Chemotherapeutic Dose Scheduling Based on Tumor Growth Rates Provides a Case for Low-Dose Metronomic High-Entropy Therapies

Optimizing cancer therapy: a review of the multifaceted effects of metronomic chemotherapy

Metronomic chemotherapy (MCT), characterized by the continuous administration of chemotherapeutics at a lower dose without prolonged drug-free periods, has garnered significant attention over the last 2 decades. Extensive evidence from both pre-clinical and clinical settings indicates that MCT induces distinct biological effects than the standard Maximum Tolerated Dose (MTD) chemotherapy. The low toxicity profile, reduced likelihood of inducing acquired therapeutic resistance, and low cost of MCT render it an attractive chemotherapeutic regimen option. One of the most prominent aspects of MCT is its anti-angiogenesis effects. It has been shown to stimulate the expression of anti-angiogenic molecules, thereby inhibiting angiogenesis. In addition, MCT has been shown to decrease the regulatory T-cell population and promote anti-tumor immune response through inducing dendritic cell maturation and increasing the number of cytotoxic T-cells. Combination therapies utilizing MCT along with oncolytic virotherapy, radiotherapy or other chemotherapeutic regimens have been studied extensively. This review provides an overview of the current status of MCT research and the established mechanisms of action of MCT treatment and also offers insights into potential avenues of development for MCT in the future.

Low-Dose Chemotherapy Preferentially Shapes the Ileal Microbiome and Augments the Response to Immune Checkpoint Blockade by Activating AIM2 Inflammasome in Ileal Epithelial Cells

Intervention of the gut microbiome is a promising adjuvant strategy in cancer immunotherapy. Chemotherapeutic agents are recognized for their substantial impacts on the gut microbiome, yet their therapeutic potential as microbiome modulators remains uncertain, due to the complexity of microbiome-host-drug interactions. Here, it is showed that low-dose chemotherapy preferentially shapes the ileal microbiome to augment the extraintestinal immune response to anti-programmed death-1 (anti-PD-1) therapy without causing intestinal toxicity. Mechanistically, low-dose chemotherapy causes DNA damage restricted to highly-proliferative ileal epithelial cells, resulting in the accumulation of cytosolic dsDNA and the activation of the absent in melanoma 2 (AIM2) inflammasome. AIM2-dependent IL-18 secretion triggers the interplay between proximal Th1 cells and Paneth cells in ileal crypts, impairing the local antimicrobial host defense and resulting in ileal microbiome change. Intestinal epithelium-specific knockout of AIM2 in mice significantly attenuates CPT-11-caused IL-18 secretion, Paneth cell dysfunction, and ileal microbiome alteration. Moreover, AIM2 deficiency in mice or antibiotic microbial depletion attenuates chemotherapy-augmented antitumor responses to anti-PD1 therapy. Collectively, these findings provide mechanistic insights into how chemotherapy-induced genomic stress is transduced to gut microbiome change and support the rationale of applying low-dose chemotherapy as a promising adjuvant strategy in cancer immunotherapy with minimal toxicity.

Adaptive therapy: a tumor therapy strategy based on Darwinian evolution theory

Cancer progression is a dynamic process of continuous evolution, in which genetic diversity and heterogeneity are generated by clonal and subclonal amplification based on random mutations. Traditional cancer treatment strategies have a great challenge, which often leads to treatment failure due to drug resistance. Integrating evolutionary dynamics into treatment regimens may be an effective way to overcome the problem of drug resistance. In particular, a potential treatment is adaptive therapy, which strategy advocates containment strategies that adjust the treatment cycles according to tumor evolution to control the growth of treatment-resistant cells. In this review, we first summarize the shortcomings of traditional tumor treatment methods in evolution and then introduce the theoretical basis and research status of adaptive therapy. By analyzing the limitations of adaptive therapy and exploring possible solutions, we can broaden people's understanding of adaptive therapy and provide new insights and strategies for tumor treatment.

Stochastic competitive release and adaptive chemotherapy

We develop a finite-cell model of tumor natural selection dynamics to investigate the stochastic fluctuations associated with multiple rounds of adaptive chemotherapy. The adaptive cycles are designed to avoid chemoresistance in the tumor by managing the ecological mechanism of competitive release of a resistant subpopulation. Our model is based on a three-component evolutionary game played among healthy (H), sensitive (S), and resistant (R) populations of N cells, with a chemotherapy control parameter, C(t), which we use to dynamically impose selection pressure on the sensitive subpopulation to slow tumor growth and manage competitive release of the resistant population. The adaptive chemoschedule is designed based on the deterministic (N→∞) adjusted replicator dynamical system, then implemented using the finite-cell stochastic frequency dependent Moran process model (N=10K-50K) to ascertain the cumulative effect of the stochastic fluctuations on the efficacy of the adaptive schedules over multiple rounds. We quantify the stochastic fixation probability regions of the R and S populations in the HSR trilinear phase plane as a function of the control parameter C∈[0,1], showing that the size of the R region increases with increasing C. We then implement an adaptive time-dependent schedule C(t) for the stochastic model and quantify the variances (using principal component coordinates) associated with the evolutionary cycles over multiple rounds of adaptive therapy. The variances increase subquadratically through several rounds before the evolutionary cycle begins to break down. Despite this, we show the stochastic adaptive schedules are more effective at delaying resistance than standard maximum tolerated dose and low-dose metronomic schedules. The simplified low-dimensional model provides some insights on how well multiple rounds of adaptive therapies are likely to perform over a range of tumor sizes (i.e., different values of N) if the goal is to maintain a sustained balance among competing subpopulations of cells to avoid chemoresistance via competitive release in a stochastic environment.

COVID-19 vaccine incentive scheduling using an optimally controlled reinforcement learning model

We model Covid-19 vaccine uptake as a reinforcement learning dynamic between two populations: the vaccine adopters, and the vaccine hesitant. Using data available from the Center for Disease Control (CDC), we estimate the payoff matrix governing the interaction between these two groups over time and show they are playing a Hawk-Dove evolutionary game with an internal evolutionarily stable Nash equilibrium (the asymptotic percentage of vaccinated in the population). We then ask whether vaccine adoption can be improved by implementing dynamic incentive schedules that reward/punish the vaccine hesitant, and if so, what schedules are optimal and how effective are they likely to be? When is the optimal time to start an incentive program, how large should the incentives be, and is there a point of diminishing returns? By using a tailored replicator dynamic reinforcement learning model together with optimal control theory, we show that well designed and timed incentive programs can improve vaccine uptake by shifting the Nash equilibrium upward in large populations, but only so much, and incentive sizes above a certain threshold show diminishing returns.

Induction chemotherapy increases efficacy and survival rate of patients with locally advanced esophageal squamous cell carcinoma

Objective

The efficacy of concurrent chemoradiotherapy (CRT) after induction chemotherapy (IC) in the treatment of esophageal squamous cell carcinoma (ESCC) remains unclear. The purpose of this study was to explore the efficacy of IC in patients with ESCC.

Methods

124 patients with ESCC receiving CRT were included. Patients were divided into IC+CRT group and CRT group. Short-term and long-term efficacy as well as survival time of the two groups were compared, influencing factors of IC efficacy were investigated, and overall survival (OS) and progression-free survival (PFS) between the two groups were compared in different subgroups.

Results

There was no significant difference in the objective response rate (ORR) between the two groups. After IC, the ORR was higher in patients with single-drug concurrent chemotherapy weekly and patients with effective IC. In the long-term efficacy, advanced clinical stage patients had a shorter PFS compared to early-stage patients, and chemoradiotherapy mode ameliorates patients' PFS. OS and PFS of IC+CRT group were longer than that of CRT group in both tumor diameter <5cm and single-drug chemotherapy weekly subgroups. In addition, OS of IC+CRT group was longer than that of CRT group in pathological grade G1-2 subgroup.

Conclusions

IC improve the efficacy and survival rate of patients with locally advanced ESCC, and the benefits are more advantageous in subgroups of effective IC, pathological grade G1-2, tumor diameter < 5cm, single-drug concurrent chemotherapy weekly.

Organelle-inspired supramolecular nanomedicine to precisely abolish liver tumor growth and metastasis

Organelles are responsible for the efficient storage and transport of substances in living systems. A myriad of extracellular vesicles (EVs) acts as a bridge to exchange signaling molecules in cell–cell communication, and the highly dynamic tubulins and actins contribute to efficient intracellular substance transport. The inexhaustible cues of natural cargo delivery by organelles inspire researchers to explore the construction of biomimetic architectures for “smart” delivery carriers. Herein, we report a 10-hydroxycamptothecin (HCPT)-peptide conjugate HpYss that simulates the artificial EV-to-filament transformation process for precise liver cancer therapy. Under the sequential stimulus of extracellular alkaline phosphatase (ALP) and intracellular glutathione (GSH), HpYss proceeds via tandem self-assembly with a morphological transformation from nanoparticles to nanofibers. The experimental phase diagram elucidates the influence of ALP and GSH contents on the self-assembled nanostructures. In addition, the dynamic transformation of organelle-mimetic architectures that are formed by HpYss in HepG2 cells enables the efficient delivery of the anticancer drug HCPT to the nucleus, and the size–shape change from extracellular nanoparticles (50–100 nm) to intracellular nanofibers (4–9 nm) is verified to be of key importance for nuclear delivery. Nuclear targeting of HpYss amplifies apoptosis, thus significantly enhancing the inhibitory effect of HCPT (>10-fold) to HepG2 cells. Benefitting from the spatiotemporally controlled nanostructures, HpYss exhibited deep penetration, enhanced accumulation, and long-term retention in multicellular spheroid and xenograft models, potently abolishing liver tumor growth and preventing lung metastasis. We envision that our organelle-mimicking delivery strategy provides a novel paradigm for designing nanomedicine to cancer therapy.

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An organelle-inspired nanomedicine for precise liver cancer therapy is proposed.

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The delivery process mimics the transport of extracellular vesicles and filaments.

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The extra- and intracellular tandem self-assembly influence the nanostructures.

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The dynamic size–shape change of nanomedicine actuates the nuclear delivery.

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Spatiotemporally controlled nanomedicine abolishes liver tumor growth and lung metastasis.

Evaluation of solid tumor response to sequential treatment cycles via a new computational hybrid approach

The development of an in silico approach that evaluates and identifies appropriate treatment protocols for individuals could help grow personalized treatment and increase cancer patient lifespans. With this motivation, the present study introduces a novel approach for sequential treatment cycles based on simultaneously examining drug delivery, tumor growth, and chemotherapy efficacy. This model incorporates the physical conditions of tumor geometry, including tumor, capillary network, and normal tissue assuming real circumstances, as well as the intravascular and interstitial fluid flow, drug concentration, chemotherapy efficacy, and tumor recurrence. Three treatment approaches-maximum tolerated dose (MTD), metronomic chemotherapy (MC), and chemo-switching (CS)-as well as different chemotherapy schedules are investigated on a real tumor geometry extracted from image. Additionally, a sensitivity analysis of effective parameters of drug is carried out to evaluate the potential of using different other drugs in cancer treatment. The main findings are: (i) CS, MC, and MTD have the best performance in reducing tumor cells, respectively; (ii) multiple doses raise the efficacy of drugs that have slower clearance, higher diffusivity, and lower to medium binding affinities; (iii) the suggested approach to eradicating tumors is to reduce their cells to a predetermined rate through chemotherapy and then apply adjunct therapy.

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

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→∞ 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.

Role of synergy and antagonism in designing multidrug adaptive chemotherapy schedules

Chemotherapeutic resistance via the mechanism of competitive release of resistant tumor cell subpopulations is a major problem associated with cancer treatments and one of the main causes of tumor recurrence. Often, chemoresistance is mitigated by using multidrug schedules (two or more combination therapies) that can act synergistically, additively, or antagonistically on the heterogeneous population of cells as they evolve. In this paper, we develop a three-component evolutionary game theory model to design two-drug adaptive schedules that mitigate chemoresistance and delay tumor recurrence in an evolving collection of tumor cells with two resistant subpopulations and one chemosensitive population that has a higher baseline fitness but is not resistant to either drug. Using the nonlinear replicator dynamical system with a payoff matrix of Prisoner's Dilemma (PD) type (enforcing a cost to resistance), we investigate the nonlinear dynamics of this three-component system along with an additional tumor growth model whose growth rate is a function of the fitness landscape of the tumor cell populations. A key parameter determines whether the two drugs interact synergistically, additively, or antagonistically. We show that antagonistic drug interactions generally result in slower rates of adaptation of the resistant cells than synergistic ones, making them more effective in combating the evolution of resistance. We then design evolutionary cycles (closed loops) in the three-component phase space by shaping the fitness landscape of the cell populations (i.e., altering the evolutionary stable states of the game) using appropriately designed time-dependent schedules (adaptive therapy), altering the dosages and timing of the two drugs. We describe two key bifurcations associated with our drug interaction parameter which help explain why antagonistic interactions are more effective at controlling competitive release of the resistant population than synergistic interactions in the context of an evolving tumor.

The Immune Checkpoint Kick Start: Optimization of Neoadjuvant Combination Therapy Using Game Theory

PURPOSE

In an upcoming clinical trial at the Moffitt Cancer Center for women with stage 2/3 estrogen receptor-positive breast cancer, treatment with an aromatase inhibitor and a PD-L1 checkpoint inhibitor combination will be investigated to lower a preoperative endocrine prognostic index (PEPI) that correlates with relapse-free survival. PEPI is fundamentally a static index, measured at the end of neoadjuvant therapy before surgery. We have developed a mathematical model of the essential components of the PEPI score to identify successful combination therapy regimens that minimize tumor burden and metastatic potential, on the basis of time-dependent trade-offs in the system.

METHODS

We considered two molecular traits, CCR7 and PD-L1, which correlate with treatment response and increased metastatic risk. We used a matrix game model with the four phenotypic strategies to examine the frequency-dependent interactions of cancer cells. This game was embedded in an ecological model of tumor population-growth dynamics. The resulting model predicts evolutionary and ecological dynamics that track with changes in the PEPI score.

RESULTS

We considered various treatment regimens on the basis of combinations of the two therapies with drug holidays. By considering the trade off between tumor burden and metastatic potential, the optimal therapy plan was a 1-month kick start of the immune checkpoint inhibitor followed by 5 months of continuous combination therapy. Relative to a protocol giving both therapeutics together from the start, this delayed regimen resulted in transient suboptimal tumor regression while maintaining a phenotypic constitution that is more amenable to fast tumor regression for the final 5 months of therapy.

CONCLUSION

The mathematical model provides a useful abstraction of clinical intuition, enabling hypothesis generation and testing of clinical assumptions.

The pro-tumorigenic host response to cancer therapies

Resistance to cancer therapy remains a major challenge in clinical oncology. Although the initial treatment phase is often successful, eventual resistance, characterized by tumour relapse or spread, is discouraging. The majority of studies devoted to investigating the basis of resistance have focused on tumour-related changes that contribute to therapy resistance and tumour aggressiveness. However, over the last decade, the diverse roles of various host cells in promoting therapy resistance have become more appreciated. A growing body of evidence demonstrates that cancer therapy can induce host-mediated local and systemic responses, many of which shift the delicate balance within the tumour microenvironment, ultimately facilitating or supporting tumour progression. In this Review, recent advances in understanding how the host response to different cancer therapies may promote therapy resistance are discussed, with a focus on therapy-induced immunological, angiogenic and metastatic effects. Also summarized is the potential of evaluating the host response to cancer therapy in an era of precision medicine in oncology.

A Quasi Birth-and-Death model for tumor recurrence

A major cause of chemoresistance and recurrence in tumors is the presence of dormant tumor foci that survive chemotherapy and can eventually transition to active growth to regenerate the cancer. In this paper, we propose a Quasi Birth-and-Death (QBD) model for the dynamics of tumor growth and recurrence/remission of the cancer. Starting from a discrete-state master equation that describes the time-dependent transition probabilities between states with different numbers of dormant and active tumor foci, we develop a framework based on a continuum-limit approach to determine the time-dependent probability that an undetectable residual tumor will become large enough to be detectable. We derive an exact formula for the probability of recurrence at large times and show that it displays a phase transition as a function of the ratio of the death rate μ_A of an active tumor focus to its doubling rate λ. We also derive forward and backward Kolmogorov equations for the transition probability density in the continuum limit and, using a first-passage time formalism, we obtain a drift-diffusion equation for the mean recurrence time and solve it analytically to leading order for a large detectable tumor size N. We show that simulations of the discrete-state model agree with the analytical results, except for O(1/N) corrections. As an example of the use of our model in a clinical setting, we show that a range of model parameters can fit Kaplan-Meier recurrence-free survival data for ovarian cancer. Finally, we show in simulations that extending the duration of chemotherapy increases both the mean recurrence time and the asymptotic (large-time) probability of no recurrence. Our results should be useful in planning optimized chemotherapy dosing and duration for cancer treatment, especially in cancer types for which no targeted therapy is available.

Nonlinear adaptive control of competitive release and chemotherapeutic resistance

We use a three-component replicator system with healthy cells, sensitive cells, and resistant cells, with a prisoner's dilemma payoff matrix from evolutionary game theory, to model and control the nonlinear dynamical system governing the ecological mechanism of competitive release by which tumors develop chemotherapeutic resistance. The control method we describe is based on nonlinear trajectory design and energy transfer methods first introduced in the orbital mechanics literature for Hamiltonian systems. For continuous therapy, the basin boundaries of attraction associated with the chemo-sensitive population and the chemo-resistant population for increasing values of chemo-concentrations have an intertwined spiral structure with extreme sensitivity to changes in chemo-concentration level as well as sensitivity with respect to resistant mutations. For time-dependent therapies, we introduce an orbit transfer method to construct continuous families of periodic (closed) orbits by switching the chemo-dose at carefully chosen times and appropriate levels to design schedules that are superior to both maximum tolerated dose (MTD) schedules and low-dose metronomic (LDM) schedules, both of which ultimately lead to fixation of sensitive cells or resistant cells. By keeping the three subpopulations of cells in competition with each other indefinitely, we avoid fixation of the cancer cell population and regrowth of a resistant tumor. The method can be viewed as a way to dynamically shape the average population fitness landscape of a tumor to steer the chemotherapeutic response curve. We show that the method is remarkably insensitive to initial conditions and small changes in chemo-dosages, an important criterion for turning the method into an actionable strategy.

Capitalizing on competition: An evolutionary model of competitive release in metastatic castration resistant prostate cancer treatment

The development of chemotherapeutic resistance resulting in tumor relapse is largely the consequence of the mechanism of competitive release of pre-existing resistant tumor cells selected for regrowth after chemotherapeutic agents attack the previously dominant chemo-sensitive population. We introduce a prisoner's dilemma game theoretic mathematical model based on the replicator of three competing cell populations: healthy (cooperators), sensitive (defectors), and resistant (defectors) cells. The model is shown to recapitulate prostate-specific antigen measurement data from three clinical trials for metastatic castration-resistant prostate cancer patients treated with 1) prednisone, 2) mitoxantrone and prednisone and 3) docetaxel and prednisone. Continuous maximum tolerated dose schedules reduce the sensitive cell population, initially shrinking tumor burden, but subsequently "release" the resistant cells from competition to re-populate and re-grow the tumor in a resistant form. The evolutionary model allows us to quantify responses to conventional (continuous) therapeutic strategies as well as to design adaptive strategies.These novel adaptive strategies are robust to small perturbations in timing and extend simulated time to relapse from continuous therapy administration.

Role of textural heterogeneity parameters in patient selection for 177Lu-PSMA therapy via response prediction

Purpose

Prostate cancer is most common tumor in men causing significant patient mortality and morbidity. In newer diagnostic/therapeutic agents PSMA linked ones are specifically important. Analysis of textural heterogeneity parameters is associated with determination of innately aggressive and therapy resistant cell lines thus emphasizing their importance in therapy planning. The objective of current study was to assess predictive ability of tumor textural heterogeneity parameters from baseline ⁶⁸Ga-PSMA PET prior to ¹⁷⁷Lu-PSMA therapy.

Results

Entropy showed a negative correlation (r_s = −0.327, p = 0.006, AUC = 0.695) and homogeneity showed a positive correlation (r_s = 0.315, p = 0.008, AUC = 0.683) with change in pre and post therapy PSA levels.

Conclusions

Study showed potential for response prediction through baseline PET scan using textural features. It suggested that increase in heterogeneity of PSMA expression seems to be associated with an increased response to PSMA radionuclide therapy.

Materials and Methods

Retrospective analysis of 70 patients was performed. All patients had metastatic prostate cancer and were planned to undergo ¹⁷⁷Lu-PSMA therapy. Pre-therapeutic ⁶⁸Ga- PSMA PET scans were used for analysis. 3D volumes (VOIs) of 3 lesions each in bones and lymph nodes were manually delineated in static PET images. Five PET based textural heterogeneity parameters (COV, entropy, homogeneity, contrast, size variation) were determined. Results obtained were then compared with clinical parameters including pre and post therapy PSA, alkaline phosphate, bone specific alkaline phosphate levels and ECOG criteria. Spearman correlation was used to determine statistical dependence among variables. ROC analysis was performed to estimate the optimal cutoff value and AUC.

Pharmacokinetic/pharmacodynamic modeling of combination-chemotherapy for lung cancer

Chemotherapy for non-small cell lung cancer (NSCLC) typically involves a doublet regimen for a number of cycles. For any particular patient, a course of treatment is usually chosen from a large number of combinational protocols with drugs in concomitant or sequential administration. In spite of newer drugs and protocols, half of patients with early disease will live less than five years and 95% of those with advanced disease survive for less than one year. Here, we apply mathematical modeling to simulate tumor response to multiple drug regimens, with the capability to assess maximum tolerated dose (MTD) as well as metronomic drug administration. We couple pharmacokinetic-pharmacodynamic intracellular multi-compartment models with a model of vascularized tumor growth, setting input parameters from in vitro data, and using the models to project potential response in vivo. This represents an initial step towards the development of a comprehensive virtual system to evaluate tumor response to combinatorial drug regimens, with the goal to more efficiently identify optimal course of treatment with patient tumor-specific data. We evaluate cisplatin and gemcitabine with clinically-relevant dosages, and simulate four treatment NSCLC scenarios combining MTD and metronomic therapy. This work thus establishes a framework for systematic evaluation of tumor response to combination chemotherapy. The results with the chosen parameter set indicate that although a metronomic regimen may provide advantage over MTD, the combination of these regimens may not necessarily offer improved response. Future model evaluation of chemotherapy possibilities may help to assess their potential value to obtain sustained NSCLC regression for particular patients, with the ultimate goal of optimizing multiple-drug chemotherapy regimens in clinical practice.