Gene Therapy Offers Hope for Children with Rare, Incurable Disorder

Children with a devastating genetic disorder characterized by severe motor disability and developmental delay have experienced sometimes dramatic improvements in a gene therapy trial launched at UC San Francisco Benioff Children’s Hospitals. The trial includes seven children aged 4 to 9 born with deficiency of AADC, an enzyme involved in the synthesis of neurotransmitters, particularly dopamine, that leaves them unable to speak, feed themselves or hold up their head. Six of the children were treated at UCSF and one at Ohio State Wexner Medical Center.

Children in the study experienced improved motor function, better mood, and longer sleep, and were able to interact more fully with their parents and siblings. Oculogyric crisis, a hallmark of the disorder involving involuntary upward fixed gaze that may last for hours and may be accompanied by seizure-like episodes, ceased in all but one patient. Just 135 children worldwide are known to be missing the AADC enzyme, with the condition affecting more people of Asian descent.

The trial borrowed from gene delivery techniques used to treat Parkinson’s disease, pioneered by senior author Krystof Bankiewicz, MD, PhD, of the UCSF Department of Neurological Surgery and the Weill Institute for Neurosciences, and of the Department of Neurological Surgery at Ohio State University. Both conditions are associated with deficiencies of AADC, which converts levodopa into dopamine, a neurotransmitter involved in movement, mood, learning and concentration. In treating both conditions, Bankiewicz developed a viral vector containing the AADC gene. The vector is infused into the brain via a small hole in the skull, using real-time MR imaging to enable the neurosurgeon to map the target region and plan canula insertion and infusion.

Children with primary AADC deficiency lack a functional copy of the gene, but we had presumed that their actual neuronal pathway was intact,” said co-first author Nalin Gupta, MD, PhD, of the UCSF Department of Neurological Surgery and the surgical principal investigator. “This is unlike Parkinson’s disease, where the neurons that produce dopamine undergo degeneration.

While the Parkinson’s trial focused on the putamen, a part of the brain that plays a key role in this degeneration, Gupta said the AADC gene therapy trial targeted neurons in the substantia nigra and ventral tegmental area of the brainstem, sites that may have more therapeutic benefits.

The approach for treating AADC deficiency is much more straightforward than it is for Parkinson’s,” said Bankiewicz. “In AADC deficiency, the wiring of the brain is normal, it’s just the neurons don’t know how to produce dopamine because they lack AADC.”

Results appear in Nature Communications.

Source: https://www.ucsf.edu/

AI Detects Alzheimer’s Six Years In Advance

Using a common type of brain scan, researchers programmed a machine-learning algorithm to diagnose early-stage Alzheimer’s disease about six years before a clinical diagnosis is made – potentially giving doctors a chance to intervene with treatment. No cure exists for Alzheimer’s disease, but promising drugs have emerged in recent years that can help stem the condition’s progression. However, these treatments must be administered early in the course of the disease in order to do any good. This race against the clock has inspired scientists to search for ways to diagnose the condition earlier.

A PET scan of the brain of a person with Alzheimer’s disease

One of the difficulties with Alzheimer’s disease is that by the time all the clinical symptoms manifest and we can make a definitive diagnosis, too many neurons have died, making it essentially irreversible,” says Jae Ho Sohn, MD, MS, a resident in the Department of Radiology and Biomedical Imaging at UC San Francisco.

 

Positron emission tomography (PET) scans, which measure the levels of specific molecules, like glucose, in the brain, have been investigated as one tool to help diagnose Alzheimer’s disease before the symptoms become severe. Glucose is the primary source of fuel for brain cells, and the more active a cell is, the more glucose it uses. As brain cells become diseased and die, they use less and, eventually, no glucose.

Other types of PET scans look for proteins specifically related to Alzheimer’s disease, but glucose PET scans are much more common and cheaper, especially in smaller health care facilities and developing countries, because they’re also used for cancer staging.

Radiologists have used these scans to try to detect Alzheimer’s by looking for reduced glucose levels across the brain, especially in the frontal and parietal lobes of the brain. However, because the disease is a slow progressive disorder, the changes in glucose are very subtle and so difficult to spot with the naked eye. To solve this problem, Sohn applied a machine learning algorithm to PET scans to help diagnose early-stage Alzheimer’s disease more reliably.

This is an ideal application of deep learning because it is particularly strong at finding very subtle but diffuse processes. Human radiologists are really strong at identifying tiny focal finding like a brain tumor, but we struggle at detecting more slow, global changes,” says Sohn. “Given the strength of deep learning in this type of application, especially compared to humans, it seemed like a natural application.

To train the algorithm, Sohn fed it images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), a massive public dataset of PET scans from patients who were eventually diagnosed with either Alzheimer’s disease, mild cognitive impairment or no disorder. Eventually, the algorithm began to learn on its own which features are important for predicting the diagnosis of Alzheimer’s disease and which are not.

Once the algorithm was trained on 1,921 scans, the scientists tested it on two novel datasets to evaluate its performance. The first were 188 images that came from the same ADNI database but had not been presented to the algorithm yet. The second was an entirely novel set of scans from 40 patients who had presented to the UCSF Memory and Aging Center with possible cognitive impairment.

The algorithm performed with flying colors. It correctly identified 92 percent of patients who developed Alzheimer’s disease in the first test set and 98 percent in the second test set. What’s more, it made these correct predictions on average 75.8 months – a little more than six yearsbefore the patient received their final diagnosis.

Source: https://www.ucsf.edu/