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Abstract IA001: Exploiting pathway activation as a new form of synthetic lethality

2024· article· en· W4399505334 on OpenAlexaboutno aff
William R. Sellers

Bibliographic record

VenueMolecular Cancer Therapeutics · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related gene regulation
Canadian institutionsnot available
Fundersnot available
KeywordsSynthetic lethalityKRASCancerCancer researchContext (archaeology)BiologyGenetic screenNeuroblastoma RAS viral oncogene homologGene knockoutMAPK/ERK pathwayPTENGeneticsDNA repairGenePhenotypeKinasePI3K/AKT/mTOR pathwaySignal transductionColorectal cancer

Abstract

fetched live from OpenAlex

Abstract Targeting activated oncogenes is an effective treatment strategy across many cancers now including therapeutics targeting KRAS. Genetic events including DNA damage deficiencies and tumor suppressor mutations require alternative strategies and the concept of synthetic lethality has been applied to these alterations. PARPi inhibitors were a founding member of this class of therapeutics demonstrating enhanced activity in the setting of BRCA-deficiency. By applying large-scale loss-of-function approaches1 we and other discovered the vulnerability to PRMT5 and WRN inhibition imposed by co-deletions of MTAP and CDKN2A, and the MSI+ genotype respectively2,3. Such inhibitors are now in clinical trials. To explore the potential for paralogous genes to act as synthetic lethal gene pairs we enacted dual knockout screens. These early efforts led to the discovery of DUSP4/6 paralog dependence in the setting of BRAF and NRAS mutations in melanoma4. Intriguingly the loss of DUSP4/6 impaired cancer cell viability through the increased activation of ERK highlighting the susceptibility of cancers to pathway activation in addition to the more common sensitivity to pathway inhibition. This inappropriate activation of the ERK signaling pathway, the conflict between EGFR and KRAS activation, the synthetic lethality enacted by TRIM8 knockouts, and the effects of AR agonists on prostate cancer viability, points to a wider than expected vulnerability of cancer to inappropriate gene activation. To systematically identify context-specific gene activation induced lethalities in cancer, we developed methods for enacting gain-of-function perturbations across ∼500 barcoded cancer cell lines. With this approach, we queried the pan-cancer vulnerability landscape upon activating 10 key cancer pathway revealing activation dependencies in MAPK and PI3K pathways. Notably, we discovered novel pathway hyperactivation dependencies in subsets of APC-mutant colorectal cancers where further activation of the WNT pathway by APC knockdown or direct β-catenin overexpression led to robust antitumor effects in xenograft and patient-derived organoid models. These latter discoveries paradoxically point to the residual activity of the APC ubiquitin-ligase complex as a target in APC-mutant CRC5. 1. McDonald, E. R., 3rd et al. Project DRIVE: A Compendium of Cancer Dependencies and Synthetic Lethal Relationships Uncovered by Large-Scale, Deep RNAi Screening. Cell 170, 577-592.e10 (2017). 2. Mavrakis, K. J. et al. Disordered methionine metabolism in MTAP/CDKN2A-deleted cancers leads to dependence on PRMT5. Science 351, 1208–1213 (2016). 3. Chan, E. M. et al. WRN helicase is a synthetic lethal target in microsatellite unstable cancers. Nature 568, 551–556 (2019). 4. Ito, T. et al. Paralog knockout profiling identifies DUSP4 and DUSP6 as a digenic dependence in MAPK pathway-driven cancers. Nat. Genet. 53, 1664–1672 (2021). 5. Chang, L. et al. Systematic profiling of conditional pathway activation identifies contextdependent synthetic lethalities. Nat. Genet. 55, 1709–1720 (2023). Citation Format: William R. Sellers. Exploiting pathway activation as a new form of synthetic lethality [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr IA001.

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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.024
GPT teacher head0.295
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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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