For the lay public, amyotrophic lateral sclerosis (ALS) appears to be a single disease entity. But clinicians have a front row seat to the heterogeneous clinical presentations that ALS offers up. The differences in presentation make both studying the disease and treating it a challenge.
At the annual meeting of AANEM last fall, Matthew Harms, MD, discussed how this clinical variability can be used to better develop care for ALS.
The goal, according to Harms, is to break ALS into homogenous subgroups so patients can be assigned to the most appropriate treatments and interventions.
Getting to the bottom of clinical variability
To achieve the goals of treatment, we need to determine which patients are at risk for which disease course. Who will have a rapid progression of symptoms? Who will experience early speech loss? Who will require early tracheostomy?
By looking at subgroups who have a similar disease course, researchers hope to determine if the shared characteristics of the members of these groups have biological relevance. Multiple studies are looking at just this information.
Genomic Translation for ALS Care (GTAC)
The GTAC study is summarized like so:
The purpose of this study is to look for abnormal genes and gene expression profiles that help determine why a person develops amyotrophic lateral sclerosis (ALS) and related motor neuron diseases (MND) and why their symptoms present and progress with a particular pattern.
In short, researchers look for specific genetic markers and associate them with variations in the clinical course of the disease. For three years, beginning in 2015, the study followed 1,500 people living with ALS. Researchers collected clinical data, sequenced DNA, and stored blood samples with the goal of stratifying patients for future clinical trials.
Of note, the ALS Association provided $3.5 million in funds for the study, which was raised through the Ice Bucket Challenge—a viral meme that was lots of fun and did some good!
Computer Analysis of Clinical Data
Multiple studies have used data analysis to create computer predictions for the clinical course in subgroups of patients. A 2018 study published in The Lancet Neurology used computer modeling of patients from 14 centers to create an algorithm to predict outcomes. They predicted time between onset of symptoms and non-invasive ventilation for more than 23 h per day, tracheostomy, or death.
Researchers assessed 16 patient characteristics to create the model, and Harms notes that the predictions were remarkably accurate regarding who would need tracheostomy in earlier stages of the disease.
Crowdsourcing is also coming into play as a way to gather data for analysis. The DREAM-Phil Bowen Prediction Prize4Life Challenge provided a $50,000 prize allowing researchers to use demographics, medical and family history data, functional measures, vital signs, and lab data from patients with ALS in order to predict the progression of disease based on the patient’s current disease status.
Biomarker Discovery Efforts
Body fluid constituents, immune cell profiles, imaging characteristics, electrical properties and other markers are prime targets for understanding who is likely to follow which path once diagnosed with ALS. The earlier these are identified after diagnosis, the better for treatment.
Researchers hope to identify markers that appear earlier in the disease and can be used to guide treatment and planning for patients.
Where We Want to Go
There are many variants of unknown significance in ALS, and this complicates genetic testing. But using genetic testing in combination with what we are learning about the course of disease in various subgroups changes the game. While ALS may appear to be a simple disease or just one disease, clinicians understand the presentation contains huge variety. Now we have researchers making progress, parsing out this variability. We look forward to what is to come.