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Record W2804079254 · doi:10.1016/j.stem.2018.04.020

Fixation and Spread of Somatic Mutations in Adult Human Colonic Epithelium

2018· article· en· W2804079254 on OpenAlexfundno aff
Anna M. Nicholson, Cora Olpe, Alice Hoyle, Ann-Sofie Thorsen, Teja Rus, Mathilde Colombé, Roxanne Brunton-Sim, Richard Kemp, Kate Marks, Philip Quirke, Shalini Malhotra, Rogier ten Hoopen, Ashraf E.K. Ibrahim, Cecilia Lindskog, Meagan B. Myers, Barbara L. Parsons, Simon Tavaré, Mark Wilkinson, Edward Morrissey, Douglas J. Winton

Bibliographic record

VenueCell stem cell · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsnot available
FundersMedical Research CouncilBiotechnology and Biological Sciences Research CouncilUniversity of East AngliaNational Institute for Health and Care ResearchMedical Research Council CanadaCancer Research UKCancer Research UK Cambridge Institute, University of CambridgeNIHR Cambridge Biomedical Research CentreNorfolk and Norwich University Hospitals NHS Foundation TrustWellcome Trust
KeywordsBiologyCryptStem cellSomatic cellGeneticsSomatic evolution in cancerGermline mutationMutantAlleleGermlineFixation (population genetics)Cell biologyMutationGeneCancer research

Abstract

fetched live from OpenAlex

We investigated the means and timing by which mutations become fixed in the human colonic epithelium by visualizing somatic clones and mathematical inference. Fixation requires two sequential steps. First, one of approximately seven active stem cells residing within each colonic crypt has to be mutated. Second, the mutated stem cell has to replace neighbors to populate the entire crypt in a process that takes several years. Subsequent clonal expansion due to crypt fission is infrequent for neutral mutations (around 0.7% of all crypts undergo fission in a single year). Pro-oncogenic mutations subvert both stem cell replacement to accelerate fixation and clonal expansion by crypt fission to achieve high mutant allele frequencies with age. The benchmarking of these behaviors allows the advantage associated with different gene-specific mutations to be compared irrespective of the cellular mechanisms by which they are conferred.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.229
Teacher spread0.222 · 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 designObservational
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

Citations126
Published2018
Admission routes1
Has abstractyes

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