Tau Mutations as a Novel Risk Factor for Cancer—Letter
Notice bibliographique
Résumé
Rossi and colleagues (2018) recently reported an increased cancer frequency in 15 families with frontotemporal lobar degeneration (FTLD) and mutations in the microtubule-associated protein tau (MAPT) gene (15% of 162 subjects of MAPT FTLD kindreds; 9% of 717 controls; ref. 1). They concluded that MAPT mutations raise the risk for cancer nearly fourfold (multivariate Cox proportional hazard model: HR = 3.72); some mutations may be less cancer predisposing than others (e.g., due to differential microtubule-binding capacity or DNA chaperone ability of mutated tau). The MAPT P301L mutation leads to increased tau phosphorylation and reduces microtubule polymerization. Microtubules are critical for mitotic spindle formation. The MAPT N279K mutation affects exon 10 splicing, resulting in increased 4R tau (2). Both mutations result in increased aneuploidy and apoptosis of neuronal and glia cells (3).The “Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects” (LEFFTDS) study is a multicenter study of symptomatic and asymptomatic subjects of families with MAPT, granulin precursor (GRN), or C9orf72 mutations, conducted in accordance with the Declaration of Helsinki and approved by Institutional Review Boards of all sites. Written informed consent was obtained from all participants. Currently, 294 LEFFTDS subjects have data available on cancer and mutation status (50.6 ± 14.0 years, 155 f; 170/294 mutation carriers: 51.0 ± 14.4 years, 89 f). Sixty-three subjects (45.8 ± 13.2 years, 33 f) have MAPT mutations (N279K: n = 5; P301L: n = 19); 1/63 reported possible cancer (melanoma). Forty-four subjects (57.7 ± 3.5 years, 19 f) have GRN mutations; 2 of 44 reported cancer (prostate and colon). Sixty-three subjects (51.6 ± 14.4 years, 37 f) have the C9orf72 repeat expansion; 8 of 63 reported cancer (colon, nonmelanoma skin carcinoma, melanoma, and breast cancer). Finally, 124 subjects from families with MAPT (n = 41), GRN (n = 40), or C9orf72 (n = 43) mutations (50.0 ± 13.3 years, 66 f) had no mutation in these genes; 10 of 124 reported cancer (CLL, breast, C9orf72 kindreds; colon, skin, ovarian, breast, thyroid, GRN kindreds; 1 report of breast cancer, MAPT kindred). Thus, we found the lowest cancer risk in MAPT mutation carriers.Furthermore, we are not aware of a single report of cancer in a large American family with pallido-ponto-nigral degeneration (PPND) due to the N279K MAPT mutation. This family (n = 332 individuals; 65 N279K MAPT mutation carriers; 60 symptomatic) has been closely followed since 1987; the phenotype consists of rapidly progressive parkinsonism and frontotemporal dementia (4). Our letter thus reports mainly on individuals harboring the MAPT N279K mutation (PPND family), whereas Rossi and colleagues (2018) included eight families with MAPT P301L mutations. Differential MAPT mutations may contribute differentially to cancer risk.See the Response, p. 6525A.B. Deutschländer reports receiving a commercial research grant from Allergan Educational Grant. H.J. Rosen reports receiving a commercial research grant from Biogen Pharmceuticals and is a consultant/advisory board member for Ionis Pharmaceuticals and Wave Pharmaceuticals. A.L. Boxer reports receiving a commercial research grant from Genentech, Roche, Biogen, BMS, Lilly and has ownership interest (including stocks and patents) in Alector, Aeton Therapeutics. He is a consultant/advisory board member for Abbvie, Arkuda, Merck, Novartis, Samumed, Toyama, UCB, Asceneuro, and Wave. No potential conflicts of interest were disclosed by the other authors.Danielle Brushaber1, Giovanni Coppola2, Bradford Dickerson3, Jamie Fong4, Tatiana Foroud5, Leah Forsberg1, Ralitza Gavrilova1, Nupur Ghoshal6, Jill Goldman7, Jon Graff-Radford1, Neill Graff-Radford8, Murray Grossman9, Hilary W. Heuer4, Ging-Yuek R. Hsiung10, Edward D. Huey7, David Irwin9, David Jones1, Kejal Kantarci1, Anna Karydas4, David Knopman1, John Kornak4, Joel Kramer4, Walter Kremers1, Walter Kukull11, Maria Lapid1, Diane Lucente3, Ian Mackenzie10, Scott McGinnis3, Bruce Miller4, Len Petrucelli8, Rosa Rademakers8, Eliana M. Ramos2, Katherine Rankin4, Katya Rascovsky9, Les Shaw9, Jeremy Syrjanen1, Joanne Taylor4, Arthur Toga12, John Trojanowski9, Sandra Weintraub13, and Bonnie Wong31Mayo Clinic, Rochester, Minnesota.2University of California, Los Angeles (UCLA), Los Angeles, California.3Harvard University/MGH, Boston, Massachusetts.4University of California, San Francisco (UCSF), San Francisco, California.5National Cell Repository for Alzheimer's Disease (NCRAD), Indiana University, Indianapolis, Indianapolis6Washington University, St. Louis, Missouri.7Columbia University, New York, New York.8Mayo Clinic, Jacksonville, Florida.9University of Pennsylvania, Philadelphia, Philadelphia.10University of British Columbia, Vancouver, British Columbia, Canada.11National Alzheimer Coordinating Center (NACC), University of Washington, Seattle, Washington.12Laboratory of Neuroimaging (LONI), USC, Los Angeles, California.13Northwestern University, Chicago, Illinois.The LEFFTDS study is supported by the following NIH grants: U01 AG045390-01A1, U54 NS092089, U24 AG021886, U01 AG016976, and P50 NS072187 (to B. Boeve, H. Rosen, A. Boxer, and Z.K. Wszolek).
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,011 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».