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Record W2893692730 · doi:10.1002/gcc.22685

Clonal evolution through genetic bottlenecks and telomere attrition: Potential threats to in vitro data reproducibility

2018· review· en· W2893692730 on OpenAlexafffund
David Gisselsson, Daniel Lichtenzstejn, Polina Kachko, Jenny Karlsson, Esther Manor, Sabine Mai

Bibliographic record

VenueGenes Chromosomes and Cancer · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity of ManitobaResearch Institute in Oncology and HematologyCancerCare Manitoba
FundersCanadian Institutes of Health ResearchCancerCare Manitoba FoundationVetenskapsrådetBarncancerfondenCrafoordska StiftelsenCancerfondenGunnar Nilssons Cancerstiftelse
KeywordsTelomereBiologyPopulationIn vitroGeneticsCell cultureAttritionMutationGenotypeComputational biologyDNAMedicineGene

Abstract

fetched live from OpenAlex

Tissue cultures of immortalized human cells, also known as established cell lines, are broadly accessible and cost-efficient tools for biomedical research. We here review potential genetic sources of systematic error in cell line experiments due to clonal evolution in vitro. In particular, the authors highlight alterations in telomere function over prolonged culture and population bottlenecks, respectively, as two commonly overlooked phenomena that can result in significant alterations in cell line genotypes over just one or a few passages in vitro. These alterations may include changes in mutation status of oncogenes and large scale chromosomal imbalances. We introduce a simple list of factors to be avoided in order to reduce the risk of data misinterpretation due to clonal evolution, including unacknowledged in vitro selection pressures, prolonged culture per se, harsh population size reductions, experiments at early phases after establishment, and the employment of cell lines not sufficiently analyzed by high resolution genetic techniques.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.057
GPT teacher head0.347
Teacher spread0.290 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations16
Published2018
Admission routes2
Has abstractyes

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