Biomarker-Guided Non-Adaptive Trial Designs in Phase II and Phase III: A Methodological Review

New insights into adaptive enrichment designs

The transition towards personalized medicine is happening and the new experimental framework is raising several challenges, from a clinical, ethical, logistical, regulatory, and statistical perspective. To face these challenges, innovative study designs with increasing complexity have been proposed. In particular, adaptive enrichment designs are becoming more attractive for their flexibility. However, these procedures rely on an increasing number of parameters that are unknown at the planning stage of the clinical trial, so the study design requires particular care. This review is dedicated to adaptive enrichment studies with a focus on design aspects. While many papers deal with methods for the analysis, the sample size determination and the optimal allocation problem have been overlooked. We discuss the multiple aspects involved in adaptive enrichment designs that contribute to their advantages and disadvantages. The decision-making process of whether or not it is worth enriching should be driven by clinical and ethical considerations as well as scientific and statistical concerns.

Design and analysis of umbrella trials: Where do we stand?

Background

The efficiencies that master protocol designs can bring to modern drug development have seen their increased utilization in oncology. Growing interest has also resulted in their consideration in non-oncology settings. Umbrella trials are one class of master protocol design that evaluates multiple targeted therapies in a single disease setting. Despite the existence of several reviews of master protocols, the statistical considerations of umbrella trials have received more limited attention.

Methods

We conduct a systematic review of the literature on umbrella trials, examining both the statistical methods that are available for their design and analysis, and also their use in practice. We pay particular attention to considerations for umbrella designs applied outside of oncology.

Findings

We identified 38 umbrella trials. To date, most umbrella trials have been conducted in early phase settings (73.7%, 28/38) and in oncology (92.1%, 35/38). The quality of statistical information available about conducted umbrella trials to date is poor; for example, it was impossible to ascertain how sample size was determined in the majority of trials (55.3%, 21/38). The literature on statistical methods for umbrella trials is currently sparse.

Conclusions

Umbrella trials have potentially great utility to expedite drug development, including outside of oncology. However, to enable lessons to be effectively learned from early use of such designs, there is a need for higher-quality reporting of umbrella trials. Furthermore, if the potential of umbrella trials is to be realized, further methodological research is required.

Study designs for clinical trials applied to personalised medicine: a scoping review

OBJECTIVE

Personalised medicine (PM) allows treating patients based on their individual demographic, genomic or biological characteristics for tailoring the 'right treatment for the right person at the right time'. Robust methodology is required for PM clinical trials, to correctly identify groups of participants and treatments. As an initial step for the development of new recommendations on trial designs for PM, we aimed to present an overview of the study designs that have been used in this field.

DESIGN

Scoping review.

METHODS

We searched (April 2020) PubMed, Embase and the Cochrane Library for all reports in English, French, German, Italian and Spanish, describing study designs for clinical trials applied to PM. Study selection and data extraction were performed in duplicate resolving disagreements by consensus or by involving a third expert reviewer. We extracted information on the characteristics of trial designs and examples of current applications of these approaches. The extracted information was used to generate a new classification of trial designs for PM.

RESULTS

We identified 21 trial designs, 10 subtypes and 30 variations of trial designs applied to PM, which we classified into four core categories (namely, Master protocol, Randomise-all, Biomarker strategy and Enrichment). We found 131 clinical trials using these designs, of which the great majority were master protocols (86/131, 65.6%). Most of the trials were phase II studies (75/131, 57.2%) in the field of oncology (113/131, 86.3%). We identified 34 main features of trial designs regarding different aspects (eg, framework, control group, randomisation). The four core categories and 34 features were merged into a double-entry table to create a new classification of trial designs for PM.

CONCLUSIONS

A variety of trial designs exists and is applied to PM. A new classification of trial designs is proposed to help readers to navigate the complex field of PM clinical trials.

Application of machine learning methods in clinical trials for precision medicine

OBJECTIVE

A key component for precision medicine is a good prediction algorithm for patients' response to treatments. We aim to implement machine learning (ML) algorithms into the response-adaptive randomization (RAR) design and improve the treatment outcomes.

MATERIALS AND METHODS

We incorporated 9 ML algorithms to model the relationship of patient responses and biomarkers in clinical trial design. Such a model predicted the response rate of each treatment for each new patient and provide guidance for treatment assignment. Realizing that no single method may fit all trials well, we also built an ensemble of these 9 methods. We evaluated their performance through quantifying the benefits for trial participants, such as the overall response rate and the percentage of patients who receive their optimal treatments.

RESULTS

Simulation studies showed that the adoption of ML methods resulted in more personalized optimal treatment assignments and higher overall response rates among trial participants. Compared with each individual ML method, the ensemble approach achieved the highest response rate and assigned the largest percentage of patients to their optimal treatments. For the real-world study, we successfully showed the potential improvements if the proposed design had been implemented in the study.

CONCLUSION

In summary, the ML-based RAR design is a promising approach for assigning more patients to their personalized effective treatments, which makes the clinical trial more ethical and appealing. These features are especially desirable for late-stage cancer patients who have failed all the Food and Drug Administration (FDA)-approved treatment options and only can get new treatments through clinical trials.

Can Systems Biology Advance Clinical Precision Oncology?

Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems' level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research.

Of some innovations in clinical trial design in hematology and oncology

The design of clinical trials, formalized in the immediate post-war period, has undergone major changes due to therapeutic innovations, particularly the arrival of targeted therapies in onco-hematology. The traditional phase I-II-III regimen is regularly questioned and multiple adaptations are proposed. This article proposes to expose some of these modifications and the issues they lead to.

Molecular profiling of advanced soft-tissue sarcomas: the MULTISARC randomized trial

BACKGROUND

Soft-tissue sarcomas (STS) represent a heterogeneous group of rare tumors including more than 70 different histological subtypes. High throughput molecular analysis (next generation sequencing exome [NGS]) is a unique opportunity to identify driver mutations that can change the usual one-size-fits-all treatment paradigm to a patient-driven therapeutic strategy. The primary objective of the MULTISARC trial is to assess whether NGS can be conducted for a large proportion of metastatic STS participants within a reasonable time, and, secondarily to determine whether a NGS-guided therapeutic strategy improves participant's outcome.

METHODS

This is a randomized, multicentre, phase II/III trial inspired by the design of umbrella and biomarker-driven trials. The setting plans up to 17 investigational centres across France and the recruitment of 960 participants. Participants aged at least 18 years, with unresectable locally advanced and/or metastatic STS confirmed by the French sarcoma pathological reference network, are randomized according to 1:1 allocation ratio between the experimental arm "NGS" and the standard "No NGS". NGS will be considered feasible if (i) NGS results are available and interpretable, and (ii) a report of exome sequencing including a clinical recommendation from a multidisciplinary tumor board is provided to investigators within 7 weeks from reception of the samples on the biopathological platform. A feasibility rate of more than 70% is expected (null hypothesis: 70% versus alternative hypothesis: 80%). In terms of care, participants randomized in "No NGS" arm and who fail treatment will be able to switch to the NGS arm at the request of the investigator.

DISCUSSION

The MULTISARC trial is a prospective study designed to provide high-level evidence to support the implementation of NGS in routine clinical practice for advanced STS participants, on a large scale.

TRIAL REGISTRATION

clinicaltrial.gov NCT03784014 .

Epigenetics and postsurgical pain: A scoping review

OBJECTIVE

Multiple factors are involved in the physiology and variability of postsurgical pain, a great part of which can be explained by genetic and environmental factors and their interaction. Epigenetics refers to the mechanism by which the environment alters the stability and expression of genes. We conducted a scoping review to examine the available evidence in both animal models and clinical studies on epigenetic mechanisms involved in regulation of postsurgical and chronic postsurgical pain.

METHODS

The Arksey & ÓMalley framework and the PRISMA-ScR (Preferred Reporting Items for Systematic Review and Meta-Analysis, scoping reviews extension) guidelines were used. The PubMed, Web of Science and Google Scholar databases were searched, and the original articles cited in reviews located through the search were also reviewed. English-language articles without time limits were retrieved. Articles were selected if the abstract addressed information on the epigenetic or epigenomic mechanisms, histone, or DNA methylation and microribonucleic acids involved in postsurgical and chronic postsurgical pain in animal models and clinical studies.

RESULTS

The initial search provided 174 articles, and 81 were used. The available studies to date, mostly in animal models, have shown that epigenetics contributes to regulation of gene expression in the pathways involved in postsurgical pain and in maintaining long-term pain.

CONCLUSION

Research on possible epigenetic mechanisms involved in postsurgical pain and chronic postsurgical pain in humans is scarce. In view of the evidence available in animal models, there is a need to evaluate epigenetic pain mechanisms in the context of human and clinical studies.

Prostate Cancer Biomarkers: From diagnosis to prognosis and precision-guided therapeutics

Prostate cancer (PCa) is one of the most commonly diagnosed malignancies and among the leading causes of cancer-related death worldwide. It is a highly heterogeneous disease, ranging from remarkably slow progression or inertia to highly aggressive and fatal disease. As therapeutic decision-making, clinical trial design and outcome highly depend on the appropriate stratification of patients to risk groups, it is imperative to differentiate between benign versus more aggressive states. The incorporation of clinically valuable prognostic and predictive biomarkers is also potentially amenable in this process, in the timely prevention of metastatic disease and in the decision for therapy selection. This review summarizes the progress that has so far been made in the identification of the genomic events that can be used for the classification, prediction and prognostication of PCa, and as major targets for clinical intervention. We include an extensive list of emerging biomarkers for which there is enough preclinical evidence to suggest that they may constitute crucial targets for achieving significant advances in the management of the disease. Finally, we highlight the main challenges that are associated with the identification of clinically significant PCa biomarkers and recommend possible ways to overcome such limitations.

Translational precision medicine: an industry perspective

In the era of precision medicine, digital technologies and artificial intelligence, drug discovery and development face unprecedented opportunities for product and business model innovation, fundamentally changing the traditional approach of how drugs are discovered, developed and marketed. Critical to this transformation is the adoption of new technologies in the drug development process, catalyzing the transition from serendipity-driven to data-driven medicine. This paradigm shift comes with a need for both translation and precision, leading to a modern Translational Precision Medicine approach to drug discovery and development. Key components of Translational Precision Medicine are multi-omics profiling, digital biomarkers, model-based data integration, artificial intelligence, biomarker-guided trial designs and patient-centric companion diagnostics. In this review, we summarize and critically discuss the potential and challenges of Translational Precision Medicine from a cross-industry perspective.

Time to reality check the promises of machine learning-powered precision medicine

Machine learning methods, combined with large electronic health databases, could enable a personalised approach to medicine through improved diagnosis and prediction of individual responses to therapies. If successful, this strategy would represent a revolution in clinical research and practice. However, although the vision of individually tailored medicine is alluring, there is a need to distinguish genuine potential from hype. We argue that the goal of personalised medical care faces serious challenges, many of which cannot be addressed through algorithmic complexity, and call for collaboration between traditional methodologists and experts in medical machine learning to avoid extensive research waste.

Transforming clinical trials in rheumatology: towards patient-centric precision medicine

Despite the success of targeted therapies in the treatment of inflammatory arthritides, the lack of predictive biomarkers drives a 'trial and error' approach to treatment allocation, leading to variable and/or unsatisfactory responses. In-depth characterization of the synovial tissue in rheumatoid arthritis, as well as psoriatic arthritis and spondyloarthritis, is bringing new insights into the diverse cellular and molecular features of these diseases and their potential links with different clinical and treatment-response phenotypes. Such progress raises the tantalizing prospect of improving response rates by matching the use of specific agents to the cognate target pathways that might drive particular disease subtypes in specific patient groups. Innovative patient-centric, molecular pathology-driven clinical trial approaches are needed to achieve this goal. Whilst progress is clearly being made, it is important to emphasize that this field is still in its infancy and there are a number of potential barriers to realizing the premise of patient-centric clinical trials.

Modeling neuroaffective biomarkers of drug addiction: A Bayesian nonparametric approach using dirichlet process mixtures

BACKGROUND

The properties of neurophysiological processes related to addiction have received much attention in the literature. However, empirical evidence of meaningful and useful characterization of these processes is limited. Recent studies have found that electrophysiological responses to emotional and drug-related cues can be used to create profiles that reliably predict smoking relapse.

NEW METHOD

This paper evaluates the validity of classifying electrophysiological responses into distinct profiles using a Bayesian dirichlet process mixture (DPM) model. The DPM is a Bayesian nonparametric (BNP) method to modeling unknown number of profiles characterized by uncertainty in cluster membership and in cluster number.

RESULTS

The DPM model confirmed previously identified neuroaffective reactivity profiles, but also revealed a finer level of granularity in the clustering. Specifically, in addition to the two clusters previously identified in the literature, the BNP methods identified a cluster of individuals showing similar responses to smoking, pleasant, neutral and unpleasant cues.

COMPARISON WITH EXISTING METHODS

BNP models provide an alternative to the k-mean clustering approach to modeling EEG-based neuroaffective profiles. Unlike k-means clustering, BNP models compute the probability that a subject belongs to a cluster while taking into consideration uncertainty in the number of clusters.

CONCLUSIONS

Our results confirm the reliability of the two clusters previously identified in these data, but also provide new insights by revealing a cluster that presented similar responses to stimuli with different contents. This finding may be related to the uncertainty in classification or overlapping brain-reactivity profiles.

Specific requirements for translation of biological research into clinical radiation oncology

Radiotherapy has been optimized over the last decades not only through technological advances, but also through the translation of biological knowledge into clinical treatment schedules. Optimization of fractionation schedules and/or the introduction of simultaneous combined systemic treatment have significantly improved tumour cure rates in several cancer types. With modern techniques, we are currently able to measure factors of radiation resistance or radiation sensitivity in patient tumours; the definition of new biomarkers is expected to further enable personalized treatments. In this Review article, we overview important translation paths and summarize the quality requirements for preclinical and translational studies that will help to avoid bias in trial results.

How to Ease the Pain of Taking a Diagnostic Point of Care Test to the Market: A Framework for Evidence Development

Bringing a diagnostic point of care test (POCT) to a healthcare market can be a painful experience as it requires the manufacturer to meet considerable technical, financial, managerial, and regulatory challenges. In this opinion article we propose a framework for developing the evidence needed to support product development, marketing, and adoption. We discuss each step in the evidence development pathway from the invention phase to the implementation of a new POCT in the healthcare system. We highlight the importance of articulating the value propositions and documenting the care pathway. We provide guidance on how to conduct care pathway analysis as little has been published on this. We summarize the clinical, economic and qualitative studies to be considered for developing evidence, and provide useful links to relevant software, on-line applications, websites, and give practical advice. We also provide advice on patient and public involvement and engagement (PPIE), and on product management. Our aim is to help device manufacturers to understand the concepts and terminology used in evaluation of in vitro diagnostics (IVDs) so that they can communicate effectively with evaluation methodologists, statisticians, and health economists. Manufacturers of medical tests and devices can use the proposed framework to plan their evidence development strategy in alignment with device development, applications for regulatory approval, and publication.

Biomarker-guided trials: Challenges in practice

Biomarker-guided trials have drawn considerable attention as they promise to lead to improvements in the benefit-risk ratio of treatments and enhanced opportunities for drug development. A variety of such designs have been proposed in the literature, many of which have been adopted in practice. Implementing such trial designs in practice can be challenging, and identifying those challenges was the main objective of a workshop organised by the MRC Hubs for Trials Methodology Research Network's Stratified Medicine Working Group in March 2017. Participants reflected on completed and ongoing biomarker-guided trials to identify the practical challenges encountered. Here, the key challenges identified during the workshop including those related to funding, ethical and regulatory issues, recruitment, monitoring of samples and laboratories, biomarker assessment, and data sharing and resources, are discussed. Despite the complexities often associated with biomarker-guided trials, the workshop concluded that they can play an important role in advancing the field of personalised medicine. Therefore, it is important that the practical challenges surrounding their implementation are acknowledged and addressed.

Don’t abandon RCTs in IVF. We don’t even understand them

The conclusion of the Human Fertilisation and Embryology Authority that ‘add-on’ therapies in IVF are not supported by high-quality evidence has prompted new questions regarding the role of the randomized controlled trial (RCT) in evaluating infertility treatments. Critics argue that trials are cumbersome tools that provide irrelevant answers. Instead, they argue that greater emphasis should be placed on large observational databases, which can be analysed using powerful algorithms to determine which treatments work and for whom. Although the validity of these arguments rests upon the sciences of statistics and epidemiology, the discussion to date has largely been conducted without reference to these fields. We aim to remedy this omission, by evaluating the arguments against RCTs in IVF from a primarily methodological perspective. We suggest that, while criticism of the status quo is warranted, a retreat from RCTs is more likely to make things worse for patients and clinicians.

Evidence to Support Inclusion of Pharmacogenetic Biomarkers in Randomised Controlled Trials

Pharmacogenetics and biomarkers are becoming normalised as important technologies to improve drug efficacy rates, reduce the incidence of adverse drug reactions, and make informed choices for targeted therapies. However, their wider clinical implementation has been limited by a lack of robust evidence. Suitable evidence is required before a biomarker’s clinical use, and also before its use in a clinical trial. We have undertaken a review of five pharmacogenetic biomarker-guided randomised controlled trials (RCTs) and evaluated the evidence used by these trials to justify biomarker inclusion. We assessed and quantified the evidence cited in published rationale papers, or where these were not available, obtained protocols from trial authors. Very different levels of evidence were provided by the trials. We used these observations to write recommendations for future justifications of biomarker use in RCTs and encourage regulatory authorities to write clear guidelines.

Adaptive Signature Design- review of the biomarker guided adaptive phase -III controlled design

Genomics having a profound impact on oncology drug development necessitates the use of genomic signatures for therapeutic strategy and emerging medicine proposals. Since its advent in the arena of clinical trials biomarker-related predictive methods for the identification and selection of patient subgroups, with optimal treatment response, are widely used. Genetic signatures which are accountable for the differential response to treatments are experimentally recognizable and analytically validated in phase II stage of clinical trials. The availability of robust and validated biomarkers in phase III is limited. Hence, the development of a clinical trial design without the availability of biomarker identity for treatment-sensitive patients becomes indispensable. Adaptive Signature Design (ASD) is a design procedure of developing and validating a predictive classifier (diagnostic testing strategy) when the signature of subjects responding differentially to treatment is remote in the context of the study. This review provides a detailed methodology and statistical background of this pioneering design developed by Freidlin and Simon (2005). In addition, it concentrates on the advances in ASD regarding statistical issues such as predictive assay identification, classification techniques, statistical methods, subgroup search, choice of differentially expressed genes, and multiplicity correction. The statistical methodology behind the design is explained with the intent of building the ground steps for future research approachable, especially for beginning researchers. Most of the existing research articles give a microcosmic view of the design and lack in describing the details behind the methodology. This study covers those details and marks the novelty of our research.

Effects of biomarker diagnostic accuracy on biomarker-guided phase 2 trials

Recent advancements in genomics have attracted attention towards biomarker-guided trials. These trials aim to identify therapies that target diseases based on their genetic profile, and are especially common in cancer research. Careful incorporation of biomarkers in phase II studies is critical to the selection of candidates for further phase III investigation. This short communication focuses on problems of biomarker test accuracy in biomarker-guided trials. We assessed how diagnostic accuracy of biomarker tests affects type I error rate, statistical power, and sample size requirements of single-arm biomarker-guided trials. In particular, we report how false positive rates (FPRs) of biomarker tests reduce statistical power and type I error for Simon's two-stage design, and the degree of sample size correction required to achieve pre-specified power and type I error with varying FPRs. This was done using a case study based on a previous biomarker-guided single-arm trial that was designed with an assumed tumor response rate of 10% under the null hypothesis and 40% for the alternative hypothesis for the mutant group for 5% type I error and 90% power. With varying FPRs of biomarker tests, we considered two scenarios in which the response rate for the wild-type group was assumed to be lower than the response rate for the mutant group at 5% and 10%. We also developed a simple open-source online trial planner for future investigators to use for their biomarker-guided phase II trials (https://mtek.shinyapps.io/Biomarker_Trial_Planner/).

Key enablers and barriers to implementing adaptive pathways in the European setting

In 2016, the European Medicines Agency published the conclusions of its pilot on adaptive pathways, with products in early stages of development still building up to their marketing authorisation. Adaptive pathways rests on three principles: iterative development; gathering evidence through real-life use to supplement clinical trial data; and early engagement of patients, payers and health technology assessment bodies in discussions on a medicine's development. While the pilot has now finished, the practical system-wide implications of employing the adaptive pathways approach are not known and further consideration of these three principles is required. In this paper we used the three principles that underpin adaptive pathways to discuss main scientific and European policy developments likely to determine progress on further implementing adaptive pathways in the European setting.

Design of experiments for a confirmatory trial of precision medicine

Precision medicine, aka stratified/personalized medicine, is becoming more pronounced in the medical field due to advancement in computational ability to learn about patient genomic backgrounds. A biomaker, i.e. a type of biological process indicator, is often used in precision medicine to classify patient population into several subgroups. The aim of precision medicine is to tailor treatment regimes for different patient subgroups who suffer from the same disease. A multi-arm design could be conducted to explore the effect of treatment regimes on different biomarker subgroups. However, if treatments work only on certain subgroups, which is often the case, enrolling all patient subgroups in a confirmatory trial would increase the burden of a study. Having observed a phase II trial, we propose a design framework for finding an optimal design that could be implemented in a phase III study or a confirmatory trial. We consider two elements in our approach: Bayesian data analysis of observed data, and design of experiments. The first tool selects subgroups and treatments to be enrolled in the future trial whereas the second tool provides an optimal treatment randomization scheme for each selected/enrolled subgroups. Considering two independent treatments and two independent biomarkers, we illustrate our approach using simulation studies. We demonstrate efficiency gain, i.e. high probability of recommending truly effective treatments in the right subgroup, of the optimal design found by our framework over a randomized controlled trial and a biomarker–treatment linked trial.

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A classical randomized controlled trial fails to identify subgroup treatment effect.

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Standard enriched designs may miss out potential patient subgroups.

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A standard multi-arm design could be inefficient for a trial of precision medicine.

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A data-driven design framework could provide efficient designs for future trials.

Fixed and Adaptive Parallel Subgroup-Specific Design for Survival Outcomes: Power and Sample Size

Biomarker-guided clinical trial designs, which focus on testing the effectiveness of a biomarker-guided approach to treatment in improving patient health, have drawn considerable attention in the era of stratified medicine with many different designs being proposed in the literature. However, planning such trials to ensure they have sufficient power to test the relevant hypotheses can be challenging and the literature often lacks guidance in this regard. In this study, we focus on the parallel subgroup-specific design, which allows the evaluation of separate treatment effects in the biomarker-positive subgroup and biomarker-negative subgroup simultaneously. We also explore an adaptive version of the design, where an interim analysis is undertaken based on a fixed percentage of target events, with the option to stop each biomarker-defined subgroup early for futility or efficacy. We calculate the number of events and patients required to ensure sufficient power in each of the biomarker-defined subgroups under different scenarios when the primary outcome is time-to-event. For the adaptive version, stopping probabilities are also explored. Since multiple hypotheses are being tested simultaneously, and multiple interim analyses are undertaken, we also focus on controlling the overall type I error rate by way of multiplicity adjustment.

Promise, Progress, and Pitfalls in the Search for Central Nervous System Biomarkers in Neuroimmunological Diseases: A Role for Cerebrospinal Fluid Immunophenotyping

Biomarkers are central to the translational medicine strategic focus, though strict criteria need to be applied to their designation and utility. They are one of the most promising areas of medical research, but the "biomarker life-cycle" must be understood to avoid false-positive and false-negative results. Molecular biomarkers will revolutionize the treatment of neurological diseases, but the rate of progress depends on a bold, visionary stance by neurologists, as well as scientists, biotech and pharmaceutical industries, funding agencies, and regulators. One important tool in studying cell-specific biomarkers is multiparameter flow cytometry. Cerebrospinal fluid immunophenotyping, or immune phenotypic subsets, captures the biology of intrathecal inflammatory processes, and has the potential to guide personalized immunotherapeutic selection and monitor treatment efficacy. Though data exist for some disorders, they are surprisingly lacking in many others, identifying a serious deficit to be overcome. Flow cytometric immunophenotyping provides a valuable, available, and feasible "window" into both adaptive and innate components of neuroinflammation that is currently underutilized.