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1.AbstractBrain metastases (BM) remain a significant cause of morbidity and mortality in breast cancer (BC) patients. Specific factors promoting the process of BM and predilection for selected neuro-anatomical regions remain unknown, yet may have major implications for prevention or treatment. Anatomical spatial distributions of BM from BC suggest a predominance of metastases in the hindbrain and cerebellum. Systematic approaches to quantifying BM location or location-based analyses based on molecular subtypes, however, remain largely unavailable. We analyzed stereotactic Cartesian coordinates derived from 134 patients undergoing gamma-knife radiosurgery (GKRS) for treatment of 407 breast cancer BMs to quantitatively study BM spatial distribution along principal component axes and by intrinsic molecular subtype (ER,PR,Herceptin). We corroborated that BC BMs show a consistent propensity to arise posteriorly and caudally, and that Her2+ tumors are relatively more likely to arise medially rather than laterally. To compare the distributions among varying BC molecular subtypes, we used the notion of mutual information, which revealed that the ER-PR-Her2+ and ER-PR-Her2-subtypes showed the smallest amount of mutual information and were most molecularly distinct. Using kernel density estimators, we found a propensity for triple negative BC to arise in more superiorly or cranially situated BMs. BM location maps according to vascular and anatomical distributions using cartesian coordinates to aid in systematic classification of tumor locations were additionally developed. Further characterization of these patterns may have major impacts on treatment or management of cancer patients.SignificanceThe quantitative spatial distribution of breast cancer metastases to the brain, and the effects of breast cancer molecular subtype on distribution frequencies remain poorly understood. We present a novel and shareable workflow for characterizing and comparing spatial distributions of BM which may aid in identifying therapeutic or diagnostic targets and interactions with the tumor microenvironment.

A new ecologically inspired paradigm in cancer treatment known as “adaptive therapy” capitalizes on competitive interactions between drug-sensitive and drug-resistant subclones. The goal of adaptive therapy is to maintain a controllable stable tumor burden by allowing a significant population of treatment-sensitive cells to survive. These, in turn, suppress proliferation of the less-fit resistant populations. However, there remain several open challenges in designing adaptive therapies, particularly in extending these therapeutic concepts to multiple treatments. We present a cancer treatment case study (metastatic castrate-resistant prostate cancer) as a point of departure to illustrate three novel concepts to aid the design of multidrug adaptive therapies. First, frequency-dependent “cycles” of tumor evolution can trap tumor evolution in a periodic, controllable loop. Second, the availability and selection of treatments may limit the evolutionary “absorbing region” reachable by the tumor. Third, the velocity of evolution significantly influences the optimal timing of drug sequences. These three conceptual advances provide a path forward for multidrug adaptive therapy.Significance:Driving tumor evolution into periodic, repeatable treatment cycles provides a path forward for multidrug adaptive therapy.

Figure S1, Tables S1, S2. Table S1: Competition parameters for Lupron and Abiraterone treatment; Table S2: Competition parameters for no treatment; Figure S1: After-action analysis of patient-specific subpopulations Normalized subpopulations over time (equation 4) for testosterone-dependent (T+, yellow), testosterone-producing (TP, blue), and testosterone-independent cells (T-, green). Timing of treatment received under clinical protocol is indicated (top) for Lupron & Abiraterone (red) and Lupron only (blue). Parameterization of the model is performed by least-squares fit to PSA data from clinical trial NCT02415621 (see figure 3).

PDF file - 232K, The supporting material describes in more detail the Markov chain model, and includes a full Table and two additional figures. Figure S1. Convergence plot for the lung cancer matrix. Figure S2. Metastatic tumor distributions after two-steps, compared with steady-state. Table S1. Comparative table of top two-step metastatic pathways of all types from Lung.

Background Bladder cancer (BCa), the sixth commonest cancer in the USA, is highly lethal when metastatic. Spatial and temporal patterns of patient-specific metastatic spread are deemed random and unpredictable. Whether BCa metastatic patterns can be quantified and predicted more accurately is unknown. Objective To develop a web-based calculator for forecasting metastatic progression in individual BCa patients. Design, setting, and participants We used a prospectively collected longitudinal dataset of 3503 BCa patients who underwent a radical cystectomy following diagnosis and were enrolled continuously. We subdivided patients by their pathologic subgroup stages of organ confined (OC), extravesical (EV), and node positive (N+). We illustrated metastatic pathway progression using color-coded, circular, tree ring diagrams. We created a dynamical, data-visualization, web-based platform that displays temporal, spatial, and Markov modeling figures with predictive capability. Outcome measurements and statistical analysis Patients underwent history and physical examination, serum studies, and liver function tests. Surveillance follow-up included computed tomography scans, chest x-rays, and radiographic evaluation of the reservoir and upper tracts, with bone scans performed only if clinically indicated. Outcomes were measured by time to clinical recurrence and overall or progression-free survival. Results and limitations Metastases developed in 29% of patients (n = 812; median follow-up 15.3 yr), with 5-yr overall survival of 20.2%, compared with 78.6% in those without metastases (n = 1983; median follow-up 10.9 yr). The three commonest sites of spread at the time of first progression were bone (n = 214; 26.4%), pelvis (n = 194; 23.9%), and lung (n = 194; 23.9%). The order and frequency of these sites vary when divided by pathologic subgroup stages of OC (lung [n = 65; 25.1%], urethra [n = 45; 17.4%], and bone [n = 29; 11.2%]), EV (pelvis [n = 63; 33.0%], bone [n = 45; 23.6%], and lung [n = 29; 15.2%]), and N+ (bone [n = 111; 30.7%], retroperitoneum [n = 70; 19.3%], and pelvis [n = 60; 16.6%]). Markov chain modeling indicated a higher probability of spread from bladder to bone (15.5%), pelvis (14.7%), and lung (14.2%). Conclusions Our web-based calculator allows real-time analyses in the clinic based on individual patient-specific demographic and cancer data elements. For contrasting subgroups, the models indicated differences in Markov transition probabilities. Spatiotemporal patterns of BCa metastasis and sites of spread indicated underlying organotropic mechanisms in the prediction of response. This recognition opens the possibility of organ site–specific therapeutic targeting in the oligometastatic BCa setting. In the precision medicine era, visualization of complex, time-resolved clinical data will enhance management of postoperative metastatic BCa patients. Patient summary We developed a web-based calculator to forecast metastatic progression for individual bladder cancer (BCa) patients, based on the clinical and demographic information obtained at diagnosis. This can help in predicting disease status and survival, and improving management in postoperative metastatic BCa patients. Take Home Message Future pathways of metastatic progression for individual bladder cancer patients can be determined based on currently available clinical and demographic information obtained at diagnosis. In focused subgroups of patients, these metastatic spread patterns can also portend disease status and survival.

We use the Bernstein polynomials of degree d as the basis for constructing a uniform approximation to the rate of evolution (related to the fixation probability) of a species in a two-component finite-population frequency-dependent evolutionary game setting. The approximation is valid over the full range 0 ≤ w ≤ 1, where w is the selection pressure parameter, and converges uniformly to the exact solution as d → ∞. We compare it to a widely used non-uniform approximation formula in the weak-selection limit (w ∼ 0) as well as numerically computed values of the exact solution. Because of a boundary layer that occurs in the weak-selection limit, the Bernstein polynomial method is more efficient at approximating the rate of evolution in the strong selection region (w ∼ 1) (requiring the use of fewer modes to obtain the same level of accuracy) than in the weak selection regime.

Anyone who has visited a science museum has seen the demonstration where a beach (or ping-pong) ball is suspended in mid-air at a fixed position by constant blowing from below. After a while, the ball inevitably tumbles to the ground but can easily be rebalanced, by hand, again at the suspension point. Here, we ask a different more delicate question. Can we blow the ball from rest, starting at the nozzle opening (x = 0), moving it up to the suspension point x = x* above the nozzle? We show that it is not possible to do this using constant blowing because the point at which the downward gravitational force balances the upward blowing force is an elliptic fixed point of the governing equations, so there is no transfer trajectory that connects the origin to x*. To overcome this problem, we design time-dependent blowing schedules that achieve the transfer, making use of orbit transfer ideas developed in the orbital mechanics literature. Then, we ask which of these time-dependent schedules are optimal? We show that, generally, it is bang–bang (on–off) blowing schedules that achieve the transfer in minimal time, using minimal energy (action) and minimal air volume. For certain parameter values, however, there are more complicated blowing schedules that are optimal (with respect to energy), which can be designed using the Pontryagin Maximum Principle (PMP) and singular control. We use elementary concepts from mechanics, nonlinear dynamics, and control theory and challenge the inclined experimentalist to try to implement some of these nonconstant blowing schedules in the lab.

Background While it has been suspected that different primary cancers have varying predilections for metastasis in certain brain regions, recent advances in neuroimaging and spatial modeling analytics have facilitated further exploration into this field. Methods A systematic electronic database search for studies analyzing the distribution of brain metastases (BMs) from any primary systematic cancer published between January 1990 and July 2020 was conducted using PRISMA guidelines. Results Two authors independently reviewed 1957 abstracts, 46 of which underwent full-text analysis. A third author arbitrated both lists; 13 studies met inclusion/exclusion criteria. All were retrospective single- or multi-institution database reviews analyzing over 8227 BMs from 2599 patients with breast (8 studies), lung (7 studies), melanoma (5 studies), gastrointestinal (4 studies), renal (3 studies), and prostate (1 study) cancers. Breast, lung, and colorectal cancers tended to metastasize to more posterior/caudal topographic and vascular neuroanatomical regions, particularly the cerebellum, with notable differences based on subtype and receptor expression. HER-2-positive breast cancers were less likely to arise in the frontal lobes or subcortical region, while ER-positive and PR-positive breast metastases were less likely to arise in the occipital lobe or cerebellum. BM from lung adenocarcinoma tended to arise in the frontal lobes and squamous cell carcinoma in the cerebellum. Melanoma metastasized more to the frontal and temporal lobes. Conclusion The observed topographical distribution of BM likely develops based on primary cancer type, molecular subtype, and genetic profile. Further studies analyzing this association and relationships to vascular distribution are merited to potentially improve patient treatment and outcomes.

The Hawk-Dove mathematical game offers a paradigm of the trade-offs associated with aggressive and passive behaviors. When two (or more) populations of players (animals, insect populations, countries in military conflict, economic competitors, microbial communities, populations of co-evolving tumor cells, or reinforcement learners adopting different strategies) compete, their success or failure can be measured by their frequency in the population (successful behavior is reinforced, unsuccessful behavior is not), and the system is governed by the replicator dynamical system. We develop a time-dependent optimal-adaptive control theory for this nonlinear dynamical system in which the payoffs of the Hawk-Dove payoff matrix are dynamically altered (dynamic incentives) to produce (bang-bang) control schedules that (i) maximize the aggressive population at the end of timeT, and (ii) minimize the aggressive population at the end of timeT. These two distinct time-dependent strategies produce upper and lower bounds on the outcomes from all strategies since they represent two extremizers of the cost function using the Pontryagin maximum (minimum) principle. We extend the results forward to timesnT(n= 1, …, 5) in an adaptive way that uses the optimal value at the end of timenTto produce the new schedule for time (n+ 1)T. Two special schedules and initial conditions are identified that produce absolute maximizers and minimizers over an arbitrary number of cycles for 0 ≤T≤ 3. ForT> 3, our optimum schedules can drive either population to extinction or fixation. The method described can be used to produce optimal dynamic incentive schedules for many different applications in which the 2 × 2 replicator dynamics is used as a governing model.

Summary Organoid model systems recapitulate key features of mammalian tissues and enable high throughput experiments. However, the impact of these experiments may be limited by manual, non-standardized, static or qualitative phenotypic analysis. OrgDyn is an open-source and modular pipeline to quantify organoid shape dynamics using a combination of feature- and model-based approaches on time series of 2D organoid contour images. Our pipeline consists of (i) geometrical and signal processing feature extraction, (ii) dimensionality reduction to differentiate dynamical paths, (iii) time series clustering to identify coherent groups of organoids and (iv) dynamical modeling using point distribution models to explain temporal shape variation. OrgDyn can characterize, cluster and model differences among unique dynamical paths that define diverse final shapes, thus enabling quantitative analysis of the molecular basis of tissue development and disease. Availability and Implementation https://github.com/zakih/organoidDynamics (BSD 3-Clause License). Supplementary information Supplementary data are available at Bioinformatics online.

Simple Summary We describe an evolutionary game theory mathematical model to investigate the robustness and accuracy of adaptive chemotherapy schedules in a stochastic environment in a tumor. The model assumes tumors are made up of a finite cell population of chemo-sensitive cells, and two populations of resistant cells, each resistant to one of two separate drugs that can be administered on different schedules. The goal of the adaptive schedules are to delay chemoresistance in the tumor by keeping the cell populations in competition with each other without any of the populations reaching fixation leading to treatment failure. Abstract We investigate the robustness of adaptive chemotherapy schedules over repeated cycles and a wide range of tumor sizes. Using a non-stationary stochastic three-component fitness-dependent Moran process model (to track frequencies), we quantify the variance of the response to treatment associated with multidrug adaptive schedules that are designed to mitigate chemotherapeutic resistance in an idealized (well-mixed) setting. The finite cell (N tumor cells) stochastic process consists of populations of chemosensitive cells, chemoresistant cells to drug 1, and chemoresistant cells to drug 2, and the drug interactions can be synergistic, additive, or antagonistic. Tumor growth rates in this model are proportional to the average fitness of the tumor as measured by the three populations of cancer cells compared to a background microenvironment average value. An adaptive chemoschedule is determined by using the N→∞ limit of the finite-cell process (i.e., the adjusted replicator equations) which is constructed by finding closed treatment response loops (which we call evolutionary cycles) in the three component phase-space. The schedules that give rise to these cycles are designed to manage chemoresistance by avoiding competitive release of the resistant cell populations. To address the question of how these cycles perform in practice over large patient populations with tumors across a range of sizes, we consider the variances associated with the approximate stochastic cycles for finite N, repeating the idealized adaptive schedule over multiple periods. For finite cell populations, the distributions remain approximately multi-Gaussian in the principal component coordinates through the first three cycles, with variances increasing exponentially with each cycle. As the number of cycles increases, the multi-Gaussian nature of the distribution breaks down due to the fact that one of the three sub-populations typically saturates the tumor (competitive release) resulting in treatment failure. This suggests that to design an effective and repeatable adaptive chemoschedule in practice will require a highly accurate tumor model and accurate measurements of the sub-population frequencies or the errors will quickly (exponentially) degrade its effectiveness, particularly when the drug interactions are synergistic. Possible ways to extend the efficacy of the stochastic cycles in light of the computational simulations are discussed.

The prisoner's dilemma (PD) game offers a simple paradigm of competition between two players who can either cooperate or defect. Since defection is a strict Nash equilibrium, it is an asymptotically stable state of the replicator dynamical system that uses the PD payoff matrix to define the fitness landscape of two interacting evolving populations. The dilemma arises from the fact that the average payoff of this asymptotically stable state is suboptimal. Coaxing the players to cooperate would result in a higher payoff for both. Here we develop an optimal control theory for the prisoner's dilemma evolutionary game in order to maximize cooperation (minimize the defector population) over a given cycle time $T$, subject to constraints. Our two time-dependent controllers are applied to the off-diagonal elements of the payoff matrix in a bang-bang sequence that dynamically changes the game being played by dynamically adjusting the payoffs, with optimal timing that depends on the initial population distributions. Over multiple cycles $nT$ $(ng1)$, the method is adaptive as it uses the defector population at the end of the $n\mathrm{th}$ cycle to calculate the optimal schedule over the $n+1\mathrm{st}$ cycle. The control method, based on Pontryagin's maximum principle, can be viewed as determining the optimal way to dynamically alter incentives and penalties in order to maximize the probability of cooperation in settings that track dynamic changes in the frequency of strategists, with potential applications in evolutionary biology, economics, theoretical ecology, social sciences, reinforcement learning, and other fields where the replicator system is used.

PURPOSE Unplanned health care encounters (UHEs) such as emergency room visits can occur commonly during cancer chemotherapy treatments. Patients at an increased risk of UHEs are typically identified by clinicians using performance status (PS) assessments based on a descriptive scale, such as the Eastern Cooperative Oncology Group (ECOG) scale. Such assessments can be bias prone, resulting in PS score disagreements between assessors. We therefore propose to evaluate PS using physical activity measurements (eg, energy expenditure) from wearable activity trackers. Specifically, we examined the feasibility of using a wristband (band) and a smartphone app for PS assessments. METHODS We conducted an observational study on a cohort of patients with solid tumor receiving highly emetogenic chemotherapy. Patients were instructed to wear the band for a 60-day activity-tracking period. During clinic visits, we obtained ECOG scores assessed by physicians, coordinators, and patients themselves. UHEs occurring during the activity-tracking period plus a 90-day follow-up period were later compiled. We defined our primary outcome as the percentage of patients adherent to band-wear ≥ 80% of 10 am to 8 pm for ≥ 80% of the activity-tracking period. In an exploratory analysis, we computed hourly metabolic equivalent of task (MET) and counted 10 am to 8 pm hours with > 1.5 METs as nonsedentary physical activity hours. RESULTS Forty-one patients completed the study (56.1% female; 61.0% age 40-60 years); 68% were adherent to band-wear. ECOG score disagreement between assessors ranged from 35.3% to 50.0%. In our exploratory analysis, lower average METs and nonsedentary hours, but not higher ECOG scores, were associated with higher 150-day UHEs. CONCLUSION The use of a wearable activity tracker is generally feasible in a similar population of patients with cancer. A larger randomized controlled trial should be conducted to confirm the association between lower nonsedentary hours and higher UHEs.

The prisoner’s dilemma (PD) game offers a simple paradigm of competition between two players who can either cooperate or defect. Since defection is a strict Nash equilibrium, it is an asymptotically stable state of the replicator dynamical system that uses the PD payoff matrix to define the fitness landscape of two interacting evolving populations. The dilemma arises from the fact that the average payoff of this asymptotically stable state is sub-optimal. Coaxing the players to cooperate would result in a higher payoff for both. Here we develop an optimal control theory for the prisoner’s dilemma evolutionary game in order to maximize cooperation (minimize the defector population) over a given cycle-time T, subject to constraints. Our two time-dependent controllers are applied to the off-diagonal elements of the payoff matrix in a bang-bang sequence that dynamically changes the game being played by dynamically adjusting the payoffs, with optimal timing that depends on the initial population distributions. Over multiple cycles nT (n > 1), the method is adaptive as it uses the defector population at the end of the nth cycle to calculate the optimal schedule over the n + 1st cycle. The control method, based on Pontryagin’s maximum principle, can be viewed as determining the optimal way to dynamically alter incentives and penalties in order to maximize the probability of cooperation in settings that track dynamic changes in the frequency of strategists, with potential applications in evolutionary biology, economics, theoretical ecology, and other fields where the replicator system is used.PACS numbers02.50.Le; 02.30.Yy; 05.45.-a; 87.23.Kg; 87.23.Cc

PURPOSE Performance status (PS) is a key factor in oncologic decision making, but conventional scales used to measure PS vary among observers. Consumer-grade biometric sensors have previously been identified as objective alternatives to the assessment of PS. Here, we investigate how one such biometric sensor can be used during a clinic visit to identify patients who are at risk for complications, particularly unexpected hospitalizations that may delay treatment or result in low physical activity. We aim to provide a novel and objective means of predicting tolerability to chemotherapy. METHODS Thirty-eight patients across three centers in the United States who were diagnosed with a solid tumor with plans for treatment with two cycles of highly emetogenic chemotherapy were included in this single-arm, observational prospective study. A noninvasive motion-capture system quantified patient movement from chair to table and during the get-up-and-walk test. Activity levels were recorded using a wearable sensor over a 2-month period. Changes in kinematics from two motion-capture data points pre- and post-treatment were tested for correlation with unexpected hospitalizations and physical activity levels as measured by a wearable activity sensor. RESULTS Among 38 patients (mean age, 48.3 years; 53% female), kinematic features from chair to table were the best predictors for unexpected health care encounters (area under the curve, 0.775 ± 0.029) and physical activity (area under the curve, 0.830 ± 0.080). Chair-to-table acceleration of the nonpivoting knee ( t = 3.39; P = .002) was most correlated with unexpected health care encounters. Get-up-and-walk kinematics were most correlated with physical activity, particularly the right knee acceleration ( t = −2.95; P = .006) and left arm angular velocity ( t = −2.4; P = .025). CONCLUSION Chair-to-table kinematics are good predictors of unexpected hospitalizations, whereas the get-up-and-walk kinematics are good predictors of low physical activity.

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

Chemotherapeutic resistance via the mechanism of competitive release of resistant tumor cell subpopulations is a major problem associated with cancer treatments and one of the main causes of tumor recurrence. Often, chemoresistance is mitigated by using multidrug schedules (two or more combination therapies) that can act synergistically, additively, or antagonistically on the heterogeneous population of cells as they evolve. In this paper, we develop a three-component evolutionary game theory model to design two-drug adaptive schedules (timing and dose levels associated with C1(t) and C2(t)) that mitigate chemoresistance and delay tumor recurrence in an evolving collection of tumor cells with two resistant subpopulations: R1 (sensitive to drug 1, resistant to drug 2), and R2 (sensitive to drug 2, resistant to drug 1). A key parameter, e, takes us from synergistic (e > 0), to additive (e = 0), to antagonistic (e < 0) drug interactions. In addition to the two resistant populations, the model includes a population of chemosensitive cells, S that have higher baseline fitness but are not resistant to either drug. Using the nonlinear replicator dynamical system with a payoff matrix of Prisoner’s Dilemma (PD) type (enforcing a cost to resistance), we investigate the nonlinear dynamics of the three-component system (S, R1, R2), along with an additional tumor growth model whose growth rate is a function of the fitness landscape of the tumor cell populations. We show that antagonistic drug interactions generally result in slower rates of adaptation of the resistant cells than synergistic ones, making them more effective in combating the evolution of resistance. We then design closed loops in the three-component phase space by shaping the fitness landscape of the cell populations (i.e. altering the evolutionary stable states of the game) using appropriately designed time-dependent schedules (adaptive therapy), altering the dosages and timing of the two drugs using information gleaned from constant dosing schedules. We show that the bifurcations associated with the evolutionary stable states are transcritical, and we detail a typical antagonistic bifurcation that takes place between the sensitive cell population S and the R1 population, and a synergistic bifurcation that takes place between the sensitive cell population S and the R2 population for fixed values of C1 and C2. These bifurcations help us further understand why antagonistic interactions are more effective at controlling competitive release of the resistant population than synergistic interactions in the context of an evolving tumor.

Background Biomechanical characterization of human performance with respect to fatigue and fitness is relevant in many settings, however is usually limited to either fully qualitative assessments or invasive methods which require a significant experimental setup consisting of numerous sensors, force plates, and motion detectors. Qualitative assessments are difficult to standardize due to their intrinsic subjective nature, on the other hand, invasive methods provide reliable metrics but are not feasible for large scale applications. Methods Presented here is a dynamical toolset for detecting performance groups using a non-invasive system based on the Microsoft Kinect motion capture sensor, and a case study of 37 cancer patients performing two clinically monitored tasks before and after therapy regimens. Dynamical features are extracted from the motion time series data and evaluated based on their ability to i) cluster patients into coherent fitness groups using unsupervised learning algorithms and to ii) predict Eastern Cooperative Oncology Group performance status via supervised learning. Findings The unsupervised patient clustering is comparable to clustering based on physician assigned Eastern Cooperative Oncology Group status in that they both have similar concordance with change in weight before and after therapy as well as unexpected hospitalizations throughout the study. The extracted dynamical features can predict physician, coordinator, and patient Eastern Cooperative Oncology Group status with an accuracy of approximately 80%. Interpretation The non-invasive Microsoft Kinect sensor and the proposed dynamical toolset comprised of data preprocessing, feature extraction, dimensionality reduction, and machine learning offers a low-cost and general method for performance segregation and can complement existing qualitative clinical assessments.

We develop Bayesian methodologies for constructing and estimating a stochastic quasi-chemical model (QCM) for bacterial growth. The deterministic QCM, described as a nonlinear system of ODEs, is treated as a dynamical system with random parameters, and a variational approach is used to approximate their probability distributions and explore the propagation of uncertainty through the model. The approach consists of approximating the parameters’ posterior distribution by a probability measure chosen from a parametric family, through minimization of their Kullback–Leibler divergence.

The subject of fluid mechanics is a rich, vibrant, and rapidly developing branch of applied mathematics. Historically, it has developed hand-in-hand with the elegant subject of complex variable the...

A new ecologically inspired paradigm in cancer treatment known as “adaptive therapy” capitalizes on competitive interactions between drug-sensitive and drug-resistant subclones. The goal of adaptive therapy is to maintain a controllable stable tumor burden by allowing a significant population of treatment-sensitive cells to survive. These, in turn, suppress proliferation of the less-fit resistant populations. However, there remain several open challenges in designing adaptive therapies, particularly in extending these therapeutic concepts to multiple treatments. We present a cancer treatment case study (metastatic castrate-resistant prostate cancer) as a point of departure to illustrate three novel concepts to aid the design of multidrug adaptive therapies. First, frequency-dependent “cycles” of tumor evolution can trap tumor evolution in a periodic, controllable loop. Second, the availability and selection of treatments may limit the evolutionary “absorbing region” reachable by the tumor. Third, the velocity of evolution significantly influences the optimal timing of drug sequences. These three conceptual advances provide a path forward for multidrug adaptive therapy. Significance: Driving tumor evolution into periodic, repeatable treatment cycles provides a path forward for multidrug adaptive therapy.

Background Inflammatory breast cancer (IBC) is a rare yet aggressive variant of breast cancer with a high recurrence rate. We hypothesized that patterns of metastasis differ between IBC and non-IBC. We focused on the patterns of bone metastasis throughout disease progression to determine statistical differences that can lead to clinically relevant outcomes. Our primary outcome of this study is to quantify and describe this difference with a view to applying the findings to clinically relevant outcomes for patients. Subjects, Materials, and Methods We retrospectively collected data of patients with nonmetastatic IBC (n = 299) and non-IBC (n = 3,436). Probabilities of future site-specific metastases were calculated. Spread patterns were visualized to quantify the most probable metastatic pathways of progression and to categorize spread pattern based on their propensity to subsequent dissemination of cancer. Results In patients with IBC, the probabilities of developing bone metastasis after chest wall, lung, or liver metastasis as the first site of progression were high: 28%, 21%, and 21%, respectively. For patients with non-IBC, the probability of developing bone metastasis was fairly consistent regardless of initial metastasis site. Conclusion Metastatic patterns of spread differ between patients with IBC and non-IBC. Selection of patients with IBC with known liver, chest wall, and/or lung metastasis would create a population in whom to investigate effective methods for preventing future bone metastasis. Implications for Practice This study demonstrated that the patterns of metastasis leading to and following bone metastasis differ significantly between patients with inflammatory breast cancer (IBC) and those with non-IBC. Patients with IBC had a progression pattern that tended toward the development of bone metastasis if they had previously developed metastases in the liver, chest wall, and lung, rather than in other sites. Selection of patients with IBC with known liver, chest wall, and/or lung metastasis would create a population in whom to investigate effective methods for preventing future bone metastasis.

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

A tumor is made up of a heterogeneous collection of cell types, all competing on a fitness landscape mediated by microenvironmental conditions that dictate their interactions. Despite the fact that much is known about cell signaling, cellular cooperation, and the functional constraints that affect cellular behavior, the specifics of how these constraints (and the range over which they act) affect the macroscopic tumor growth laws that govern total volume, mass, and carrying capacity remain poorly understood. We develop a statistical mechanics approach that focuses on the total number of possible states each cell can occupy and show how different assumptions on correlations of these states give rise to the many different macroscopic tumor growth laws used in the literature. Although it is widely understood that molecular and cellular heterogeneity within a tumor is a driver of growth, here we emphasize that focusing on the functional coupling of states at the cellular level is what determines macroscopic growth characteristics.

A new ecologically inspired paradigm in cancer treatment known as "adaptive therapy" capitalizes on competitive interactions between drug-sensitive and drug-resistant subclones. The goal of adaptive therapy is to maintain a controllable stable tumor burden by allowing a significant population of treatment sensitive cells to survive which in turn suppress proliferation of the less fit resistant populations. This adaptive approach means that each patient9s treatment is truly personalized based on the tumor9s response rather than a one-size-fits-all fixed treatment regime. However, there remain several open challenges in designing adaptive therapies, particularly in extending these therapeutic concepts to multiple treatments. We present a simple extension of a previously developed model of metastatic prostate cancer to a frequency-dependent domain as a case study and point of departure to illustrate three novel concepts to aid the design of multi-drug adaptive therapies. First, frequency-dependent "cycles" of tumor evolution can trap tumor evolution in a periodic, controllable loop. Second, the availability and selection of treatments may limit the evolutionary "search space" reachable by the tumor. Third, the velocity of evolution is important to the timing of drug sequences. By noting that the stated goal of adaptive therapy is to maintain a stable tumor volume while controlling the tumor composition, we can make the simplifying assumption of a (relatively) constant tumor volume. We explain the importance of modeling frequency-dependent dynamics at play within a tumor, and show how these three concepts (evolutionary cycles, evolutionary search space, and evolutionary velocity) aid the selection of drugs and design of treatment schedules in multi-drug adaptive therapy.

Lung cancer is often classified by the presence of oncogenic drivers, such as epidermal growth factor receptor (EGFR), rather than patterns of anatomical distribution. While metastatic spread may seem a random and unpredictable process, we explored the possibility of using its quantifiable nature as a measure of describing and comparing different subsets of disease. We constructed a database of 664 non-small cell lung cancer (NSCLC) patients treated at the University of Southern California Norris Comprehensive Cancer Center and the Los Angeles County Medical Center. Markov mathematical modeling was employed to assess metastatic sites in a spatiotemporal manner through every time point in progression of disease. Our findings identified a preferential pattern of primary lung disease progressing through lung metastases to the brain amongst EGFR mutated (EGFRm) NSCLC patients, with exon 19 deletions or exon 21 L858R mutations, as compared to EGFR wild type (EGFRwt). The brain was classified as an anatomic “sponge”, with a higher ratio of incoming to outgoing spread, for EGFRm NSCLC. Bone metastases were more commonly identified in EGFRwt patients. Our study supports a link between the anatomical and molecular characterization of lung metastatic cancer. Improved understanding of the differential biology that drives discordant patterns of anatomic spread, based on genotype specific profiling, has the potential to improve personalized oncologic care.

We describe a cell-molecular based evolutionary mathematical model of tumor development driven by a stochastic Moran birth-death process. The cells in the tumor carry molecular information in the form of a numerical genome which we represent as a four-digit binary string used to differentiate cells into 16 molecular types. The binary string is able to undergo stochastic point mutations that are passed to a daughter cell after each birth event. The value of the binary string determines the cell fitness, with lower fit cells (e.g. 0000) defined as healthy phenotypes, and higher fit cells (e.g. 1111) defined as malignant phenotypes. At each step of the birth-death process, the two phenotypic sub-populations compete in a prisoner's dilemma evolutionary game with the healthy cells playing the role of cooperators, and the cancer cells playing the role of defectors. Fitness, birth-death rates of the cell populations, and overall tumor fitness are defined via the prisoner's dilemma payoff matrix. Mutation parameters include passenger mutations (mutations conferring no fitness advantage) and driver mutations (mutations which increase cell fitness). The model is used to explore key emergent features associated with tumor development, including tumor growth rates as it relates to intratumor molecular heterogeneity. The tumor growth equation states that the growth rate is proportional to the logarithm of cellular diversity/heterogeneity. The Shannon entropy from information theory is used as a quantitative measure of heterogeneity and tumor complexity based on the distribution of the 4-digit binary sequences produced by the cell population. To track the development of heterogeneity from an initial population of healthy cells (0000), we use dynamic phylogenetic trees which show clonal and sub-clonal expansions of cancer cell sub-populations from an initial malignant cell. We show tumor growth rates are not constant throughout tumor development, and are generally much higher in the subclinical range than in later stages of development, which leads to a Gompertzian growth curve. We explain the early exponential growth of the tumor and the later saturation associated with the Gompertzian curve which results from our evolutionary simulations using simple statistical mechanics principles related to the degree of functional coupling of the cell states. We then compare dosing strategies at early stage development, mid-stage (clinical stage), and late stage development of the tumor. If used early during tumor development in the subclinical stage, well before the cancer cell population is selected for growth, therapy is most effective at disrupting key emergent features of tumor development.

We introduce a method of designing treatment schedules for a model three-component replicator dynamical system that avoids chemotherapeutic resistance by controlling and managing the competitive release of resistant cells in the tumor. We use an evolutionary game theory model with prisoner’s dilemma payoff matrix that governs the competition among healthy cells, chemo-sensitive cells, and chemo-resistant cells and the goal is to control the evolution of chemo-resistance via the competitive release mechanism. The method is based on nonlinear trajectory design and energy transfer methods first introduced in the orbital mechanics literature for Hamiltonian systems. By using the structure of the trajectories defined by solutions of the replicator system for different constant chemotherapeutic concentrations (which produces a curvilinear coordinate system spanning the full region), we construct periodic (closed) orbits by switching the chemo-dose at carefully chosen times and appropriate levels to design schedules that are superior to both maximum tolerated dose (MTD) schedules and low-dose metronomic (LDM) schedules, both of which ultimately lead to fixation of either sensitive cells or resistant cells. By keeping the three sub-populations of cells in competition with each other, neither the sensitive cell population nor the resitant cell population are able to dominate as we balance the populations indefinitely (closed periodic orbits), thereby avoiding fixation of the cancer cell population and re-growth of a resistant tumor. The schedules we design have the feature that they maintain a higher average population fitness than either the MTD or the LDM schedules.PACS numbers: 87.23.Kg; 87.55.de; 87.19.Xj; 87.19.lr

We use a three-component replicator dynamical system with healthy cells, sensitive cells, and resistant cells, with a prisoner’s dilemma payoff matrix from evolutionary game theory to understand the phenomenon of competitive release, which is the main mechanism by which tumors develop chemotherapeutic resistance. By comparing the phase portraits of the system without therapy compared to continuous therapy above a certain threshold, we show that chemotherapeutic resistance develops if there are pre-exisiting resistance cells in the population. We examine the basin boundaries of attraction associated with the chemo-sensitive population and the chemo-resistant population for increasing values of chemo-concentrations and show their spiral intertwined structure. We also examine the fitness landscapes both with and without continuous therapy and show that with therapy, the average fitness as well as the fitness functions of each of the subpopulations initially increases, but eventually decreases monotonically as the resistant subpopulation saturates the tumor.

A tumor is made up of a heterogeneous collection of cell types all competing on a fitness landscape mediated by micro-environmental conditions that dictate their interactions. Despite the fact that much is known about cell signaling and cellular cooperation, the specifics of how the cell-to-cell coupling and the range over which this coupling acts affect the macroscopic tumor growth laws that govern total volume, mass, and carrying capacity remain poorly understood. We develop a statistical mechanics approach that focuses on the total number of possible states each cell can occupy, and show how different assumptions on correlations of these states gives rise to the many different macroscopic tumor growth laws used in the literature. Although it is widely understood that molecular and cellular heterogeneity within a tumor is a driver of growth, here we emphasize that focusing on the functional coupling of these states at the cellular level is what determines macroscopic growth characteristics.Significance statementA mathematical model relating tumor heterogeneity at the cellular level to tumor growth at the macroscopic level is described based on a statistical mechanics framework. The model takes into account the number of accessible states available to each cell as well as their long-range coupling (population cooperation) to other cells. We show that the degree to which cell populations cooperate determine the number of independent cell states, which in turn dictates the macroscopic (volumetric) growth law. It follows that targeting cell-to-cell interactions could be a way of mitigating and controlling tumor growth.

The development of chemotherapeutic resistance resulting in tumor relapse is thought largely to be a consequence of the mechanism of competitive release of pre-existing resistant cells in the tumor selected for growth after chemotherapeutic agents attack the previously dom- inant population of chemo-sensitive cells. To study this process, we use a mathematical model based on the replicator equations with a prisoner's dilemma payoff matrix defining fitness levels of three competing cell populations: healthy cells (cooperators), sensitive cells (defectors), and resistant cells (defectors). The model is shown to recapitulate prostate- specific antigen (PSA) measurement data of patients (a surrogate for tumor growth) from three randomized clinical trials with metastatic castration-resistant prostate cancer for pa- tients treated with 1) prednisone only, 2) mitoxantrone and prednisone and 3) docetaxel and prednisone. In each trial, continuous maximum tolerated dose (MTD) schedules reduce the sensitive cell population, initially shrinking tumor volume, but subsequently release the resistant cells to re-populate and eventually re-grow the tumor in a resistant form. The evolutionary model allows us to quantify responses to conventional therapeutic strategies as well as to design novel adaptive strategies which are able to maintain the tumor volume at reduced levels by keeping a sufficient number of sensitive cells to prevent tumor re-growth from the resistant population.

We describe the use of Markov chain models for the purpose of quantitative forecasting of metastatic cancer progression. Each site (node) in the Markov network (directed graph) is an organ site where a secondary tumor could develop with some probability. The Markov matrix is an N x N matrix where each entry represents a transition probability of the disease progressing from one site to another during the course of the disease. The initial state-vector has a 1 at the position corresponding to the primary tumor, and 0s elsewhere (no initial metastases). The spread of the disease to other sites (metastases) is modeled as a directed random walk on the Markov network, moving from site to site with the estimated transition probabilities obtained from longitudinal data. The stochastic model produces probabilistic predictions of the likelihood of each metastatic pathway and corresponding time sequences obtained from computer Monte Carlo simulations. The main challenge is to empirically estimate the N^2 transition probabilities in the Markov matrix using appropriate longitudinal data.

We review the classic tumor growth and regression laws of Skipper and Schable based on fixed exponential growth assumptions, and Norton and Simon’s law based on a Gompertzian growth assumption. We then discuss ways to optimize chemotherapeutic scheduling using a Moran process evolutionary game-theory model of tumor growth that incorporates more general dynamical and evolutionary features of tumor cell kinetics. Using this model, and employing the quantitative notion of Shannon entropy which assigns high values to low-dose metronomic (LDM) therapies, and low values to maximum tolerated dose (MTD) therapies, we show that low-dose metronomic strategies can outperform maximum tolerated dose strategies, particularly for faster growing tumors. The general concept of designing different chemotherapeutic strategies for tumors with different growth characteristics is discussed.

Competitive release is a bedrock principle of coevolutionary ecology and population dynamics. It is also the main mechanism by which heterogeneous tumors develop chemotherapeutic resistance. Understanding, controlling, and exploiting this important mechanism represents one of the key challenges and potential opportunities of current medical oncology. The development of sophisticated mathematical and computational models of coevolution among clonal and sub-clonal cell populations in the tumor ecosystem can guide us in predicting and shaping various responses to perturbations in the fitness landscape which is altered by chemo-toxic agents. This in turn can help us design adaptive chemotherapeutic strategies to combat the release resistant cells.

The paper by Dritschel et al. (J. Fluid Mech., vol. 783, 2015, pp. 1–22) describes the long-time behaviour of inviscid two-dimensional fluid dynamics on the surface of a sphere. At issue is whether the flow settles down to an equilibrium or whether, for generic (random) initial conditions, the long-time solution is periodic, quasi-periodic or chaotic. While it might be surprising that this issue is not settled in the literature, it is important to keep in mind that the Euler equations form a dissipationless Hamiltonian system, hence the set of equations only redistributes the initial vorticity, generating smaller and smaller scales, while keeping kinetic energy, angular impulse and an infinite family of vorticity moments (Casimirs) intact. While special solutions that never settle down to an equilibrium state can be constructed using point vortices, vortex patches and other distributions, the fate of random initial conditions is a trickier problem. Previous statistical theories indicate that the long-time state should be a stationary large-scale distribution of vorticity. By carrying out careful numerical simulations using two different methods, the authors make a compelling case that the generic long-time state resembles a large-scale oscillating quadrupolar vorticity field, surrounded by persistent small-scale vortices. While numerical simulations can never conclusively settle this issue, the results might help guide future theories that seek to prove the existence of such an interesting dynamical long-time state.