Model discovery approach enables noninvasive measurement of intra-tumoral fluid transport in dynamic MRI.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a routine method to noninvasively quantify perfusion dynamics in tissues. The standard practice for analyzing DCE-MRI data is to fit an ordinary differential equation to each voxel. Recent advances in data science provide an opportunity to move beyond existing methods to obtain more accurate measurements of fluid properties. Here, we developed a localized convolutional function regression that enables simultaneous measurement of interstitial fluid velocity, diffusion, and perfusion in 3D. We validated the method computationally and experimentally, demonstrating accurate measurement of fluid dynamics in situ and in vivo. Applying the method to human MRIs, we observed tissue-specific differences in fluid dynamics, with an increased fluid velocity in breast cancer as compared to brain cancer. Overall, our method represents an improved strategy for studying interstitial flows and interstitial transport in tumors and patients. We expect that our method will contribute to the better understanding of cancer progression and therapeutic response.

Demonstration of chemotherapeutic mediated lymphatic changes in meningeal lymphatics in vitro, ex vivo, and in vivo.

Systemic chemotherapeutics target cancer cells but are also known to impact other cells away from the tumor. Questions remain whether systemic chemotherapy crosses the blood-brain barrier and causes inflammation in the periphery that impacts the central nervous system (CNS) downstream. The meningeal lymphatics are a critical component that drain cerebrospinal fluid from the CNS to the cervical lymph nodes for immunosurveillence. To develop new tools for understanding chemotherapy-mediated effects on the meningeal lymphatics, we present two novel models that examine cellular and tissue level changes. Our in vitro tissue engineered model of a meningeal lymphatic vessel lumen, using a simple tissue culture insert system with both lymphatic endothelial and meningeal cells, examines cell disruption. Our ex vivo model culturing mouse meningeal layers probes structural changes and remodeling, correlating to an explant tissue level. To gain a holistic understanding, we compare our in vitro and ex vivo models to in vivo studies for validation and a three-tier methodology for examining the chemotherapeutic response of the meningeal lymphatics. We have demonstrated that the meningeal lymphatics can be disrupted by systemic chemotherapy but show differential responses to platinum and taxane chemotherapies, emphasizing the need for further study of off-target impacts in the CNS.

A Phase 1b Adaptive Androgen Deprivation Therapy Trial in Metastatic Castration Sensitive Prostate Cancer.

Background: We hypothesize that cancer survival can be improved through adapting treatment strategies to cancer evolutionary dynamics and conducted a phase 1b study in metastatic castration sensitive prostate cancer (mCSPC). Methods: Men with asymptomatic mCSPC were enrolled and proceeded with a treatment break after achieving > 75% PSA decline with LHRH analog plus an NHA. ADT was restarted at the time of PSA or radiographic progression and held again after achieving >50% PSA decline. This on-off cycling of ADT continued until on treatment imaging progression. Results: At data cut off in August 2022, only 2 of the 16 evaluable patients were off study due to imaging progression at 28 months from first dose of LHRH analog for mCSPC. Two additional patients showed PSA progression at 12.4 and 20.5 months and remain on trial. Since none of the 16 patients developed imaging progression at 12 months, the study succeeded in its primary objective of feasibility. The secondary endpoints of median time to PSA progression and median time to radiographic progression have not been reached at a median follow up of 26 months. Conclusions: It is feasible to use an individual's PSA response and testosterone levels to guide intermittent ADT in mCSPC.

Frequency-dependent interactions determine outcome of competition between two breast cancer cell lines.

Tumors are highly dynamic ecosystems in which diverse cancer cell subpopulations compete for space and resources. These complex, often non-linear interactions govern continuous spatial and temporal changes in the size and phenotypic properties of these subpopulations. Because intra-tumoral blood flow is often chaotic, competition for resources may be a critical selection factor in progression and prognosis. Here, we quantify resource competition using 3D spheroid cultures with MDA-MB-231 and MCF-7 breast cancer cells. We hypothesized that MCF-7 cells, which primarily rely on efficient aerobic glucose metabolism, would dominate the population under normal pH and low glucose conditions; and MDA-MB-231 cells, which exhibit high levels of glycolytic metabolism, would dominate under low pH and high glucose conditions. In spheroids with single populations, MCF-7 cells exhibited equal or superior intrinsic growth rates (density-independent measure of success) and carrying capacities (density-dependent measure of success) when compared to MDA-MB-231 cells under all pH and nutrient conditions. Despite these advantages, when grown together, MCF-7 cells do not always outcompete MDA-MB-231 cells. MDA-MB-231 cells outcompete MCF-7 cells in low glucose conditions and coexistence is achieved in low pH conditions. Under all conditions, MDA-MB-231 has a stronger competitive effect (frequency-dependent interaction) on MCF-7 cells than vice-versa. This, and the inability of growth rate or carrying capacity when grown individually to predict the outcome of competition, suggests a reliance on frequency-dependent interactions and the need for competition assays. We frame these results in a game-theoretic (frequency-dependent) model of cancer cell interactions and conclude that competition assays can demonstrate critical density-independent, density-dependent and frequency-dependent interactions that likely contribute to in vivo outcomes.

Fighting microbial pathogens by integrating host ecosystem interactions and evolution.

Successful therapies to combat microbial diseases and cancers require incorporating ecological and evolutionary principles. Drawing upon the fields of ecology and evolutionary biology, we present a systems-based approach in which host and disease-causing factors are considered as part of a complex network of interactions, analogous to studies of "classical" ecosystems. Centering this approach around empirical examples of disease treatment, we present evidence that successful therapies invariably engage multiple interactions with other components of the host ecosystem. Many of these factors interact nonlinearly to yield synergistic benefits and curative outcomes. We argue that these synergies and nonlinear feedbacks must be leveraged to improve the study of pathogenesis in situ and to develop more effective therapies. An eco-evolutionary systems perspective has surprising and important consequences, and we use it to articulate areas of high research priority for improving treatment strategies.

Resistance is not the end: lessons from pest management.

The "war on cancer" began over 40 years ago with the signing of the National Cancer Act of 1971. Currently, complete eradication has proven possible in early stage premetastatic disease with increasingly successful early detection and surgery protocols; however, late stage metastatic disease remains invariably fatal. One of the main causes of treatment failure in metastatic disease is the ability of cancer cells to evolve resistance to currently available therapies. Evolution of resistance to control measures is a universal problem. While it may seem that the mechanisms of resistance employed by cancer cells are impossible to control, we show that many of the resistance mechanisms are mirrored in agricultural pests. In this way, we argue that measures developed in the agricultural industry to slow or prevent pesticide resistance could be adopted in clinical cancer biology to do the same. The agriculture industry recognized the problem of pesticide resistance and responded by developing and enforcing guidelines on resistance management and prevention. These guidelines, known as integrated pest management (IPM), do not encourage eradication of pests but instead strive to maintain pests, even with the presence of resistant strains, at a level that does not cause economic damage to the crops. Integrated pest management inspired management of metastatic cancer could result in the slowing or curtailing of widespread resistance to treatment, reducing overall drug usage, and increasing the survival and quality of life of patients with cancer. Using IPM principles as a foundation and shifting the goal of treatment of metastatic disease to long-term management will require close monitoring of evolving tumor populations, judicious application of currently available therapies, and development of new criteria of success.

Optimal control to develop therapeutic strategies for metastatic castrate resistant prostate cancer.

In metastatic castrate resistant prostate cancer (mCRPC), abiraterone is conventionally administered continuously at maximal tolerated dose until treatment failure. The majority of patients initially respond well to abiraterone but the cancer cells evolve resistance and the cancer progresses within a median time of 16 months. Incorporating techniques that attempt to delay or prevent the growth of the resistant cancer cell phenotype responsible for disease progression have only recently entered the clinical setting. Here we use evolutionary game theory to model the evolutionary dynamics of patients with mCRPC subject to abiraterone therapy. In evaluating therapy options, we adopt an optimal control theory approach and seek the best treatment schedule using nonlinear constrained optimization. We compare patient outcomes from standard clinical treatments to those with other treatment objectives, such as maintaining a constant total tumor volume or minimizing the fraction of resistant cancer cells within the tumor. Our model predicts that continuous high doses of abiraterone as well as other therapies aimed at curing the patient result in accelerated competitive release of the resistant phenotype and rapid subsequent tumor progression. We find that long term control is achievable using optimized therapy through the restrained and judicious application of abiraterone, maintaining its effectiveness while providing acceptable patient quality of life. Implementing this strategy will require overcoming psychological and emotional barriers in patients and physicians as well as acquisition of a new class of clinical data designed to accurately estimate intratumoral eco-evolutionary dynamics during therapy.

Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer.

Abiraterone treats metastatic castrate-resistant prostate cancer by inhibiting CYP17A, an enzyme for testosterone auto-production. With standard dosing, evolution of resistance with treatment failure (radiographic progression) occurs at a median of ~16.5 months. We hypothesize time to progression (TTP) could be increased by integrating evolutionary dynamics into therapy. We developed an evolutionary game theory model using Lotka-Volterra equations with three competing cancer "species": androgen dependent, androgen producing, and androgen independent. Simulations with standard abiraterone dosing demonstrate strong selection for androgen-independent cells and rapid treatment failure. Adaptive therapy, using patient-specific tumor dynamics to inform on/off treatment cycles, suppresses proliferation of androgen-independent cells and lowers cumulative drug dose. In a pilot clinical trial, 10 of 11 patients maintained stable oscillations of tumor burdens; median TTP is at least 27 months with reduced cumulative drug use of 47% of standard dosing. The outcomes show significant improvement over published studies and a contemporaneous population.

Spatial vs. non-spatial eco-evolutionary dynamics in a tumor growth model.

Metastatic prostate cancer is initially treated with androgen deprivation therapy (ADT). However, resistance typically develops in about 1 year - a clinical condition termed metastatic castrate-resistant prostate cancer (mCRPC). We develop and investigate a spatial game (agent based continuous space) of mCRPC that considers three distinct cancer cell types: (1) those dependent on exogenous testosterone (T+), (2) those with increased CYP17A expression that produce testosterone and provide it to the environment as a public good (TP), and (3) those independent of testosterone (T-). The interactions within and between cancer cell types can be represented by a 3 × 3 matrix. Based on the known biology of this cancer there are 22 potential matrices that give roughly three major outcomes depending upon the absence (good prognosis), near absence or high frequency (poor prognosis) of T- cells at the evolutionarily stable strategy (ESS). When just two cell types coexist the spatial game faithfully reproduces the ESS of the corresponding matrix game. With three cell types divergences occur, in some cases just two strategies coexist in the spatial game even as a non-spatial matrix game supports all three. Discrepancies between the spatial game and non-spatial ESS happen because different cell types become more or less clumped in the spatial game - leading to non-random assortative interactions between cell types. Three key spatial scales influence the distribution and abundance of cell types in the spatial game: i. Increasing the radius at which cells interact with each other can lead to higher clumping of each type, ii. Increasing the radius at which cells experience limits to population growth can cause densely packed tumor clusters in space, iii. Increasing the dispersal radius of daughter cells promotes increased mixing of cell types. To our knowledge the effects of these spatial scales on eco-evolutionary dynamics have not been explored in cancer models. The fact that cancer interactions are spatially explicit and that our spatial game of mCRPC provides in general different outcomes than the non-spatial game might suggest that non-spatial models are insufficient for capturing key elements of tumorigenesis.

Aggregation Effects and Population-Based Dynamics as a Source of Therapy Resistance in Cancer.

OBJECTIVE: Evolution of resistance allows cancer cells to adapt and continue proliferating even when therapy is initially very effective. Most investigations of treatment resistance focus on the adaptive phenotypic properties of individual cells. We propose that the resistance of a single cell to therapy may extend beyond its own phenotypic and molecular properties and be influenced by the phenotypic properties of surrounding cells and variations in cell density. Similar variation exists in population densities of animals living in groups and can significantly affect the outcome of an external threat. METHODS: We investigate aggregation effects in cancer therapy using Darwinian models that integrate phenotypic properties of individual cells and common population effects found in nature to simulate the dynamics of resistance and sensitivity in the diverse cellular environments within cancers. RESULTS: We demonstrate that the density of cancer cell populations can profoundly influence response to chemotherapy independent of the properties of individual cells. Most commonly, these aggregation effects benefit the tumor allowing cells to survive even with phenotypic properties that would render them highly vulnerable to therapy in the absence of population effects. CONCLUSION: We demonstrate aggregation effects likely play a significant role in conferring resistance to therapy on tumor cells that would otherwise be sensitive to treatment. SIGNIFICANCE: The potential role of aggregation in outcomes from cancer therapy has not been previously investigated. Our results demonstrate these dynamics may play a key role in resistance to therapy and could be used to design evolutionarily-enlightened therapies that exploit aggregation effects to improve treatment outcomes.

Darwinian Dynamics of Intratumoral Heterogeneity: Not Solely Random Mutations but Also Variable Environmental Selection Forces.

Spatial heterogeneity in tumors is generally thought to result from branching clonal evolution driven by random mutations that accumulate during tumor development. However, this concept rests on the implicit assumption that cancer cells never evolve to a fitness maximum because they can always acquire mutations that increase proliferative capacity. In this study, we investigated the validity of this assumption. Using evolutionary game theory, we demonstrate that local cancer cell populations will rapidly converge to the fittest phenotype given a stable environment. In such settings, cellular spatial heterogeneity in a tumor will be largely governed by regional variations in environmental conditions, for example, alterations in blood flow. Model simulations specifically predict a common spatial pattern in which cancer cells at the tumor-host interface exhibit invasion-promoting, rapidly proliferating phenotypic properties, whereas cells in the tumor core maximize their population density by promoting supportive tissue infrastructures, for example, to promote angiogenesis. We tested model predictions through detailed quantitative image analysis of phenotypic spatial distribution in histologic sections of 10 patients with stage 2 invasive breast cancers. CAIX, GLUT1, and Ki67 were upregulated in the tumor edge, consistent with an acid-producing invasive, proliferative phenotype. Cells in the tumor core were 20% denser than the edge, exhibiting upregulation of CAXII, HIF-1α, and cleaved caspase-3, consistent with a more static and less proliferative phenotype. Similarly, vascularity was consistently lower in the tumor center compared with the tumor edges. Lymphocytic immune responses to tumor antigens also trended to higher level in the tumor edge, although this effect did not reach statistical significance. Like invasive species in nature, cancer cells at the leading edge of the tumor possess a different phenotype from cells in the tumor core. Our results suggest that at least some of the molecular heterogeneity in cancer cells in tumors is governed by predictable regional variations in environmental selection forces, arguing against the assumption that cancer cells can evolve toward a local fitness maximum by random accumulation of mutations. Cancer Res; 76(11); 3136-44. ©2016 AACR.

The multiple facets of Peto's paradox: a life-history model for the evolution of cancer suppression.

Large animals should have higher lifetime probabilities of cancer than small animals because each cell division carries an attendant risk of mutating towards a tumour lineage. However, this is not observed--a (Peto's) paradox that suggests large and/or long-lived species have evolved effective cancer suppression mechanisms. Using the Euler-Lotka population model, we demonstrate the evolutionary value of cancer suppression as determined by the 'cost' (decreased fecundity) of suppression verses the 'cost' of cancer (reduced survivorship). Body size per se will not select for sufficient cancer suppression to explain the paradox. Rather, cancer suppression should be most extreme when the probability of non-cancer death decreases with age (e.g. alligators), maturation is delayed, fecundity rates are low and fecundity increases with age. Thus, the value of cancer suppression is predicted to be lowest in the vole (short lifespan, high fecundity) and highest in the naked mole rat (long lived with late female sexual maturity). The life history of pre-industrial humans likely selected for quite low levels of cancer suppression. In modern humans that live much longer, this level results in unusually high lifetime cancer risks. The model predicts a lifetime risk of 49% compared with the current empirical value of 43%.

Divergent and convergent evolution in metastases suggest treatment strategies based on specific metastatic sites.

BACKGROUND AND OBJECTIVE: Systemic therapy for metastatic cancer is currently determined exclusively by the site of tumor origin. Yet, there is increasing evidence that the molecular characteristics of metastases significantly differ from the primary tumor. We define the evolutionary dynamics of metastases that govern this molecular divergence and examine their potential contribution to variations in response to targeted therapies. METHODOLOGY: Darwinian interactions of transformed cells with the tissue microenvironments at primary and metastatic sites are analyzed using evolutionary game theory. Computational models simulate responses to targeted therapies in different organs within the same patient. RESULTS: Tumor cells, although maximally fit at their primary site, typically have lower fitness on the adaptive landscapes offered by the metastatic sites due to organ-specific variations in mesenchymal properties and signaling pathways. Clinically evident metastases usually exhibit time-dependent divergence from the phenotypic mean of the primary population as the tumor cells evolve and adapt to their new circumstances. In contrast, tumors from different primary sites evolving on identical metastatic adaptive landscapes exhibit phenotypic convergence. Thus, metastases in the liver from different primary tumors and even in different hosts will evolve toward similar adaptive phenotypes. The combination of evolutionary divergence from the primary cancer phenotype and convergence towards similar adaptive strategies in the same tissue cause significant variations in treatment responses particularly for highly targeted therapies. CONCLUSION AND IMPLICATIONS: The results suggest that optimal therapies for disseminated cancer must take into account the site(s) of metastatic growth as well as the primary organ.

Evolutionary dynamics in cancer therapy.

Disseminated cancer remains a largely fatal disease. While systemic therapy can have some initial success, it is rarely durable. Typically, populations of cancer cells resistant to therapy emerge quickly requiring progressively less effective second, third, and fourth line therapies until the patient succumbs. Cancer cells possess a large repertoire of heritable phenotypic strategies that can be used to confer resistance to one or more therapeutic drugs. In addition, environmental factors such as ischemia and hypoxia can reduce therapeutic effects by limiting drug delivery or toxicity. Here, we use a fitness generating function (G-function) approach to model tumor response with respect to evolutionary adaptation and microenvironmental conditions in response to various therapeutic strategies. We examine tumor cell death and the evolution of resistance in single and two drug therapies as well as alternative "evolutionary" approaches. We demonstrate that even monotherapy would be highly successful in the absence of tumor evolution or environmentally mediated resistance. However, environmental and evolutionary factors dramatically reduce the effectiveness of therapy. Two-drug therapy in which adaptation requires two different phenotypic changes will maximally reduce tumor size and delay onset of resistance, but actual eradication of the tumor population is rare. We demonstrate that multiagent therapies in which the first drug both achieves tumor cell toxicity and drives phenotypic adaptation that renders the cell more vulnerable to a second therapy can be highly successful in maintaining durable tumor control. Examples of clinical trials that exploit these results are presented. We conclude that the development of more lethal (cytotoxic) drugs is not likely to fundamentally change the outcome of therapy. Instead, new approaches that incorporate evolutionary strategies into target and drug selection are needed.