maintenance of tumoral heterogeneity. While these strategies have shown promise in recent clinical trials, they often rely on biological conjecture and intuition to derive parameters. Reproducibility of the success seen with this treatment paradigm is contingent on formal elucidation of underlying subclonal interactions. One such consequence of these interactions, “competitive release”, is an evolutionary phenomenon that describes the unopposed proliferation of resistant populations following maximally tolerated systemic therapies. While often assumed in evolutionary models of cancer, here we show the first empiric evidence of “competitive release” occurring in an in vitro tumor environment. We found that this phenomenon is modulated by both drug dose and initial population composition. As such, we observed that monotypic fitness differentials were insufficient to accurately predict the outcomes of this phenomenon. Instead, derivation of underlying frequency dependent evolutionary game dynamics is essential to understand resulting sub-population shifts through time. To evaluate the impact of these non-autonomous growth behaviors over longer time series, we used a range of commonly employed growth models, some of which are the foundation of ongoing clinical trials. While useful for identifying persistent qualitative features, we observed significant fragility and model specific behaviors that limited the ability of these models to make consistent quantitative predictions, even when the parameters were empirically derived.

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Section: Discussionmentioning

confidence: 99%

Measuring competitive exclusion in non-small cell lung cancer

Farrokhian

Maltas

Dinh

et al. 2020

Preprint

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“…The central issues of optimal time for drug switching to maintain an optimal sub-population structure is apropos to the issues explored in this and our own previous work. Adaptive therapy has demonstrated clinical utility in markedly improving tumor response for a subset of cancers in theory, [37] in pre-clinical models [38] and clinically [39]. While this purely theoretical study should not be used to inform clinical care at this time, [40] we submit that further application of the techniques herein present a way forward for precise timing of drug switching to further optimize single drug adaptive therapy and higher order drug sequences alike.…”

Section: Conclusion and Discussionmentioning

confidence: 93%

Modeling collaterally sensitive drug cycles: shaping heterogeneity to allow adaptive therapy

Yoon

Krishnan

Scott

2020

Preprint

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AbstractIn previous work, we focused on the optimal therapeutic strategy with a pair of drugs which are collaterally sensitive to each other, that is, a situation in which evolution of resistance to one drug induces sensitivity to the other, and vice versa. [1] Here, we have extended this exploration to the optimal strategy with a collaterally sensitive drug sequence of an arbitrary length, N(≥2). To explore this, we have developed a dynamical model of sequential drug therapies with N drugs. In this model, tumor cells are classified as one of N subpopulations represented as {Ri|i = 1, 2, …, N}. Each subpopulation, Ri, is resistant to ‘Drug i’ and each subpopulation, Ri−1 (or RN, if i = 1), is sensitive to it, so that Ri increases under ‘Drug i’ as it is resistant to it, and after drug-switching, decreases under ‘Drug i + 1’ as it is sensitive to that drug(s).Similar to our previous work examining optimal therapy with two drugs, we found that there is an initial period of time in which the tumor is ‘shaped’ into a specific makeup of each subpopulation, at which time all the drugs are equally effective . After this shaping period, all the drugs are quickly switched with duration relative to their efficacy in order to maintain each subpopulation, consistent with the ideas underlying adaptive therapy. [2]Additionally, we have developed methodologies to administer the optimal regimen under clinical or experimental situations in which no drug parameters and limited information of trackable populations data (all the subpopulations or only total population) are known. The therapy simulation based on these methodologies showed consistency with the theoretical effect of optimal therapy.

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“…The former is a c-MET, VEGFR2 and RET MKI that was demonstrated to significantly prolong PFS in patients with progressive MTC (11.2 months vs 4.0 months with placebo) (Elisei et al 2013). Vandetanib is an MKI for RET, VEGF and EGF receptors and was demonstrated in the trial setting to also significantly prolong PFS in metastatic MTC (30.2 months vs 19.2 months with placebo) (Wells et al 2012).…”

Section: Pharmacological Agents For Targeted Therapy In Nenmentioning

confidence: 99%

Using genetics to inform the pharmacological targeting of neuroendocrine neoplasms

Clift

Frilling

2020

Endocrine-Related Cancer

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Neuroendocrine neoplasms (NEN) are a class of tumours heterogeneous in terms of their anatomical sites of origin and clinical behaviour. Outdated perspectives of indolence have been superseded by appreciation for their myriad clinical challenges, such as the high rates of regional and distant metastases at initial diagnosis, lack of clarity on optimal treatment strategies/sequencing, and incompletely elucidated genetic/other pathophysiological drivers. The first randomised controlled trials in this arena were published approximately a decade ago – since then, increased understanding of the genetic drivers and signalling pathway perturbations in these tumours have suggested promise for precision therapy influenced by an individual tumour’s molecular sub-type, but this is yet to be realised for manifold reasons. In this article, the authors review the genetic landscapes as currently understood for selected forms of NEN and discuss the current and developing evidence to support the use of genetic information to influence therapy. They provide a critical assessment of the potential limitations of using such approaches, and also posit avenues for future developments in this arena.

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Towards Multidrug Adaptive Therapy

Cited by 167 publications

References 58 publications

Measuring competitive exclusion in non-small cell lung cancer

Measuring competitive exclusion in non-small cell lung cancer

Modeling collaterally sensitive drug cycles: shaping heterogeneity to allow adaptive therapy

Using genetics to inform the pharmacological targeting of neuroendocrine neoplasms

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