Bibliographic record
Abstract
Abstract Chromatin-associated multi-protein complexes, which often comprise proteins involved in epigenetic regulation, play an important role in various cancers, including leukemia. Thus, pharmacologic inhibition of proteins involved in these complexes can lead to new anti-cancer therapies. We have focused on therapeutic targeting of various chromatin-bound multi-protein complexes, including Mixed Lineage Leukemia 1 (MLL1) and Polycomb Repressive Complex 1 (PRC1) complexes, as a novel treatment for leukemia. These efforts led to the development of small molecule inhibitors targeting the menin-MLL1 interaction, which were translated to leukemia patients, demonstrating promising efficacy in Acute Myeloid Leukemia (AML) with NPM1 mutations or MLL1 translocations. However, clinical studies revealed resistance to the single agent menin inhibitor in a sub-set of AML patients, suggesting that combinatorial treatments or new generations of menin inhibitors might be required to overcome resistance. Our recent efforts to address resistance to menin inhibitors will be presented, including combinatorial studies with other targeted agents (e.g. kinases inhibitors), which resulted in synergistic effects. Furthermore, discovery of a new generation of menin inhibitors with promising activity against menin patient mutations will also be presented. Citation Format: Jolanta Grembecka. Targeting epigenetic complexes in leukemia [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr IA023.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".