The Huntington’s Disease Integrated Staging System (HD-ISS): A New Staging System for More Targeted Research
The HD-ISS is based on a biological definition of HD that considers the full course of HD starting at birth, and provides an evidence-based staging system that fills a gap in HD clinical research
In a recent symposium at Vanderbilt University Medical Center (Nashville, TN), Jeff Long, Ph.D, Professor of Psychiatry and Professor of Biostatistics at the University of Iowa, presented an overview of a newly proposed staging system for HD: the HD Integrated Staging System (HD-ISS). Long is one of the academic members of a larger working group formed under the auspices of the Critical Path Institute (C-PATH), a non-profit that advances regulatory science opportunities. The HD Regulatory Science Consortium (HD-RSC), a precompetitive C-PATH initiative, created the Regulatory Science Forum working group (RSF), which includes industry and academic representatives (Long being among them).
The RSF was tasked with generating the new evidenced-based HD-ISS. The group used a formal consensus methodology to consider which prognostic biomarkers, signs, and symptoms of HD would be best for defining stages. Empirical data analysis was used to calculate healthy-control-based landmark variable cut-offs for stage classification and to internally validate the framework. Recently, the RSF completed the initial work on the new system, which will be detailed in a soon-to-be-released professional publication (a preprint is freely available at MedRxiv, Huntington’s Disease Integrated Staging System (HD-ISS): A Novel Evidence-Based Classification System For Staging).
Specifics of the Staging System
Dr. Long presented the details of the HD-ISS, which starts at birth with Stage 0 (“Huntington’s Disease”) in which individuals have CAG ≥40, but before they have any detectible changes. Stage 1 (“Biomarker of Pathogenesis”) is marked by brain atrophy, which is reached after dropping below an age-specific volume threshold for the caudate or putamen (this is prior to clinical signs/symptoms). Stage 2 (“Clinical Sign or Symptom”) is HD signs/symptoms, which is reached after surpassing thresholds for either the Total Motor Score or the Symbol Digit Modalities Test. Finally, Stage 3 (“Functional Change”) is marked by functional decline, which is reached after dropping below thresholds for the Total Functional Capacity or the Independence Scale.
Motivation for the Staging System
Long says that a major motivation for the new staging system was to enable the planning of clinical trials earlier in HD progression. “To date, HD clinical research has been structured around clinical motor diagnosis, which occurs late in the disease,” he said. “There is ample evidence that changes occur many years prior to clinical manifestation. The goal was to have the staging system reflect the early changes, with the hope that eventually clinical trials might be planned in earlier stages, such as Stage 1. This would allow the development of trials for testing preventative interventions that might be effective prior to clinical manifestation.” Another motivation was to establish clear criteria that can be used for clinical trial planning. “We wanted to remove ambiguity about the types of cases and stages of disease that might be included or excluded from a clinical trial.”
Developing the Staging System
Long explained that the Regulatory Science Forum used the NIH consensus methodology in developing the HD-ISS. All published HD research studies were surveyed to identify a pool of potential landmarks for defining the stages. The initial pool was reduced by considering landmarks that were from longitudinal studies with a sufficient sample size and showed prognostic value. The final set of landmarks was selected based on a consensus among the members of the RSF.
Once the landmarks had been identified, analysis was conducted using the data sets from four HD repositories: ENROLL-HD, IMAGE-HD, PREDICT-HD, and TRACK-HD. Three-quarters of these patients had multiple research visits, which allowed the inclusion of data on their progression over time.
The first goal of the analysis was to establish cutoffs for reaching a stage. This analysis used only healthy controls, so that the cutoffs were CAG-independent. The second goal was to assess the internal validity of the HD-ISS, especially the success of classification. “The results of the analysis were positive, showing rates of correct classification on par with staging systems in Alzheimer’s and Parkinson’s disease,” Long said. Long cautioned that the system is for research purposes and not for clinical practice. “The system does not affect how a doctor would diagnose a patient,” he said. “The tool is also not meant for diagnostic criteria or clinical guidelines, such as qualifying someone for disability.”
Benefits for Drug Developers
The ability to isolate populations in different stages has multiple benefits for clinical trials. For example, in the recently suspended tominersen trial, an earlier progression subgroup was identified as potentially benefitting from lower dosing. Identifying appropriate treatment populations has benefits. “Placing the subgroup within the HD-ISS can help to define the window of progression that might be beneficial for future trials,” he said.
What’s next?
The next step in Long’s work is to apply the staging system to patients in ENROLL-HD, the largest database in the HD community. Researchers often rely on that database for the planning of clinical trials, and it will facilitate the use of the system to stage the 20,000+ people in that database. Thanks to the HD Community Long expressed his gratitude to the community of HD patients and their families. He is impressed by the scores of volunteers for the naturalistic studies, and stressed how important this is for helping to characterize how the disease changes over time.
“Because of the willingness to participate in research studies, our knowledge database in HD is large compared to other diseases. Researchers can make good use of such information, which we hope will bring us closer to benefits for the HD community.”
BENEFITS OF HD STAGING SYSTEM
- Classifies patients into homogeneous groups that share similar expected outcomes
- Unifies research terminology for HD
- Facilitates the gathering and accumulation of evidence across studies
- Provides a framework for intervention before clinical motor diagnosis
- Provides possible new endpoints for clinical research, such as transition into Stage 3 (functional loss)
THE NEW STAGING SYSTEM IS:
- A model that includes both a research definition and a staging system
- A framework for researchers to use to analyze data
- A means of helping drug companies formulate inclusion criteria for a trial
THE NEW STAGING SYSTEM IS NOT:
- Diagnostic criteria
- Clinical guidelines
What Does the HD-ISS Leave Out?
PSYCHIATRIC VARIABLES
- Depression, anxiety, aggression
- Apathy: Best progression candidate, but limited [Schobel et al., 2017]
- Aggression, arguably important for caretakers [Brownstein et al., 2020]
ALTERNATIVE BIOMARKERS
- Neurofilament light chain (NfL) [Byrne et al., 2017]
- mHTT, total tau, YKL-40, glial fibrillary acidic protein, interleukin-6 & -8, neurogranin,ubiquitin carboxyl-terminal hydrolase L1, etc.
- Challenge: We need lots of longitudinal data on controls and cases
C-PATH Aims to Streamline Research and FDA Consideration
C-PATH is a non-profit, public-private partnership with the US Food and Drug Administration (FDA), created in response to the FDA’s Critical Path Initiative program in 2005. C-PATH works to accelerate and de-risk the medical product development process for many diseases, including Huntington’s disease. A working group under the auspices of C-PATH launched the development of the HD staging system described here.
This staging system is just part of C-PATH’s portfolio. C-PATH works to speed up product development using a collaborative model specific to the unmet needs of a disease area, such as developing improved patient selection criteria for clinical trials and improved patient-centered outcome measures. C-Path attempts to build consensus among participating members from industry and academia with regulatory participation and iterative feedback.
