CoChat
Sign Up
Cited by 60Cerebrovascular and genetic disordersGenetic Neurodegenerative DiseasesNeurological Disease Mechanisms and Treatments

Three-tiered EGFr domain risk stratification for individualized NOTCH3-small vessel disease prediction

Open access
Hack, Remco J, Gravesteijn, Gido, Cerfontaine, Minne N, Santcroos, Mark A, Gatti, Laura, Kopczak, Anna, Bersano, Anna, Duering, Marco +2 more·2023·Brain
DOI 10.1093/brain/awac486PMC PMC10316769
Open this paper in CoChat — free accountOpen PDF

Abstract

Cysteine-altering missense variants (NOTCH3cys) in one of the 34 epidermal growth-factor-like repeat (EGFr) domains of the NOTCH3 protein are the cause of NOTCH3-associated small vessel disease (NOTCH3-SVD). NOTCH3-SVD is highly variable, ranging from cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) at the severe end of the spectrum to non-penetrance. The strongest known NOTCH3-SVD modifier is NOTCH3cys variant position: NOTCH3cys variants located in EGFr domains 1–6 are associated with a more severe phenotype than NOTCH3cys variants located in EGFr domains 7–34. The objective of this study was to further improve NOTCH3-SVD genotype-based risk prediction by using relative differences in NOTCH3cys variant frequencies between large CADASIL and population cohorts as a starting point. Scientific CADASIL literature, cohorts and population databases were queried for NOTCH3cys variants. For each EGFr domain, the relative difference in NOTCH3cys variant frequency (NVFOR) was calculated using genotypes of 2574 CADASIL patients and 1647 individuals from population databases. Based on NVFOR cut-off values, EGFr domains were classified as either low (LR-EGFr), medium (MR-EGFr) or high risk (HR-EGFr). The clinical relevance of this new three-tiered EGFr risk classification was cross-sectionally validated by comparing SVD imaging markers and clinical outcomes between EGFr risk categories using a genotype-phenotype data set of 434 CADASIL patients and 1003 NOTCH3cys positive community-dwelling individuals. CADASIL patients and community-dwelling individuals harboured 379 unique NOTCH3cys variants. Nine EGFr domains were classified as an HR-EGFr, which included EGFr domains 1–6, but additionally also EGFr domains 8, 11 and 26. Ten EGFr domains were classified as MR-EGFr and 11 as LR-EGFr. In the population genotype–phenotype data set, HR-EGFr individuals had the highest risk of stroke [odds ratio (OR) = 10.81, 95% confidence interval (CI): 5.46–21.37], followed by MR-EGFr individuals (OR = 1.81, 95% CI: 0.84–3.88) and LR-EGFr individuals (OR = 1 [reference]). MR-EGFr individuals had a significantly higher normalized white matter hyperintensity volume (nWMHv; P = 0.005) and peak width of skeletonized mean diffusivity (PSMD; P = 0.035) than LR-EGFr individuals. In the CADASIL genotype–phenotype data set, HR-EGFr domains 8, 11 and 26 patients had a significantly higher risk of stroke (P = 0.002), disability (P = 0.041), nWMHv (P = 1.8 × 10−8), PSMD (P = 2.6 × 10−8) and lacune volume (P = 0.006) than MR-EGFr patients. SVD imaging marker load and clinical outcomes were similar between HR-EGFr 1–6 patients and HR-EGFr 8, 11 and 26 patients. NVFOR was significantly associated with vascular NOTCH3 aggregation load (P = 0.006), but not with NOTCH3 signalling activity (P = 0.88). In conclusion, we identified three clinically distinct NOTCH3-SVD EGFr risk categories based on NFVOR cut-off values, and identified three additional HR-EGFr domains located outside of EGFr domains 1–6. This EGFr risk classification will provide an important key to individualized NOTCH3-SVD disease prediction.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/brain/awac486

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • NOTCH3 variants were classified by exon and EGFr domain and stratified into high-risk, medium-risk, low-risk, or undetermined-risk categories according to published criteria.

    “For patients who underwent genetic testing, NOTCH3 variants were classified by exon and epidermal growth factor–like repeat (EGFr) domain and stratified into high-risk, medium-risk, low-risk, or undetermined-risk categories according to published criteria.9”

    From Multiple Sclerosis Misdiagnosis in Cerebral Autosomal Dominant Arteriopathy With Subcortical Infarcts and Leukoencephalopathy (2026) · cites this paper as [9]

    The Materials and methods section (page unavailable) defines high-, medium-, low-, and unknown-risk EGFr domains; the Results Figure 2 caption (page unavailable) identifies the exons encoding those domains. However, the risk criteria apply specifically to cysteine-altering NOTCH3 variants, not to NOTCH3 variants generally, and the paper does not set out a separate exon-based risk classification. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Medium-risk genetic variants may result in atypical phenotypes that complicate recognition of CADASIL and predispose patients to MS misdiagnosis.

    “Owing to their intermediate penetrance and variable expressivity,9 medium-risk genetic variants may result in atypical phenotypes that complicate the recognition of CADASIL and predispose patients to MS misdiagnosis.”

    From Multiple Sclerosis Misdiagnosis in Cerebral Autosomal Dominant Arteriopathy With Subcortical Infarcts and Leukoencephalopathy (2026) · cites this paper as [9]

    The Discussion (page unavailable) says medium-risk NOTCH3 variants are associated with a broad disease spectrum, including very mild subclinical phenotypes. That supports variable presentation, but our copy does not link these variants to difficulty recognizing CADASIL or to MS misdiagnosis. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 42

  1. [1]Rutten JW, Hack RJ, Duering M, et al Broad phenotype of cysteine altering NOTCH3 variants in UK biobank: CADASIL to non-penetrance. Neurology. 2020; 95(13): e1835– e1843. 32732295 PMC7682826
  2. [2]Hack RJ, Rutten JW, Person TN, et al Cysteine-altering NOTCH3 variants are a risk factor for stroke in the elderly population. Stroke. 2020; 51: 3562– 3569. 33161844 PMC7678653
  3. [3]Chabriat H, Joutel A, Dichgans M, Tournier-Lasserve E, Bousser MG. CADASIL. Lancet Neurol. 2009; 8: 643– 653. 19539236
  4. [4]Rutten JW, Dauwerse HG, Gravesteijn G, et al Archetypal NOTCH3 mutations frequent in public exome: Implications for CADASIL. Ann Clin Transl Neurol. 2016; 3: 844– 853. 27844030 PMC5099530
  5. [6]Hack RJ, Cerfontaine MN, Gravesteijn G, et al Effect of NOTCH3 EGFr group, sex, and cardiovascular risk factors on CADASIL clinical and neuroimaging outcomes. Stroke. 2022; 53: 3133– 3144. 35862191 PMC9508953
  6. [7]Cho BPH, Nannoni S, Harshfield EL, et al NOTCH3 variants are more common than expected in the general population and associated with stroke and vascular dementia: An analysis of 200 000 participants. J Neurol Neurosurg Psychiatry. 2021; 92(7): 694– 701. PMC8223663 33712516
  7. [8]Adib-Samii P, Brice G, Martin RJ, Markus HS. Clinical spectrum of CADASIL and the effect of cardiovascular risk factors on phenotype: Study in 200 consecutively recruited individuals. Stroke. 2010; 41: 630– 634. 20167921
  8. [9]Mukai M, Mizuta I, Watanabe-Hosomi A, et al Genotype–phenotype correlations and effect of mutation location in Japanese CADASIL patients. J Hum Genet. 2020; 65: 637– 646. 32277177
  9. [10]Min JY, Park SJ, Kang EJ, Hwang SY, Han SH. Mutation spectrum and genotype–phenotype correlations in 157 Korean CADASIL patients: A multicenter study. Neurogenetics. 2022; 23(1): 45– 58. 34741685
  10. [11]Bycroft C, Freeman C, Petkova D, et al The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018; 562: 203– 209. 30305743 PMC6786975
  11. [12]Karczewski KJ, Francioli LC, Tiao G, et al The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020; 581: 434– 443. 32461654 PMC7334197
  12. [13]Carey DJ, Fetterolf SN, Davis FD, et al The Geisinger MyCode community health initiative: An electronic health record-linked biobank for precision medicine research. Genet Med. 2016; 18: 906– 913. 26866580 PMC4981567
  13. [14]Bianchi S, Zicari E, Carluccio A, et al CADASIL in central Italy: A retrospective clinical and genetic study in 229 patients. J Neurol. 2015; 262: 134– 141. 25344745
  14. [15]Chen S, Ni W, Yin XZ, et al Clinical features and mutation spectrum in Chinese patients with CADASIL: A multicenter retrospective study. CNS Neurosci Ther. 2017; 23: 707– 716. 28710804 PMC6492642
  15. [16]Moreton FC, Razvi SS, Davidson R, Muir KW. Changing clinical patterns and increasing prevalence in CADASIL. Acta Neurol Scand. 2014; 130: 197– 203. 24840674
  16. [17]Opherk C, Peters N, Herzog J, Luedtke R, Dichgans M. Long-term prognosis and causes of death in CADASIL: A retrospective study in 411 patients. Brain. 2004; 127(Pt 11): 2533– 2539. 15364702
  17. [18]Hu Y, Sun Q, Zhou Y, et al NOTCH3 variants and genotype–phenotype features in Chinese CADASIL patients. Front Genet. 2021; 12: 705284. PMC8320595 34335700
  18. [19]Liu X, Zuo Y, Sun W, et al The genetic spectrum and the evaluation of CADASIL screening scale in Chinese patients with NOTCH3 mutations. J Neurol Sci. 2015; 354(1–2): 63– 69. 25982499
  19. [20]Singhal S, Bevan S, Barrick T, Rich P, Markus HS. The influence of genetic and cardiovascular risk factors on the CADASIL phenotype. Brain. 2004; 127(Pt 9): 2031– 2038. 15229130
  20. [21]Liao YC, Hsiao CT, Fuh JL, et al Characterization of CADASIL among the Han Chinese in Taiwan: Distinct genotypic and phenotypic profiles. PLoS One. 2015; 10: e0136501. PMC4550240 26308724
  21. [22]Kim YE, Yoon CW, Seo SW, et al Spectrum of NOTCH3 mutations in Korean patients with clinically suspicious cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy. Neurobiol Aging. 2014; 35: 726.e1– 6. 24139282
  22. [23]Joutel A, Vahedi K, Corpechot C, et al Strong clustering and stereotyped nature of Notch3 mutations in CADASIL patients. Lancet. 1997; 350: 1511– 1515. 9388399
  23. [24]Wardlaw JM, Smith EE, Biessels GJ, et al Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 2013; 12: 822– 838. 23867200 PMC3714437
  24. [26]Duering M, Csanadi E, Gesierich B, et al Incident lacunes preferentially localize to the edge of white matter hyperintensities: Insights into the pathophysiology of cerebral small vessel disease. Brain. 2013; 136(Pt 9): 2717– 2726. 23864274
  25. [27]van den Brink H, Kopczak A, Arts T, et al Zooming in on cerebral small vessel function in small vessel diseases with 7 T MRI: Rationale and design of the “ZOOM@SVDs” study. Cereb Circ Cogn Behav. 2021; 2: 100013. PMC9616370 36324717
  26. [28]Griffanti L, Zamboni G, Khan A, et al BIANCA (Brain intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities. Neuroimage. 2016; 141: 191– 205. 27402600 PMC5035138
  27. [29]Gravesteijn G, Hack RJ, Mulder AA, et al NOTCH3 Variant position is associated with NOTCH3 aggregation load in CADASIL vasculature. Neuropathol Appl Neurobiol. 2022; 48. PMC9291091 34297860
  28. [30]Gravesteijn G, Dauwerse JG, Overzier M, et al Naturally occurring NOTCH3 exon skipping attenuates NOTCH3 protein aggregation and disease severity in CADASIL patients. Hum Mol Genet. 2020; 29: 1853– 1863. 31960911 PMC7372551
  29. [32]Pettersen EF, Goddard TD, Huang CC, et al UCSF Chimerax: Structure visualization for researchers, educators, and developers. Protein Sci. 2021; 30: 70– 82. 32881101 PMC7737788
  30. [33]McNeish D, Stapleton LM. Modeling clustered data with very few clusters. Multivariate Behav Res. 2016; 51: 495– 518. 27269278
  31. [34]Chabriat H, Herve D, Duering M, et al Predictors of clinical worsening in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy: Prospective cohort study. Stroke. 2016; 47: 4– 11. 26578659
  32. [36]Hack RJ, Gravesteijn G, Cerfontaine MN, et al Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy family members with a pathogenic NOTCH3 variant can have a normal brain magnetic resonance imaging and skin biopsy beyond age 50 years. Stroke. 2022; 53: 1964– 1974. 35300531 PMC9126263
  33. [37]Monet-Lepretre M, Haddad I, Baron-Menguy C, et al Abnormal recruitment of extracellular matrix proteins by excess Notch3 ECD: A new pathomechanism in CADASIL. Brain. 2013; 136(Pt 6): 1830– 1845. 23649698 PMC3673461
  34. [38]Kast J, Hanecker P, Beaufort N, et al Sequestration of latent TGF-beta binding protein 1 into CADASIL-related Notch3-ECD deposits. Acta Neuropathol Commun. 2014; 2: 96. 25190493 PMC4243959
  35. [39]Zellner A, Scharrer E, Arzberger T, et al CADASIL brain vessels show a HTRA1 loss-of-function profile. Acta Neuropathol. 2018; 136: 111– 125. 29725820
  36. [40]Young KZ, Lee SJ, Zhang X, et al NOTCH3 is non-enzymatically fragmented in inherited cerebral small-vessel disease. J Biol Chem. 2020; 295: 1960– 1972. 31901894 PMC7029126
  37. [41]Zhang X, Lee SJ, Wang MM. Hydrolysis of a second Asp–Pro site at the N-terminus of NOTCH3 in inherited vascular dementia. Sci Rep. 2021; 11: 17246. 34446744 PMC8390697
  38. [42]Arboleda-Velasquez JF, Manent J, Lee JH, et al Hypomorphic notch 3 alleles link notch signaling to ischemic cerebral small-vessel disease. Proc Natl Acad Sci U S A. 2011; 108: E128– E135. 21555590 PMC3102344
  39. [43]Peters N, Opherk C, Zacherle S, Capell A, Gempel P, Dichgans M. CADASIL-associated Notch3 mutations have differential effects both on ligand binding and ligand-induced Notch3 receptor signaling through RBP-Jk. Exp Cell Res. 2004; 299: 454– 464. 15350543
  40. [44]Cognat E, Baron-Menguy C, Domenga-Denier V, et al Archetypal Arg169Cys mutation in NOTCH3 does not drive the pathogenesis in cerebral autosomal dominant arteriopathy with subcortical infarcts and leucoencephalopathy via a loss-of-function mechanism. Stroke. 2014; 45: 842– 849. 24425116
  41. [45]Joutel A, Monet M, Domenga V, Riant F, Tournier-Lasserve E. Pathogenic mutations associated with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy differently affect Jagged1 binding and Notch3 activity via the RBP/JK signaling pathway. Am J Hum Genet. 2004; 74: 338– 347. 14714274 PMC1181931
  42. [46]Baron-Menguy C, Domenga-Denier V, Ghezali L, Faraci FM, Joutel A. Increased Notch3 activity mediates pathological changes in structure of cerebral arteries. Hypertension. 2017; 69: 60– 70. 27821617 PMC5145742

Cited by

Sorted by citations

Authors
Hack, R. J., Gravesteijn, G., Cerfontaine, M. N., Santcroos, M. A., Gatti, L., Kopczak, A., Bersano, A., Duering, M., Rutten, J. W., & Lesnik Oberstein, S. A. J.
Affiliations
  • Leiden University Medical Center (NL)
  • Fondazione IRCCS Istituto Neurologico Carlo Besta (IT)
  • LMU Klinikum (DE)
  • University of Basel (CH)
  • Department of Clinical Genetics, Leiden University Medical Center , 2333 ZA Leiden , The Netherlands
ORCID
Venue
Brain
Publisher
Oxford University Press (OUP)
Published
2022-12-20
Volume
146
Issue
7
Pages
2913-2927
ISSN
  • 0006-8950 (ISSN-L)
  • 1460-2156
DOI
10.1093/brain/awac486
PMC
PMC10316769
OpenAlex
W4312019058
Cited by
60
References
5
Citations by year
2026: 19 · 2025: 22 · 2024: 15 · 2023: 4
FWCI
5.40
Type
journal article
Language
en
Open access
Licence
Locations
  • Brain (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • Leiden Repository (Leiden University) (submittedVersion, cc-by-nc)
Funders
  • Netherlands Organisation for Health Research and Development (91717325)
  • The Netherlands Brain Foundation (HA2016-02-03)
Topics
  • Health Sciences › Medicine › Neurology › Cerebrovascular and genetic disorders
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Genetic Neurodegenerative Diseases
  • Life Sciences › Neuroscience › Neurology › Neurological Disease Mechanisms and Treatments
Keywords
epidermal growth factor-like repeats, CADASIL, risk stratification, genotype-phenotype correlation, stroke risk, cerebral small vessel disease, personalized risk prediction, leukoencephalopathy, white matter hyperintensities, cerebral microbleeds
MeSH terms
Receptor, Notch3, Epidermal Growth Factor, Humans, Magnetic Resonance Imaging, Mutation, Risk Assessment, Stroke, CADASIL, Receptors, Notch

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review