The role of cellular proteostasis in antitumor immunity
Bibliographic record
Abstract
Immune checkpoint blockade therapy is perhaps the most important development in cancer treatment in recent memory. It is based on decades of investigation into the biology of immune cells and the role of the immune system in controlling cancer growth. While the molecular circuitry that governs the immune system in general-and antitumor immunity in particular-is intensely studied, far less attention has been paid to the role of cellular stress in this process. Proteostasis, intimately linked to cell stress responses, refers to the dynamic regulation of the cellular proteome and is maintained through a complex network of systems that govern the synthesis, folding, and degradation of proteins in the cell. Disruption of these systems can result in the loss of protein function, altered protein function, the formation of toxic aggregates, or pathologies associated with cell stress. However, the importance of proteostasis extends beyond its role in maintaining proper protein function; proteostasis governs how tolerant cells may be to mutations in protein-coding genes and the overall half-life of proteins. Such gene expression changes may be associated with human diseases including neurodegenerative diseases, metabolic disease, and cancer and manifest at the protein level against the backdrop of the proteostasis network in any given cellular environment. In this review, we focus on the role of proteostasis in regulating immune responses against cancer as well the role of proteostasis in determining immunogenicity of cancer cells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".