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Record W3097937986 · doi:10.1093/narcan/zcaa033

APOBEC3 enzymes mediate efficacy of cisplatin and are epistatic with base excision repair and mismatch repair in platinum response

2020· article· en· W3097937986 on OpenAlexaff
Kayla L. Conner, Asra N. Shaik, Katie A Marshall, Ashley M. Floyd, Elmira Ekinci, Jacob Lindquist, Akshada Sawant, Wen Lei, Madison B. Adolph, Linda Chelico, Sachini U. Siriwardena, Ashok S. Bhagwat, Seongho Kim, Michele L. Coté, Steve M. Patrick

Bibliographic record

VenueNAR Cancer · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsUniversity of Saskatchewan
FundersNational Cancer InstituteNational Institutes of HealthSusan G. Komen for the Cure
KeywordsCisplatinBase excision repairDNA mismatch repairDNA repairUracilCancer researchNucleotide excision repairDNA damageChemistryDNABiologyGeneticsBiochemistryChemotherapy

Abstract

fetched live from OpenAlex

Identifying the mechanisms mediating cisplatin response is essential for improving patient response. Previous research has identified base excision repair (BER) and mismatch repair (MMR) activity in sensitizing cells to cisplatin. Cisplatin forms DNA adducts including interstrand cross-links (ICLs) that distort the DNA helix, forcing adjacent cytosines to become extrahelical. These extrahelical cytosines provide a substrate for cytosine deaminases. Herein, we show that APOBEC3 (A3) enzymes are capable of deaminating the extrahelical cytosines to uracils and sensitizing breast cancer cells to cisplatin. Knockdown of A3s results in resistance to cisplatin and induction of A3 expression in cells with low A3 expression increases sensitivity to cisplatin. We show that the actions of A3s are epistatic with BER and MMR. We propose that A3-induced cytosine deamination to uracil at cisplatin ICLs results in repair of uracils by BER, which blocks ICL DNA repair and enhances cisplatin efficacy and improves breast cancer outcomes.

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 score0.071
Threshold uncertainty score0.560

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 head0.013
GPT teacher head0.254
Teacher spread0.241 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

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