Autophagy mediates cancer cell resistance to doxorubicin induced by the Programmed Death 1/Programmed Death Ligand 1 immune checkpoint axis
Bibliographic record
Abstract
ABSTRACT Background While the Programmed Death 1/Programmed Death Ligand 1 (PD-1/PD-L1) immune checkpoint is an important mechanism of immune evasion in cancer, recent studies have shown that it can also lead to resistance to chemotherapy in cancer cells via reverse signaling. Here we describe a novel mechanism by which autophagy mediates cancer cell drug resistance induced by PD-1/PD-L1 signaling. Methods Human and mouse breast cancer cells were treated with recombinant PD-1 (rPD-1) to stimulate PD-1/PD-L1 signaling. Activation of autophagy was assessed by immunoblot analysis of microtubule-associated protein 1A/1B-light chain 3 (LC3)-II and Beclin 1 protein levels, two important markers of autophagy. Moreover, autophagosome formation was assessed in human breast cancer cells using green fluorescence protein (GFP)-tagged LC3. Cells were either treated with Beclin 1 or Atg7 shRNA to assess the role of autophagy on resistance to doxorubicin mediated by PD-1/PD-L1 signalling. We then investigated signaling mechanisms upstream of PD-1/PD-L1 induced autophagy by assessing phosphorylation of extracellular signal-related kinase (ERK). Results Treatment of cells with rPD-1 resulted in a time-dependent increase in LC3-II as well as Beclin 1, and an increase in autophagosome formation. Knockdown of Beclin 1 or Atg7 prevented drug resistance induced by PD-1/PD-L1 signaling. Exposure of breast cancer cells to rPD-1 resulted in increased ERK phosphorylation and inhibition of ERK activation abolished autophagy induced by PD-1/PD-L1 signaling. Conclusions These studies provide a rationale for the use of PD-1/PD-L1 immune checkpoint blockers and autophagy inhibitors as potential chemosensitizers in cancer therapy.
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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".