Biology subjects

Singleton, A. B.

Publications and source records attributed to Singleton, A. B..

3 recordsLinked to original sources

bioRxiv2018

Genetic variability and potential effects on clinical trial outcomes: perspectives in Parkinson’s disease

BackgroundImproper randomization in clinical trials can result in the failure of the trial to meet its primary end-point. The last [~]10 years have revealed that common and rare genetic variants are an important disease factor and sometimes account for a substantial portion of disease risk variance. However, the burden of common genetic risk variants is not often considered in the randomization of clinical trials and can therefore lead to additional unwanted variance between trial arms. We simulated clinical trials to estimate false negative and false positive rates and investigated differences in single variants and mean genetic risk scores (GRS) between trial arms to investigate the potential effect of genetic variance on clinical trial outcomes at different sample sizes.\n\nMethodsSingle variant and genetic risk score analyses were conducted in a clinical trial simulation environment using data from 5851 Parkinsons Disease patients as well as two simulated virtual cohorts based on public data. The virtual cohorts included a GBA variant cohort and a two variant interaction cohort. Data was resampled at different sizes (n = 200-5000 for the Parkinsons Disease cohort) and (n = 50-800 and n = 50-2000 for virtual cohorts) for 1000 iterations and randomly assigned to the two arms of a trial. False negative and false positive rates were estimated using simulated clinical trials, and percent difference in genetic risk score and allele frequency was calculated to quantify disparity between arms.\n\nFindingsSignificant genetic differences between the two arms of a trial are found at all sample sizes. Approximately 90% of the iterations had at least one statistically significant difference in individual risk SNPs between each trial arm. Approximately 10% of iterations had a statistically significant difference between trial arms in polygenic risk score mean or variance. For significant iterations at sample size 200, the average percent difference for mean GRS between trial arms was 130.87%, decreasing to 29.87% as sample size reached 5000. In the GBA only simulations we see an average 18.86% difference in GRS scores between trial arms at n = 50, decreasing to 3.09% as sample size reaches 2000. Balancing patients by genotype reduced mean percent difference in GRS between arms to 36.71% for the main cohort and 2.00% for the GBA cohort at n = 200. When adding a drug effect to the simulations, we found that unbalanced genetics with an effect on the chosen measurable clinical outcome can result in high false negative rates among trials, especially at small sample sizes. At a sample size of n = 50 and a targeted drug effect of -0.5 points in UPDRS per year, we discovered 33.9% of trials resulted in false negatives.\n\nInterpretationsOur data support the hypothesis that within genetically unmatched clinical trials, particularly those below 1000 participants, heterogeneity could confound true therapeutic effects as expected. This is particularly important in the changing environment of drug approvals. Clinical trials should undergo pre-trial genetic adjustment or, at the minimum, post-trial adjustment and analysis for failed trials. Clinical trial arms should be balanced on genetic risk variants, as well as cumulative variant distributions represented by GRS, in order to ensure the maximum reduction in trial arm disparities. The reduction in variance after balancing allows smaller sample sizes to be utilized without risking the large disparities between trial arms witnessed in typical randomized trials. As the cost of genotyping will likely be far less than greatly increasing sample size, genetically balancing trial arms can lead to more cost-effective clinical trials as well as better outcomes.

genomics↗

bioRxiv2018

Predicting onset, progression, and clinical subtypes of Parkinson disease using machine learning

BackgroundThe clinical manifestations of Parkinson disease are characterized by heterogeneity in age at onset, disease duration, rate of progression, and constellation of motor versus nonmotor features. Due to these variable presentations, counseling of patients about their individual risks and prognosis is limited. There is an unmet need for predictive tests that facilitate early detection and characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. The emergence of machine learning to detect hidden patterns in complex, multi-dimensional datasets provides unparalleled opportunities to address this critical need.\n\nMethods and FindingsWe used unsupervised and supervised machine learning approaches for subtype identification and prediction. We used machine learning methods on comprehensive, longitudinal clinical data from the Parkinson Disease Progression Marker Initiative (PPMI) (n=328 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinson Disease Biomarker Program (PDBP) (n=112 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate and fast disease progressors. We achieved highly accurate projections of disease progression four years after initial diagnosis with an average Area Under the Curve of 0.93 (95% CI: 0.96 {+/-} 0.01 for PDvec1, 0.87 {+/-} 0.03 for PDvec2, and 0.96 {+/-} 0.02 for PDvec3). We have demonstrated robust replication of these findings in the independent validation cohort.\n\nConclusionsThese data-driven results enable clinicians to deconstruct the heterogeneity within their patient cohorts. This knowledge could have immediate implications for clinical trials by improving the detection of significant clinical outcomes that might have been masked by cohort heterogeneity. We anticipate that machine learning models will improve patient counseling, clinical trial design, allocation of healthcare resources and ultimately individualized clinical care.

neuroscience↗

bioRxiv2017

Mitochondrial impairment and rescue in riboflavin responsive neuropathy

Brown-Vialetto-Van Laere syndrome (BVVLS) represents a phenotypic spectrum of motor, sensory, and cranial nerve neuropathy, often with ataxia, optic atrophy and respiratory problems leading to ventilator-dependence. Loss-of-function mutations in two riboflavin transporter (RFVT) genes, SLC52A2 and SLC52A3, have recently been linked to BVVLS. However, the genetic frequency, neuropathology and downstream consequences of RFVT mutations have previously been undefined. By screening a large cohort of 132 patients with early-onset severe sensory, motor and cranial nerve neuropathy we confirmed the strong genetic link between RFVT mutations and BVVLS, identifying twenty-two pathogenic mutations in SLC52A2 and SLC52A3, fourteen of which were novel. Brain and spinal cord neuropathological examination of two cases with SLC52A3 mutations showed classical symmetrical brainstem lesions resembling pathology seen in mitochondrial disease, including severe neuronal loss in the lower cranial nerve nuclei, anterior horns and corresponding nerves, atrophy of the spinothalamic and spinocerebellar tracts and posterior column-medial lemniscus pathways. Mitochondrial dysfunction has previously been implicated in an array of neurodegenerative disorders. Since riboflavin metabolites are critical components of the mitochondrial electron transport chain (ETC), we hypothesized that reduced riboflavin transport would result in impaired mitochondrial activity, and confirmed this using in vitro and in vivo models. ETC complex I and complex II activity were decreased in SLC52A2 patient fibroblasts, while global knockdown of the single Drosophila RFVT homologue revealed reduced levels of riboflavin, downstream metabolites, and ETC complex I activity. RFVT knockdown in Drosophila also resulted in severely impaired locomotor activity and reduced lifespan, mirroring patient pathology, and these phenotypes could be partially rescued using a novel esterified derivative of riboflavin. Our findings indicate mitochondrial dysfunction as a downstream consequence of RFVT gene defects in BVVLS and validate riboflavin esters as a potential therapeutic strategy.

neuroscience↗