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Record W2890347012 · doi:10.1016/j.isci.2018.09.012

A Potent and Selective ULK1 Inhibitor Suppresses Autophagy and Sensitizes Cancer Cells to Nutrient Stress

2018· article· en· W2890347012 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueiScience · 2018
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsGilead Sciences (Canada)
FundersNational Cancer InstituteNational Institutes of Health
KeywordsAutophagyULK1Cancer cellCell biologyChemistryKinaseRegulatorCancer researchKRASCancerBiologyProtein kinase ABiochemistryAMPKMutationApoptosisGene

Abstract

fetched live from OpenAlex

In response to stress, cancer cells generate nutrients and energy through a cellular recycling process called autophagy, which can promote survival and tumor progression. Accordingly, autophagy inhibition has emerged as a potential cancer treatment strategy. Inhibitors targeting ULK1, an essential and early autophagy regulator, have provided proof of concept for targeting this kinase to inhibit autophagy; however, these are limited individually in their potency, selectivity, or cellular activity. In this study, we report two small molecule ULK1 inhibitors, ULK-100 and ULK-101, and establish superior potency and selectivity over a noteworthy published inhibitor. Moreover, we show that ULK-101 suppresses autophagy induction and autophagic flux in response to different stimuli. Finally, we use ULK-101 to demonstrate that ULK1 inhibition sensitizes KRAS mutant lung cancer cells to nutrient stress. ULK-101 represents a powerful molecular tool to study the role of autophagy in cancer cells and to evaluate the therapeutic potential of autophagy inhibition.

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.

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 score0.046
Threshold uncertainty score0.347

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 head0.009
GPT teacher head0.285
Teacher spread0.276 · 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