Cyclin E amplification/overexpression is a mechanism of trastuzumab resistance in HER2 <sup>+</sup> breast cancer patients
Bibliographic record
Abstract
Clinical benefits from trastuzumab and other anti-HER2 therapies in patients with HER2 amplified breast cancer remain limited by primary or acquired resistance. To identify potential mechanisms of resistance, we established trastuzumab-resistant HER2 amplified breast cancer cells by chronic exposure to trastuzumab treatment. Genomewide copy-number variation analyses of the resistant cells compared with parental cells revealed a focal amplification of genomic DNA containing the cyclin E gene. In a cohort of 34 HER2(+) patients treated with trastuzumab-based therapy, we found that cyclin E amplification/overexpression was associated with a worse clinical benefit (33.3% compared with 87.5%, P < 0.02) and a lower progression-free survival (6 mo vs. 14 mo, P < 0.002) compared with nonoverexpressing cyclin E tumors. To dissect the potential role of cyclin E in trastuzumab resistance, we studied the effects of cyclin E overexpression and cyclin E suppression. Cyclin E overexpression resulted in resistance to trastuzumab both in vitro and in vivo. Inhibition of cyclin E activity in cyclin E-amplified trastuzumab resistant clones, either by knockdown of cyclin E expression or treatment with cyclin-dependent kinase 2 (CDK2) inhibitors, led to a dramatic decrease in proliferation and enhanced apoptosis. In vivo, CDK2 inhibition significantly reduced tumor growth of trastuzumab-resistant xenografts. Our findings point to a causative role for cyclin E overexpression and the consequent increase in CDK2 activity in trastuzumab resistance and suggest that treatment with CDK2 inhibitors may be a valid strategy in patients with breast tumors with HER2 and cyclin E coamplification/overexpression.
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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".