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Data from Acquired <i>RAD51C</i> Promoter Methylation Loss Causes PARP Inhibitor Resistance in High-Grade Serous Ovarian Carcinoma

2023· preprint· en· W4361826965 on OpenAlexfundno aff
Ksenija Nešić, Olga Kondrashova, Rachel M. Hurley, Cordelia D. McGehee, Cassandra J. Vandenberg, Gwo‐Yaw Ho, Elizabeth Lieschke, Genevieve Dall, Nirashaa Bound, Kristy Shield‐Artin, Marc R. Radke, Ashan Musafer, Zi Qing Chai, Mohammad Reza Eftekhariyan Ghamsari, Maria I. Harrell, Damien Kee, Inger Olesen, Orla McNally, Nadia Traficante, Australian Ovarian Cancer Study, Anna DeFazio, David D.L. Bowtell, Elizabeth M. Swisher, S. John Weroha, Kátia Nones, Nicola Waddell, Scott H. Kaufmann, Alexander Dobrovic, Matthew J. Wakefield, Clare L. Scott

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsnot available
FundersCancer Council TasmaniaCancer Council VictoriaCancer Council South AustraliaCancer Research InstituteAstraZenecaNational Health and Medical Research CouncilEntertainment Industry FoundationStand Up To CancerMedical Research CouncilPeter MacCallum FoundationAstraZeneca AustraliaMedical Research and Materiel CommandEisaiOvarian Cancer Research FundCancer AustraliaNational Cancer InstituteAustralian Cancer Research FoundationNational Breast Cancer FoundationClovis OncologyCancer Council NSWOvarian Cancer AustraliaSierra OncologyState Government of VictoriaMayo Foundation for Medical Education and ResearchNational Ovarian Cancer CoalitionAmerican Association for Cancer ResearchAustralian GovernmentOvarian Cancer Research Fund AllianceNational Institutes of Health
KeywordsCancer researchDNA methylationMethylationGene silencingCpG siteBiologyMolecular biologyGeneGeneticsGene expression

Abstract

fetched live from OpenAlex

Abstract In high-grade serous ovarian carcinoma (HGSC), deleterious mutations in DNA repair gene RAD51C are established drivers of defective homologous recombination and are emerging biomarkers of PARP inhibitor (PARPi) sensitivity. RAD51C promoter methylation (meRAD51C) is detected at similar frequencies to mutations, yet its effects on PARPi responses remain unresolved. In this study, three HGSC patient-derived xenograft (PDX) models with methylation at most or all examined CpG sites in the RAD51C promoter show responses to PARPi. Both complete and heterogeneous methylation patterns were associated with RAD51C gene silencing and homologous recombination deficiency (HRD). PDX models lost meRAD51C following treatment with PARPi rucaparib or niraparib, where a single unmethylated copy of RAD51C was sufficient to drive PARPi resistance. Genomic copy number profiling of one of the PDX models using SNP arrays revealed that this resistance was acquired independently in two genetically distinct lineages. In a cohort of 12 patients with RAD51C-methylated HGSC, various patterns of meRAD51C were associated with genomic “scarring,” indicative of HRD history, but exhibited no clear correlations with clinical outcome. Differences in methylation stability under treatment pressure were also observed between patients, where one HGSC was found to maintain meRAD51C after six lines of therapy (four platinum-based), whereas another HGSC sample was found to have heterozygous meRAD51C and elevated RAD51C gene expression (relative to homozygous meRAD51C controls) after only neoadjuvant chemotherapy. As meRAD51C loss in a single gene copy was sufficient to cause PARPi resistance in PDX, methylation zygosity should be carefully assessed in previously treated patients when considering PARPi therapy. Significance: Homozygous RAD51C methylation is a positive predictive biomarker for sensitivity to PARP inhibitors, whereas a single unmethylated gene copy is sufficient to confer resistance.

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)0.001
metaresearch head score (Gemma)0.001
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: Dataset · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0240.005

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 head0.086
GPT teacher head0.328
Teacher spread0.242 · 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
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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