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Record W2522885072 · doi:10.1038/ncomms12918

The DNA cytosine deaminase APOBEC3H haplotype I likely contributes to breast and lung cancer mutagenesis

2016· article· en· W2522885072 on OpenAlexafffund
Gabriel J. Starrett, Elizabeth M. Luengas, Jennifer L. McCann, Diako Ebrahimi, Nuri A. Temiz, Robin P. Love, Yuqing Feng, Madison B. Adolph, Linda Chelico, Emily K. Law, Michael A. Carpenter, Reuben S. Harris

Bibliographic record

VenueNature Communications · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsUniversity of Saskatchewan
FundersU.S. Department of DefenseUniversity of MinnesotaHoward Hughes Medical InstituteNatural Sciences and Engineering Research Council of CanadaNational Cancer InstituteNational Science Foundation
KeywordsAPOBECMutagenesisCytosine deaminaseGeneticsBiologyMutationCancer researchGeneCytosineCancerGenome

Abstract

fetched live from OpenAlex

Cytosine mutations within TCA/T motifs are common in cancer. A likely cause is the DNA cytosine deaminase APOBEC3B (A3B). However, A3B-null breast tumours still have this mutational bias. Here we show that APOBEC3H haplotype I (A3H-I) provides a likely solution to this paradox. A3B-null tumours with this mutational bias have at least one copy of A3H-I despite little genetic linkage between these genes. Although deemed inactive previously, A3H-I has robust activity in biochemical and cellular assays, similar to A3H-II after compensation for lower protein expression levels. Gly105 in A3H-I (versus Arg105 in A3H-II) results in lower protein expression levels and increased nuclear localization, providing a mechanism for accessing genomic DNA. A3H-I also associates with clonal TCA/T-biased mutations in lung adenocarcinoma suggesting this enzyme makes broader contributions to cancer mutagenesis. These studies combine to suggest that A3B and A3H-I, together, explain the bulk of 'APOBEC signature' mutations in cancer.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.295
Teacher spread0.288 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations170
Published2016
Admission routes2
Has abstractyes

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