<i>Here, There Be Dragons</i> : Charting Autophagy-Related Alterations in Human Tumors
Bibliographic record
Abstract
Macroautophagy (or autophagy) is a catabolic cellular process that is both homeostatic and stress adaptive. Normal cells rely on basal levels of autophagy to maintain cellular integrity (via turnover of long-lived proteins and damaged organelles) and increased levels of autophagy to buoy cell survival during various metabolic stresses (via nutrient and energy provision through lysosomal degradation of cytoplasmic components). Autophagy can function in both tumor suppression and tumor progression, and is under investigation in clinical trials as a novel target for anticancer therapy. However, its role in cancer pathogenesis has yet to be fully explored. In particular, it remains unknown whether in vitro observations will be applicable to human cancer patients. Another outstanding question is whether there exists tumor-specific selection for alterations in autophagy function. In this review, we survey reported mutations in autophagy genes and key autophagy regulators identified in human tumor samples and summarize the literature regarding expression levels of autophagy genes and proteins in various cancer tissues. Although it is too early to draw inferences from this collection of in vivo studies of autophagy-related alterations in human cancers, their results highlight the challenges that must be overcome before we can accurately assess the scope of autophagy's predicted role in tumorigenesis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".