Immune checkpoints between epithelial-mesenchymal transition and autophagy: A conflicting triangle
Bibliographic record
Abstract
Inhibitory immune checkpoint (ICP) molecules are pivotal in inhibiting innate and acquired antitumor immune responses, a mechanism frequently exploited by cancer cells to evade host immunity. These evasion strategies contribute to the complexity of cancer progression and therapeutic resistance. For this reason, ICP molecules have become targets for antitumor drugs, particularly monoclonal antibodies, collectively referred to as immune checkpoint inhibitors (ICI), that counteract such cancer-associated immune suppression and restore antitumor immune responses. Over the last decade, however, it has become clear that tumor cell-associated ICPs can also induce tumor cell-intrinsic effects, in particular epithelial-mesenchymal transition (EMT) and macroautophagy (hereafter autophagy). Both of these processes have profound implications for cancer metastasis and drug responsiveness. This article reviews the positive or negative cross-talk that tumor cell-associated ICPs undergo with autophagy and EMT. We discuss that tumor cell-associated ICPs are upregulated in response to the same stimuli that induce EMT. Moreover, ICPs themselves, when overexpressed, become an EMT-inducing stimulus. As regards the cross-talk with autophagy, ICPs have been shown to either stimulate or inhibit autophagy, while autophagy itself can either up- or downregulate the expression of ICPs. This dynamic equilibrium also extends to the autophagy-apoptosis axis, further emphasizing the complexities of cellular responses. Eventually, we delve into the intricate balance between autophagy and apoptosis, elucidating its role in the broader interplay of cellular dynamics influenced by ICPs. In the final part of this article, we speculate about the driving forces underlying the contradictory outcomes of the reciprocal, inhibitory, or stimulatory effects between ICPs, EMT, and autophagy. A conclusive identification of these driving forces may allow to achieve improved antitumor effects when using combinations of ICIs and compounds acting on EMT and/or autophagy. Prospectively, this may translate into increased and/or broadened therapeutic efficacy compared to what is currently achieved with ICI-based clinical protocols.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".