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Record W3118051387 · doi:10.3126/jpn.v10i2.29862

A brief overview of homologous recombination deficiency testing in cancers for the ‘Next-Generation’ Pathologist

2020· article· en· W3118051387 on OpenAlexaff
Poornima Vijayan, Luisa Bonilla

Bibliographic record

VenueJournal of Pathology of Nepal · 2020
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsHomologous recombinationGenome instabilityDNA repairHomologous chromosomeSynthetic lethalityCancerMedicineGeneticsNon-allelic homologous recombinationPoly ADP ribose polymeraseCancer researchComputational biologyBioinformaticsBiologyGeneDNADNA damageRecombinationPolymeraseGenetic recombination

Abstract

fetched live from OpenAlex

Genomic instability is one of the hallmarks of cancer, having a crucial role in cancer pathogenesis as well as tumor proliferation. This essential feature is secondary to dysregulation of DNA damage repair pathways. Homologous repair represents the most reliable double-strand break repair mechanism. Homologous recombination deficiency is responsible for generating and perpetuating DNA damage in cancer, posing an opportunity for targeting treatment with poly(ADP-ribose) polymerase inhibitors through ‘synthetic lethality’, as well as platinum-based agents. Comprehensive genomic analysis has made it possible to discover molecular biomarkers that assist in the identification of Homologous recombination deficient tumors, allowing for the expansion of such treatment strategies to various other malignancies. Leveraging the improvement of genomic analysis methods to be more efficient in identifying Homologous recombination deficiency is crucial in the advancement of cancer care. The current review highlights the current strategies for Homologous recombination deficiency detection, clinical implications, limitations, and applicability.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.705
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.322
GPT teacher head0.385
Teacher spread0.063 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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