Hypoxic Activation of the PERK/eIF2α Arm of the Unfolded Protein Response Promotes Metastasis through Induction of LAMP3
Bibliographic record
Abstract
PURPOSE: Conditions of poor oxygenation (hypoxia) are present in many human tumors, including cervix cancer, and are associated with increased risk of metastasis and poor prognosis. Hypoxia is a potent activator of the PERK/eIF2α signaling pathway, a component of the unfolded protein response (UPR) and an important mediator of hypoxia tolerance and tumor growth. Here, the importance of this pathway in the metastasis of human cervix carcinoma was investigated. EXPERIMENTAL DESIGN: Amplification and expression of LAMP3, a UPR metastasis-associated gene, was examined using FISH and immunofluorescence in a cohort of human cervix tumors from patients who had received oxygen needle electrode tumor oxygenation measurements. To evaluate the importance of this pathway in metastasis in vivo, we constructed a series of inducible cell lines to interfere with PERK signaling during hypoxia and used these in an orthotopic cervix cancer model of hypoxia-driven metastasis. RESULTS: We show that LAMP3 expression in human cervix tumors is augmented both by gene copy number alterations and by hypoxia. Induced disruption of PERK signaling in established orthotopic xenografts resulted in complete inhibition of hypoxia-induced metastasis to the lymph nodes. This is due, in part, to a direct influence of the UPR pathway on hypoxia tolerance. However, we also find that LAMP3 is a key mediator of hypoxia-driven nodal metastasis, through its ability to promote metastatic properties including cell migration. CONCLUSION: These data suggest that the association between hypoxia, metastasis, and poor prognosis is due, in part, to hypoxic activation of the UPR and expression of LAMP3. Clin Cancer Res; 19(22); 6126-37. ©2013 AACR.
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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) | 0.000 | 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".