Bibliographic record
Abstract
Abstract Standard-of-care therapy in high-risk stage II-III ER+/HER2-negative and Triple-Negative Breast Cancer (TNBC) includes neoadjuvant chemotherapy (NAC) prior to surgery, which is then followed by adjuvant endocrine (for ER+) +/- salvage chemotherapy (for TNBC). These treatments are delivered with limited stratification by disease histology and, while many patients are cured, more than one third of those in whom invasive disease remains in the breast at the time of surgery experience early relapses of metastatic disease within the first few years. The poor survival rate stems from incomplete response to NAC and the lack of alternative tailored therapy against the chemo-resistant disease that recurs. Hence, there is an urgent need for new therapies to treat recurrence and improve outcome. Tumor progression following standard-of-care therapy arises from pre-existing resistant and/or adapting persister cancer cells committing to an expansion phase. These differ from treatment sensitive cells in the epigenetic landscape of their genome, regulating the on/off state of genes for instance. This underlies epigenetic variants, akin to genetic variants, that define vulnerabilities to new treatment strategies. Here, we demonstrate the value of epigenetic therapy in preclinical settings against high-risk breast cancer, putting forward new treatment options to be tested in clinical trials. Citation Format: M Lupien. Epigenetic therapy against high-risk breast cancer [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr SP087.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.008 |
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".