Anti-tumor effects of an Id antagonist with no acquired resistance
Bibliographic record
Abstract
Summary Id proteins are helix-loop-helix (HLH) transcriptional regulators frequently overexpressed in cancer. Id proteins inhibit basic HLH transcription factors through protein-protein interactions, often inhibiting differentiation and sustaining proliferation. We recently identified a small-molecule, AGX51, which targets Id proteins for degradation and impairs ocular neovascularization in mouse models. Here we show that AGX51 treatment of cancer cell lines impaired cell growth and viability that results from a dramatic increase in ROS production upon Id degradation. In mouse models, AGX51 treatment suppressed breast cancer colonization in the lung, regressed the growth of paclitaxel-resistant breast tumors when combined with paclitaxel and reduced tumor burden in a model of sporadic colorectal neoplasia. Furthermore, in cells and mice, we failed to observe acquired resistance to AGX51 likely the result of the immutability of the binding pocket and efficient degradation of the Id proteins. Thus, AGX51 is a first-in-class compound that antagonizes Id proteins, shows strong anti-tumor effects and may be further developed for the management of multiple cancers.
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) | 0.003 | 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".