Biomarkers for Sanfilippo
Being able to easily and accurately confirm a diagnosis and predict and monitor an individual’s disease trajectory can be incredibly helpful to families whose child has been diagnosed with a progressive condition like Sanfilippo. It can help them to plan and understand what lies ahead. It can also help with treatment decisions, clinical management, and support effective clinical trials.
Biomarkers are tools that can help with all these goals, but they are not always easy to identify.
Recently, a review article on disease-predicting biomarkers for Sanfilippo was published by Leanne Winner, Flinders University, Australia and her colleagues. Leanne is a recipient of a Sanfilippo Children’s Foundation PhD top-up Scholarship and is working on identifying biomarkers for Sanfilippo from easy to access sources such as blood and saliva.
As Leanne notes in her article, biomarkers are an ‘urgent unmet need’ for Sanfilippo and this is a very active area of research.
What are biomarkers and why are they needed?
Biomarkers can be genes, proteins, or other molecules found in bodily fluids or tissues, or other features, such as those seen in medical imaging scans. They are capable of indicating the presence or absence of a disease (diagnosis), predicting its severity (prognosis), or tracking whether it is getting worse (progressing) or improving (e.g. responding to treatment).
Biomarkers for Sanfilippo would support the development of therapies and clinical practice for Sanfilippo, but they are also likely to have applications for similar childhood dementias.
Because the earliest symptoms of Sanfilippo are similar to those seen in other conditions like developmental delay, ADHD and autism, families affected by Sanfilippo often encounter long diagnostic odysseys and misdiagnoses. However, once there is a clinical suspicion of Sanfilippo, there are clear biomarkers that allow a quick and accurate diagnosis.
The global consensus clinical care guidelines for Sanfilippo, note that a diagnosis can be made with two of three possible disease biomarkers: 1) high levels of complex sugar (e.g. heparan sulfate, HS) in blood or urine; 2) a deficiency of one of four enzymes associated with Sanfilippo; and 3) genetic testing that shows changes in genes associated with Sanfilippo.
Having additional diagnostic biomarkers for Sanfilippo could be helpful, but more awareness among primary healthcare providers to recognise the signs and symptoms would also support early diagnosis.
Once a diagnosis has been made, it is currently much harder to provide parents with a clear prognosis for their child. Parents may be told of a ‘typical’ disease course, but in reality the rate of disease progression can vary greatly for every individual.
In cancer, for example, biomarkers such as tumour size and grade, or the presence of certain molecules can be used to predict the long term prognosis and guide treatment decisions. However, there are few such tools available for Sanfilippo.
While total HS is often used for diagnosis, it is not as useful as a predictor of disease severity. Currently, one of the best tools to predict disease progression is the age that symptoms first appear, with earlier onset and more severe early symptoms predicting faster disease progression.
Certain gene variants are known to be associated with either a faster or a slower disease progression. However, new variants are often found, particularly in Sanfilippo type B, so the gene mutation is not always useful to predict disease course. The level of residual enzyme activity is also not considered an accurate prognostic biomarker for Sanfilippo.
More accurate prognostic biomarkers are urgently needed and will be particularly important once newborn screening becomes available and children with Sanfilippo are identified before the onset of symptoms.
Disease progression biomarkers
The symptoms of Sanfilippo vary as the disease advances and progression is not always linear, making it hard to objectively measure where an individual is along their disease trajectory. Biomarkers could assist clinical teams to better tailor care to an individual’s needs at different stages of the disease and help with decisions around eligibility for clinical trials.
Biomarkers like grey matter volume and a child’s developmental quotient (DQ) can be used to track disease progression. Grey matter is the part of the brain involved in information processing, and can be measured using MRI. DQ assesses a child's performance on tasks compared with typical results from children the same age.
Reduced volume of brain grey matter and a lower DQ are associated with more advanced disease. However, both can be difficult to measure in very young children, and not all patients may have access to an MRI.
Some molecules or their amounts may change with the disease, and further basic laboratory research is needed to identify and validate these molecules as biomarkers. One team, led by Associate Professor Jan Kaslin (Monash University), is using cell and animal models of Sanfilippo and patient blood samples to find new potential biomarkers for Sanfilippo.
Pharmacodynamic (response-to-treatment) biomarkers
Developing and testing new therapies for Sanfilippo relies on the ability to accurately measure whether a therapy is working. Often, follow-up periods in clinical trials are not long enough to clearly determine whether a clinical change has been achieved. In these cases, pharmacodynamic biomarkers can be useful to measure if a therapy changes features of a disease and predicts whether a clinical benefit is likely.
HS levels are used in clinical trials to measure whether an experimental therapy such as gene therapy or enzyme replacement therapy is having a biological effect in restoring enzyme activity. However, reductions in total HS in Sanfilippo trials have not always correlated well with improvements in neurocognitive development.
One example of a molecule that is being used to predict treatment response in other neurodegenerative diseases is the protein neurofilament light chain (NfL). NfL is only released from damaged neurons and can be detected in the blood. Results presented from the Lysogene clinical trial for Sanfilippo type A showed that blood NfL levels were two times lower following treatment, potentially indicating a positive response to the gene therapy. Further work is needed to validate NfL as a biomarker for Sanfilippo.
Grey matter volume and a child’s developmental quotient (DQ) are also being used in ongoing clinical trials for Sanfilippo to predict whether the therapy will have a clinical benefit. Early results from the Abeona/Ultragenyx gene therapy clinical trial for Sanfilippo type A showed increases in total brain volume, in contrast to the brain shrinkage seen in untreated children. This was also supported by DQ data, suggesting that children treated at the youngest ages might achieve a normal cognitive development trajectory.
For Sanfilippo, more biomarkers would allow greater power when evaluating a potential therapy’s effect in clinical trials and in the laboratory when developing and testing treatments for Sanfilippo in cell or animal models of the disease.
- Biomarkers can be genes, proteins, or other molecules found in bodily fluids or tissues, or other features, such as those seen in medical imaging scans, that provide information about a disease.
- Biomarkers can be categorised into those that can help diagnose a disease, predict disease progression, track progression, or track response to treatment.
- The biomarker heparan sulfate (HS) can be used to diagnose the disease, but does not appear to be as useful in tracking disease progression or treatment response.
- The impact of new biomarkers will be felt most by people living with Sanfilippo and their families, helping with earlier diagnosis, certainty regarding disease outlook, and supporting faster, more efficient clinical trials.
- Sanfilippo Children’s Foundation is supporting biomarker discovery through several projects including a PhD top-up scholarship to Leanne Winner (Flinders University, Adelaide) and a Translational grant led by A/Prof Jan Kaslin (Monash University).