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Record W2899215722 · doi:10.1158/0008-5472.can-18-2313

Tau Mutations as a Novel Risk Factor for Cancer—Letter

2018· letter· en· W2899215722 on OpenAlexaboutno aff
Angela Deutschländer, Bradley F. Boeve, Howard J. Rosen, Adam L. Boxer, Zbigniew K. Wszołek

Bibliographic record

VenueCancer Research · 2018
Typeletter
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeMedical Research CouncilNational Institute on AgingNational Institutes of Health
KeywordsTau proteinFrontotemporal dementiaBiologyMutationCancerGeneticsCancer researchOncologyFrontotemporal lobar degenerationInternal medicineMedicineDementiaGeneAlzheimer's diseaseDisease

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.011
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0110.011
Insufficient payload (model declined to judge)0.0020.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.166
GPT teacher head0.480
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2018
Admission routes1
Has abstractyes

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