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Record W4409629766 · doi:10.1158/1538-7445.am2025-1467

Abstract 1467: Revealing therapeutically relevant states of cohesin through dominant-negative mutational mapping of Smc1 as a synthetic lethal anti-cancer approach

2025· article· en· W4409629766 on OpenAlexaff
Elizabeth Stephens, Nigel J. O’Neil, Peter C. Stirling, Philip Hieter

Bibliographic record

VenueCancer Research · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCohesinCancerGeneticsBiologyComputational biologyCancer researchGeneChromosome

Abstract

fetched live from OpenAlex

Abstract Leveraging synthetic lethal interactions is an approach to selectively kill tumor cells. A SL interaction between two genes occurs when the disruption of either gene alone is viable, but the disruption of both genes simultaneously results in cell death. Although targeting SL interactions holds immense therapeutic potential, only PARP inhibitors have translated to the clinic. PARP inhibitors are unique in that they cause a gain-of-function (GoF) change in the PARP protein resulting in DNA trapping and selective cytotoxicity in BRCA1/2-mutated tumors. This study employs a novel method of dominant SL screening that aims to identify GoF protein states, modelled by missense mutations, to guide inhibitor design. Here, we evaluate the potential of the cohesin complex as a drug target. Cohesin is composed of four core subunits (SMC1, SMC3, RAD21, and STAG2) and plays roles in sister chromatid cohesion, DNA damage repair, and chromatin organization. This study shows that mutations in the C-terminal ATPase domain cause dominant effects and cluster in a druggable domain, suggesting this state could be phenocopied therapeutically. Using S. Cerevisiae as a model, dominant SL screening of a random mutagenesis library was used to identify states of the target protein, Smc1, that cause dominant lethality in a cohesin-compromised background. This revealed 17 key residues, all residing in the C-terminal ATPase domain, with specific clustering in the signature motif. Directed SL testing of 11 mutants was performed with a panel of cancer-like backgrounds. These data showed that expression of the mutants cause dominant SL in backgrounds with altered cohesin regulation, mitotic defects and replication deficiencies. Further, cell cycle analysis was performed on select mutants, showing that mutations are causing dominant G2 arrest, leading to SL cell death in a cohesin-compromised background. To test if these mutants rely on cohesin complex formation, we tested reliance on scMcd1(hsRAD21) and Smc3. Overexpressing the cohesin ring rate-limiting factor Mcd1 in conjunction with mutant protein shows worsened phenotypes, while introducing a secondary mutation that disrupts the Smc1-Smc3 interface abolishes dominant lethality. These data suggest that the mutant Smc1 proteins rely on the creation of complexes to elicit their dominant negative properties. Importantly, structural mapping reveals that the hits from the screen are conserved and map to ATP interacting residues on the human structure, revealing a potentially druggable domain. The performed mutational mapping can guide in silico molecular docking to design small molecules that can then be tested for phenocopying of the dominant state for better clinical translation. Citation Format: Elizabeth Stephens, Nigel O’Neil, Peter Stirling, Philip Hieter. Revealing therapeutically relevant states of cohesin through dominant-negative mutational mapping of Smc1 as a synthetic lethal anti-cancer approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1467.

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 score0.001
Threshold uncertainty score0.004

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.073
GPT teacher head0.397
Teacher spread0.324 · 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
Published2025
Admission routes1
Has abstractyes

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