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Record W2950587411 · doi:10.1128/mcb.00105-19

<i>RB1</i> Deletion in Retinoblastoma Protein Pathway-Disrupted Cells Results in DNA Damage and Cancer Progression

2019· article· en· W2950587411 on OpenAlexafffund
Aren E. Marshall, Michael V. Roes, Daniel T. Passos, Megan C. DeWeerd, Andrea C. Chaikovsky, Julien Sage, Christopher J. Howlett, Frederick A. Dick

Bibliographic record

VenueMolecular and Cellular Biology · 2019
Typearticle
Languageen
FieldMedicine
TopicOcular Oncology and Treatments
Canadian institutionsChildren’s Health Research InstituteLawson Health Research InstituteWestern University
FundersNational Cancer InstituteNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaCanadian Institutes of Health ResearchCancer Research Society
KeywordsBiologyRetinoblastomaGenome instabilityDNA damageCarcinogenesisDNA repairMutationCancerRetinoblastoma proteinCancer researchCancer cellHomologous recombinationMutantGeneticsMolecular biologyCell biologyCell cycleDNAGene

Abstract

fetched live from OpenAlex

Proliferative control in cancer cells is frequently disrupted by mutations in the retinoblastoma protein (RB) pathway. Intriguingly, RB1 mutations can arise late in tumorigenesis in cancer cells whose RB pathway is already compromised by another mutation. In this study, we present evidence for increased DNA damage and instability in cancer cells with RB pathway defects when RB1 mutations are induced. We generated isogenic RB1 mutant genotypes with CRISPR/Cas9 in a number of cell lines. Cells with even one mutant copy of RB1 have increased basal levels of DNA damage and increased mitotic errors. Elevated levels of reactive oxygen species as well as impaired homologous recombination repair underlie this DNA damage. When xenografted into immunocompromised mice, RB1 mutant cells exhibit an elevated propensity to seed new tumors in recipient lungs. This study offers evidence that late-arising RB1 mutations can facilitate genome instability and cancer progression that are beyond the preexisting proliferative control deficit.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.566

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 head0.006
GPT teacher head0.260
Teacher spread0.255 · 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

Citations39
Published2019
Admission routes2
Has abstractyes

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