Inhibition of GPX4 enhances CDK4/6 inhibitor and endocrine therapy activity in breast cancer
Bibliographic record
Abstract
CDK4/6 inhibition in combination with endocrine therapy is the standard of care for estrogen receptor (ER+) breast cancer, and although cytostasis is frequently observed, new treatment strategies that enhance efficacy are required. Here, we perform two independent genome-wide CRISPR screens to identify genetic determinants of CDK4/6 and endocrine therapy sensitivity. Genes involved in oxidative stress and ferroptosis modulate sensitivity, with GPX4 as the top sensitiser in both screens. Depletion or inhibition of GPX4 increases sensitivity to palbociclib and giredestrant, and their combination, in ER+ breast cancer models, with GPX4 null xenografts being highly sensitive to palbociclib. GPX4 perturbation additionally sensitises triple negative breast cancer (TNBC) models to palbociclib. Palbociclib and giredestrant induced oxidative stress and disordered lipid metabolism, leading to a ferroptosis-sensitive state. Lipid peroxidation is promoted by a peroxisome AGPAT3-dependent pathway in ER+ breast cancer models, rather than the classical ACSL4 pathway. Our data demonstrate that CDK4/6 and ER inhibition creates vulnerability to ferroptosis induction, that could be exploited through combination with GPX4 inhibitors, to enhance sensitivity to the current therapies in breast cancer. While CDK4/6 inhibitors (CDK4/6i) have improved outcomes for breast cancer patients, estrogen receptor (ER + ) breast cancers often develop resistance, and triple negative breast cancer (TNBC) show poor sensitivity. Here, the authors identify a vulnerability of CDK4/6i treated ER+ and TNBC on ferroptosis and identify the combination of CDK4/6i and GPX4 inhibition as synergistic.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".