A Multiplexed Luciferase-based Screening Platform for Interrogating Cancer-associated Signal Transduction in Cultured Cells

Genome-scale interrogation of gene function using RNA interference (RNAi) holds tremendous promise for the rapid identification of chemically tractable cancer cell vulnerabilities. Limiting the potential of this technology is the inability to rapidly delineate the mechanistic basis of phenotypic outcomes and thus inform the development of molecularly targeted therapeutic strategies. We outline here methods to deconstruct cellular phenotypes induced by RNAi-mediated gene targeting using multiplexed reporter systems that allow monitoring of key cancer cell-associated processes. This high-content screening methodology is versatile and can be readily adapted for the screening of other types of large molecular libraries.     

Optimal individualized dosing strategies: A pharmacologic approach to developing dynamic treatment regimens for continuous‐valued treatments

There have been considerable advances in the methodology for estimating dynamic treatment regimens, and for the design of sequential trials that can be used to collect unconfounded data to inform such regimens. However, relatively little attention has been paid to how such methodology could be used to advance understanding of optimal treatment strategies in a continuous dose setting, even though it is often the case that considerable patient heterogeneity in drug response along with a narrow therapeutic window may necessitate the tailoring of dosing over time. Such is the case with warfarin, a common oral anticoagulant. We propose novel, realistic simulation models based on pharmacokinetic‐pharmacodynamic properties of the drug that can be used to evaluate potentially optimal dosing strategies. Our results suggest that this methodology can lead to a dosing strategy that performs well both within and across populations with different pharmacokinetic characteristics, and may assist in the design of randomized trials by narrowing the list of potential dosing strategies to those which are most promising.     

A general framework for subgroup detection via one-step value difference estimation

Recent statistical methodology for precision medicine has focused on either identification of subgroups with enhanced treatment effects or estimating optimal treatment decision rules so that treatment is allocated in a way that maximizes, on average, predefined patient outcomes. Less attention has been given to subgroup testing, which involves evaluation of whether at least a subgroup of the population benefits from an investigative treatment, compared to some control or standard of care. In this work, we propose a general framework for testing for the existence of a subgroup with enhanced treatment effects based on the difference of the estimated value functions under an estimated optimal treatment regime and a fixed regime that assigns everyone to the same treatment. Our proposed test does not require specification of the parametric form of the subgroup and allows heterogeneous treatment effects within the subgroup. The test applies to cases when the outcome of interest is either a time-to-event or a (uncensored) scalar, and is valid at the exceptional law. To demonstrate the empirical performance of the proposed test, we study the type I error and power of the test statistics in simulations and also apply our test to data from a Phase III trial in patients with hematological malignancies.     

Evolution of Resistance to Targeted Anti-Cancer Therapies during Continuous and Pulsed Administration Strategies

The discovery of small molecules targeted to specific oncogenic pathways has revolutionized anti-cancer therapy. However, such therapy often fails due to the evolution of acquired resistance. One long-standing question in clinical cancer research is the identification of optimum therapeutic administration strategies so that the risk of resistance is minimized. In this paper, we investigate optimal drug dosing schedules to prevent, or at least delay, the emergence of resistance. We design and analyze a stochastic mathematical model describing the evolutionary dynamics of a tumor cell population during therapy. We consider drug resistance emerging due to a single (epi)genetic alteration and calculate the probability of resistance arising during specific dosing strategies. We then optimize treatment protocols such that the risk of resistance is minimal while considering drug toxicity and side effects as constraints. Our methodology can be used to identify optimum drug administration schedules to avoid resistance conferred by one (epi)genetic alteration for any cancer and treatment type.     

Depression screening and patient outcomes in cancer: a systematic review

Background

Several practice guidelines recommend screening for depression in cancer care, but no systematic reviews have examined whether there is evidence that depression screening benefits cancer patients. The objective was to evaluate the potential benefits of depression screening in cancer patients by assessing the (1) accuracy of depression screening tools; (2) effectiveness of depression treatment; and (3) effect of depression screening, either alone or in the context of comprehensive depression care, on depression outcomes.

Methods

Data sources were CINAHL, Cochrane, EMBASE, ISI, MEDLINE, PsycINFO and SCOPUS databases through January 24, 2011; manual journal searches; reference lists; citation tracking; trial registry reviews. Articles on cancer patients were included if they (1) compared a depression screening instrument to a valid criterion for major depressive disorder (MDD); (2) compared depression treatment with placebo or usual care in a randomized controlled trial (RCT); (3) assessed the effect of screening on depression outcomes in a RCT.

Results

There were 19 studies of screening accuracy, 1 MDD treatment RCT, but no RCTs that investigated effects of screening on depression outcomes. Screening accuracy studies generally had small sample sizes (median = 17 depression cases) and used exploratory methods to set sample-specific cutoff scores that varied substantially across studies. A nurse-delivered intervention for MDD reduced depressive symptoms moderately (effect size = 0.37).

Conclusions

The one treatment study reviewed reported modest improvement in depressive symptoms, but no evidence was found on whether or not depression screening in cancer patients, either alone or in the context of optimal depression care, improves depression outcomes compared to usual care. Depression screening in cancer should be evaluated in a RCT in which all patients identified as depressed, either through screening or via physician recognition and referral in a control group, have access to comprehensive depression care.     

Significance tests for cancer screening trials

The goal of a cancer screening program is to reduce cancer mortality by detecting tumors at earlier stages of their development. For some types of cancer, screening tests may allow the preclinical detection of benign precursors of a tumor, and thus a screening program could result in reductions in both cancer incidence and mortality. For other types of cancer, a screening program will not reduce cancer incidence, and thus the expected outcome in a randomized cancer screening trial would be equal cancer incidence rates in control and study groups, but reduced cancer mortality in the study group. For the latter situation, we employ a variety of Poisson models for cancer incidence and mortality to derive optimal tests for equality of cancer mortality rates in a cancer screening trial, and we compare the asymptotic relative efficiencies of the test statistics under various alternatives. We demonstrate that testing equality of case mortality rates using Fisher's exact test or its Pearson chi-square approximation is nearly optimal when cancer incidence rates are equal and is fully efficient when cancer incidence rates are unequal. When valid, this comparison of case mortality rates in the study and control groups can be considerably more powerful than the standard comparison of population mortality rates. We illustrate the results using data from a clinical trial of a breast cancer screening program.     

Evaluation of screening strategies for pre-malignant lesions using a biomathematical approach

We derive mathematical expressions for the size distribution of screen-detectable pre-malignant lesions, both conditional and unconditional on no prior detection of cancer in the tissue of interest, based on a general multistage clonal expansion model of carcinogenesis. We apply these expressions to simulate the natural history of colorectal cancer and to evaluate the effect of a screen for adenomatous polyps and concomitant intervention on cancer risk. Our approach allows the efficient simulation of multiple screens and interventions and determination of the optimal timing of the screens. We further demonstrate the utility of our approach by computing the benefits of up to two colonoscopies on the lifetime risk of colorectal cancer.     

Different strategies for screening and prevention of type 2 diabetes in adults: cost effectiveness analysis

Objective To compare four potential screening strategies, and subsequent interventions, for the prevention and treatment of type 2 diabetes: (a) screening for type 2 diabetes to enable early detection and treatment, (b) screening for type 2 diabetes and impaired glucose tolerance, intervening with lifestyle interventions in those with a diagnosis of impaired glucose tolerance to delay or prevent diabetes, (c) as for (b) but with pharmacological interventions, and (d) no screening.Design Cost effectiveness analysis based on development and evaluation of probabilistic, comprehensive economic decision analytic model, from screening to death.Setting A hypothetical population, aged 45 at time of screening, with above average risk of diabetes.Data sources Published clinical trials and epidemiological studies retrieved from electronic bibliographic databases; supplementary data obtained from the Department of Health statistics for England and Wales, the screening those at risk (STAR) study, and the Leicester division of the ADDITION study.Methods A hybrid decision tree/Markov model was developed to simulate the long term effects of each screening strategy, in terms of both clinical and cost effectiveness outcomes. The base case model assumed a 50 year time horizon with discounting of both costs and benefits at 3.5%. Sensitivity analyses were carried out to investigate assumptions of the model and to identify which model inputs had most impact on the results.Results Estimated costs for each quality adjusted life year (QALY) gained (discounted at 3.5% a year for both costs and benefits) were £14 150 (€17 560; $27 860) for screening for type 2 diabetes, £6242 for screening for diabetes and impaired glucose tolerance followed by lifestyle interventions, and £7023 for screening for diabetes and impaired glucose tolerance followed by pharmacological interventions, all compared with no screening. At a willingness-to-pay threshold of £20 000 the probability of the intervention being cost effective was 49%, 93%, and 85% for each of the active screening strategies respectively.Conclusions Screening for type 2 diabetes and impaired glucose tolerance, with appropriate intervention for those with impaired glucose tolerance, in an above average risk population aged 45, seems to be cost effective. The cost effectiveness of a policy of screening for diabetes alone, which offered no intervention to those with impaired glucose tolerance, is still uncertain.     

Nonparametric estimation of asymptomatic duration from a randomized prospective cancer screening trial

We propose a nonparametric estimation of preclinical duration distribution in cancer based on data from a randomized early detection trial. In cancer screening studies, the preclinical duration of a disease is of great interest for better understanding the natural history of the disease, and for developing optimal screening strategies. To estimate the sojourn time distribution nonparametrically, we first estimate the distribution of the age at onset of preclinical disease nonparametrically using data from the screening arm in a randomized screening trial, and the distribution for the age at onset of clinical disease from the control arm of the randomized screening trial. Finally, by using deconvolution the two estimated distributions lead to a nonparametric estimate of the distribution for the gap time between the onset of preclinical disease and the onset of clinical disease. We illustrate the methodology using data from a randomized breast cancer screening trial.     

Prevention. How much harm? How much benefit? 1. Influence of reporting methods on perception of benefits.

Before a physician or a patient can decide whether a preventive program is worth while, each must understand the nature and degree of its benefits and the frequency and magnitude of its adverse effects. Preventive interventions can be divided into two major categories: those with infrequent or minor adverse effects and those with adverse effects that are frequent or serious. Accident prevention, avoidance of high-risk behaviour and healthy lifestyle choices such as breast-feeding and moderate exercise are associated with few adverse consequences. By contrast, screening populations for disease, risk classification for the purpose of selective preventive interventions, dietary intervention and prophylactic drug treatment may lead to more frequent and serious adverse effects. When assessing whether the benefits of a preventive intervention outweigh the harm, one must be aware that the methods used to report benefits of clinical trials may distort the reader''s perception of their magnitude. The relative reduction of morbidity or mortality rate often grossly exaggerates benefits and should never be used as a basis for clinical decision making. More realistic ways of recording benefits are the absolute reduction of morbidity or mortality rate, the number of patients that need to be treated to avoid one adverse event, and the total cohort mortality rate.     

Rational Design and Adaptive Management of Combination Therapies for Hepatitis C Virus Infection

Recent discoveries of direct acting antivirals against Hepatitis C virus (HCV) have raised hopes of effective treatment via combination therapies. Yet rapid evolution and high diversity of HCV populations, combined with the reality of suboptimal treatment adherence, make drug resistance a clinical and public health concern. We develop a general model incorporating viral dynamics and pharmacokinetics/ pharmacodynamics to assess how suboptimal adherence affects resistance development and clinical outcomes. We derive design principles and adaptive treatment strategies, identifying a high-risk period when missing doses is particularly risky for de novo resistance, and quantifying the number of additional doses needed to compensate when doses are missed. Using data from large-scale resistance assays, we demonstrate that the risk of resistance can be reduced substantially by applying these principles to a combination therapy of daclatasvir and asunaprevir. By providing a mechanistic framework to link patient characteristics to the risk of resistance, these findings show the potential of rational treatment design.     

CAERUS: predicting CAncER oUtcomeS using relationship between protein structural information, protein networks, gene expression data, and mutation data

Carcinogenesis is a complex process with multiple genetic and environmental factors contributing to the development of one or more tumors. Understanding the underlying mechanism of this process and identifying related markers to assess the outcome of this process would lead to more directed treatment and thus significantly reduce the mortality rate of cancers. Recently, molecular diagnostics and prognostics based on the identification of patterns within gene expression profiles in the context of protein interaction networks were reported. However, the predictive performances of these approaches were limited. In this study we propose a novel integrated approach, named CAERUS, for the identification of gene signatures to predict cancer outcomes based on the domain interaction network in human proteome. We first developed a model to score each protein by quantifying the domain connections to its interacting partners and the somatic mutations present in the domain. We then defined proteins as gene signatures if their scores were above a preset threshold. Next, for each gene signature, we quantified the correlation of the expression levels between this gene signature and its neighboring proteins. The results of the quantification in each patient were then used to predict cancer outcome by a modified naïve Bayes classifier. In this study we achieved a favorable accuracy of 88.3%, sensitivity of 87.2%, and specificity of 88.9% on a set of well-documented gene expression profiles of 253 consecutive breast cancer patients with different outcomes. We also compiled a list of cancer-associated gene signatures and domains, which provided testable hypotheses for further experimental investigation. Our approach proved successful on different independent breast cancer data sets as well as an ovarian cancer data set. This study constitutes the first predictive method to classify cancer outcomes based on the relationship between the domain organization and protein network.     

Convenience polyandry or convenience polygyny? Costly sex under female control in a promiscuous primate

Classic sex roles depict females as choosy, but polyandry is widespread. Empirical attempts to understand the evolution of polyandry have often focused on its adaptive value to females, whereas 'convenience polyandry' might simply decrease the costs of sexual harassment. We tested whether constraint-free female strategies favour promiscuity over mating selectivity through an original experimental design. We investigated variation in mating behaviour in response to a reversible alteration of sexual dimorphism in body mass in the grey mouse lemur, a small primate where female brief sexual receptivity allows quantifying polyandry. We manipulated body condition in captive females, predicting that convenience polyandry would increase when females are weaker than males, thus less likely to resist their solicitations. Our results rather support the alternative hypothesis of 'adaptive polyandry': females in better condition are more polyandrous. Furthermore, we reveal that multiple mating incurs significant energetic costs, which are strikingly symmetrical between the sexes. Our study shows that mouse lemur females exert tight control over mating and actively seek multiple mates. The benefits of remating are nevertheless not offset by its costs in low-condition females, suggesting that polyandry is a flexible strategy yielding moderate fitness benefits in this small mammal.     

Efficient algorithms for optimal designs with correlated observations in pharmacokinetics and dose-finding studies

Random effects models are widely used in population pharmacokinetics and dose-finding studies. However, when more than one observation is taken per patient, the presence of correlated observations (due to shared random effects and possibly residual serial correlation) usually makes the explicit determination of optimal designs difficult. In this article, we introduce a class of multiplicative algorithms to be able to handle correlated data and thus allow numerical calculation of optimal experimental designs in such situations. In particular, we demonstrate its application in a concrete example of a crossover dose-finding trial, as well as in a typical population pharmacokinetics example. Additionally, we derive a lower bound for the efficiency of any given design in this context, which allows us on the one hand to monitor the progress of the algorithm, and on the other hand to investigate the efficiency of a given design without knowing the optimal one. Finally, we extend the methodology such that it can be used to determine optimal designs if there exist some requirements regarding the minimal number of treatments for several (in some cases all) experimental conditions.     

Marine reserves with ecological uncertainty

To help manage the fluctuations inherent in fish populations scientists have argued for both an ecosystem approach to management and the greater use of marine reserves. Support for reserves includes empirical evidence that they can raise the spawning biomass and mean size of exploited populations, increase the abundance of species and, relative to reference sites, raise population density, biomass, fish size and diversity. By contrast, fishers often oppose the establishment and expansion of marine reserves and claim that reserves provide few, if any, economic payoffs. Using a stochastic optimal control model with two forms of ecological uncertainty we demonstrate that reserves create a resilience effect that allows for the population to recover faster, and can also raise the harvest immediately following a negative shock. The tradeoff of a larger reserve is a reduced harvest in the absence of a negative shock such that a reserve will never encompass the entire population if the goal is to maximize the economic returns from harvesting, and fishing is profitable. Under a wide range of parameter values with ecological uncertainty, and in the ‘worst case’ scenario for a reserve, we show that a marine reserve can increase the economic payoff to fishers even when the harvested population is not initially overexploited, harvesting is economically optimal and the population is persistent. Moreover, we show that the benefits of a reserve cannot be achieved by existing effort or output controls. Our results demonstrate that, in many cases, there is no tradeoff between the economic payoff of fishers and ecological benefits when a reserve is established at equal to, or less than, its optimum size.     

Lentiviral and targeted cellular barcoding reveals ongoing clonal dynamics of cell lines in vitro and in vivo

Background

Cell lines are often regarded as clonal, even though this simplifies what is known about mutagenesis, transformation and other processes that destabilize them over time. Monitoring these clonal dynamics is important for multiple areas of biomedical research, including stem cell and cancer biology. Tracking the contributions of individual cells to large populations, however, has been constrained by limitations in sensitivity and complexity.

Results

We utilize cellular barcoding methods to simultaneously track the clonal contributions of tens of thousands of cells. We demonstrate that even with optimal culturing conditions, common cell lines including HeLa, K562 and HEK-293 T exhibit ongoing clonal dynamics. Starting a population with a single clone diminishes but does not eradicate this phenomenon. Next, we compare lentiviral and zinc-finger nuclease barcode insertion approaches, finding that the zinc-finger nuclease protocol surprisingly results in reduced clonal diversity. We also document the expected reduction in clonal complexity when cells are challenged with genotoxic stress. Finally, we demonstrate that xenografts maintain clonal diversity to a greater extent than in vitro culturing of the human non-small-cell lung cancer cell line HCC827.

Conclusions

We demonstrate the feasibility of tracking and quantifying the clonal dynamics of entire cell populations within multiple cultured cell lines. Our results suggest that cell heterogeneity should be considered in the design and interpretation of in vitro culture experiments. Aside from clonal cell lines, we propose that cellular barcoding could prove valuable in modeling the clonal behavior of heterogeneous cell populations over time, including tumor populations treated with chemotherapeutic agents.     

A cluster randomized controlled trial comparing three methods of disseminating practice guidelines for children with croup [ISRCTN73394937]

Background

The optimal management of croup – a common respiratory illness in young children – is well established. In particular, treatment with corticosteroids has been shown to significantly reduce the rate and duration of intubation, hospitalization, and return to care for on-going croup symptoms. Furthermore treatment with a single dose of corticosteroids does not appear to result in any significant adverse outcomes, and yields overall cost-savings for both families and the health care system. However, as has been shown with many other diseases, there is a significant gap between what we know and what we do. The overall aim of this study is to identify, from a societal perspective, the costs and associated benefits of three strategies for implementing a practice guideline that addresses the management of croup.

Methods/designs

We propose to use a matched pair cluster trial in 24 Alberta hospitals randomized into three intervention groups. We will use mixed methods to assess outcomes including linkage and analysis of administrative databases obtained from Alberta Health and Wellness, retrospective medical chart audit, and prospective telephone surveys of the parents of children diagnosed to have croup. The intervention strategies to be compared will be mailing of printed educational materials (low intensity intervention), mailing plus a combination of interactive educational meetings, educational outreach visits, and reminders (intermediate intensity intervention), and a combination of mailing, interactive sessions, outreach visits, reminders plus identification of local opinion leaders and establishment of local consensus processes (high intensity intervention). The primary objective is to determine which of the three intervention strategies are most effective at lowering the rate of hospital days per 1,000 disease episodes. Secondary objectives are to determine which of the three dissemination strategies are most effective at increasing the use of therapies of known benefit. An economic analysis will be conducted to determine which of the three intervention strategies will most effectively reduce total societal costs including all health care costs, costs borne by the family, and costs stemming from the strategies for disseminating guidelines.     

Promiscuity, paternity and personality in the great tit

Understanding causes of variation in promiscuity within populations remain a major challenge. While most studies have focused on quantifying fitness costs and benefits of promiscuous behaviour, an alternative possibility--that variation in promiscuity within populations is maintained because of linkage with other traits-has received little attention. Here, we examine whether promiscuity in male and female great tits (Parus major)--quantified as extra-pair paternity (EPP) within and between nests--is associated with variation in a well-documented personality trait: exploration behaviour in a novel environment. Exploration behaviour has been shown to correlate with activity levels, risk-taking and boldness, and these are behaviours that may plausibly influence EPP. Exploration behaviour correlated positively with paternity gained outside the social pair among males in our population, but there was also a negative correlation with paternity in the social nest. Hence, while variation in male personality predicted the relative importance of paternity gain within and outside the pair bond, total paternity gained was unrelated to exploration behaviour. We found evidence that males paired with bold females were more likely to sire extra-pair young. Our data thus demonstrate a link between personality and promiscuity, with no net effects on reproductive success, suggesting personality-dependent mating tactics, in contrast with traditional adaptive explanations for promiscuity.     

Optimal vaccine allocation for COVID-19 in the Netherlands: A data-driven prioritization

For the control of COVID-19, vaccination programmes provide a long-term solution. The amount of available vaccines is often limited, and thus it is crucial to determine the allocation strategy. While mathematical modelling approaches have been used to find an optimal distribution of vaccines, there is an excessively large number of possible allocation schemes to be simulated. Here, we propose an algorithm to find a near-optimal allocation scheme given an intervention objective such as minimization of new infections, hospitalizations, or deaths, where multiple vaccines are available. The proposed principle for allocating vaccines is to target subgroups with the largest reduction in the outcome of interest. We use an approximation method to reconstruct the age-specific transmission intensity (the next generation matrix), and express the expected impact of vaccinating each subgroup in terms of the observed incidence of infection and force of infection. The proposed approach is firstly evaluated with a simulated epidemic and then applied to the epidemiological data on COVID-19 in the Netherlands. Our results reveal how the optimal allocation depends on the objective of infection control. In the case of COVID-19, if we wish to minimize deaths, the optimal allocation strategy is not efficient for minimizing other outcomes, such as infections. In simulated epidemics, an allocation strategy optimized for an outcome outperforms other strategies such as the allocation from young to old, from old to young, and at random. Our simulations clarify that the current policy in the Netherlands (i.e., allocation from old to young) was concordant with the allocation scheme that minimizes deaths. The proposed method provides an optimal allocation scheme, given routine surveillance data that reflect ongoing transmissions. This approach to allocation is useful for providing plausible simulation scenarios for complex models, which give a more robust basis to determine intervention strategies.     

Optimal screening of surface-displayed polypeptide libraries

Cell surface display of polypeptide libraries combined with flow cytometric cell sorting presents remarkable potential for enhancement of protein-ligand recognition properties. To maximize the utility of this approach, screening and purification conditions must be optimized to take full advantage of the quantitative feature of this technique. In particular, discrimination of improved library mutants from an excess of wild-type polypeptides is dependent upon an effective screening methodology. Fluorescence discrimination profiles for improved library mutants were derived from a mathematical model of expected cell fluorescence intensities for polypeptide libraries screened with fluorescent ligand. Profiles for surface-displayed libraries under equilibrium or kinetic screening conditions demonstrate distinct discrimination optima from which optimal equilibrium and kinetic screening parameters were derived. In addition, a statistical model of low cytometrically analyzed cell populations indicates the importance of low-stringency sorting followed by amplification through regrowth and resorting at increased stringency. This analysis further yields quantitative recommendations for cell-sorting stringency.