Leukodystrophies, and other heritable disorders of the white matter of the brain, were previously resistant to genetic characterization, largely due to the extreme genetic heterogeneity of molecular causes. While recent work has demonstrated that whole genome sequencing (WGS), has the potential to dramatically increase diagnostic efficiency, significant questions remain around the impact on downstream clinical management approaches versus standard diagnostic approaches.

Leukodystrophies are a group of approximately 30 genetic diseases that primarily affect the white matter of the brain, a complex structure composed of axons sheathed in myelin, a glial cell-derived lipid-rich membrane. Leukodystrophies are frequently characterized by early onset, spasticity and developmental delay, and are degenerative in nature. As a whole, leukodystrophies are relatively common (approximately 1 in 7000 births or almost twice as prevalent as Prader-Willi Syndrome, which has been far more extensively studied) with high associated health-care costs; however, more than half of the suspected leukodystrophies do not have a definitive diagnosis, and are generally classified as "leukodystrophies of unknown etiology". Even when a diagnosis is achieved, the diagnostic process lasts an average of eight years and results in test expenses in excess of $8,000 on average per patient, including the majority of patients who never achieve a diagnosis at all. These diagnostic challenges represent an urgent and unresolved gap in knowledge and disease characterization, as obtaining a definitive diagnosis is of paramount importance for leukodystrophy patients. The diagnostic workup begins with findings on cranial Magnetic Resonance Imaging (MRI) followed by sequential targeted genetic testing, however next generation sequencing (NGS) technologies offer the promise of rapid and more cost effective approaches.

Despite significant advances in diagnostic efficacy, there are still significant issues with respect to implementation of NGS in clinical settings. First, sample cohorts demonstrating diagnostic efficacy are generally small, retrospective, and susceptible to ascertainment bias, ultimately rendering them poor candidates for utility analyses (to determine how efficient a test is at producing a diagnosis). Second, historic sample cohorts have not been examined prospectively for information about impact on clinical management (whether the test results in different clinical monitoring, a change in medications, or alternate clinical interventions).

To address these issues, the study team conducted an investigation of patients with suspected leukodystrophies or other genetic disorders affecting the white matter of the brain at the time of initial confirmation of MRI abnormalities, with prospective collection of patients randomly received on a "first come, first served" basis from a network of expert clinical sites. Subjects were randomized to receive early (1 month) or late (6 months) WGS, with SoC clinical analyses conducted alongside WGS testing. An interim analysis performed in May 2018 assessed these study outcomes for a cohort of thirty-four (34) enrolled subjects. Two of these subjects were resolved before complete enrollment and were retained as controls. Nine subjects were stratified to the Immediate Arm, of which 5 (55.6%) were resolved by WGS and 4 (44.4%) were persistently unresolved. Of the 23 subjects randomized to the Delayed Arm, 14 (60.9%) were resolved by WGS and 5 (21.7%) by SoC, while the remaining 4 (17.4%) remained undiagnosed. The diagnostic efficacy of WGS in both arms was significant relative to SoC (p<0.005). The time to diagnosis was significantly shorter in the immediate WGS group (p<0.05). The overall diagnostic efficacy of the combination of WGS and SoC approaches was 26/34 (76.5%; 95% CI = 58.8% to 89.3%) over <4 months, greater than historical norms of <50% over more than 5 years.

The study now seeks to determine whether WGS results in changes to diagnostic status and clinical management in subjects affected by undiagnosed genetic disorders of the white matter of the brain. We anticipate that WGS will produce measurable downstream changes in diagnostic status and clinical management, as defined by disease-specific screening for complications or implementation of disease-specific therapeutic approaches.

Inclusion Criteria:

1. Abnormalities of the white matter signal on neuroimaging (MRI) with T2 hyperintensity which must be diffuse or involve specific anatomical tracts consistent with a genetic diagnosis;
2. No pre-existing genetic diagnosis;
3. A clinical decision has been made to perform WGS;
4. Less than 18 years of age (exception for the affected sibling of the proband);
5. Availability of both biologic parents for blood sampling;
6. Availability of both biological parents to provide informed consent;
7. Concurrently enrolled in CHOP IRB 14-011236 (Myelin Disorders Biorepository Project)

Exclusion Criteria:

1. Candidates with acquired disorders, including infection, acute disseminated encephalomyelitis (ADEM), multiple sclerosis, vasculitis or toxic leukoencephalopathies;

2. Patients who have had previous genetic testing*, including WES or WGS;

3. Those with no third-party payer insurance, unable to receive standard of care diagnosis and therapeutic approaches;

4. Candidates who have already received a diagnosis.

   • Note: Karyotype or microarray testing that did not yield a definitive diagnosis should not be considered as an excluding factor.

We expect participants to be identified during their initial presentation and preliminary diagnostic workup. Leukodystrophies are heritable conditions that - with only few exceptions - are not gender-specific. We therefore expect males and females to be equally represented in the study population. The age of presentation is variable ranging from infancy to adulthood, though enrollment for the study is limited to individuals who have not yet reached the age of 18. All ethnicities are equally represented in these disorders, and we expect ethnicities to be represented based on US census data of population distribution.

Individual participant data (IPD) only available to principal investigator, co-investigators, and trial staff.