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Record W2118010724 · doi:10.1158/1078-0432.ccr-10-0527

“Contextual” Synthetic Lethality and/or Loss of Heterozygosity: Tumor Hypoxia and Modification of DNA Repair

2010· review· en· W2118010724 on OpenAlexaff
Norman Chan, Robert G. Bristow

Bibliographic record

VenueClinical Cancer Research · 2010
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsSynthetic lethalityLoss of heterozygosityDNA repairCancer researchCarcinogenesisGenome instabilityBiologyHypoxia (environmental)PhenotypeDNA damageDNA mismatch repairContext (archaeology)Tumor hypoxiaDNAGeneticsCancerMedicineChemistryInternal medicineGeneRadiation therapy

Abstract

fetched live from OpenAlex

Hypoxia exists in every solid tumor and is associated with poor prognosis because of both local and systemic therapeutic resistance. Recent studies have focused on the interaction between tumor cell genetics and the dynamic state of oxygenation and metabolism. Hypoxia generates aggressive tumor cell phenotypes in part owing to ongoing genetic instability and a "mutator" phenotype. The latter may be due to suppression of DNA mismatch repair (MMR), nucleotide excision repair (NER), and double-strand break (DSB) repair. We propose a theoretical model in which hypoxia-mediated defects in DNA repair can lead to "contextual loss of heterozygosity" and drive oncogenesis. Additionally, hypoxia-mediated repair defects can be specifically targeted by DNA damaging agents and/or "contextual synthetic lethality" to kill repair-deficient cells and preserve the therapeutic ratio. These proposed concepts support the interrogation of solid tumors to document repair defects in both oxic and hypoxic tumor subregions as a conduit to novel clinical trials within the context of personalized medicine.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.281
GPT teacher head0.514
Teacher spread0.233 · 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 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

Citations121
Published2010
Admission routes1
Has abstractyes

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