Measurements of Tumor Cell Autophagy Predict Invasiveness, Resistance to Chemotherapy, and Survival in Melanoma
Bibliographic record
Abstract
PURPOSE: Autophagy consists of lysosome-dependent degradation of cytoplasmic contents sequestered by autophagic vesicles (AV). The role of autophagy in determining tumor aggressiveness and response to therapy in melanoma was investigated in this study. EXPERIMENTAL DESIGN: Autophagy was measured in tumor biopsies obtained from metastatic melanoma patients enrolled on a phase II trial of temozolomide and sorafenib and correlated to clinical outcome. These results were compared with autophagy measurements in aggressive and indolent melanoma cells grown in two- and three-dimensional (3D) culture and as xenograft tumors. The effects of autophagy inhibition with either hydroxychloroquine or inducible shRNA (short hairpin RNA) against the autophagy gene ATG5 were assessed in three-dimensional spheroids. RESULTS: Patients whose tumors had a high autophagic index were less likely to respond to treatment and had a shorter survival compared with those with a low autophagic index. Differences in autophagy were less evident in aggressive and indolent melanoma cells grown in monolayer culture. In contrast, autophagy was increased in aggressive compared with indolent melanoma xenograft tumors. This difference was recapitulated when aggressive and indolent melanoma cells were grown as spheroids. Autophagy inhibition with either hydroxychloroquine or inducible shRNA against ATG5 resulted in cell death in aggressive melanoma spheroids, and significantly augmented temozolomide-induced cell death. CONCLUSIONS: Autophagy is a potential prognostic factor and therapeutic target in melanoma. Three dimensional culture mimics the tumor microenvironment better than monolayer culture and is an appropriate model for studying therapeutic combinations involving autophagy modulators. Autophagy inhibition should be tested clinically in patients with melanoma.
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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) | 0.000 | 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".