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Abstract PR002: EP300 loss of function is a pan-cancer sensitizer to BET inhibition

2024· article· en· W4405182221 on OpenAlexaffabout
Tomas Babak, Peter Truesdell, Greg Vontz, Peter Heinecke, Doris Coto Villa, Meiou Dai

Bibliographic record

VenueMolecular Cancer Therapeutics · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Degradation and Inhibitors
Canadian institutionsGoogle (Canada)
Fundersnot available
KeywordsSynthetic lethalityCancerCancer researchBiologyLoss functionRegulatorDNA replicationCancer cellGeneGeneticsPhenotypeDNA repair

Abstract

fetched live from OpenAlex

Abstract Genetically targeted therapies have proven to be a successful approach to treating cancer because they inhibit the causal (driver) biology of tumors that is absent in healthy cells. While targeted therapies exist for most oncogenes, only a small fraction of loss-of-function (LoF) tumor suppressor genes (TSGs) have been drugged to date, despite TSG mutations causing 2/3 of all cancer. Here we report a novel 1st-in-class genetically targeted opportunity using BET inhibitors to treat EP300 LoF cancers. Among the hundreds of pharmacogenetic screens that we have conducted, this interaction is similar to PARP-BRCA1/HRD in significance, and among the strongest we have discovered. It is cancer-type agnostic and holds up with all potent BET inhibitors in various pre-clinical models, including PDXs. Mechanistically, we demonstrate that apoptosis induced by DNA catastrophe arising from pre-mature entry into S phase plays a significant role. BET inhibition blocks timely assembly of DNA replication factors, which leads to G1 arrest in healthy cells; cancer cells that lack p300, a key regulator of CDK2, and CDK7, proceed through G1-S before replication deficiencies can be resolved. EP300 loss of function is a causal driver in more than 25,000 new cancer cases per year in the US and there currently are no genetically targeted options for these patients. This presentation will focus on the discovery, characterization, and clinical re-entry plan for our BET inhibitor LFB-190, to treat EP300 LoF solid tumors. Citation Format: Tomas Babak, Peter Truesdell, Greg Vontz, Peter Heinecke, Doris Coto Villa, Meiou Dai. EP300 loss of function is a pan-cancer sensitizer to BET inhibition. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr PR002

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 score0.009
Threshold uncertainty score0.030

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)0.0090.002

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 head0.017
GPT teacher head0.288
Teacher spread0.271 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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