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Record W2040023167 · doi:10.1158/1538-7445.am2014-2382

Abstract 2382: Synthetic lethal killing of RAD54B-deficient colorectal cancer cells by targeting SOD1

2014· article· en· W2040023167 on OpenAlexaff
Babu V. Sajesh, Kirk J. McManus

Bibliographic record

VenueCancer Research · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsResearch Manitoba
Fundersnot available
KeywordsSynthetic lethalitySOD1Cancer cellGene silencingCancerRNA interferenceBiologyCancer researchHomologous recombinationGeneGeneticsDNA repairMutantRNA

Abstract

fetched live from OpenAlex

Abstract Synthetic lethality is a rare genetic interaction that results when two independently viable mutations occur within the same organism/cell and results in cell death. Synthetic lethality is thus a rational approach to identify drug targets that can specifically kill cancer cells harboring somatic mutations in specific genes. RAD54B encodes a protein involved in homologous recombination repair whose expression is normally required to maintain genome integrity. RAD54B is somatically altered in ∼4% of the colorectal cancers and numerous other tumor types including breast, prostate and lung. Accordingly, a synthetic lethal approach designed to uncover novel drug targets that selectively exploit and kill cancer cells harboring defects in RAD54B is highly warranted. In budding yeast, Rad54B is synthetic lethal with Superoxide dismutase 1 (Sod1). We hypothesized that silencing or targeting SOD1 would result in specific killing of RAD54B-deficient cells in a human cancer context. Using a combination of cross-species gene approaches, RNAi and high-content imaging we identified and validated a synthetic lethal interaction between RAD54B and SOD1 in colorectal cancer cells. We demonstrated that silencing SOD1 resulted in specific synthetic lethal killing of RAD54B-deficient colorectal cancer cells while RAD54B-proficient colorectal cancer cells remained viable. Additionally, chemical compounds (e.g. ATTM and 2ME2) that induce reactive oxygen species phenocopied the synthetic lethal interactions observed using RNAi-based approaches. In fact, RAD54B-deficient cells were >10-fold more sensitive to these chemicals when compared to RAD54B-proficient cells. SOD1 is an enzyme responsible for maintaining the levels of superoxide radicals within tolerable limits in cells. We reasoned that targeting SOD1 would lead to excessive superoxide anions, leading to DNA double-strand break and render RAD54B-deficient cells amenable to synthetic lethal killing. To determine if defects in DNA double-strand break repair occurred in these cells, semi-quantitative imaging microscopy was performed and we confirmed the persistence of two surrogate markers of DNA damage, namely γ-H2A.X and 53BP1, following treatment with either ATTM or 2ME2 relative to controls. Finally, we show that apoptosis as reflected by an increase in cleaved Caspase 3, has a key role in the synthetic lethal killing of the RAD54B-deficient cells relative to controls. Together these results indicate that RAD54B and SOD1 are synthetic lethal interactors, and further identify SOD1 as a novel candidate therapeutic target. The pharmacological targeting of SOD1 has implications beyond the colorectal cancer context employed above as RAD54B is altered in many tumor types. Citation Format: Babu V. Sajesh, Kirk McManus. Synthetic lethal killing of RAD54B-deficient colorectal cancer cells by targeting SOD1. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2382. doi:10.1158/1538-7445.AM2014-2382

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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.021
GPT teacher head0.358
Teacher spread0.337 · 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
Published2014
Admission routes1
Has abstractyes

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