CDK4/6 inhibitors and the pRB-E2F1 axis suppress PVR and PD-L1 expression in triple-negative breast cancer
Bibliographic record
Abstract
Immune-checkpoint (IC) modulators like the poliovirus receptor (PVR) and programmed death ligand 1 (PD-L1) attenuate innate and adaptive immune responses and are potential therapeutic targets for diverse malignancies, including triple-negative breast cancer (TNBC). The retinoblastoma tumor suppressor, pRB, controls cell growth through E2F1-3 transcription factors, and its inactivation drives metastatic cancer, yet its effect on IC modulators is contentious. Here, we show that RB-loss and high E2F1/E2F2 signatures correlate with expression of PVR, CD274 (PD-L1 gene) and other IC modulators and that pRB represses whereas RB depletion and E2F1 induce PVR and CD274 in TNBC cells. Accordingly, the CDK4/6 inhibitor, palbociclib, suppresses both PVR and PD-L1 expression. Palbociclib also counteracts the effect of CDK4 on SPOP, leading to its depletion, but the overall effect of palbociclib is a net reduction in PD-L1 level. Hydrochloric acid, commonly used to solubilize palbociclib, counteracts its effect and induces PD-L1 expression. Remarkably, lactic acid, a by-product of glycolysis, also induces PD-L1 as well as PVR. Our results suggest a model in which CDK4/6 regulates PD-L1 turnover by promoting its transcription via pRB-E2F1 and degradation via SPOP and that the CDK4/6-pRB-E2F pathway couples cell proliferation with the induction of multiple innate and adaptive immunomodulators, with direct implications for cancer progression, anti-CDK4/6- and IC-therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".