Abstract B017: Tumor-secreted collagen VI weakens endothelium and promotes metastasis
Bibliographic record
Abstract
Abstract Metastasis is the leading cause of cancer-related deaths. However, the mechanisms behind the metastatic cascade remain poorly understood. Tumor’s metastatic potential is strongly influenced by microenvironmental cues such as low oxygen (hypoxia). Our published work reported that lung metastases in sarcoma are associated with increased primary tumor expression of the hypoxia-inducible collagen-modifying lysyl hydroxylase, Plod2; in multiple subtypes of sarcoma, excessive collagen lysyl hydroxylation results in secretion of immature collagen aggregates able to physically associate with tumor cells and promote both intravasation and extravasation. In the current study, we observed that Plod2 is colocalized with collagen VI and is important for its extracellular network structure, suggesting that collagen VI is a putative substrate of Plod2. Using an impedance sensing assay and a zebrafish intravital microinjection model, we demonstrated that tumoral Plod2 and collagen VI weaken the endothelial barrier and promote extravasation. With a tail vein injection mouse model, we determined that collagen VI is essential for lung metastasis. Clinically, we detected high levels of collagen VI in metastatic sarcoma in surgically resected patient lungs. Furthermore, by analyzing patient data in The Cancer Genome Atlas (TCGA), we found a strong correlation between the expression of both PLOD2 and collagen VI with disease outcomes. Together, our study identifies a novel mechanism of sarcoma lung metastasis, opening up opportunities for therapeutic intervention. Citation Format: Ying Liu, Ashley M. Fuller, Ileana Murazzi, Ann Devine, Nicolas Skuli. Tumor-secreted collagen VI weakens endothelium and promotes metastasis [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr B017.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".