Abstract MS1-1: Targeting DNA repair deficiency in triple negative breast cancers (TNBC) with G-quadruplex stabilisers
Bibliographic record
Abstract
Abstract Triple negative breast cancers (TNBC) represent a heterogeneous group of patients where pathway targeted interventions are lacking. We have recently identified new subgroups of TNBC tumours based on patterns of genomic instability, suggesting that deficiencies in DNA repair or genome maintenance could be exploited in specific subgroups of TNBC patients. We recently discovered synthetic lethal activity for small molecules that bind and stabilise G-quadruplex (G4) structures in the human genome. Stabilized G4 structures are considered "at risk" during DNA replication, leading to single strand and double strand breaks in the absence of DNA repair. This has specific application in cancers with DNA repair deficiency. We have shown that loss of homologous recombination repair capacity in breast cancers is associated with ∼ 1 log order increase in sensitivity to G4 binders. Moreover this activity has been observed in platinum pre-treated tumours, and in PARP insensitive tumours. Recently through CRISPR screening approaches we have defined additional DNA repair lesions that confer synthetic lethality to G4 binders. This mechanism of G4 stabilisation is now being tested in a phase 1/2 clinical trial of CX5461 in breast cancer patients. Citation Format: Aparicio S. Targeting DNA repair deficiency in triple negative breast cancers (TNBC) with G-quadruplex stabilisers [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr MS1-1.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".