Autophagy inhibition in cancer therapy: metabolic considerations for antitumor immunity
Bibliographic record
Abstract
Tumors and the immune system are intertwined in a competition where tilting the fine balance between tumor-specific immunity and tolerance can ultimately decide the fate of the host. Defensive and suppressive immunological responses to cancer are exquisitely sensitive to metabolic features of rapidly growing tumors, such as hypoxia, low nutrient availability, and aberrant growth factor signaling. As a result, clinical therapies impacting these properties change the in situ antitumor immune response by virtue of disrupting the tumor environment. To compensate for disruptions in cellular metabolism, cells activate autophagy to promote survival. On the basis of this notion, strategies designed to block autophagy in tumor cells are currently being tested in several human clinical trials. However, therapies that impair tumor metabolism must also take into account their effect on lymphocytes activated in the immune response to cancer. Given that a strong antitumor immune response is a positive prognostic factor in overall patient survival, identifying ways to block essential processes in tumor cells and suppressive immune cells while promoting those that are important for a robust immune response are of critical importance. Herein, we review the effects of anti-cancer agents that impact metabolism administered concurrently with autophagy inhibitors on immune cells and consider the implications for patient response to 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".