LAMC2 Regulates Key Transcriptional and Targetable Effectors to Support Pancreatic Cancer Growth
Bibliographic record
Abstract
PURPOSE: The identification of pancreatic ductal adenocarcinoma (PDAC) dysregulated genes may unveil novel molecular targets entering inhibitory strategies. Laminins are emerging as potential targets in PDAC given their role as diagnostic and prognostic markers. Here, we investigated the cellular, functional, and clinical relevance of LAMC2 and its regulated network, with the ultimate goal of identifying potential therapies. EXPERIMENTAL DESIGN: LAMC2 expression was analyzed in PDAC tissues, a panel of human and mouse cell lines, and a genetically engineered mouse model. Genetic perturbation in 2D, 3D, and in vivo allograft and xenograft models was done. Expression profiling of a LAMC2 network was performed by RNA-sequencing, and publicly available gene expression datasets from experimental and clinical studies examined to query its human relevance. Dual inhibition of pharmacologically targetable LAMC2-regulated effectors was investigated. RESULTS: LAMC2 was consistently upregulated in human and mouse experimental models as well as in human PDAC specimens, and associated with tumor grade and survival. LAMC2 inhibition impaired cell cycle, induced apoptosis, and sensitized PDAC to MEK1/2 inhibitors (MEK1/2i). A LAMC2-regulated network was featured in PDAC, including both classical and quasi-mesenchymal subtypes, and contained downstream effectors transcriptionally shared by the KRAS signaling pathway. LAMC2 regulated a functional FOSL1-AXL axis via AKT phosphorylation. Furthermore, genetic LAMC2 or pharmacological AXL inhibition elicited a synergistic antiproliferative effect in combination with MEK1/2is that was consistent across 2D and 3D human and mouse PDAC models, including primary patient-derived organoids. CONCLUSIONS: LAMC2 is a molecular target in PDAC that regulates a transcriptional network that unveils a dual drug combination for cancer treatment.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".