Loss of Cell-Surface Laminin Anchoring Promotes Tumor Growth and Is Associated with Poor Clinical Outcomes
Bibliographic record
Abstract
Perturbations in the composition and assembly of extracellular matrices (ECM) contribute to progression of numerous diseases, including cancers. Anchoring of laminins at the cell surface enables assembly and signaling of many ECMs, but the possible contributions of altered laminin anchoring to cancer progression remain undetermined. In this study, we investigated the prominence and origins of defective laminin anchoring in cancer cells and its association with cancer subtypes and clinical outcomes. We found loss of laminin anchoring to be widespread in cancer cells. Perturbation of laminin anchoring originated from several distinct defects, which all led to dysfunctional glycosylation of the ECM receptor dystroglycan. In aggressive breast and brain cancers, defective laminin anchoring was often due to suppressed expression of the glycosyltransferase LARGE. Reduced expression of LARGE characterized a broad array of human tumors in which it was associated with aggressive cancer subtypes and poor clinical outcomes. Notably, this defect robustly predicted poor survival in patients with brain cancers. Restoring LARGE expression repaired anchoring of exogenous and endogenous laminin and modulated cell proliferation and tumor growth. Together, our findings suggest that defects in laminin anchoring occur commonly in cancer cells, are characteristic of aggressive cancer subtypes, and are important drivers of disease progression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".