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Background: Caused by duplications of the gene encoding peripheral myelin protein 22 (PMP22), Charcot–Marie–Tooth disease type 1A (CMT1A) is the most common hereditary neuropathy. Despite this shared genetic origin, there is considerable variability in clinical severity. It is hypothesized that genetic modifiers contribute to this heterogeneity, the identification of which may reveal novel therapeutic targets. In this study, we present a comprehensive analysis of clinical examination results from 1564 CMT1A patients sourced from a prospective natural history study conducted by the RDCRN‐INC (Inherited Neuropathy Consortium). Our primary objective is to delineate extreme phenotype profiles (mild and severe) within this patient cohort, thereby enhancing our ability to detect genetic modifiers with large effects. Methods: We have conducted large‐scale statistical analyses of the RDCRN‐INC database to characterize CMT1A severity across multiple metrics. Results: We defined patients below the 10th (mild) and above the 90th (severe) percentiles of age‐normalized disease severity based on the CMT Examination Score V2 and foot dorsiflexion strength (MRC scale). Based on extreme phenotype categories, we defined a statistically justified recruitment strategy, which we propose to use in future modifier studies. Interpretation: Leveraging whole genome sequencing with base pair resolution, a future genetic modifier evaluation will include single nucleotide association, gene burden tests, and structural variant analysis. The present work not only provides insight into the severity and course of CMT1A, but also elucidates the statistical foundation and practical considerations for a cost‐efficient and straightforward patient enrollment strategy that we intend to conduct on additional patients recruited globally. [ABSTRACT FROM AUTHOR]

Quantitative T2‐weighted MRI (T2W) interpretation is impeded by the variability of acquisition‐related features, such as field strength, coil type, signal amplification, and pulse sequence parameters. The main purpose of this work is to develop an automated method for prostate T2W intensity normalization. The procedure includes the following: (i) a deep learning‐based network utilizing MASK R‐CNN for automatic segmentation of three reference tissues: gluteus maximus muscle, femur, and bladder; (ii) fitting a spline function between average intensities in these structures and reference values; and (iii) using the function to transform all T2W intensities. The T2W distributions in the prostate cancer regions of interest (ROIs) and normal appearing prostate tissue (NAT) were compared before and after normalization using Student's t‐test. The ROIs' T2W associations with the Gleason Score (GS), Decipher genomic score, and a three‐tier prostate cancer risk were evaluated with Spearman's correlation coefficient (rS). T2W differences in indolent and aggressive prostate cancer lesions were also assessed. The MASK R‐CNN was trained with manual contours from 32 patients. The normalization procedure was applied to an independent MRI dataset from 83 patients. T2W differences between ROIs and NAT significantly increased after normalization. T2W intensities in 231 biopsy ROIs were significantly negatively correlated with GS (rS = −0.21, p = 0.001), Decipher (rS = −0.193, p = 0.003), and three‐tier risk (rS = −0.235, p < 0.001). The average T2W intensities in the aggressive ROIs were significantly lower than in the indolent ROIs after normalization. In conclusion, the automated triple‐reference tissue normalization method significantly improved the discrimination between prostate cancer and normal prostate tissue. In addition, the normalized T2W intensities of cancer exhibited a significant association with tumor aggressiveness. By improving the quantitative utilization of the T2W in the assessment of prostate cancer on MRI, the new normalization method represents an important advance over clinical protocols that do not include sequences for the measurement of T2 relaxation times. [ABSTRACT FROM AUTHOR]

DNA-based therapeutics have emerged as a revolutionary approach for addressing the treatment gap in rare inherited conditions by targeting the fundamental genetic causes of disease. Charcot-Marie-Tooth (CMT) disease, a group of inherited neuropathies, represents one of the most prevalent Mendelian disease groups in neurology and is characterized by diverse genetic etiology. Axonal forms of CMT, known as CMT2, are caused by dominant mutations in over 30 different genes which lead to degeneration of lower motor neuron axons. Recent advances in antisense oligonucleotide (ASO) therapeutics have shown promise in targeting neurodegenerative disorders. Here we elucidate pathomechanistic changes contributing to variant specific molecular phenotypes in CMT2E, caused by a single nucleotide substitution (p.N98S) in the neurofilament light chain gene (NEFL). We used a patient-derived pluripotent stem cell (iPSC)-induced motor neuron model, which recapitulates several cellular and biomarker phenotypes associated with CMT2E. Using an ASO treatment strategy targeting a heterozygous gain-of-function variant, we aimed to resolve molecular phenotypic changes observed in the CMT2E p.N98S subtype. To determine ASO therapeutic potential, we employed our treatment strategy in iPSC-derived motor neurons and used established as well as novel biomarkers of peripheral nervous system axonal degeneration. Our findings have demonstrated a significant decrease in clinically relevant biomarkers of axonal degeneration, presenting the first clinically viable genetic therapeutic for CMT2E. Similar strategies could be used to develop precision medicine approaches for otherwise untreatable gain of function inherited disorders., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)