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Record W3015673613 · doi:10.1007/s42764-020-00011-0

ATM-deficient lung, prostate and pancreatic cancer cells are acutely sensitive to the combination of olaparib and the ATR inhibitor AZD6738

2020· article· en· W3015673613 on OpenAlexafffund
Nicholas Jette, Suraj Radhamani, Ruiqiong Ye, Yaping Yu, Greydon Arthur, Siddhartha Goutam, Tarek A. Bismar, Mehul Kumar, Pinaki Bose, Steven Yip, Michael Kolinsky, Susan P. Lees‐Miller

Bibliographic record

VenueGenome Instability & Disease · 2020
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsUniversity of Calgary
FundersCumming School of Medicine, University of CalgaryAlberta Cancer FoundationCancer Research Society
KeywordsOlaparibPARP inhibitorCancer researchProstate cancerPoly ADP ribose polymeraseApoptosisPancreatic cancerLung cancerProgrammed cell deathMedicineCancerChemistryInternal medicinePolymeraseBiochemistryDNA

Abstract

fetched live from OpenAlex

Abstract The Ataxia Telangiectasia Mutated (ATM) protein kinase is mutated in several human cancers, presenting potential opportunities for targeted cancer therapy. We previously reported that the poly-ADP-ribose polymerase (PARP) inhibitor olaparib induces transient G2 arrest but not cell death in ATM-deficient lung cancer cells, while the combination of olaparib with the ATM- and Rad3-related (ATR) inhibitor VE-821 induced cell death. Here, we show that combination of olaparib plus the clinically relevant ATR inhibitor AZD6738 also induces cell death in ATM-deficient lung, prostate and pancreatic cancer cells with little effect on their ATM-proficient counterparts. Together, our data suggest that lung, prostate and pancreatic patients whose tumours exhibit loss or inactivation of ATM may benefit from combination of a PARP inhibitor plus an ATR inhibitor.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.263
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2020
Admission routes2
Has abstractyes

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