A Precision Medicine Initiative for Alzheimer's disease: the road ahead to biomarker-guided integrative disease modeling.

After intense scientific exploration and more than a decade of failed trials, Alzheimer's disease (AD) remains a fatal global epidemic. A traditional research and drug development paradigm continues to target heterogeneous late-stage clinically phenotyped patients with single 'magic bullet' drugs. Here, we propose that it is time for a paradigm shift towards the implementation of precision medicine (PM) for enhanced risk screening, detection, treatment, and prevention of AD. The overarching structure of how PM for AD can be achieved will be provided through the convergence of breakthrough technological advances, including big data science, systems biology, genomic sequencing, blood-based biomarkers, integrated disease modeling and P4 medicine. It is hypothesized that deconstructing AD into multiple genetic and biological subsets existing within this heterogeneous target population will provide an effective PM strategy for treating individual patients with the specific agent(s) that are likely to work best based on the specific individual biological make-up. The Alzheimer's Precision Medicine Initiative (APMI) is an international collaboration of leading interdisciplinary clinicians and scientists devoted towards the implementation of PM in Neurology, Psychiatry and Neuroscience. It is hypothesized that successful realization of PM in AD and other neurodegenerative diseases will result in breakthrough therapies, such as in oncology, with optimized safety profiles, better responder rates and treatment responses, particularly through biomarker-guided early preclinical disease-stage clinical trials.

A Mixed-Effects Model with Time Reparametrization for Longitudinal Univariate Manifold-Valued Data.

Mixed-effects models provide a rich theoretical framework for the analysis of longitudinal data. However, when used to analyze or predict the progression of a neurodegenerative disease such as Alzheimer's disease, these models usually do not take into account the fact that subjects may be at different stages of disease progression and the interpretation of the model may depend on some implicit reference time. In this paper, we propose a generative statistical model for longitudinal data, described in a univariate Riemannian manifold setting, which estimates an average disease progression model, subject-specific time shifts and acceleration factors. The time shifts account for variability in age at disease-onset time. The acceleration factors account for variability in speed of disease progression. For a given individual, the estimated time shift and acceleration factor define an affine reparametrization of the average disease progression model. This statistical model has been used to analyze neuropsychological assessments scores and cortical thickness measurements from the Alzheimer's Disease Neuroimaging Initiative database. The numerical results showed that we can distinguish between slow versus fast progressing and early versus late-onset individuals.

Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data.

This paper proposes an original approach for the statistical analysis of longitudinal shape data. The proposed method allows the characterization of typical growth patterns and subject-specific shape changes in repeated time-series observations of several subjects. This can be seen as the extension of usual longitudinal statistics of scalar measurements to high-dimensional shape or image data. The method is based on the estimation of continuous subject-specific growth trajectories and the comparison of such temporal shape changes across subjects. Differences between growth trajectories are decomposed into morphological deformations, which account for shape changes independent of the time, and time warps, which account for different rates of shape changes over time. Given a longitudinal shape data set, we estimate a mean growth scenario representative of the population, and the variations of this scenario both in terms of shape changes and in terms of change in growth speed. Then, intrinsic statistics are derived in the space of spatiotemporal deformations, which characterize the typical variations in shape and in growth speed within the studied population. They can be used to detect systematic developmental delays across subjects. In the context of neuroscience, we apply this method to analyze the differences in the growth of the hippocampus in children diagnosed with autism, developmental delays and in controls. Result suggest that group differences may be better characterized by a different speed of maturation rather than shape differences at a given age. In the context of anthropology, we assess the differences in the typical growth of the endocranium between chimpanzees and bonobos. We take advantage of this study to show the robustness of the method with respect to change of parameters and perturbation of the age estimates.

A statistical model for quantification and prediction of cardiac remodelling: application to tetralogy of Fallot.

Cardiac remodelling plays a crucial role in heart diseases. Analyzing how the heart grows and remodels over time can provide precious insights into pathological mechanisms, eventually resulting in quantitative metrics for disease evaluation and therapy planning. This study aims to quantify the regional impacts of valve regurgitation and heart growth upon the end-diastolic right ventricle (RV) in patients with tetralogy of Fallot, a severe congenital heart defect. The ultimate goal is to determine, among clinical variables, predictors for the RV shape from which a statistical model that predicts RV remodelling is built. Our approach relies on a forward model based on currents and a diffeomorphic surface registration algorithm to estimate an unbiased template. Local effects of RV regurgitation upon the RV shape were assessed with Principal Component Analysis (PCA) and cross-sectional multivariate design. A generative 3-D model of RV growth was then estimated using partial least squares (PLS) and canonical correlation analysis (CCA). Applied on a retrospective population of 49 patients, cross-effects between growth and pathology could be identified. Qualitatively, the statistical findings were found realistic by cardiologists. 10-fold cross-validation demonstrated a promising generalization and stability of the growth model. Compared to PCA regression, PLS was more compact, more precise and provided better predictions.

Study design in osteoporosis: a European perspective.

The advent of effective agents for the treatment of osteoporosis has led to the view that placebo-controlled trials to test new agents for efficacy are no longer appropriate. Rather, studies of superiority, equivalence, or non-inferiority have been recommended. Such studies require very large sample sizes, and the burden of osteoporotic fracture in a trial setting is substantially increased. Studies of equivalence cannot be unambiguously interpreted because the variance in effect of active comparator agents is too large in osteoporosis. If fracture studies are required by regulatory agencies, there is still a requirement for placebo-controlled studies, although perhaps of shorter duration than demanded at present.

A confirmatory dose-ranging study of riluzole in ALS. ALS/Riluzole Study Group-II.

ALS is a progressive motor neuron disease with no effective treatment. The anti-excitotoxic drug riluzole (100 mg/day) has been shown to decrease mortality and muscular deterioration in ALS patients. To confirm and extend the therapeutic effect of riluzole, we performed a double-blind, placebo-controlled, multicenter, international, dose-ranging (50, 100, 200 mg/day), stratified study in 959 ALS outpatients treated for up to 18 months. Primary efficacy criterion was survival and the effect of treatment was analyzed before (Wilcoxon and log rank tests) and after adjustment on prognostic factors (Cox model). Secondary efficacy criterion was disease progression assessed through change in functional measures. Tracheostomy-free survival rates were: 50.4% (placebo), 55.3% (50 mg riluzole) (p = 0.23, Wilcoxon test; p = 0.25, log-rank test), 56.8% (100 mg riluzole) (p = 0.05, Wilcoxon test; p = 0.076, log-rank test), and 57.8% (200 mg riluzole) (p = 0.061, Wilcoxon test; p = 0.075, log-rank test). At the end of the 18-month study, there was a significant dose-related decrease in risk of death or tracheostomy (p = 0.04). Adjustment for baseline prognostic factors showed a 35% decreased risk of death with the 100-mg dose compared with placebo (p = 0.002). No significant treatment effects were detected for the functional assessments. The most frequent dose-related adverse events included nausea, asthenia, and elevated liver enzyme levels. This study confirms the therapeutic effect of riluzole in a large representative ALS sample, over an 18-month period. Riluzole is well tolerated and decreases the risk of death or tracheostomy in ALS patients.

[Monitoring of clinical trials and interim analysis. 1. Monitoring committee]. / Surveillance d'essais cliniques et analyses intermédiaires. 1. Les comités de surveillance.

In order to fulfil the ethical principles linked to the protection of patients randomized in a controlled clinical trial, monitoring procedures need to be set up. In this context, a committee of experts, called the data monitoring committee is in charge of reviewing regularly unblinded data to assess the quality and the relevance of the trial, to evaluate the evidence of an emerging treatment difference and to control the rate of occurrence of serious adverse events. After each meeting, the monitoring committee reports to the steering committee its recommendation to continue or to stop the trial prematurely. Protocol modifications might be proposed as well. Illustrated with several examples, this article reviews different situations a monitoring committee might have to tackle with.

[Monitoring of clinical trials and interim analysis. 2. Statistic methods]. / Surveillance d'essais cliniques et analyses intermédiaires. 2. Méthodes statistiques.

Although the decision to continue or to stop prematurely a clinical trial is not solely based on statistical tests, they bring useful objective arguments to the data monitoring board. However, the multiple use of statistical tests leads to increase the risk of false positive conclusions in favor of one of the treatments, and several methods have been developed to address this problem. This article presents the four major strategies that are being used for monitoring clinical trials, as well as the rationale for planning and using such statistical monitoring procedures.

When to randomize?

Many clinical trials address the question of the duration of therapy or whether initial therapy can be improved by the addition of a consolidation or maintenance regimen. For such clinical trials, the question of when to perform the randomization is often difficult. Conventional statistical wisdom prescribes that randomization should take place as late as possible before treatment is effected. This is not always possible or desirable, however. In this report, we describe the factors that are influenced by the timing of randomization, quantify how timing affects these factors, and attempt to provide a tool to help investigators and statisticians determine the appropriate time to randomize for individual studies and particular circumstances.

Planning and monitoring of equivalence studies.

Demonstrating therapeutic equivalence of two treatments is the goal of many clinical trials. For instance, when the toxicity of an effective standard treatment is of concern, much effort is devoted to developing new therapies that would be both as effective and less toxic. In this paper we review the special characteristics of these trials and describe sequential monitoring of equivalence studies using repeated confidence intervals. We show how sequential monitoring may be of particular value in this setting and critically discuss the choice of some important design parameters. We also provide tables for use when planning a sequential equivalence trial.

Prognostic factors for patients with diffuse large cell or immunoblastic non-Hodgkin's lymphomas: experience of the non-Hodgkin's Lymphoma Pathologic Classification Project.

Prognostic factors for long-term survival of 312 patients with diffuse large cell or immunoblastic non-Hodgkin's lymphoma are presented based on analysis of the multiinstitution clinicopathologic study sponsored by the National Cancer Institute. At the time of analysis, 75% of the patients had died and the median follow-up for patients still alive was 11 years. The distribution of Ann Arbor stages was 21% stage I, 32% stage II, 17% stage III, and 30% stage IV. Factors of prognostic significance for survival included age, stage, histologic subtype, presence of B symptoms, size of the largest lesion, number of extra-lymphoid organs involved, and extent of lymphatic involvement. Recursive partitioning analysis suggested a prognostic classification system based on stage, age, size of the largest lesion, and presence of mediastinal involvement. Stage I patient less than 50 years of age had a 10 year survival rate of 65% compared to 36% for older stage I patients. Stage II patients less than 65 years old without bulky lesions or mediastinal involvement had a 10 year survival rate of 45% compared to 10% for the poorer risk stage II patients. Although statistically significant prognostic factors were identified for the stage III/IV patients, they were not strong discriminants of 5-10 year survival rate. Because of the correlation among potential prognostic factors, there is no uniquely best classification system. Reasons for discrepancies among reported prognostic factor analyses are discussed, and a prognostic grouping that synthesizes our results with those of others is proposed.

2'-Deoxycoformycin after failure of alpha-interferon in hairy cell leukemia.

Alpha-interferon (IFN) and 2'-deoxycoformycin (dCF) both exhibit substantial activity in the treatment of hairy cell leukemia (HCL). Anecdotal reports have suggested that patients who failed IFN could achieve durable responses with dCF, although the frequency with which this was said to have occurred was unknown. We reviewed the available data on the responsiveness of HCL to dCF after IFN therapy by analyzing cases reported in the literature and those treated under the Special Exception mechanism of the National Cancer Institute, Division of Cancer Treatment. Of 60 such cases identified there were 22 (37%) "compete responses" and 22 (37%) "partial responses" for a total response rate of 74%. Responses appeared to be durable in many cases, lasting up to 2 years at the time of reporting. dCF is an active agent in HCL both as initial therapy and for the salvage of patients who have failed IFN. The relative activity of these two agents and the optimal strategies for their use are currently under investigation in ongoing clinical trials.

Increasing trend of ALS in France and elsewhere: are the changes real?

We analyzed 9,005 deaths from amyotrophic lateral sclerosis recorded in France between the years 1968 and 1982. The overall adjusted mortality rates were 1.45/100,000 for men and 0.90/100,000 for women. We found excess male mortality in every age group. Age-specific mortality rates increased with age until 65-74 years and then declined in the older population. There was no meaningful regional pattern. We found a substantial increase in ALS mortality over time: the adjusted rates (per 100,000) in the period 1968 to 1971 were 1.11 for men and 0.63 for women. In the period 1979 to 1982, the corresponding figures were 1.92 and 1.12. The increase was mainly due to persons over 55 years of age and affected mostly the women during the first part of the study (1968 to 1978). In the recent years, increase appeared similar in both sexes. The temporal trends are consistent across studies in different countries.

Flexible regression models with cubic splines.

We describe the use of cubic splines in regression models to represent the relationship between the response variable and a vector of covariates. This simple method can help prevent the problems that result from inappropriate linearity assumptions. We compare restricted cubic spline regression to non-parametric procedures for characterizing the relationship between age and survival in the Stanford Heart Transplant data. We also provide an illustrative example in cancer therapeutics.

The Non-Hodgkin Lymphoma Pathologic Classification Project. Long-term follow-up of 1153 patients with non-Hodgkin lymphomas.

STUDY OBJECTIVE: To document the long-term prognosis of patients with non-Hodgkin lymphoma treated between 1971 and 1975 and to determine how the prognosis varies by histologic subtype and stage. SETTING: Three cancer referral centers in the United States and one center in Italy. PATIENTS: A consecutive sample of 1153 previously untreated patients with non-Hodgkin lymphoma. At the time of analysis, 71% of the patients had died and the median follow-up for patients still alive was 11.2 years. MEASUREMENTS AND MAIN RESULTS: The 10-year survival proportions were 45% (CI, 40% to 50%); 26% (CI, 22% to 30%); and 23% (CI, 18% to 30%) for patients with low-, intermediate-, and high-grade lymphomas, respectively. Patients with intermediate- and high-grade lymphomas were curable, but this was not apparent for patients with advanced stage low-grade lymphomas. For the low-grade follicular small cleaved and follicular mixed lymphomas, the Ann Arbor staging system distinguished the prognosis of patients with stage I disease from those with more extensive involvement. For the diffuse large cell and immunoblastic lymphomas, the Ann Arbor staging system distinguished long-term prognosis for patients with stage I disease from patients with stage II disease and those with more disseminated involvement. CONCLUSIONS: The probability of long-term survival for unselected patients with non-Hodgkin lymphoma can be substantial. Long-term prognosis depends on the histologic subtype of the tumor and the extent of dissemination. The Working Formulation for non-Hodgkin lymphomas is a simple and useful nomenclature for selecting treatment and reporting results. The Ann Arbor staging system is a useful but imperfect prognostic indicator.

[An expert system of aiding decision making in breast pathology connected to a clinical data base]. / Conception d'un système expert d'aide à la décision en pathologie mammaire connecté à une base de données cliniques.

The René Huguenin Cancer Center holds a medical file for each patient which is intended to store and process medical data. Since 1970, we introduced computerization: a development plan was elaborated and simultaneously a statistical software (Clotilde--GSI/CFRO) was selected. Thus, we now have access to a large database, structured according to medical rationale, and utilizable with methods of artificial intelligence towards three objectives: improved data acquisition, decision making and exploitation. The first application was to breast pathology, which represents one of the Center's primary activities. The structure of the data concerning patients is by all criteria part of the medical knowledge. This information needs to be presented as well as processed with a suitable language. To this end, we chose a language-oriented object, Mering II, usable with Apple and IBM 4 micro-computers. This project has already allowed to work out an operational model.