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Abstract A27: Assessing somatic tumor-associated RAD51 mutations and screening for novel dominant-interfering RAD51 proteins

2017· article· en· W2604603435 on OpenAlexaboutno aff
Pei Xin Lim, Jeanette Sutherland, Raymond J. Noonan, Alexandra Dananberg, William K. Holloman, Agata Smogorzewska, Maria Jasin

Bibliographic record

VenueMolecular Cancer Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsRAD51BiologyDNA damageDNA repairCancer researchMutantCancerDNAGeneticsGene

Abstract

fetched live from OpenAlex

Abstract The responsiveness of cancer cells to chemotherapy and targeted treatment, as well as the development of resistance, is determined by the state of DNA damage response pathways. Homology-directed repair (HDR) is crucial for error-free repair of DNA double-strand breaks that arise during replication stress or are induced by exogenous genotoxins. Therefore, HDR efficacy status in cancer cells could potentially be utilized as an indicator to predict tumor responsiveness to standard chemotherapies and to poly(ADP-ribose) polymerases (PARP) inhibitors. We are interested in understanding the influence of different levels of functional activity of the HDR pathway on treatment responses. To this end, we have investigated the functional consequence of RAD51 point mutations identified in tumors. RAD51 is an effector of the key tumor suppressor BRCA2 and contributes to genome integrity in actively dividing cells. RAD51 mutations are identified from Memorial Sloan Kettering clinical sequencing cohort (MSK-IMPACT), the Catalogue of Somatic Mutations in Cancer (COSMIC) and the International Fanconi Anemia Registry (IFAR). To understand the implications of these altered residues on RAD51 function in HDR, we tested repair efficiency using a previously established DR-GFP reporter assay in mammalian cells. Of eighteen novel RAD51 mutants examined, ten displayed decreased HDR efficiency, three had wild-type levels of HDR, and five had increased HDR levels. To test whether the HDR-deficient RAD51 mutants lead to sensitivity to genotoxins, we assayed the survival of corn smut U. maydis that expressed these RAD51 mutants after treatment with DNA damaging agents ultraviolet light, diepoxybutane, or methylmethane sulfonate. All ten HDR-defective mutants were found to be sensitive to one or more genotoxins. In addition, six were found to promote sensitivity to DNA damaging agents when they were expressed in wild-type cells—i.e., a dominant negative phenotype. Interestingly, these dominant negative mutants confer selective sensitivity to specific DNA damaging agent(s). The underlying mechanism is still under investigation; nevertheless, these mutations can be used as biomarkers for HDR efficiency and to identify novel interactions of RAD51. The identification of these somatic tumor-associated RAD51 mutants warrants further testing for sensitivity towards chemotherapy drugs such as PARP inhibitors and gemcitabine. Furthermore, a random mutagenesis screening is in progress to identify more novel dominant negative RAD51 mutations. The functional analysis of RAD51 is critical as readouts for HDR efficiency, which impacts personalized chemotherapeutic choices. The result of this study will hopefully expand the benefits of using PARP inhibitors not just on BRCA1/2- and RAD51 paralog-deficient tumors but also on tumors with RAD51 mutations. Citation Format: Pei Xin Lim, Jeanette Sutherland, Raymond Noonan, Alexandra Dananberg, William Holloman, Agata Smogorzewska, Maria Jasin. Assessing somatic tumor-associated RAD51 mutations and screening for novel dominant-interfering RAD51 proteins [abstract]. In: Proceedings of the AACR Special Conference on DNA Repair: Tumor Development and Therapeutic Response; 2016 Nov 2-5; Montreal, QC, Canada. Philadelphia (PA): AACR; Mol Cancer Res 2017;15(4_Suppl):Abstract nr A27.

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 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)0.001
metaresearch head score (Gemma)0.001
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 score0.036
Threshold uncertainty score0.857

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.081
GPT teacher head0.409
Teacher spread0.329 · 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 teacher head, 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

Citations1
Published2017
Admission routes1
Has abstractyes

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