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Understanding the Evolutionary Dynamics and Ecology of Cancer in Treatment Resistance

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A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 30359

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Special Issue Editor

Prof. Dr. David Basanta

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Guest Editor

Integrated Mathematical Oncology department, H. Lee Moffit Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL-33612, USA
Interests: cancer evolution; metastasis; tumor microenvironment; non-cell autonomous interactions
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

As the importance of somatic evolution in driving cancer initiation and progression is being increasingly recognized, we are now in a place where we can begin to understand its potential role in treatment. Virtually all advanced tumors become resistant to clinically available treatments. Evolution and ecology are key elements in explaining this resistance: in a sufficiently heterogeneous tumor, certain tumor cells will always avoid the full impact of the treatment. This could be the result of insufficient delivery due to the microenvironment, the cells having a (relative) degree of resistance to said treatment through genetic and epigenetic mechanisms, or as a result of the interactions between tumor cells with cells in the stroma. A better understanding of these factors could help us delay or even prevent the emergence of resistance and, potentially, to steer the evolution of the tumor towards clinically desirable outcomes in a patient-specific manner.

For this Special Issue of Cancers, we invite research articles and focused reviews on all aspects of cancer evolution and ecology related to treatment.

Prof. Dr. David Basanta
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

cancer
evolution
treatment
resistance
heterogeneity

Published Papers (10 papers)

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Research

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8 pages, 1314 KiB

Open AccessArticle

Range-Bounded Adaptive Therapy in Metastatic Prostate Cancer

by Renee Brady-Nicholls and Heiko Enderling

Cancers 2022, 14(21), 5319; https://doi.org/10.3390/cancers14215319 - 28 Oct 2022

Cited by 3 | Viewed by 1618

Abstract

Adaptive therapy with abiraterone acetate (AA), whereby treatment is cycled on and off, has been presented as an alternative to continuous therapy for metastatic castration resistant prostate cancer (mCRPC). It is hypothesized that cycling through treatment allows sensitive cells to competitively suppress resistant cells, thereby increasing the amount of time that treatment is effective. It has been proposed that there exists a subset of patients for whom this competition can be enhanced through slight modifications. Here, we investigate how adaptive AA can be modified to extend time to progression using a simple mathematical model of stem cell, non-stem cell, and prostate-specific antigen (PSA) dynamics. The model is calibrated to longitudinal PSA data from 16 mCRPC patients undergoing adaptive AA in a pilot clinical study at Moffitt Cancer Center. Model parameters are then used to simulate range-bounded adaptive therapy (RBAT) whereby treatment is modulated to maintain PSA levels between pre-determined patient-specific bounds. Model simulations of RBAT are compared to the clinically applied adaptive therapy and show that RBAT can further extend time to progression, while reducing the cumulative dose patients received in 11/16 patients. Simulations also show that the cumulative dose can be reduced by up to 40% under RBAT. Through small modifications to the conventional adaptive therapy design, our study demonstrates that RBAT offers the opportunity to improve patient care, particularly in those patients who do not respond well to conventional adaptive therapy. Full article

(This article belongs to the Special Issue Understanding the Evolutionary Dynamics and Ecology of Cancer in Treatment Resistance)

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29 pages, 7665 KiB

Open AccessArticle

In Silico Investigations of Multi-Drug Adaptive Therapy Protocols

by Daniel S. Thomas, Luis H. Cisneros, Alexander R. A. Anderson and Carlo C. Maley

Cancers 2022, 14(11), 2699; https://doi.org/10.3390/cancers14112699 - 30 May 2022

Cited by 5 | Viewed by 2442

Abstract

The standard of care for cancer patients aims to eradicate the tumor by killing the maximum number of cancer cells using the maximum tolerated dose (MTD) of a drug. MTD causes significant toxicity and selects for resistant cells, eventually making the tumor refractory to treatment. Adaptive therapy aims to maximize time to progression (TTP), by maintaining sensitive cells to compete with resistant cells. We explored both dose modulation (DM) protocols and fixed dose (FD) interspersed with drug holiday protocols. In contrast to previous single drug protocols, we explored the determinants of success of two-drug adaptive therapy protocols, using an agent-based model. In almost all cases, DM protocols (but not FD protocols) increased TTP relative to MTD. DM protocols worked well when there was more competition, with a higher cost of resistance, greater cell turnover, and when crowded proliferating cells could replace their neighbors. The amount that the drug dose was changed, mattered less. The more sensitive the protocol was to tumor burden changes, the better. In general, protocols that used as little drug as possible, worked best. Preclinical experiments should test these predictions, especially dose modulation protocols, with the goal of generating successful clinical trials for greater cancer control. Full article

(This article belongs to the Special Issue Understanding the Evolutionary Dynamics and Ecology of Cancer in Treatment Resistance)

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19 pages, 4312 KiB

Open AccessArticle

Optimizing Adaptive Therapy Based on the Reachability to Tumor Resistant Subpopulation

by Jiali Wang, Yixuan Zhang, Xiaoquan Liu and Haochen Liu

Cancers 2021, 13(21), 5262; https://doi.org/10.3390/cancers13215262 - 20 Oct 2021

Cited by 5 | Viewed by 1860

Abstract

Adaptive therapy exploits the self-organization of tumor cells to delay the outgrowth of resistant subpopulations successfully. When the tumor has aggressive resistant subpopulations, the outcome of adaptive therapy was not superior to maximum tolerated dose therapy (MTD). To explore methods to improve the adaptive therapy’s performance of this case, the tumor system was constructed by osimertinib-sensitive and resistant cell lines and illustrated by the Lotka-Volterra model in this study. Restore index proposed to assess the system reachability can predict the duration of each treatment cycle. Then the threshold of the restore index was estimated to evaluate the timing of interrupting the treatment cycle and switching to high-frequency administration. The introduced reachability-based adaptive therapy and classic adaptive therapy were compared through simulation and animal experiments. The results suggested that reachability-based adaptive therapy showed advantages when the tumor has an aggressive resistant subpopulation. This study provides a feasible method for evaluating whether to continue the adaptive therapy treatment cycle or switch to high-frequency administration. This method improves the gain of adaptive therapy by taking into account the benefits of tumor intra-competition and the tumor control of killing sensitive subpopulation. Full article

(This article belongs to the Special Issue Understanding the Evolutionary Dynamics and Ecology of Cancer in Treatment Resistance)

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18 pages, 4440 KiB

Open AccessArticle

Drug-Induced Resistance and Phenotypic Switch in Triple-Negative Breast Cancer Can Be Controlled via Resolution and Targeting of Individualized Signaling Signatures

by Swetha Vasudevan, Ibukun A. Adejumobi, Heba Alkhatib, Sangita Roy Chowdhury, Shira Stefansky, Ariel M. Rubinstein and Nataly Kravchenko-Balasha

Cancers 2021, 13(19), 5009; https://doi.org/10.3390/cancers13195009 - 06 Oct 2021

Cited by 14 | Viewed by 2401

Abstract

Triple-negative breast cancer (TNBC) is an aggressive subgroup of breast cancers which is treated mainly with chemotherapy and radiotherapy. Epidermal growth factor receptor (EGFR) was considered to be frequently expressed in TNBC, and therefore was suggested as a therapeutic target. However, clinical trials of EGFR inhibitors have failed. In this study, we examine the relationship between the patient-specific TNBC network structures and possible mechanisms of resistance to anti-EGFR therapy. Using an information-theoretical analysis of 747 breast tumors from the TCGA dataset, we resolved individualized protein network structures, namely patient-specific signaling signatures (PaSSS) for each tumor. Each PaSSS was characterized by a set of 1–4 altered protein–protein subnetworks. Thirty-one percent of TNBC PaSSSs were found to harbor EGFR as a part of the network and were predicted to benefit from anti-EGFR therapy as long as it is combined with anti-estrogen receptor (ER) therapy. Using a series of single-cell experiments, followed by in vivo support, we show that drug combinations which are not tailored accurately to each PaSSS may generate evolutionary pressure in malignancies leading to an expansion of the previously undetected or untargeted subpopulations, such as ER+ populations. This corresponds to the PaSSS-based predictions suggesting to incorporate anti-ER drugs in certain anti-TNBC treatments. These findings highlight the need to tailor anti-TNBC targeted therapy to each PaSSS to prevent diverse evolutions of TNBC tumors and drug resistance development. Full article

(This article belongs to the Special Issue Understanding the Evolutionary Dynamics and Ecology of Cancer in Treatment Resistance)

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18 pages, 1944 KiB

Open AccessArticle

Collapse of Intra-Tumor Cooperation Induced by Engineered Defector Cells

by Marco Archetti

Cancers 2021, 13(15), 3674; https://doi.org/10.3390/cancers13153674 - 22 Jul 2021

Cited by 6 | Viewed by 2224

Abstract

Anti-cancer therapies promote clonal selection of resistant cells that evade treatment. Effective therapy must be stable against the evolution of resistance. A potential strategy based on concepts from evolutionary game theory is to impair intra-tumor cooperation using genetically modified cells in which genes coding for essential growth factors have been knocked out. Such engineered cells would spread by clonal selection, driving the collapse of intra-tumor cooperation and a consequent reduction in tumor growth. Here, I test this idea in vitro in four cancer types (neuroendocrine pancreatic cancer, mesothelioma, lung adenocarcinoma and multiple myeloma). A reduction, or even complete eradication, of the producer clone and the consequent reduction in cell proliferation, is achieved in some but not all cases by introducing a small fraction of non-producer cells in the population. I show that the collapse of intra-tumor cooperation depends on the cost/benefit ratio of growth factor production. When stable cooperation among producer and non-producer cells occurs, its collapse can be induced by increasing the number of growth factors available to the cells. Considerations on nonlinear dynamics in the framework of evolutionary game theory explain this as the result of perturbation of the equilibrium of a system that resembles a public goods game, in which the production of growth factors is a cooperative phenotype. Inducing collapse of intra-tumor cooperation by engineering cancer cells will require the identification of growth factors that are essential for the tumor and that have a high cost of production for the cell. Full article

(This article belongs to the Special Issue Understanding the Evolutionary Dynamics and Ecology of Cancer in Treatment Resistance)

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16 pages, 1590 KiB

Open AccessArticle

Are Adaptive Chemotherapy Schedules Robust? A Three-Strategy Stochastic Evolutionary Game Theory Model

by Rajvir Dua, Yongqian Ma and Paul K. Newton

Cancers 2021, 13(12), 2880; https://doi.org/10.3390/cancers13122880 - 09 Jun 2021

Cited by 8 | Viewed by 2385

Abstract

We investigate the robustness of adaptive chemotherapy schedules over repeated cycles and a wide range of tumor sizes. Using a non-stationary stochastic three-component fitness-dependent Moran process model (to track frequencies), we quantify the variance of the response to treatment associated with multidrug adaptive schedules that are designed to mitigate chemotherapeutic resistance in an idealized (well-mixed) setting. The finite cell (N tumor cells) stochastic process consists of populations of chemosensitive cells, chemoresistant cells to drug 1, and chemoresistant cells to drug 2, and the drug interactions can be synergistic, additive, or antagonistic. Tumor growth rates in this model are proportional to the average fitness of the tumor as measured by the three populations of cancer cells compared to a background microenvironment average value. An adaptive chemoschedule is determined by using the

N \to \infty

limit of the finite-cell process (i.e., the adjusted replicator equations) which is constructed by finding closed treatment response loops (which we call evolutionary cycles) in the three component phase-space. The schedules that give rise to these cycles are designed to manage chemoresistance by avoiding competitive release of the resistant cell populations. To address the question of how these cycles perform in practice over large patient populations with tumors across a range of sizes, we consider the variances associated with the approximate stochastic cycles for finite N, repeating the idealized adaptive schedule over multiple periods. For finite cell populations, the distributions remain approximately multi-Gaussian in the principal component coordinates through the first three cycles, with variances increasing exponentially with each cycle. As the number of cycles increases, the multi-Gaussian nature of the distribution breaks down due to the fact that one of the three sub-populations typically saturates the tumor (competitive release) resulting in treatment failure. This suggests that to design an effective and repeatable adaptive chemoschedule in practice will require a highly accurate tumor model and accurate measurements of the sub-population frequencies or the errors will quickly (exponentially) degrade its effectiveness, particularly when the drug interactions are synergistic. Possible ways to extend the efficacy of the stochastic cycles in light of the computational simulations are discussed. Full article

(This article belongs to the Special Issue Understanding the Evolutionary Dynamics and Ecology of Cancer in Treatment Resistance)

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16 pages, 2707 KiB

Open AccessArticle

Quantification and Optimization of Standard-of-Care Therapy to Delay the Emergence of Resistant Bone Metastatic Prostate Cancer

by Arturo Araujo, Leah M. Cook, Jeremy S. Frieling, Winston Tan, John A. Copland II, Manish Kohli, Shilpa Gupta, Jasreman Dhillon, Julio Pow-Sang, Conor C. Lynch and David Basanta

Cancers 2021, 13(4), 677; https://doi.org/10.3390/cancers13040677 - 08 Feb 2021

Cited by 3 | Viewed by 3486

Abstract

Background: Bone metastatic prostate cancer (BMPCa), despite the initial responsiveness to androgen deprivation therapy (ADT), inevitably becomes resistant. Recent clinical trials with upfront treatment of ADT combined with chemotherapy or novel hormonal therapies (NHTs) have extended overall patient survival. These results indicate that there is significant potential for the optimization of standard-of-care therapies to delay the emergence of progressive metastatic disease. Methods: Here, we used data extracted from human bone metastatic biopsies pre- and post-abiraterone acetate/prednisone to generate a mathematical model of bone metastatic prostate cancer that can unravel the treatment impact on disease progression. Intra-tumor heterogeneity in regard to ADT and chemotherapy resistance was derived from biopsy data at a cellular level, permitting the model to track the dynamics of resistant phenotypes in response to treatment from biological first-principles without relying on data fitting. These cellular data were mathematically correlated with a clinical proxy for tumor burden, utilizing prostate-specific antigen (PSA) production as an example. Results: Using this correlation, our model recapitulated the individual patient response to applied treatments in a separate and independent cohort of patients (n = 24), and was able to estimate the initial resistance to the ADT of each patient. Combined with an intervention-decision algorithm informed by patient-specific prediction of initial resistance, we propose to optimize the sequence of treatments for each patient with the goal of delaying the evolution of resistant disease and limit cancer cell growth, offering evidence for an improvement against retrospective data. Conclusions: Our results show how minimal but widely available patient information can be used to model and track the progression of BMPCa in real time, offering a clinically relevant insight into the patient-specific evolutionary dynamics of the disease and suggesting new therapeutic options for intervention. Trial registration: NCT # 01953640. Funding: Funded by an NCI U01 (NCI) U01CA202958-01 and a Moffitt Team Science Award. CCL and DB were partly funded by an NCI PSON U01 (U01CA244101). AA was partly funded by a Department of Defense Prostate Cancer Research Program (W81XWH-15-1-0184) fellowship. LC was partly funded by a postdoctoral fellowship (PF-13-175-01-CSM) from the American Cancer Society. Full article

(This article belongs to the Special Issue Understanding the Evolutionary Dynamics and Ecology of Cancer in Treatment Resistance)

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13 pages, 817 KiB

Open AccessArticle

Modifying Adaptive Therapy to Enhance Competitive Suppression

by Elsa Hansen and Andrew F. Read

Cancers 2020, 12(12), 3556; https://doi.org/10.3390/cancers12123556 - 28 Nov 2020

Cited by 25 | Viewed by 5104

Abstract

Adaptive therapy is a promising new approach to cancer treatment. It is designed to leverage competition between drug-sensitive and drug-resistant cells in order to suppress resistance and maintain tumor control for longer. Prompted by encouraging results from a recent pilot clinical trial, we evaluate the design of this initial test of adaptive therapy and identify three simple modifications that should improve performance. These modifications are designed to increase competition and are easy to implement. Using the mathematical model that supported the recent adaptive therapy trial, we show that the suggested modifications further delay time to tumor progression and also increase the range of patients who can benefit from adaptive therapy. Full article

(This article belongs to the Special Issue Understanding the Evolutionary Dynamics and Ecology of Cancer in Treatment Resistance)

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Review

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16 pages, 1104 KiB

Open AccessReview

Scales of Cancer Evolution: Selfish Genome or Cooperating Cells?

by Branislav Brutovský

Cancers 2022, 14(13), 3253; https://doi.org/10.3390/cancers14133253 - 01 Jul 2022

Cited by 4 | Viewed by 2378

Abstract

The exploitation of the evolutionary modus operandi of cancer to steer its progression towards drug sensitive cancer cells is a challenging research topic. Integrating evolutionary principles into cancer therapy requires properly identified selection level, the relevant timescale, and the respective fitness of the principal selection unit on that timescale. Interpretation of some features of cancer progression, such as increased heterogeneity of isogenic cancer cells, is difficult from the most straightforward evolutionary view with the cancer cell as the principal selection unit. In the paper, the relation between the two levels of intratumour heterogeneity, genetic, due to genetic instability, and non-genetic, due to phenotypic plasticity, is reviewed and the evolutionary role of the latter is outlined. In analogy to the evolutionary optimization in a changing environment, the cell state dynamics in cancer clones are interpreted as the risk diversifying strategy bet hedging, optimizing the balance between the exploitation and exploration of the cell state space. Full article

(This article belongs to the Special Issue Understanding the Evolutionary Dynamics and Ecology of Cancer in Treatment Resistance)

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19 pages, 1279 KiB

Open AccessReview

Darwinian Approaches for Cancer Treatment: Benefits of Mathematical Modeling

by Sophia Belkhir, Frederic Thomas and Benjamin Roche

Cancers 2021, 13(17), 4448; https://doi.org/10.3390/cancers13174448 - 03 Sep 2021

Cited by 13 | Viewed by 3742

Abstract

One of the major problems of traditional anti-cancer treatments is that they lead to the emergence of treatment-resistant cells, which results in treatment failure. To avoid or delay this phenomenon, it is relevant to take into account the eco-evolutionary dynamics of tumors. Designing evolution-based treatment strategies may help overcoming the problem of drug resistance. In particular, a promising candidate is adaptive therapy, a containment strategy which adjusts treatment cycles to the evolution of the tumors in order to keep the population of treatment-resistant cells under control. Mathematical modeling is a crucial tool to understand the dynamics of cancer in response to treatments, and to make predictions about the outcomes of these treatments. In this review, we highlight the benefits of in silico modeling to design adaptive therapy strategies, and to assess whether they could effectively improve treatment outcomes. Specifically, we review how two main types of models (i.e., mathematical models based on Lotka–Volterra equations and agent-based models) have been used to model tumor dynamics in response to adaptive therapy. We give examples of the advances they permitted in the field of adaptive therapy and discuss about how these models can be integrated in experimental approaches and clinical trial design. Full article

(This article belongs to the Special Issue Understanding the Evolutionary Dynamics and Ecology of Cancer in Treatment Resistance)

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