Extracellular Matrix-Induced Genes May Reduce Response to Rapamycin in LAM
Bibliographic record
Abstract
Abstract Rationale Lymphangioleiomyomatosis (LAM) is a rare cystic lung disease driven by nodules containing TSC2 -/- ‘LAM cells’ and recruited LAM associated fibroblasts (LAFs). Although rapamycin reduces lung function loss, some patients continue to decline meaning additional therapies are needed. Objectives To investigate how the LAM nodule environment affects LAM cell proliferation and the response to rapamycin. Methods Changes in advanced LAM were identified using shotgun proteomics and immunohistochemistry in tissue from carefully phenotyped patients. Genes potentially associated with rapamycin insensitivity of cells grown on LAF-derived extracellular matrix were identified by RNA sequencing and validated using repurposed pharmacologic inhibitors. Main Results More advanced disease was associated with increasing nodules adjacent to lung cysts and greater decline in forced expiratory volume in 1 sec (FEV 1 ) when treated with rapamycin (p=0.005). In late-stage LAM, proteomics identified upregulation of pathways associated with accumulation of activated fibroblasts, including extracellular matrix deposition, glucose metabolism and the actin cytoskeleton. Picrosirius red staining and immunohistochemistry confirmed deposition of extracellular matrix within LAM nodules. The growth of TSC2 -/- model LAM cells was increased on LAF-derived extracellular matrix (LAF ECM), and incompletely supressed by rapamycin (p<0.0001). RNA sequencing of cells grown on LAF ECM identified upregulation of pathways driving cell cycle control, transcription and metabolism in cells. Tractable, pro-proliferative, rapamycin insensitive genes included CDK7 , GAS6 and PLAU. Repurposed inhibitors of these pathways inhibited LAM cell proliferation and enhanced the anti-proliferative effect of rapamycin. Conclusions Extracellular matrix deposited by LAM associated fibroblasts upregulates expression of genes which potentially blunt the response to rapamycin, but offer additional therapeutic opportunities for patients with established LAM.
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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) | 0.002 | 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".