Evolutionary approaches to prolong progression-free survival in breast cancer

Contrasting Dynamics of Intracellular and Extracellular Antibiotic Resistance Genes in Response to Nutrient Variations in Aquatic Environments

The propagation of antibiotic resistance in environments, particularly aquatic environments that serve as primary pathways for antibiotic resistance genes (ARGs), poses significant health risks. The impact of nutrients, as key determinants of bacterial growth and metabolism, on the propagation of ARGs, particularly extracellular ARGs (eARGs), remains poorly understood. In this study, we collected microorganisms from the Yangtze River and established a series of microcosms to investigate how variations in nutrient levels and delivery frequency affect the relative abundance of intracellular ARGs (iARGs) and eARGs in bacterial communities. Our results show that the relative abundance of 7 out of 11 representative eARGs in water exceeds that of iARGs, while 8 iARGs dominate in biofilms. Notably, iARGs and eARGs consistently exhibited opposite responses to nutrient variation. When nutrient levels increased, iARGs in the water also increased, with the polluted group (COD = 333.3 mg/L, COD:N:P = 100:3:0.6, m/m) and the eutrophic group (COD = 100 mg/L, COD:N:P = 100:25:5, m/m) showing 1.2 and 3.2 times higher levels than the normal group (COD = 100 mg/L, COD:N:P = 100:10:2, m/m), respectively. In contrast, eARGs decreased by 6.7% and 8.4% in these groups. On the other hand, in biofilms, higher nutrient levels led to an increase in eARGs by 1.5 and 1.7 times, while iARGs decreased by 17.5% and 50.1% in the polluted and eutrophic groups compared to the normal group. Moreover, while increasing the frequency of nutrient delivery (from 1 time/10 d to 20 times/10 d) generally did not favor iARGs in either water or biofilm, it selectively enhanced eARGs in both. To further understand these dynamics, we developed an ARGs-nutrient model by integrating the Lotka-Volterra and Monod equations. The results highlight the complex interplay of bacterial growth, nutrient availability, and mechanisms such as horizontal gene transfer and secretion influencing ARGs' propagation, driving the opposite trend between these two forms of ARGs. This contrasting response between iARGs and eARGs contributes to a dynamic balance that stabilizes bacterial resistance levels amid nutrient fluctuations. This study offers helpful implications regarding the persistence of bacterial resistance in the environment.

Red light-activated depletion of drug-refractory glioblastoma stem cells and chemosensitization of an acquired-resistant mesenchymal phenotype

Glioblastoma stem cells (GSCs) are potent tumor initiators resistant to radiochemotherapy, and this subpopulation is hypothesized to re-populate the tumor milieu due to selection following conventional therapies. Here, we show that 5-aminolevulinic acid (ALA) treatment-a pro-fluorophore used for fluorescence-guided cancer surgery-leads to elevated levels of fluorophore conversion in patient-derived GSC cultures, and subsequent red light-activation induces apoptosis in both intrinsically temozolomide chemotherapy-sensitive and -resistant GSC phenotypes. Red light irradiation of ALA-treated cultures also exhibits the ability to target mesenchymal GSCs (Mes-GSCs) with induced temozolomide resistance. Furthermore, sub-lethal light doses restore Mes-GSC sensitivity to temozolomide, abrogating GSC-acquired chemoresistance. These results suggest that ALA is not only useful for fluorescence-guided glioblastoma tumor resection, but that it also facilitates a GSC drug-resistance agnostic, red light-activated modality to mop up the surgical margins and prime subsequent chemotherapy.

A war on many fronts: cross disciplinary approaches for novel cancer treatment strategies

Cancer is a disease characterized by uncontrolled cellular growth where cancer cells take advantage of surrounding cellular populations to obtain resources and promote invasion. Carcinomas are the most common type of cancer accounting for almost 90% of cancer cases. One of the major subtypes of carcinomas are adenocarcinomas, which originate from glandular cells that line certain internal organs. Cancers such as breast, prostate, lung, pancreas, colon, esophageal, kidney are often adenocarcinomas. Current treatment strategies include surgery, chemotherapy, radiation, targeted therapy, and more recently immunotherapy. However, patients with adenocarcinomas often develop resistance or recur after the first line of treatment. Understanding how networks of tumor cells interact with each other and the tumor microenvironment is crucial to avoid recurrence, resistance, and high-dose therapy toxicities. In this review, we explore how mathematical modeling tools from different disciplines can aid in the development of effective and personalized cancer treatment strategies. Here, we describe how concepts from the disciplines of ecology and evolution, economics, and control engineering have been applied to mathematically model cancer dynamics and enhance treatment strategies.

Tumor containment: a more general mathematical analysis

Clinical and pre-clinical data suggest that treating some tumors at a mild, patient-specific dose might delay resistance to treatment and increase survival time. A recent mathematical model with sensitive and resistant tumor cells identified conditions under which a treatment aiming at tumor containment rather than eradication is indeed optimal. This model however neglected mutations from sensitive to resistant cells, and assumed that the growth-rate of sensitive cells is non-increasing in the size of the resistant population. The latter is not true in standard models of chemotherapy. This article shows how to dispense with this assumption and allow for mutations from sensitive to resistant cells. This is achieved by a novel mathematical analysis comparing tumor sizes across treatments not as a function of time, but as a function of the resistant population size.

On minimising tumoural growth under treatment resistance

Drug resistance is a major challenge for curative cancer treatment, representing the main reason of death in patients. Evolutionary biology suggests pauses between treatment rounds as a way to delay or even avoid resistance emergence. Indeed, this approach has already shown promising preclinical and early clinical results, and stimulated the development of mathematical models for finding optimal treatment protocols. Due to their complexity, however, these models do not lend themself to a rigorous mathematical analysis, hence so far clinical recommendations generally relied on numerical simulations and ad-hoc heuristics. Here, we derive two mathematical models describing tumour growth under genetic and epigenetic treatment resistance, respectively, which are simple enough for a complete analytical investigation. First, we find key differences in response to treatment protocols between the two modes of resistance. Second, we identify the optimal treatment protocol which leads to the largest possible tumour shrinkage rate. Third, we fit the "epigenetic model" to previously published xenograft experiment data, finding excellent agreement, underscoring the biological validity of our approach. Finally, we use the fitted model to calculate the optimal treatment protocol for this specific experiment, which we demonstrate to cause curative treatment, making it superior to previous approaches which generally aimed at stabilising tumour burden. Overall, our approach underscores the usefulness of simple mathematical models and their analytical examination, and we anticipate our findings to guide future preclinical and, ultimately, clinical research in optimising treatment regimes.

Drug dependence in cancer is exploitable by optimally constructed treatment holidays

Cancers with acquired resistance to targeted therapy can become simultaneously dependent on the presence of the targeted therapy drug for survival, suggesting that intermittent therapy may slow resistance. However, relatively little is known about which tumours are likely to become dependent and how to schedule intermittent therapy optimally. Here we characterized drug dependence across a panel of over 75 MAPK-inhibitor-resistant BRAFV600E mutant melanoma models at the population and single-clone levels. Melanocytic differentiated models exhibited a much greater tendency to give rise to drug-dependent progeny than their dedifferentiated counterparts. Mechanistically, acquired loss of microphthalmia-associated transcription factor in differentiated melanoma models drives ERK-JunB-p21 signalling to enforce drug dependence. We identified the optimal scheduling of 'drug holidays' using simple mathematical models that we validated across short and long timescales. Without detailed knowledge of tumour characteristics, we found that a simple adaptive therapy protocol can produce near-optimal outcomes using only measurements of total population size. Finally, a spatial agent-based model showed that optimal schedules derived from exponentially growing cells in culture remain nearly optimal in the context of tumour cell turnover and limited environmental carrying capacity. These findings may guide the implementation of improved evolution-inspired treatment strategies for drug-dependent cancers.

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.

Intermetastatic and Intrametastatic Heterogeneity Shapes Adaptive Therapy Cycling Dynamics

Adaptive therapies that alternate between drug applications and drug-free vacations can exploit competition between sensitive and resistant cells to maximize the time to progression. However, optimal dosing schedules depend on the properties of metastases, which are often not directly measurable in clinical practice. Here, we proposed a framework for estimating features of metastases through tumor response dynamics during the first adaptive therapy treatment cycle. Longitudinal prostate-specific antigen (PSA) levels in 16 patients with metastatic castration-resistant prostate cancer undergoing adaptive androgen deprivation treatment were analyzed to investigate relationships between cycle dynamics and clinical variables such as Gleason score, the change in the number of metastases over a cycle, and the total number of cycles over the course of treatment. The first cycle of adaptive therapy, which consists of a response period (applying therapy until 50% PSA reduction), and a regrowth period (removing treatment until reaching initial PSA levels), delineated several features of the computational metastatic system: larger metastases had longer cycles; a higher proportion of drug-resistant cells slowed the cycles; and a faster cell turnover rate sped up drug response time and slowed regrowth time. The number of metastases did not affect cycle times, as response dynamics were dominated by the largest tumors rather than the aggregate. In addition, systems with higher intermetastasis heterogeneity responded better to continuous therapy and correlated with dynamics from patients with high or low Gleason scores. Conversely, systems with higher intrametastasis heterogeneity responded better to adaptive therapy and correlated with dynamics from patients with intermediate Gleason scores.

SIGNIFICANCE

Multiscale mathematical modeling combined with biomarker dynamics during adaptive therapy helps identify underlying features of metastatic cancer to inform treatment decisions.

The acquisition order of leukemic drug resistance mutations is directed by the selective fitness associated with each resistance mechanism

In Chronic Myeloid Leukemia, the transition from drug sensitive to drug resistant disease is poorly understood. Here, we used exploratory sequencing of gene transcripts to determine the mechanisms of drug resistance in a dasatinib resistant cell line model. Importantly, cell samples were collected sequentially during drug exposure and dose escalation, revealing several resistance mechanisms which fluctuated over time. BCR::ABL1 overexpression, BCR::ABL1 kinase domain mutation, and overexpression of the small molecule transporter ABCG2, were identified as dasatinib resistance mechanisms. The acquisition of mutations followed an order corresponding with the increase in selective fitness associated with each resistance mechanism. Additionally, it was demonstrated that ABCG2 overexpression confers partial ponatinib resistance. The results of this study have broad applicability and help direct effective therapeutic drug usage and dosing regimens and may be useful for clinicians to select the most efficacious therapy at the most beneficial time.

Modeling the effect of acquired resistance on cancer therapy outcomes

Adaptive therapy (AT) is an evolution-based treatment strategy that exploits cell-cell competition. Acquired resistance can change the competitive nature of cancer cells in a tumor, impacting AT outcomes. We aimed to determine if adaptive therapy can still be effective with cell's acquiring resistance. We developed an agent-based model for spatial tumor growth considering three different types of acquired resistance: random genetic mutations during cell division, drug-induced reversible (plastic) phenotypic changes, and drug-induced irreversible phenotypic changes. These three resistance mechanisms lead to different spatial distributions of resistant cells. To quantify the spatial distribution, we propose an extension of Ripley's K-function, Sampled Ripley's K-function (SRKF), which calculates the non-randomness of the resistance distribution over the tumor domain. Our model predicts that the emergent spatial distribution of resistance can determine the time to progression under both adaptive and continuous therapy (CT). Notably, a high rate of random genetic mutations leads to quicker progression under AT than CT due to the emergence of many small clumps of resistant cells. Drug-induced phenotypic changes accelerate tumor progression irrespective of the treatment strategy. Low-rate switching to a sensitive state reduces the benefits of AT compared to CT. Furthermore, we also demonstrated that drug-induced resistance necessitates aggressive treatment under CT, regardless of the presence of cancer-associated fibroblasts. However, there is an optimal dose that can most effectively delay tumor relapse under AT by suppressing resistance. In conclusion, this study demonstrates that diverse resistance mechanisms can shape the distribution of resistance and thus determine the efficacy of adaptive therapy.

Adaptive therapy achieves long-term control of chemotherapy resistance in high grade ovarian cancer

Drug resistance results in poor outcomes for most patients with metastatic cancer. Adaptive Therapy (AT) proposes to address this by exploiting presumed fitness costs incurred by drug-resistant cells when drug is absent, and prescribing dose reductions to allow fitter, sensitive cells to re-grow and re-sensitise the tumour. However, empirical evidence for treatment-induced fitness change is lacking. We show that fitness costs in chemotherapy-resistant ovarian cancer cause selective decline and apoptosis of resistant populations in low-resource conditions. Moreover, carboplatin AT caused fluctuations in sensitive/resistant tumour population size in vitro and significantly extended survival of tumour-bearing mice. In sequential blood-derived cell-free DNA and tumour samples obtained longitudinally from ovarian cancer patients during treatment, we inferred resistant cancer cell population size through therapy and observed it correlated strongly with disease burden. These data have enabled us to launch a multicentre, phase 2 randomised controlled trial (ACTOv) to evaluate AT in ovarian cancer.

The role of PARP inhibitor combination therapy in ovarian cancer

The use of PARP inhibitors (PARPi) has transformed the care of advanced high-grade serous/endometrioid ovarian cancer. PARPi are now available to patients in both the first-line and recurrent platinum-sensitive disease settings; therefore, most patients will receive PARPi at some point in their treatment pathway. The majority of this expanding population of patients eventually acquire resistance to PARPi, in addition to those with primary PARPi resistance. We discuss the rationale behind developing combination therapies, to work synergistically with PARPi and overcome mechanisms of resistance to restore drug sensitivity, and clinical evidence of their efficacy to date.

Mathematical Model of Clonal Evolution Proposes a Personalised Multi-Modal Therapy for High-Risk Neuroblastoma

Neuroblastoma is the most common extra-cranial solid tumour in children. Despite multi-modal therapy, over half of the high-risk patients will succumb. One contributing factor is the one-size-fits-all nature of multi-modal therapy. For example, during the first step (induction chemotherapy), the standard regimen (rapid COJEC) administers fixed doses of chemotherapeutic agents in eight two-week cycles. Perhaps because of differences in resistance, this standard regimen results in highly heterogeneous outcomes in different tumours. In this study, we formulated a mathematical model comprising ordinary differential equations. The equations describe the clonal evolution within a neuroblastoma tumour being treated with vincristine and cyclophosphamide, which are used in the rapid COJEC regimen, including genetically conferred and phenotypic drug resistance. The equations also describe the agents’ pharmacokinetics. We devised an optimisation algorithm to find the best chemotherapy schedules for tumours with different pre-treatment clonal compositions. The optimised chemotherapy schedules exploit the cytotoxic difference between the two drugs and intra-tumoural clonal competition to shrink the tumours as much as possible during induction chemotherapy and before surgical removal. They indicate that induction chemotherapy can be improved by finding and using personalised schedules. More broadly, we propose that the overall multi-modal therapy can be enhanced by employing targeted therapies against the mutations and oncogenic pathways enriched and activated by the chemotherapeutic agents. To translate the proposed personalised multi-modal therapy into clinical use, patient-specific model calibration and treatment optimisation are necessary. This entails a decision support system informed by emerging medical technologies such as multi-region sequencing and liquid biopsies. The results and tools presented in this paper could be the foundation of this decision support system.

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

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

Adaptive therapy to circumvent drug resistance to tyrosine kinase inhibitors in cancer: is it clinically relevant?

INTRODUCTION

Cancer is highly adaptable and is constantly evolving against current targeted therapies such as tyrosine kinase inhibitors. Despite advances in recent decades, the emergence of drug resistance to tyrosine kinase inhibitors constantly hampers therapeutic efficacy of cancer treatment. Continuous therapy versus intermittent clinical regimen has been a debate in drug administration of cancer patients. An ecologically-inspired shift in cancer treatment known as 'adaptive therapy' intends to improve the drug administration of drugs to cancer patients that can delay emergence of drug resistance.

AREAS COVERED

We discuss improved understanding of the concept of drug resistance, the basis of continuous therapy, intermittent clinical regimens, and adaptive therapy will be reviewed. In addition, we discuss how adaptive therapy provides guidance for future cancer treatment.

EXPERT OPINION

The current understanding of drug resistance in cancer leads to poor prognosis and limited treatment options in patients. Fighting drug resistance mutants is constantly followed by new forms of resistance. In most reported cases, continuous therapy leads to drug resistance and an intermittent clinical regimen vaguely delays it. However, adaptive therapy, conceptually, exploits multiple parameters that can suppress the growth of drug resistance and provides safe treatment for cancer patients in the future.

Range-Bounded Adaptive Therapy in Metastatic Prostate Cancer

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.

Evolution-based mathematical models significantly prolong response to abiraterone in metastatic castrate-resistant prostate cancer and identify strategies to further improve outcomes

Background

Abiraterone acetate is an effective treatment for metastatic castrate-resistant prostate cancer (mCRPC), but evolution of resistance inevitably leads to progression. We present a pilot study in which abiraterone dosing is guided by evolution-informed mathematical models to delay onset of resistance.

Methods

In the study cohort, abiraterone was stopped when PSA was <50% of pretreatment value and resumed when PSA returned to baseline. Results are compared to a contemporaneous cohort who had >50% PSA decline after initial abiraterone administration and met trial eligibility requirements but chose standard of care (SOC) dosing.

Results

17 subjects were enrolled in the adaptive therapy group and 16 in the SOC group. All SOC subjects have progressed, but four patients in the study cohort remain stably cycling (range 53-70 months). The study cohort had significantly improved median time to progression (TTP; 33.5 months; p<0.001) and median overall survival (OS; 58.5 months; hazard ratio, 0.41, 95% confidence interval (CI), 0.20-0.83, p<0.001) compared to 14.3 and 31.3 months in the SOC cohort. On average, study subjects received no abiraterone during 46% of time on trial. Longitudinal trial data demonstrated the competition coefficient ratio (αRS/αSR) of sensitive and resistant populations, a critical factor in intratumoral evolution, was two- to threefold higher than pre-trial estimates. Computer simulations of intratumoral evolutionary dynamics in the four long-term survivors found that, due to the larger value for αRS/αSR, cycled therapy significantly decreased the resistant population. Simulations in subjects who progressed predicted further increases in OS could be achieved with prompt abiraterone withdrawal after achieving 50% PSA reduction.

Conclusions

Incorporation of evolution-based mathematical models into abiraterone monotherapy for mCRPC significantly increases TTP and OS. Computer simulations with updated parameters from longitudinal trial data can estimate intratumoral evolutionary dynamics in each subject and identify strategies to improve outcomes.

Funding

Moffitt internal grants and NIH/NCI U54CA143970-05 (Physical Science Oncology Network).

Optimal Strategy and Benefit of Pulsed Therapy Depend On Tumor Heterogeneity and Aggressiveness at Time of Treatment Initiation

Therapeutic resistance is a fundamental obstacle in cancer treatment. Tumors that initially respond to treatment may have a preexisting resistant subclone or acquire resistance during treatment, making relapse theoretically inevitable. Here, we investigate treatment strategies that may delay relapse using mathematical modeling. We find that for a single-drug therapy, pulse treatment-short, elevated doses followed by a complete break from treatment-delays relapse compared with continuous treatment with the same total dose over a length of time. For tumors treated with more than one drug, continuous combination treatment is only sometimes better than sequential treatment, while pulsed combination treatment or simply alternating between the two therapies at defined intervals delays relapse the longest. These results are independent of the fitness cost or benefit of resistance, and are robust to noise. Machine-learning analysis of simulations shows that the initial tumor response and heterogeneity at the start of treatment suffice to determine the benefit of pulsed or alternating treatment strategies over continuous treatment. Analysis of eight tumor burden trajectories of breast cancer patients treated at Memorial Sloan Kettering Cancer Center shows the model can predict time to resistance using initial responses to treatment and estimated preexisting resistant populations. The model calculated that pulse treatment would delay relapse in all eight cases. Overall, our results support that pulsed treatments optimized by mathematical models could delay therapeutic resistance.

Spatial structure impacts adaptive therapy by shaping intra-tumoral competition

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.

Cancer therapy traditionally focuses on maximising tumour cell kill with the aim of achieving a cure, but such aggressive treatment can open up space for drug-resistant cells to grow. In contrast, adaptive therapy aims to leverage competition between drug-sensitive and resistant cells by adjusting treatment to maintain the tumour at a tolerable size, whilst preserving drug-sensitive cells. This approach is being tested in trials but is not yet widely used as deeper understanding of cell-cell competition is required. Here, we used a mathematical model to investigate how strongly, and with whom, resistant cells compete during continuous and adaptive therapy, and applied our insights to hormone therapy in prostate cancer where adaptive therapy has recently been successfully trialed. Our results provide new insights into how adaptive therapy works and show that, by shaping cell competition, the tumour’s spatial architecture is important in determining therapy response.

Strobl et al. develop an agent-based spatial model of drug resistance in tumour cells under adaptive therapy. Using this model, they investigate how the tumour’s spatial architecture impacts intratumoural competitive dynamics of drug-sensitive vs. -resistant clones in response to therapy.

Intermittent treatment of BRAFV600E melanoma cells delays resistance by adaptive resensitization to drug rechallenge

Preclinical studies of metastatic melanoma treated with targeted therapeutics have suggested that alternating periods of treatment and withdrawal might delay the onset of resistance. This has been attributed to drug addiction, where cells lose fitness upon drug removal due to the resulting hyperactivation of mitogen-activated protein (MAP) kinase signaling. This study presents evidence that the intermittent treatment response can also be explained by the resensitization of cells following drug removal and enhanced cell loss upon drug rechallenge. Resensitization is accompanied by adaptive transcriptomic switching and occurs despite the sustained expression of resistance genes throughout the intermittent treatment.

Patients with melanoma receiving drugs targeting BRAF^V600E and mitogen-activated protein (MAP) kinase kinases 1 and 2 (MEK1/2) invariably develop resistance and face continued progression. Based on preclinical studies, intermittent treatment involving alternating periods of drug withdrawal and rechallenge has been proposed as a method to delay the onset of resistance. The beneficial effect of intermittent treatment has been attributed to drug addiction, where drug withdrawal reduces the viability of resistant cells due to MAP kinase pathway hyperactivation. However, the mechanistic basis of the intermittent effect is incompletely understood. We show that intermittent treatment with the BRAF^V600E inhibitor, LGX818/encorafenib, suppresses growth compared with continuous treatment in human melanoma cells engineered to express BRAF^V600E, p61-BRAF^V600E, or MEK2^C125 oncogenes. Analysis of the BRAF^V600E-overexpressing cells shows that, while drug addiction clearly occurs, it fails to account for the advantageous effect of intermittent treatment. Instead, growth suppression is best explained by resensitization during periods of drug removal, followed by cell death after drug readdition. Continuous treatment leads to transcriptional responses prominently associated with chemoresistance in melanoma. By contrast, cells treated intermittently reveal a subset of transcripts that reverse expression between successive cycles of drug removal and rechallenge and include mediators of cell invasiveness and the epithelial-to-mesenchymal transition. These transcripts change during periods of drug removal by adaptive switching, rather than selection pressure. Resensitization occurs against a background of sustained expression of melanoma resistance genes, producing a transcriptome distinct from that of the initial drug-naive cell state. We conclude that phenotypic plasticity leading to drug resensitization can underlie the beneficial effect of intermittent treatment.

Comprehensive Analysis of the Immune and Prognostic Implication of TRIM8 in Breast Cancer

Background: Breast cancer remains one of most lethal illnesses and the most common malignancies among women, making it important to discover novel biomarkers and therapeutic targets for the disease. Immunotherapy has become a promising therapeutic tool for breast cancer. The role of TRIM8 in breast cancer has rarely been reported.

Method: Here we identified TRIM8 expression and its potential function on survival in patients with breast cancer using TCGA (The cancer genome atlas), GEO (Gene expression omnibus) database and METABRIC (Molecular Taxonomy of Breast Cancer International Consortium). Then, TIMER and TISIDB databases were used to investigate the correlations between TRIM8 mRNA levels and immune characteristics. Using stepwise cox regression, we established an immune prognostic signature based on five differentially expression immune-related genes (DE-IRGs). Finally, a nomogram, accompanied by a calibration curve was proposed to predict 1-, 3-, and 5-year survival for breast cancer patients.

Results: We found that TRIM8 expression was dramatically lower in breast cancer tissues in comparison with normal tissues. Lower TRIM8 expression was related with worse prognosis in breast cancer. TIMER and TISIDB analysis showed that there were strong correlations between TRIM8 expression and immune characteristics. The receiver operating characteristic (ROC) curve confirmed the good performance in survival prediction and showed good accuracy of the immune prognostic signature. We demonstrated the model usefulness of predictions by nomogram and calibration curves. Our findings indicated that TRIM8 might be a potential link between progression and prognosis survival of breast cancer.

Conclusion: This is a comprehensive study to reveal that tripartite motif 8 (TRIM8) may serve as a potential prognostic biomarker associating with immune characteristics and provide a novel therapeutic target for the treatment of breast cancer.

The impact of the spatial heterogeneity of resistant cells and fibroblasts on treatment response

A long-standing practice in the treatment of cancer is that of hitting hard with the maximum tolerated dose to eradicate tumors. This continuous therapy, however, selects for resistant cells, leading to the failure of the treatment. A different type of treatment strategy, adaptive therapy, has recently been shown to have a degree of success in both preclinical xenograft experiments and clinical trials. Adaptive therapy is used to maintain a tumor's volume by exploiting the competition between drug-sensitive and drug-resistant cells with minimum effective drug doses or timed drug holidays. To further understand the role of competition in the outcomes of adaptive therapy, we developed a 2D on-lattice agent-based model. Our simulations show that the superiority of the adaptive strategy over continuous therapy depends on the local competition shaped by the spatial distribution of resistant cells. Intratumor competition can also be affected by fibroblasts, which produce microenvironmental factors that promote cancer cell growth. To this end, we simulated the impact of different fibroblast distributions on treatment outcomes. As a proof of principle, we focused on five types of distribution of fibroblasts characterized by different locations, shapes, and orientations of the fibroblast region with respect to the resistant cells. Our simulation shows that the spatial architecture of fibroblasts modulates tumor progression in both continuous and adaptive therapy. Finally, as a proof of concept, we simulated the outcomes of adaptive therapy of a virtual patient with four metastatic sites composed of different spatial distributions of fibroblasts and drug-resistant cell populations. Our simulation highlights the importance of undetected metastatic lesions on adaptive therapy outcomes.

Find the path of least resistance: Adaptive therapy to delay treatment failure and improve outcomes

Cytotoxic chemotherapy and targeted therapies help people with advanced cancers, but for most, treatment fails. Cancer heterogeneity is one cause of treatment failure, but also suggests an opportunity to improve outcomes; reconceptualising cancer therapy as an ecological problem offers the strategy of adaptive therapy. If an agent is active against a patient's cancer, instead of traditional continuous dosing at the maximum tolerated dose until treatment failure, the patient and their oncologist may instead choose to pause treatment as soon as the cancer responds. When tumour burden increases, the cancer is rechallenged with the same agent in hope of delivering another response, ideally before symptoms occur or quality-of-life is impacted. These 'loops' of 'pause/restart' allows an active treatment to be used strategically, to delay the development of evolutionary selection within the cancer, delaying the onset of treatment resistance, controlling the cancer for longer. Modelling predicts patients can navigate several 'loops', potentially increasing the utility of an active treatment by multiples, and early trials suggest at least doubling of progression-free survival. In this narrative review we confront how cancer heterogeneity limits treatment effectiveness, re-examine cancer as an ecological problem, review the data supporting adaptive therapy and outline the challenges and opportunities faced in clinical practice to implement this evolutionary concept. In an era where multiple novel active anti-neoplastic agents are being used with ancient inflexibile maximum tolerated dose for maximum duration approaches, adaptive dosing offers a personalised, n = 1 approach to cancer therapy selection.

Optimizing Adaptive Therapy Based on the Reachability to Tumor Resistant Subpopulation

Simple Summary

The intra-competition among tumor subpopulations is a promising target to modify and control the outgrowth of the resistant subpopulation. Adaptive therapy lives up to this principle well, but the gain of tumors with an aggressive resistant subpopulation is not superior to maximum tolerated dose therapy (MTD). How to integrate these two therapies to maximize the outcome? According to the model and system reachability, the ‘restore index’ is proposed to evaluate the timing of the transition from the treatment cycle of adaptive therapy to high-frequency administration, and to juggle the benefits of intra-competition and killing of the sensitive subpopulation. Based on the simulation and animal experiment, the effectiveness of this method in treating tumors with an aggressive resistant subpopulation has been confirmed.

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.

Darwinian Approaches for Cancer Treatment: Benefits of Mathematical Modeling

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.

The Evolutionary Ecology of Dormancy in Nature and in Cancer

Dormancy is an inactive period of an organism’s life cycle that permits it to survive through phases of unfavorable conditions in highly variable environments. Dormancy is not binary. There is a continuum of dormancy phenotypes that represent some degree of reduced metabolic activity (hypometabolism), reduced feeding, and reduced reproduction or proliferation. Similarly, normal cells and cancer cells exhibit a range of states from quiescence to long-term dormancy that permit survival in adverse environmental conditions. In contrast to organismal dormancy, which entails a reduction in metabolism, dormancy in cells (both normal and cancer) is primarily characterized by lack of cell division. “Cancer dormancy” also describes a state characterized by growth stagnation, which could arise from cells that are not necessarily hypometabolic or non-proliferative. This inconsistent terminology leads to confusion and imprecision that impedes progress in interdisciplinary research between ecologists and cancer biologists. In this paper, we draw parallels and contrasts between dormancy in cancer and other ecosystems in nature, and discuss the potential for studies in cancer to provide novel insights into the evolutionary ecology of dormancy.

How Should Cancer Models Be Constructed?

Choosing and optimizing treatment strategies for cancer requires capturing its complex dynamics sufficiently well for understanding but without being overwhelmed. Mathematical models are essential to achieve this understanding, and we discuss the challenge of choosing the right level of complexity to address the full range of tumor complexity from growth, the generation of tumor heterogeneity, and interactions within tumors and with treatments and the tumor microenvironment. We discuss the differences between conceptual and descriptive models, and compare the use of predator-prey models, evolutionary game theory, and dynamic precision medicine approaches in the face of uncertainty about mechanisms and parameter values. Although there is of course no one-size-fits-all approach, we conclude that broad and flexible thinking about cancer, based on combined modeling approaches, will play a key role in finding creative and improved treatments.

A theoretical analysis of tumour containment

Recent studies have shown that a strategy aiming for containment, not elimination, can control tumour burden more effectively in vitro, in mouse models and in the clinic. These outcomes are consistent with the hypothesis that emergence of resistance to cancer therapy may be prevented or delayed by exploiting competitive ecological interactions between drug-sensitive and drug-resistant tumour cell subpopulations. However, although various mathematical and computational models have been proposed to explain the superiority of particular containment strategies, this evolutionary approach to cancer therapy lacks a rigorous theoretical foundation. Here we combine extensive mathematical analysis and numerical simulations to establish general conditions under which a containment strategy is expected to control tumour burden more effectively than applying the maximum tolerated dose. We show that containment may substantially outperform more aggressive treatment strategies even if resistance incurs no cellular fitness cost. We further provide formulas for predicting the clinical benefits attributable to containment strategies in a wide range of scenarios and compare the outcomes of theoretically optimal treatments with those of more practical protocols. Our results strengthen the rationale for clinical trials of evolutionarily informed cancer therapy, while also clarifying conditions under which containment might fail to outperform standard of care.

The Hallmarks of Cancer as Ecologically Driven Phenotypes

Ecological fitness is the ability of individuals in a population to survive and reproduce. Individuals with increased fitness are better equipped to withstand the selective pressures of their environments. This paradigm pertains to all organismal life as we know it; however, it is also becoming increasingly clear that within multicellular organisms exist highly complex, competitive, and cooperative populations of cells under many of the same ecological and evolutionary constraints as populations of individuals in nature. In this review I discuss the parallels between populations of cancer cells and populations of individuals in the wild, highlighting how individuals in either context are constrained by their environments to converge on a small number of critical phenotypes to ensure survival and future reproductive success. I argue that the hallmarks of cancer can be distilled into key phenotypes necessary for cancer cell fitness: survival and reproduction. I posit that for therapeutic strategies to be maximally beneficial, they should seek to subvert these ecologically driven phenotypic responses.

Using ecological coexistence theory to understand antibiotic resistance and microbial competition

Tackling antibiotic resistance necessitates deep understanding of how resource competition within and between species modulates the fitness of resistant microbes. Recent advances in ecological coexistence theory offer a powerful framework to probe the mechanisms regulating intra- and interspecific competition, but the significance of this body of theory to the problem of antibiotic resistance has been largely overlooked. In this Perspective, we draw on emerging ecological theory to illustrate how changes in resource niche overlap can be equally important as changes in competitive ability for understanding costs of resistance and the persistence of resistant pathogens in microbial communities. We then show how different temporal patterns of resource and antibiotic supply, alongside trade-offs in competitive ability at high and low resource concentrations, can have diametrically opposing consequences for the coexistence and exclusion of resistant and susceptible strains. These insights highlight numerous opportunities for innovative experimental and theoretical research into the ecological dimensions of antibiotic resistance.

Adaptive Therapy for Metastatic Melanoma: Predictions from Patient Calibrated Mathematical Models

Adaptive therapy is an evolution-based treatment approach that aims to maintain tumor volume by employing minimum effective drug doses or timed drug holidays. For successful adaptive therapy outcomes, it is critical to find the optimal timing of treatment switch points in a patient-specific manner. Here we develop a combination of mathematical models that examine interactions between drug-sensitive and resistant cells to facilitate melanoma adaptive therapy dosing and switch time points. The first model assumes genetically fixed drug-sensitive and -resistant popul tions that compete for limited resources. The second model considers phenotypic switching between drug-sensitive and -resistant cells. We calibrated each model to fit melanoma patient biomarker changes over time and predicted patient-specific adaptive therapy schedules. Overall, the models predict that adaptive therapy would have delayed time to progression by 6-25 months compared to continuous therapy with dose rates of 6-74% relative to continuous therapy. We identified predictive factors driving the clinical time gained by adaptive therapy, such as the number of initial sensitive cells, competitive effect, switching rate from resistant to sensitive cells, and sensitive cell growth rate. This study highlights that there is a range of potential patient-specific benefits of adaptive therapy and identifies parameters that modulate this benefit.

Adriamycin-resistant cells are significantly less fit than adriamycin-sensitive cells in cervical cancer

Adriamycin (ADR) is an important chemotherapy agent in many advanced cancers, but the emergence of drug resistance during treatment is a major limitation to its successful use. Recent studies have suggested that drug-resistant cells become less fit and their growth could be inhibited by parental cells without cytotoxic treatment. In this study, we examined the fitness differences between HeLa and HeLa/ADR cells. Compared with the parental cell line, HeLa/ADR cells showed significantly lower growth rates, both in vitro and in vivo. There was no difference in the apoptosis rate between them, but G1 arrest and reduced DNA synthesis were found in HeLa/ADR cells. Further study indicated that HeLa/ADR cells failed to compete for space and nutrition against parental cells in vivo. Taken together, we demonstrate that HeLa/ADR cells are less fit and their growth can be inhibited by parental cells in the absence of ADR; therefore, the maintenance of a certain amount of ADR-sensitive cells during treatment may facilitate the control of the development of ADR resistance.

Searching for Goldilocks: How Evolution and Ecology Can Help Uncover More Effective Patient-Specific Chemotherapies

Deaths from cancer are mostly due to metastatic disease that becomes resistant to therapy. A mainstay treatment for many cancers is chemotherapy, for which the dosing strategy is primarily limited by patient toxicity. While this MTD approach builds upon the intuitively appealing principle that maximum therapeutic benefit is achieved by killing the largest possible number of cancer cells, there is increasing evidence that moderation might allow host-specific features to contribute to success. We believe that a "Goldilocks Window" of submaximal chemotherapy will yield improved overall outcomes. This window combines the complex interplay of cancer cell death, immune activity, emergence of chemoresistance, and metastatic dissemination. These multiple activities driven by chemotherapy have tradeoffs that depend on the specific agents used as well as their dosing levels and schedule. Here we present evidence supporting the idea that MTD may not always be the best approach and offer suggestions toward a more personalized treatment regime that integrates insights into patient-specific eco-evolutionary dynamics.

Modifying Adaptive Therapy to Enhance Competitive Suppression

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.

The Evolution and Ecology of Resistance in Cancer Therapy

Despite the continuous deployment of new treatment strategies and agents over many decades, most disseminated cancers remain fatal. Cancer cells, through their access to the vast information of the human genome, have a remarkable capacity to deploy adaptive strategies for even the most effective treatments. We note there are two critical steps in the clinical manifestation of treatment resistance. The first, which is widely investigated, requires molecular machinery necessary to eliminate the cytotoxic effect of the treatment. However, the emergence of a resistant phenotype is not in itself clinically significant. That is, resistant cells affect patient outcomes only when they succeed in the second step of resistance by proliferating into a sufficiently large population to allow tumor progression and treatment failure. Importantly, proliferation of the resistant phenotype is by no means certain and, in fact, depends on complex Darwinian dynamics governed by the costs and benefits of the resistance mechanisms in the context of the local environment and competing populations. Attempts to target the molecular machinery of resistance have had little clinical success largely because of the diversity within the human genome-therapeutic interruption of one mechanism simply results in its replacement by an alternative. Here we explore evolutionarily informed strategies (adaptive, double-bind, and extinction therapies) for overcoming treatment resistance that seek to understand and exploit the critical evolutionary dynamics that govern proliferation of the resistant phenotypes. In general, this approach has demonstrated that, while emergence of resistance mechanisms in cancer cells to every current therapy is inevitable, proliferation of the resistant phenotypes is not and can be delayed and even prevented with sufficient understanding of the underlying eco-evolutionary dynamics.

Evolution of tumor cells during AsiDNA treatment results in energy exhaustion, decrease in responsiveness to signal, and higher sensitivity to the drug

It is increasingly suggested that ecological and evolutionary sciences could inspire novel therapies against cancer but medical evidence of this remains scarce at the moment. The Achilles heel of conventional and targeted anticancer treatments is intrinsic or acquired resistance following Darwinian selection; that is, treatment toxicity places the surviving cells under intense evolutionary selective pressure to develop resistance. Here, we review a set of data that demonstrate that Darwinian principles derived from the "smoke detector" principle can instead drive the evolution of malignant cells toward a different trajectory. Specifically, long-term exposure of cancer cells to a strong alarm signal, generated by the DNA repair inhibitor AsiDNA, induces a stable new state characterized by a down-regulation of the targeted pathways and does not generate resistant clones. This property is due to the original mechanism of action of AsiDNA, which acts by overactivating a "false" signaling of DNA damage through DNA-PK and PARP enzymes, and is not observed with classical DNA repair inhibitors such as the PARP inhibitors. Long-term treatment with AsiDNA induces a new "alarm down" state in the tumor cells with decrease in NAD level and reactiveness to it. These results suggest that agonist drugs such as AsiDNA could promote a state-dependent tumor cell evolution by lowering their ability to respond to high "danger" signal. This analysis provides a compelling argument that evolutionary ecology could help drug design development in overcoming fundamental limitation of novel therapies against cancer due to the modification of the targeted tumor cell population during treatment.

Advances in tissue-based imaging: impact on oncology research and clinical practice

INTRODUCTION

Tissue-based imaging has emerged as a critical tool in translational cancer research and is rapidly gaining traction within a clinical context. Significant progress has been made in the digital pathology arena, particularly in respect of brightfield and fluorescent imaging. Critically, the cellular context of molecular alterations occurring at DNA, RNA, or protein level within tumor tissue is now being more fully appreciated. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumor microenvironment, including the potential interplay between various cell types.

AREAS COVERED

This review summarizes the recent developments within the field of tissue-based imaging, centering on the application of these approaches in oncology research and clinical practice.

EXPERT OPINION

Significant advances have been made in digital pathology during the last 10 years. These include the use of quantitative image analysis algorithms, predictive artificial intelligence (AI) on large datasets of H&E images, and quantification of fluorescence multiplexed tissue imaging data. We believe that new methodologies that can integrate AI-derived histologic data with omic data, together with other forms of imaging data (such as radiologic image data), will enhance our ability to deliver better diagnostics and treatment decisions to the cancer patient.

Human accelerated genome regions with value in medical genetics and drug discovery

Accumulated evolutionary knowledge not only benefits our understanding of the pathogenesis of diseases, but also help in the search for new drug targets. This is further supported by the recent finding that human accelerated regions (HARs) identified by comparative genomic studies are linked to human neural system evolution and are also associated with neurological disorders. Here, we analyze the associations between HARs and diseases and drugs. We found that 32.42% of approved drugs target at least one HAR gene, which is higher than the ratio of in-research drugs. More interestingly, HAR gene-targeted drugs are most significantly enriched with agents treating neurological disorders. Thus, HAR genes have important implications in medical genetics and drug discovery.

In Silico implementation of evolutionary paradigm in therapy design: Towards anti-cancer therapy as Darwinian process

In here presented in silico study we suggest a way how to implement the evolutionary principles into anti-cancer therapy design. We hypothesize that instead of its ongoing supervised adaptation, the therapy may be constructed as a self-sustaining evolutionary process in a dynamic fitness landscape established implicitly by evolving cancer cells, microenvironment and the therapy itself. For these purposes, we replace a unified therapy with the 'therapy species', which is a population of heterogeneous elementary therapies, and propose a way how to turn the toxicity of the elementary therapy into its fitness in a way conforming to evolutionary causation. As a result, not only the therapies govern the evolution of different cell phenotypes, but the cells' resistances govern the evolution of the therapies as well. We illustrate the approach by the minimalistic ad hoc evolutionary model. Its results indicate that the resistant cells could bias the evolution towards more toxic elementary therapies by inhibiting the less toxic ones. As the evolutionary causation of cancer drug resistance has been intensively studied for a few decades, we refer to cancer as a special case to illustrate purely theoretical analysis.

The Promising Connection Between Data Science and Evolutionary Theory in Oncology

Theoretical and empirical work over the past several decades suggests that oncogenesis and disease progression represents an evolutionary story. Despite this knowledge, current anti-resistance strategies to drugs are often managed through treating cancers as independent biological agents divorced from human activity. Yet once drug resistance to cancer treatment is understood as a product of artificial or anthropogenic rather than unconscious selection, oncologists could improve outcomes for their patients by consulting evolutionary studies of oncology prior to clinical trial and treatment plan design. In the setting of multiple cancer types, for example, a machine learning algorithm can predict the genetic changes known to be related to drug resistance. In this way, a unity between technology and theory might have practical clinical implications—and may pave the way for a new paradigm shift in medicine.

Beyond drugs: the evolution of genes involved in human response to medications

The genetic variation of our species reflects human demographic history and adaptation to diverse local environments. Part of this genetic variation affects individual responses to exogenous substances, such as food, pollutants and drugs, and plays an important role in drug efficacy and safety. This review provides a synthesis of the evolution of loci implicated in human pharmacological response and metabolism, interpreted within the theoretical framework of population genetics and molecular evolution. In particular, I review and discuss key evolutionary aspects of different pharmacogenes in humans and other species, such as the relationship between the type of substrates and rate of evolution; the selective pressure exerted by landscape variables or dietary habits; expected and observed patterns of rare genetic variation. Finally, I discuss how this knowledge can be translated directly or after the implementation of specific studies, into practical guidelines.

Leveraging transcriptional dynamics to improve BRAF inhibitor responses in melanoma

BACKGROUND

Melanoma is a heterogeneous tumour, but the impact of this heterogeneity upon therapeutic response is not well understood.

METHODS

Single cell mRNA analysis was used to define the transcriptional heterogeneity of melanoma and its dynamic response to BRAF inhibitor therapy and treatment holidays. Discrete transcriptional states were defined in cell lines and melanoma patient specimens that predicted initial sensitivity to BRAF inhibition and the potential for effective re-challenge following resistance. A mathematical model was developed to maintain competition between the drug-sensitive and resistant states, which was validated in vivo.

FINDINGS

Our analyses showed melanoma cell lines and patient specimens to be composed of >3 transcriptionally distinct states. The cell state composition was dynamically regulated in response to BRAF inhibitor therapy and drug holidays. Transcriptional state composition predicted for therapy response. The differences in fitness between the different transcriptional states were leveraged to develop a mathematical model that optimized therapy schedules to retain the drug sensitive population. In vivo validation demonstrated that the personalized adaptive dosing schedules outperformed continuous or fixed intermittent BRAF inhibitor schedules.

INTERPRETATION

Our study provides the first evidence that transcriptional heterogeneity at the single cell level predicts for initial BRAF inhibitor sensitivity. We further demonstrate that manipulating transcriptional heterogeneity through personalized adaptive therapy schedules can delay the time to resistance.

FUNDING

This work was funded by the National Institutes of Health. The funder played no role in assembly of the manuscript.

Multi-stage models for the failure of complex systems, cascading disasters, and the onset of disease

Complex systems can fail through different routes, often progressing through a series of (rate-limiting) steps and modified by environmental exposures. The onset of disease, cancer in particular, is no different. Multi-stage models provide a simple but very general mathematical framework for studying the failure of complex systems, or equivalently, the onset of disease. They include the Armitage-Doll multi-stage cancer model as a particular case, and have potential to provide new insights into how failures and disease, arise and progress. A method described by E.T. Jaynes is developed to provide an analytical solution for a large class of these models, and highlights connections between the convolution of Laplace transforms, sums of random variables, and Schwinger/Feynman parameterisations. Examples include: exact solutions to the Armitage-Doll model, the sum of Gamma-distributed variables with integer-valued shape parameters, a clonal-growth cancer model, and a model for cascading disasters. Applications and limitations of the approach are discussed in the context of recent cancer research. The model is sufficiently general to be used in many contexts, such as engineering, project management, disease progression, and disaster risk for example, allowing the estimation of failure rates in complex systems and projects. The intended result is a mathematical toolkit for applying multi-stage models to the study of failure rates in complex systems and to the onset of disease, cancer in particular.

Engineering Multidimensional Evolutionary Forces to Combat Cancer

With advances in technology and bioinformatics, we are now positioned to view and manage cancer through an evolutionary lens. This perspective is critical as our appreciation for the role of tumor heterogeneity, tumor immune compartment, and tumor microenvironment on cancer pathogenesis and evolution grows. Here, we explore recent knowledge on the evolutionary basis of cancer pathogenesis and progression, viewing tumors as multilineage, multicomponent organisms whose growth is regulated by subcomponent fitness relationships. We propose reconsidering some current tenets of the cancer management paradigm in order to take better advantage of crucial fitness relationships to improve outcomes of patients with cancer. SIGNIFICANCE: Tumor and tumor immune compartment and microenvironment heterogeneity, and their evolution, are critical disease features that affect treatment response. The impact and interplay of these components during treatment are viable targets to improve clinical response. In this article, we consider how tumor cells, the tumor immune compartment and microenvironment, and epigenetic factors interact and also evolve during treatment. We evaluate the convergence of these factors and suggest innovative treatment concepts that leverage evolutionary relationships to limit tumor growth and drug resistance.

Ecological principle meets cancer treatment: treating children with acute myeloid leukemia with low-dose chemotherapy

Standard chemotherapy regimens for remission induction of pediatric acute myeloid leukemia (AML) are associated with significant morbidity and mortality. We performed a cohort study to determine the impact of reducing the intensity of remission induction chemotherapy on the outcomes of selected children with AML treated with a low-dose induction regimen plus granulocyte colony stimulating factor (G-CSF) (low-dose chemotherapy (LDC)/G-CSF). Complete response (CR) after two induction courses was attained in 87.0% (40/46) of patients receiving LDC/G-CSF. Post-remission therapy was offered to all patients, and included standard consolidation and/or stem cell transplantation. During the study period, an additional 94 consecutive children with AML treated with standard chemotherapy (SDC) for induction (80/94 (85.1%) of the patients attained CR after induction II, P = 0.953) and post-remission. In this non-randomized study, there were no significant differences in 4-year event-free (67.4 vs. 70.7%; P = 0.99) and overall (70.3 vs. 74.6%, P = 0.69) survival in the LDC/G-CSF and SDC cohorts, respectively. After the first course of induction, recovery of white blood cell (WBC) and platelet counts were significantly faster in patients receiving LDC/G-CSF than in those receiving SDC (11.5 vs. 18.5 d for WBCs (P < 0.001); 15.5 vs. 22.0 d for platelets (P < 0.001)). To examine the quality of molecular response, targeted deep sequencing was performed. Of 137 mutations detected at diagnosis in 20 children who attained hematological CR after two courses of LDC/G-CSF (n = 9) or SDC (n = 11), all of the mutations were below the reference value (variant allelic frequency <2.5%) after two courses, irrespective of the treatment group. In conclusion, children with AML receiving LDC/G-CSF appear to have similar outcomes and mutation clearance levels, but significantly lower toxicity than those receiving SDC. Thus, LDC/G-CSF should be further evaluated as an effective alternative to remission induction in pediatric AML.

Detection of circulating microRNAs with Ago2 complexes to monitor the tumor dynamics of colorectal cancer patients during chemotherapy

Because of the different forms of circulating miRNAs in plasma, Argonaute2 (Ago2)-miRNAs and extracellular vesicles (EV-miRNAs), we examined the two forms of extracellular miRNAs in vitro and developed a unique methodology to detect circulating Ago2-miRNAs in small volumes of plasma. We demonstrated that Ago2-miR-21 could be released into the extracellular fluid by active export from viable cancer cells and cytolysis in vitro. As miR-21 and miR-200c were abundantly expressed in both metastatic liver sites and primary lesions, we evaluated Ago2-miR-21 as a candidate biomarker of both active export and cytolysis while Ago2-miR-200c as a biomarker of cytolysis in plasma obtained from colorectal cancer (CRC) patients before treatment and in a series of plasma obtained from CRC patients with liver metastasis who received systemic chemotherapy. The measurement of Ago2-miR-21 allowed us to distinguish CRC patients from subjects without CRC. The trend in ΔCt values for Ago2-miR-21 and -200c during chemotherapy could predict tumor response to ongoing treatment. Thus, capturing circulating Ago2-miRNAs from active export can screen patients with tumor burdens, while capturing them from passive release by cytolysis can monitor tumor dynamics during chemotherapy treatment.

Frontiers in Molecular Evolutionary Medicine

This paper surveys some of the important insights that molecular evolution has contributed to evolutionary medicine; they include phage therapy, cancer biology, helminth manipulation of the host immune system, quality control of gametes, and pathogen outbreaks. Molecular evolution has helped to revolutionize our understanding of cancer, of autoimmune disease, and of the origin, spread, and pathogenesis of emerging diseases, where it has suggested new therapies, illuminated mechanisms, and revealed historical processes: all have practical therapeutic implications. While much has been accomplished, much remains to be done.

Illuminating the Numbers: Integrating Mathematical Models to Optimize Photomedicine Dosimetry and Combination Therapies

Cancer photomedicine offers unique mechanisms for inducing local tumor damage with the potential to stimulate local and systemic anti-tumor immunity. Optically-active nanomedicine offers these features as well as spatiotemporal control of tumor-focused drug release to realize synergistic combination therapies. Achieving quantitative dosimetry is a major challenge, and dosimetry is fundamental to photomedicine for personalizing and tailoring therapeutic regimens to specific patients and anatomical locations. The challenge of dosimetry is perhaps greater for photomedicine than many standard therapies given the complexity of light delivery and light-tissue interactions as well as the resulting photochemistry responsible for tumor damage and drug-release, in addition to the usual intricacies of therapeutic agent delivery. An emerging multidisciplinary approach in oncology utilizes mathematical and computational models to iteratively and quantitively analyze complex dosimetry, and biological response parameters. These models are parameterized by preclinical and clinical observations and then tested against previously unseen data. Such calibrated and validated models can be deployed to simulate treatment doses, protocols, and combinations that have not yet been experimentally or clinically evaluated and can provide testable optimal treatment outcomes in a practical workflow. Here, we foresee the utility of these computational approaches to guide adaptive therapy, and how mathematical models might be further developed and integrated as a novel methodology to guide precision photomedicine.

The impact of proliferation-migration tradeoffs on phenotypic evolution in cancer

Tumors are not static masses of cells but dynamic ecosystems where cancer cells experience constant turnover and evolve fitness-enhancing phenotypes. Selection for different phenotypes may vary with (1) the tumor niche (edge or core), (2) cell turnover rates, (3) the nature of the tradeoff between traits, and (4) whether deaths occur in response to demographic or environmental stochasticity. Using a spatially-explicit agent-based model, we observe how two traits (proliferation rate and migration speed) evolve under different tradeoff conditions with different turnover rates. Migration rate is favored over proliferation at the tumor's edge and vice-versa for the interior. Increasing cell turnover rates slightly slows tumor growth but accelerates the rate of evolution for both proliferation and migration. The absence of a tradeoff favors ever higher values for proliferation and migration, while a convex tradeoff tends to favor proliferation, often promoting the coexistence of a generalist and specialist phenotype. A concave tradeoff favors migration at low death rates, but switches to proliferation at higher death rates. Mortality via demographic stochasticity favors proliferation, and environmental stochasticity favors migration. While all of these diverse factors contribute to the ecology, heterogeneity, and evolution of a tumor, their effects may be predictable and empirically accessible.

Low-dose eribulin reduces lung metastasis of osteosarcoma in vitro and in vivo

Lung metastasis markedly reduces the prognosis of osteosarcoma. Moreover, there is no effective treatment for lung metastasis, and a new treatment strategy for the treatment of osteosarcoma lung metastasis is required. Therefore, in this study, we investigated the suppressive effect of the microtubule inhibitor eribulin mesylate (eribulin) on lung metastasis of osteosarcoma. At concentrations >proliferation IC₅₀, eribulin induced cell cycle arrest and apoptosis in a metastatic osteosarcoma cell line, LM8. However, at concentrations <proliferation IC₅₀, (low dose), eribulin changed cell morphology and decreased LM8 migration. Low eribulin concentrations also reduced directionality during migration, peripheral localization of adenomatous polyposis coli protein, and turnover of focal adhesions. In a three-dimensional collagen culture system, low eribulin concentrations inhibited tumor cell proliferation and colony formation. Higher doses of eribulin administered on a standard schedule inhibited lung metastasis and primary tumor growth in a murine osteosarcoma metastasis model. Frequent low-dose eribulin administration (0.3 mg/kg every 4 days × 4) effectively inhibited lung metastasis but had little effect on primary tumor growth. Overall, our results indicate that eribulin could reduce osteosarcoma lung metastasis.