Parallel Accumulation of Tumor Hyaluronan, Collagen, and Other Drivers of Tumor Progression
Bibliographic record
Abstract
Abstract Purpose: The tumor microenvironment (TME) evolves to support tumor progression. One marker of more aggressive malignancy is hyaluronan (HA) accumulation. Here, we characterize biological and physical changes associated with HA-accumulating (HA-high) tumors. Experimental Design: We used immunohistochemistry, in vivo imaging of tumor pH, and microdialysis to characterize the TME of HA-high tumors, including tumor vascular structure, hypoxia, tumor perfusion by doxorubicin, pH, content of collagen. and smooth muscle actin (α-SMA). A novel method was developed to measure real-time tumor-associated soluble cytokines and growth factors. We also evaluated biopsies of murine and pancreatic cancer patients to investigate HA and collagen content, important contributors to drug resistance. Results: In immunodeficient and immunocompetent mice, increasing tumor HA content is accompanied by increasing collagen content, vascular collapse, hypoxia, and increased metastatic potential, as reflected by increased α-SMA. In vivo treatment of HA-high tumors with PEGylated recombinant human hyaluronidase (PEGPH20) dramatically reversed these changes and depleted stores of VEGF-A165, suggesting that PEGPH20 may also diminish the angiogenic potential of the TME. Finally, we observed in xenografts and in pancreatic cancer patients a coordinated increase in HA and collagen tumor content. Conclusions: The accumulation of HA in tumors is associated with high tIP, vascular collapse, hypoxia, and drug resistance. These findings may partially explain why more aggressive malignancy is observed in the HA-high phenotype. We have shown that degradation of HA by PEGPH20 partially reverses this phenotype and leads to depletion of tumor-associated VEGF-A165. These results encourage further clinical investigation of PEGPH20. Clin Cancer Res; 24(19); 4798–807. ©2018 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".