Risk stratification and subclinical phenotyping of dilated and/or arrhythmogenic cardiomyopathy mutation-positive relatives: CVON eDETECT consortium

In relatives of index patients with dilated cardiomyopathy and arrhythmogenic cardiomyopathy, early detection of disease onset is essential to prevent sudden cardiac death and facilitate early treatment of heart failure. However, the optimal screening interval and combination of diagnostic techniques are unknown. The clinical course of disease in index patients and their relatives is variable due to incomplete and age-dependent penetrance. Several biomarkers, electrocardiographic and imaging (echocardiographic deformation imaging and cardiac magnetic resonance imaging) techniques are promising non-invasive methods for detection of subclinical cardiomyopathy. However, these techniques need optimisation and integration into clinical practice. Furthermore, determining the optimal interval and intensity of cascade screening may require a personalised approach. To address this, the CVON-eDETECT (early detection of disease in cardiomyopathy mutation carriers) consortium aims to integrate electronic health record data from long-term follow-up, diagnostic data sets, tissue and plasma samples in a multidisciplinary biobank environment to provide personalised risk stratification for heart failure and sudden cardiac death. Adequate risk stratification may lead to personalised screening, treatment and optimal timing of implantable cardioverter defibrillator implantation. In this article, we describe non-invasive diagnostic techniques used for detection of subclinical disease in relatives of index patients with dilated cardiomyopathy and arrhythmogenic cardiomyopathy.     

Carcinoid heart disease: a guide for screening and timing of surgical intervention

The cardiac manifestations of a neuroendocrine tumour are referred to as carcinoid heart disease (CaHD) and are associated with a poor prognosis. Surgical intervention is the only proven therapeutic option and may prolong survival and quality of life. No consensus has been reached internationally with regard to screening for CaHD and the optimal timing for surgery. Although limited evidence is available on this matter, a trend towards early surgery and subsequent reduced mortality has been observed. In this review we provide an overview of the current understanding and propose a protocol to guide cardiologists in the screening for CaHD and the timing of referral to a specialised surgical centre.     

Judging Statistical Models of Individual Decision Making under Risk Using In- and Out-of-Sample Criteria

Despite the fact that conceptual models of individual decision making under risk are deterministic, attempts to econometrically estimate risk preferences require some assumption about the stochastic nature of choice. Unfortunately, the consequences of making different assumptions are, at present, unclear. In this paper, we compare three popular error specifications (Fechner, contextual utility, and Luce error) for three different preference functionals (expected utility, rank-dependent utility, and a mixture of those two) using in- and out-of-sample selection criteria. We find drastically different inferences about structural risk preferences across the competing functionals and error specifications. Expected utility theory is least affected by the selection of the error specification. A mixture model combining the two conceptual models assuming contextual utility provides the best fit of the data both in- and out-of-sample.     

Ergodicity-breaking reveals time optimal decision making in humans

Ergodicity describes an equivalence between the expectation value and the time average of observables. Applied to human behaviour, ergodic theories of decision-making reveal how individuals should tolerate risk in different environments. To optimize wealth over time, agents should adapt their utility function according to the dynamical setting they face. Linear utility is optimal for additive dynamics, whereas logarithmic utility is optimal for multiplicative dynamics. Whether humans approximate time optimal behavior across different dynamics is unknown. Here we compare the effects of additive versus multiplicative gamble dynamics on risky choice. We show that utility functions are modulated by gamble dynamics in ways not explained by prevailing decision theories. Instead, as predicted by time optimality, risk aversion increases under multiplicative dynamics, distributing close to the values that maximize the time average growth of in-game wealth. We suggest that our findings motivate a need for explicitly grounding theories of decision-making on ergodic considerations.     

Optimizing prostate biopsy strategies for the diagnosis of prostate cancer

The importance of prostate biopsy in urologic practice has been magnified by the routine use of serum prostate-specific antigen in prostate cancer screening. Given the potential impact of the procedure on both patient care and health care costs, an optimal strategy for accurate and judicious detection of early prostate cancer is imperative. Maintaining maximal sensitivity and negative predictive value are equally important to the patient. In this article, we review recent modifications in prostate biopsy indications and techniques that may allow for a systematic biopsy approach to the patient in whom prostate cancer is suspected.     

Risk-Specific Optimal Cancer Screening Schedules: An Application to Breast Cancer Early Detection

The optimal schedules for breast cancer screening in terms of examination frequency and ages at examination are of practical interest. A decision-theoretic approach is explored to search for optimal cancer screening programs which should achieve maximum survival benefit while balancing the associated cost to the health care system. We propose a class of utility functions that account for costs associated with screening examinations and value of survival benefit under a non-stable disease model. We consider two different optimization criteria: optimize the number of screening examinations with equal screening intervals between exams but without a prefixed total cost; and optimize the ages at which screening should be given for a fixed total cost. We show that an optimal solution exists under each of the two frameworks. The proposed methods may consider women at different levels of risk for breast cancer so that the optimal screening strategies will be tailored according to a woman’s risk of developing the disease. Results of a numerical study are presented and the proposed models are illustrated with various data inputs. We also use the data inputs from the Health Insurance Plan of New York (HIP) and Canadian National Breast Screening Study (CNBSS) to illustrate the proposed models and to compare the utility values between the optimal schedules and the actual schedules in the HIP and CNBSS trials. Here, the utility is defined as the difference in cure rates between cases found at screening examinations and cases found between screening examinations while accounting for the cost of examinations, under a given screening schedule.     

Design,synthesis, and evaluation of a selective chemosensor for leucine-rich repeat kinase 2

We describe the design, synthesis, and evaluation of a selective activity probe for leucine-rich repeat kinase 2 (LRRK2), a possible molecular target for the treatment of Parkinson’s disease. Our optimal chemosensor design, termed Nictide-S2, incorporates a phosphorylation-sensitive sulfonamido-oxine fluorophore at an engineered cysteine within the substrate sequence. This design allows for the direct, real-time analysis of LRRK2 kinase activity with a detection limit of 2.5 nM. Under optimized conditions, we measured a Z′ factor of 0.7 demonstrating the potential utility of this assay for inhibitor screening. Off-target kinases capable of phosphorylating Nictide-S2 are identified and an optimized inhibitor cocktail for suppressing background signal is provided. The resulting chemosensor could be utilized to identify LRRK2 inhibitors as well as selectively report on LRRK2 activity in the presence of off-target kinases.     

Optimal schedule for monitoring a plant incursion when detection and treatment success vary over time

Management of an invasive plant species can be viewed as two separate and successive processes. The first, survey, aims to find infested areas and remove individuals. The second, monitoring, consists of repeated visits to these areas in order to prevent possible re-emergence. As detection probability may vary over time, the timing and number of monitoring visits can dramatically impact monitoring efficacy. We explore the optimal timing and number of monitoring visits, by focusing on one infested site. Our decision-analysis framework defines an optimal monitoring schedule which accounts for a time-dependent probability of detection, based on the presence/absence of a flower. We use this framework to investigate the optimal monitoring schedule for Hieracium aurantiacum, an invasive species in the Australian Alps and many other countries. We also perform a sensitivity analysis to draw more general conclusions. For H. aurantiacum eight monitoring visits (compared to 12 visits in the current program) are sufficient to obtain a 99% monitoring efficacy. When four or fewer visits to a site are allowed, it is optimal to visit during the high season, when the weed is likely to initiate flowering. Any extra visits should be scheduled in the early season, before the plants flower. The sensitivity analysis shows that increasing the detection probability early in the season has a greater impact than increasing it late in the season. An effective treatment method increases the value of site visits late in the season, when the detection probability is higher. Our decision-analysis framework can assist invasive species managers to reduce or reallocate management resources by determining the minimum number of monitoring visits required to satisfy an acceptable risk of re-emergence.     

X chromosome choice occurs independently of asynchronous replication timing

In mammals, dosage compensation is achieved by X chromosome inactivation in female cells. Xist is required and sufficient for X inactivation, and Xist gene deletions result in completely skewed X inactivation. In this work, we analyzed skewing of X inactivation in mice with an Xist deletion encompassing sequence 5 KB upstream of the promoter through exon 3. We found that this mutation results in primary nonrandom X inactivation in which the wild-type X chromosome is always chosen for inactivation. To understand the molecular mechanisms that affect choice, we analyzed the role of replication timing in X inactivation choice. We found that the two Xist alleles and all regions tested on the X chromosome replicate asynchronously before the start of X inactivation. However, analysis of replication timing in cell lines with skewed X inactivation showed no preference for one of the two Xist alleles to replicate early in S-phase before the onset of X inactivation, indicating that asynchronous replication timing does not play a role in skewing of X inactivation.     

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.     

Autoantibody signatures: progress and perspectives for early cancer detection

Becoming invasive is a crucial step in cancer development, and the early spread of tumour cells is usually undetected by current imaging technologies. In patients with cancer and no signs of overt metastases, sensitive methods have been developed to identify circulating autoantibodies and their antigen counterparts in several cancers. These technologies are often based on proteomic approaches, and recent advances in protein and antibody microarrays have greatly facilitated the discovery of new antibody biomarkers in sera from cancer patients. Interestingly, in a clinical application setting, combinations of multiple autoantibody reactivities into panel assays have recently been proposed as relevant screening tests and validated in several independent trials. In addition, autoantibody signatures seem to be particularly relevant for early detection of cancer in high-risk cancer patients. In this review, we highlight the concept that immunogenic epitopes associated with the humoural response and key pathogenic pathways elicit serum autoantibodies that can be considered as relevant cancer biomarkers. We outline the proteomic strategies employed to identify and validate their use in clinical practice for cancer screening and diagnosis. We particularly emphasize the clinical utility of autoantibody signatures in several cancers. Finally, we discuss the challenges remaining for clinical validation.     

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.     

Cancer screening by systemic administration of a gene delivery vector encoding tumor-selective secretable biomarker expression

Cancer biomarkers facilitate screening and early detection but are known for only a few cancer types. We demonstrated the principle of inducing tumors to secrete a serum biomarker using a systemically administered gene delivery vector that targets tumors for selective expression of an engineered cassette. We exploited tumor-selective replication of a conditionally replicative Herpes simplex virus (HSV) combined with a replication-dependent late viral promoter to achieve tumor-selective biomarker expression as an example gene delivery vector. Virus replication, cytotoxicity and biomarker production were low in quiescent normal human foreskin keratinocytes and high in cancer cells in vitro. Following intravenous injection of virus >90% of tumor-bearing mice exhibited higher levels of biomarker than non-tumor-bearing mice and upon necropsy, we detected virus exclusively in tumors. Our strategy of forcing tumors to secrete a serum biomarker could be useful for cancer screening in high-risk patients, and possibly for monitoring response to therapy. In addition, because oncolytic vectors for tumor specific gene delivery are cytotoxic, they may supplement our screening strategy as a "theragnostic" agent. The cancer screening approach presented in this work introduces a paradigm shift in the utility of gene delivery which we foresee being improved by alternative vectors targeting gene delivery and expression to tumors. Refining this approach will usher a new era for clinical cancer screening that may be implemented in the developed and undeveloped world.     

Application of biomarkers in cancer risk management: evaluation from stochastic clonal evolutionary and dynamic system optimization points of view

Aside from primary prevention, early detection remains the most effective way to decrease mortality associated with the majority of solid cancers. Previous cancer screening models are largely based on classification of at-risk populations into three conceptually defined groups (normal, cancer without symptoms, and cancer with symptoms). Unfortunately, this approach has achieved limited successes in reducing cancer mortality. With advances in molecular biology and genomic technologies, many candidate somatic genetic and epigenetic "biomarkers" have been identified as potential predictors of cancer risk. However, none have yet been validated as robust predictors of progression to cancer or shown to reduce cancer mortality. In this Perspective, we first define the necessary and sufficient conditions for precise prediction of future cancer development and early cancer detection within a simple physical model framework. We then evaluate cancer risk prediction and early detection from a dynamic clonal evolution point of view, examining the implications of dynamic clonal evolution of biomarkers and the application of clonal evolution for cancer risk management in clinical practice. Finally, we propose a framework to guide future collaborative research between mathematical modelers and biomarker researchers to design studies to investigate and model dynamic clonal evolution. This approach will allow optimization of available resources for cancer control and intervention timing based on molecular biomarkers in predicting cancer among various risk subsets that dynamically evolve over time.     

Epitope presentation is an important determinant of the utility of antigens identified from protein arrays in the development of autoantibody diagnostic assays

Autoantibodies represent an attractive biomarker for diagnostic assays principally due to the stability of immunoglobulin in patient serum facilitating measurement with conventional assays. Immune responses to tumorigenesis may facilitate detection of ovarian cancer in the early stages of the disease with identification of a panel of tumour specific autoantibodies. Despite the reporting of many tumour associated autoantibodies using arrays of tumour antigens, this has not led to the advance in diagnostic capability as rapidly as was initially expected. Here we examine the potential diagnostic utility of candidate autoantibody biomarkers identified via screening of serum samples on a high content human protein array from a unique cohort of early stage and late stage ovarian cancer patients. We analyse the performance of autoantibodies to the tumour suppressor protein p53 and the novel autoantigens alpha adducin and endosulfine alpha identified in our array screen. Each antigen has different performance characteristics using conventional ELISA format and Western blot immunoassay. The high attrition rate of promising autoantigens identified by array screening can in part be explained by the presentation of the epitope of the antigen in the subsequent method of validation and this study provides directions on maximising the potential of candidate biomarkers. This article is part of a Special Issue entitled: Translational Proteomics.     

MAPPIT: a protein interaction toolbox built on insights in cytokine receptor signaling

MAPPIT (mammalian protein-protein interaction trap) is a two-hybrid interaction mapping technique based on functional complementation of a type I cytokine receptor signaling pathway. Over the last decade, the technology has been extended into a platform of complementary assays for the detection of interactions among proteins and between chemical compounds and proteins, and for the identification of small molecules that interfere with protein-protein interactions. Additionally, several screening approaches have been developed to broaden the utility of the platform. In this review we provide an overview of the different components of the MAPPIT toolbox and highlight a number of applications in interactomics, drug screening and compound target profiling.     

Investing in early human development: Timing and economic efficiency

Policy discussions to ameliorate socioeconomic (SES) inequalities are increasingly focused on investments in early childhood. Yet such interventions are costly to implement, and clear evidence on the optimal time to intervene to yield a high economic and social return in the future is meagre. The majority of successful early childhood interventions start in the preschool years. However socioeconomic gradients in cognitive skills, socio-emotional functioning and health can be observed by age three, suggesting that preventative programmes starting earlier in childhood may be even more effective. We discuss the optimal timing of early childhood intervention with reference to recent research in developmental neuroscience. We motivate the need for early intervention by providing an overview of the impact of adverse risk factors during the antenatal and early childhood periods on outcomes later in life. We provide a brief review of the economic rationale for investing early in life and propose the “antenatal investment hypothesis”. We conclude by discussing a suite of new European interventions that will inform this optimal timing debate.     

Application of a homogenous membrane potential assay to assess mitochondrial function

Decreases in mitochondrial membrane potential (MMP) have been associated with mitochondrial dysfunction that could lead to cell death. The MMP is generated by an electrochemical gradient via the mitochondrial electron transport chain coupled to a series of redox reactions. Measuring the MMP in living cells is commonly used to assess the effect of chemicals on mitochondrial function; decreases in MMP can be detected using lipophilic cationic fluorescent dyes. To identify an optimal dye for use in a high-throughput screening (HTS) format, we compared the ability of mitochondrial membrane potential sensor (Mito-MPS), 5,5',6,6'-tetrachloro-1,1',3,3' tetraethylbenzimidazolylcarbocyanine iodide, rhodamine 123, and tetramethylrhodamine to quantify a decrease in MMP in chemically exposed HepG2 cells cultured in 1,536-well plates. Under the conditions used, the optimal dye for this purpose is Mito-MPS. Next, we developed and optimized a homogenous cell-based Mito-MPS assay for use in 1,536-well plate format and demonstrated the utility of this assay by screening 1,280 compounds in the library of pharmacologically active compounds in HepG2 cells using a quantitative high-throughput screening platform. From the screening, we identified 14 compounds that disrupted the MMP, with half-maximal potencies ranging from 0.15 to 18 μM; among these, compound clusters that contained tyrphostin and 3'-substituted indolone analogs exhibited a structure-activity relationship. Our results demonstrate that this homogenous cell-based Mito-MPS assay can be used to evaluate the ability of large numbers of chemicals to decrease mitochondrial function.