Overcoming retinoic acid resistance in <scp>HER2</scp> ‐enriched breast cancers: role of <scp>MYC</scp>
Bibliographic record
Abstract
HER2‐enriched (HER2 + ) breast cancers express high levels of the growth‐promoting HER2 protein. Although these cancers are treated with the HER2‐targeted drug, trastuzumab, resistance to treatment is common. Retinoic acid (RA) is an anti‐cancer agent that has been successfully used for the treatment of leukemia and holds promise for the treatment of solid cancers, including breast cancer. The HER2 gene is frequently co‐amplified with RARA , a key determinant of RA sensitivity in breast cancers. It seems surprising, therefore, that HER2 + breast cancers are refractory to RA treatment. Here, we show that MYC mediates RA resistance by suppressing the expression of cellular retinoic acid binding protein 2 (CRABP2), resulting in RARα inactivation. CRABP2 is an intracellular RA transporter that delivers RA to the nuclear receptor RARα for its activation. Our results indicate that response to RA is enhanced by MYC depletion in HER2 + breast cancer cells and that RA treatment enhances trastuzumab responsiveness. Our findings support the use of RA and trastuzumab for the treatment of subsets of patients with breast cancers that are HER2‐RARα co‐amplified and have low levels of MYC.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".