Bibliographic record
Abstract
Abstract Genomic instability and defective DNA repair (DDR) are key hallmarks of many currently incurable malignancies, with these often associating with poorer outcomes and more aggressive disease. DDR supports tumor evolution but can generate genomic alterations leading to neoantigen generation from single nucleotide alterations and genomic rearrangements/fusions, which may lead to tumor sculpting by immune responses and tumor cell adaptations to abrogate anticancer immune responses through not only immune checkpoint expression but also the secretion of cytokines and chemokines that impact immune cell migration and function. Multiple studies have now shown that DDR can be a vulnerability, both through synthetic lethal strategies with drugs like PARP and ATR inhibition and DNA damaging agents, as well as through strategies targeting the immune response as best exemplified by mismatch repair defects and PD-1 targeting and more recently emerging evidence on CDK12 alterations. This presentation will focus on the therapeutic targeting of tumors with DNA repair defects, synthetic lethal strategies, and the challenges of developing anticancer drugs targeting DNA repair. Citation Format: Johann de Bono. Targeting DNA Repair and Defective DNA repair [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr PL03-01. doi:10.1158/1535-7163.TARG-19-PL03-01
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.024 |
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".