A geometric approach to comparing treatments for rapidly fatal diseases

In therapy of rapidly fatal diseases, early treatment efficacy often is characterized by an event, "response," which is observed relatively quickly. Since the risk of death decreases at the time of response, it is desirable not only to achieve a response, but to do so as rapidly as possible. We propose a Bayesian method for comparing treatments in this setting based on a competing risks model for response and death without response. Treatment effect is characterized by a two-dimensional parameter consisting of the probability of response within a specified time and the mean time to response. Several target parameter pairs are elicited from the physician so that, for a reference covariate vector, all elicited pairs embody the same improvement in treatment efficacy compared to a fixed standard. A curve is fit to the elicited pairs and used to determine a two-dimensional parameter set in which a new treatment is considered superior to the standard. Posterior probabilities of this set are used to construct rules for the treatment comparison and safety monitoring. The method is illustrated by a randomized trial comparing two cord blood transplantation methods.     

Theory and Experimental Validation of a Spatio-temporal Model of Chemotherapy Transport to Enhance Tumor Cell Kill

It has been hypothesized that continuously releasing drug molecules into the tumor over an extended period of time may significantly improve the chemotherapeutic efficacy by overcoming physical transport limitations of conventional bolus drug treatment. In this paper, we present a generalized space- and time-dependent mathematical model of drug transport and drug-cell interactions to quantitatively formulate this hypothesis. Model parameters describe: perfusion and tissue architecture (blood volume fraction and blood vessel radius); diffusion penetration distance of drug (i.e., a function of tissue compactness and drug uptake rates by tumor cells); and cell death rates (as function of history of drug uptake). We performed preliminary testing and validation of the mathematical model using in vivo experiments with different drug delivery methods on a breast cancer mouse model. Experimental data demonstrated a 3-fold increase in response using nano-vectored drug vs. free drug delivery, in excellent quantitative agreement with the model predictions. Our model results implicate that therapeutically targeting blood volume fraction, e.g., through vascular normalization, would achieve a better outcome due to enhanced drug delivery.

Author Summary

Cancer treatment efficacy can be significantly enhanced through the elution of drug from nano-carriers that can temporarily stay in the tumor vasculature. Here we present a relatively simple yet powerful mathematical model that accounts for both spatial and temporal heterogeneities of drug dosing to help explain, examine, and prove this concept. We find that the delivery of systemic chemotherapy through a certain form of nano-carriers would have enhanced tumor kill by a factor of 2 to 4 over the standard therapy that the patients actually received. We also find that targeting blood volume fraction (a parameter of the model) through vascular normalization can achieve more effective drug delivery and tumor kill. More importantly, this model only requires a limited number of parameters which can all be readily assessed from standard clinical diagnostic measurements (e.g., histopathology and CT). This addresses an important challenge in current translational research and justifies further development of the model towards clinical translation.     

Approximate Bayesian evaluation of multiple treatment effects

We propose an approximate Bayesian method for comparing an experimental treatment to a control based on a randomized clinical trial with multivariate patient outcomes. Overall treatment effect is characterized by a vector of parameters corresponding to effects on the individual patient outcomes. We partition the parameter space into four sets where, respectively, the experimental treatment is superior to the control, the control is superior to the experimental, the two treatments are equivalent, and the treatment effects are discordant. We compute posterior probabilities of the parameter sets by treating an estimator of the parameter vector like a random variable in the Bayesian paradigm. The approximation may be used in any setting where a consistent, asymptotically normal estimator of the parameter vector is available. The method is illustrated by application to a breast cancer data set consisting of multiple time-to-event outcomes with covariates and to count data arising from a cross-classification of response, infection, and treatment in an acute leukemia trial.     

Accelerated development of quarantine treatments for insects on poor hosts.

The probit 9 standard for quarantine treatment efficacy (99.9968% mortality) was originally recommended for tropical fruits heavily infested with fruit flies and it centers on high mortality to achieve quarantine security. This standard may be too stringent for quarantine pests in commodities that are rarely infested or are poor hosts, The alternative treatment efficacy approach measures risk as the probability of a mating pair, gravid female, or parthenogenic individual surviving in a shipment. This will be a function of many factors including infestation rate and shipment volume. Applying the risk-based alternative treatment efficacy approach to pests on rarely infested or poor hosts will lower the number of required test insects needed for developing quarantine treatments; hence data for a quarantine treatment could be generated by testing 10,000 or fewer insects with no survivors, compared with 90,000-100,000 insects to demonstrate the traditional probit 9 efficacy. Several commodity/quarantine pest systems where this approach could be applied are discussed. This approach would save time and resources, and help farmers export their crop on a more-timely basis.     

A Bayesian approach to the multiplicity problem for significance testing with binomial data

Statistical analyses of simple tumor rates from an animal experiment with one control and one treated group typically consist of hypothesis testing of many 2 X 2 tables, one for each tumor type or site. The multiplicity of significance tests may cause excessive overall false-positive rates. This paper presents a Bayesian approach to the problem of multiple significance testing. We develop a normal logistic model that accommodates the incidences of all tumor types or sites observed in the current experiment simultaneously as well as their historical control incidences. Exchangeable normal priors are assumed for certain linear terms in the model. Posterior means, standard deviations, and Bayesian P-values are computed for an average treatment effect as well as for the effects on individual tumor types or sites. Model assumptions are checked using probability plots and the sensitivity of the parameter estimates to alternative priors is studied. The method is illustrated using tumor data from a chronic animal experiment.     

Therapeutic Implications from Sensitivity Analysis of Tumor Angiogenesis Models

Anti-angiogenic cancer treatments induce tumor starvation and regression by targeting the tumor vasculature that delivers oxygen and nutrients. Mathematical models prove valuable tools to study the proof-of-concept, efficacy and underlying mechanisms of such treatment approaches. The effects of parameter value uncertainties for two models of tumor development under angiogenic signaling and anti-angiogenic treatment are studied. Data fitting is performed to compare predictions of both models and to obtain nominal parameter values for sensitivity analysis. Sensitivity analysis reveals that the success of different cancer treatments depends on tumor size and tumor intrinsic parameters. In particular, we show that tumors with ample vascular support can be successfully targeted with conventional cytotoxic treatments. On the other hand, tumors with curtailed vascular support are not limited by their growth rate and therefore interruption of neovascularization emerges as the most promising treatment target.     

A latent contingency table approach to dose finding for combinations of two agents

Summary .  Two-agent combination trials have recently attracted enormous attention in oncology research. There are several strong motivations for combining different agents in a treatment: to induce the synergistic treatment effect, to increase the dose intensity with nonoverlapping toxicities, and to target different tumor cell susceptibilities. To accommodate this growing trend in clinical trials, we propose a Bayesian adaptive design for dose finding based on latent 2 × 2 tables. In the search for the maximum tolerated dose combination, we continuously update the posterior estimates for the unknown parameters associated with marginal probabilities and the correlation parameter based on the data from successive patients. By reordering the dose toxicity probabilities in the two-dimensional space, we assign each coming cohort of patients to the most appropriate dose combination. We conduct extensive simulation studies to examine the operating characteristics of the proposed method under various practical scenarios. Finally, we illustrate our dose-finding procedure with a clinical trial of agent combinations at M. D. Anderson Cancer Center.     

Antiangiogenic cancer therapy: why do mouse and human patients respond in a different way to the same drug?

The tumor vasculature is an increasingly attractive target for development of anticancer drugs. The fundamental principle for antiangiogenic cancer therapy is based on the inhibitory effect of chemical compounds, proteins or nucleotides on tumor angiogenesis. Indeed, in almost all preclinical tumor models, antiangiogenic monotherapy with different agents shows potent effects on suppression of tumor growth. However, antiangiogenic monotherapy has barely produced any clinical benefits in cancer patients. Although in combination with chemotherapy some antiangiogenic drugs demonstrate survival improvement in patients with certain types of cancers, the overall benefits by addition of antiangiogenic drugs (ADs) to chemotherapy remain modest. The disparity of AD responses between preclinical models and clinical cancer patients has raised important issues, which include: 1) Are current animal tumor models appropriate for assessing the therapeutic efficacy of ADs for clinical development? 2) What are the key differences between mouse tumor models and human cancer patients? 3) Are anti-VEGF drugs off target in cancer patients? 4) What are alternative options for improvement of the clinical benefits of ADs? In this short review, I discuss these critical issues in relation to the clinical practice of ADs.     

Ensemble Modeling for Robustness Analysis in engineering non-native metabolic pathways

Metabolic pathways in cells must be sufficiently robust to tolerate fluctuations in expression levels and changes in environmental conditions. Perturbations in expression levels may lead to system failure due to the disappearance of a stable steady state. Increasing evidence has suggested that biological networks have evolved such that they are intrinsically robust in their network structure. In this article, we presented Ensemble Modeling for Robustness Analysis (EMRA), which combines a continuation method with the Ensemble Modeling approach, for investigating the robustness issue of non-native pathways. EMRA investigates a large ensemble of reference models with different parameters, and determines the effects of parameter drifting until a bifurcation point, beyond which a stable steady state disappears and system failure occurs. A pathway is considered to have high bifurcational robustness if the probability of system failure is low in the ensemble. To demonstrate the utility of EMRA, we investigate the bifurcational robustness of two synthetic central metabolic pathways that achieve carbon conservation: non-oxidative glycolysis and reverse glyoxylate cycle. With EMRA, we determined the probability of system failure of each design and demonstrated that alternative designs of these pathways indeed display varying degrees of bifurcational robustness. Furthermore, we demonstrated that target selection for flux improvement should consider the trade-offs between robustness and performance.     

Sequential adjuvant hormone therapy in postmenopausal breast cancer: rationale and clinical results

For the past 15 years tamoxifen has been the standard adjuvant hormone therapy for women with early-stage breast cancer and estrogen receptor (ER)-positive tumors, irrespective of nodal status and other clinicopathological parameters. Recent studies provided evidence that the optimal duration of tamoxifen treatment is 5 years. Based on the positive clinical results obtained with the administration of aromatase inhibitors (AIs) in the metastatic setting, several controlled clinical trials have evaluated the efficacy and side effects of AIs versus standard tamoxifen also as adjuvant therapy in postmenopausal breast cancer patients. The results of the above studies, suggest a therapeutic advantage of AIs over tamoxifen with regard to relapse-free survival and the risk of metachronous contralateral breast cancer. We review the rationale and the available clinical data on initial or sequential hormone treatment with AIs and we propose a novel scenario for possible therapeutic strategies based on the clinicopathological characteristics of the patients and on the biology of each single tumor.     

IL-13 receptor-targeted cytotoxin cancer therapy leads to complete eradication of tumors with the aid of phagocytic cells in nude mice model of human cancer

Tumor-directed therapeutic approaches require unique or overexpressed specific Ag or receptor as a target to achieve selective tumor killing. However, heterogeneous expression of these targets on tumor cells limits the efficacy of this form of therapy. In this study, we forced abundant expression of IL-13Ralpha2 chain by plasmid-mediated gene transfer in head and neck, as well as prostate tumors to provide a potential target. This was followed by successfully treating xenograft tumor-bearing nude mice with IL-13R-directed cytotoxin (IL13-PE38QQR). Although we did not observe an indirect cytotoxic bystander effect conveyed to nontransduced tumor cells in vitro, our approach in vivo led to a complete regression of established tumors transfected with IL-13Ralpha2 chain in most animals. We found that the tumor eradication was achieved in part by infiltration of macrophages and NK cells, assessed by immunohistochemistry. Moreover, head and neck tumors xenografted in macrophage-depleted nude mice were less sensitive to the antitumor effect of IL-13 cytotoxin. Because we did not observe vector-related toxicity in any vital organs, our novel combination strategy of gene transfer of IL-13Ralpha2 chain and receptor-directed cytotoxin therapy may be a useful approach for the treatment of localized cancer.     

Tumor Growth Rate Determines the Timing of Optimal Chronomodulated Treatment Schedules

In host and cancer tissues, drug metabolism and susceptibility to drugs vary in a circadian (24 h) manner. In particular, the efficacy of a cell cycle specific (CCS) cytotoxic agent is affected by the daily modulation of cell cycle activity in the target tissues. Anti-cancer chronotherapy, in which treatments are administered at a particular time each day, aims at exploiting these biological rhythms to reduce toxicity and improve efficacy of the treatment. The circadian status, which is the timing of physiological and behavioral activity relative to daily environmental cues, largely determines the best timing of treatments. However, the influence of variations in tumor kinetics has not been considered in determining appropriate treatment schedules. We used a simple model for cell populations under chronomodulated treatment to identify which biological parameters are important for the successful design of a chronotherapy strategy. We show that the duration of the phase of the cell cycle targeted by the treatment and the cell proliferation rate are crucial in determining the best times to administer CCS drugs. Thus, optimal treatment times depend not only on the circadian status of the patient but also on the cell cycle kinetics of the tumor. Then, we developed a theoretical analysis of treatment outcome (TATO) to relate the circadian status and cell cycle kinetic parameters to the treatment outcomes. We show that the best and the worst CCS drug administration schedules are those with 24 h intervals, implying that 24 h chronomodulated treatments can be ineffective or even harmful if administered at wrong circadian times. We show that for certain tumors, administration times at intervals different from 24 h may reduce these risks without compromising overall efficacy.     

Nelfinavir induces TRAIL receptor upregulation in ovarian cancer cells

HIV protease inhibitors are currently being discussed to be useful as new and alternative anti-cancer agents, especially as second line treatments for chemo-resistant human cancer types. Among three clinically applied HIV protease inhibitors tested, we found a high efficacy of nelfinavir on ovarian cancer cells, accompanied by apoptosis (annexin binding) and necrosis (propidium iodide permeability). In vitro, at concentrations used to induce cell death in ovarian cancer cells, nelfinavir had no effect on the cellular viability of fibroblasts or peripheral blood mononuclear leukocytes. Nelfinavir sensitized ovarian cancer cells to treatment with an apoptosis-inducing TRAIL receptor antibody due to upregulation of the TRAIL receptor DR5 as shown by RT-PCR and FACScan analysis. We conclude that nelfinavir, an already approved drug, is a highly efficient agent against ovarian cancer cells and could sensitize ovarian cancer cells to TRAIL treatment, either therapeutically applied or endogenously produced by cells of the immune system.     

Correction of confounding bias in non-randomized studies by appropriate weighting

In non-randomized studies, the assessment of a causal effect of treatment or exposure on outcome is hampered by possible confounding. Applying multiple regression models including the effects of treatment and covariates on outcome is the well-known classical approach to adjust for confounding. In recent years other approaches have been promoted. One of them is based on the propensity score and considers the effect of possible confounders on treatment as a relevant criterion for adjustment. Another proposal is based on using an instrumental variable. Here inference relies on a factor, the instrument, which affects treatment but is thought to be otherwise unrelated to outcome, so that it mimics randomization. Each of these approaches can basically be interpreted as a simple reweighting scheme, designed to address confounding. The procedures will be compared with respect to their fundamental properties, namely, which bias they aim to eliminate, which effect they aim to estimate, and which parameter is modelled. We will expand our overview of methods for analysis of non-randomized studies to methods for analysis of randomized controlled trials and show that analyses of both study types may target different effects and different parameters. The considerations will be illustrated using a breast cancer study with a so-called Comprehensive Cohort Study design, including a randomized controlled trial and a non-randomized study in the same patient population as sub-cohorts. This design offers ideal opportunities to discuss and illustrate the properties of the different approaches.     

Cancer: Towards a general theory of the target

General theories (GT) are reductionist explications of apparently independent facts. Here, in reviewing the literature, I develop a GT to simplify the cluttered landscape of cancer therapy targets by revealing they cluster parsimoniously according to only a few underlying principles. The first principle is that targets can be only exploited by either or both of two fundamentally different approaches: causality‐inhibition, and ‘acausal’ recognition of some marker or signature. Nonetheless, each approach must achieve both of two separate goals, efficacy (reduction in cancer burden) and selectivity (sparing of normal cells); if the mechanisms are known, this provides a definition of rational treatment. The second principle is target fragmentation, whereby the target may perform up to three categoric functions (cytoreduction, modulation, cytoprotection), potentially mediated by physically different target molecules, even on different cell types, or circulating freely. This GT remains incomplete until the minimal requirements for cure, or alternatively, proof that cure is impossible, become predictable.     

Does your species have memory? Analyzing capture–recapture data with memory models

We examine memory models for multisite capture–recapture data. This is an important topic, as animals may exhibit behavior that is more complex than simple first‐order Markov movement between sites, when it is necessary to devise and fit appropriate models to data. We consider the Arnason–Schwarz model for multisite capture–recapture data, which incorporates just first‐order Markov movement, and also two alternative models that allow for memory, the Brownie model and the Pradel model. We use simulation to compare two alternative tests which may be undertaken to determine whether models for multisite capture–recapture data need to incorporate memory. Increasing the complexity of models runs the risk of introducing parameters that cannot be estimated, irrespective of how much data are collected, a feature which is known as parameter redundancy. Rouan et al. (JABES, 2009, pp 338–355) suggest a constraint that may be applied to overcome parameter redundancy when it is present in multisite memory models. For this case, we apply symbolic methods to derive a simpler constraint, which allows more parameters to be estimated, and give general results not limited to a particular configuration. We also consider the effect sparse data can have on parameter redundancy and recommend minimum sample sizes. Memory models for multisite capture–recapture data can be highly complex and difficult to fit to data. We emphasize the importance of a structured approach to modeling such data, by considering a priori which parameters can be estimated, which constraints are needed in order for estimation to take place, and how much data need to be collected. We also give guidance on the amount of data needed to use two alternative families of tests for whether models for multisite capture–recapture data need to incorporate memory.     

Targeting signal transduction pathways by chemopreventive agents

Cancer is a dynamic process that involves many complex factors, which may explain why a "magic bullet" cure for cancer has not been found. Death rates are still rising for many types of cancers, which possibly contributes to the increased interest in chemoprevention as an alternative approach to the control of cancer. This strategy for cancer control is based on the presumption that because cancer develops through a multi-step process, each step may be a prospective target for reversing or suppressing the process. Thus, the design and development of chemopreventive agents that act on specific and/or multiple molecular and cellular targets is gaining support as a rational approach to control cancer. Nutritional or dietary factors have attracted a great deal of interest because of their perceived ability to act as highly effective chemopreventive agents. They are professed as being generally safe and may have efficacy as chemopreventive agents by preventing or reversing premalignant lesions and/or reducing second primary tumor incidence. Many of these dietary compounds appear to act on multiple target signaling pathways. Some of the most interesting and well documented are resveratrol and components of tea, including EGCG, theaflavins and caffeine. This review will focus on recent work regarding three well-accepted cellular/molecular mechanisms that may at least partially explain the effectiveness of selected food factors, including those indicated above, as chemopreventive anti-promotion agents. These food compounds may act by: (1) inducing apoptosis in cancer cells; (2) inhibiting neoplastic transformation through the inhibition of AP-1 and/or NF-kappaB activation; and/or (3) suppressing COX-2 overexpression in cancer cells.