Spatial transcriptomics unravels novel signaling patterns at the leading edge of oral squamous cell carcinoma.
Bibliographic record
Abstract
e18043 Background: Head and neck cancer is the 6th most common cancer worldwide. Oral squamous cell carcinoma (OSCC) is the most prevalent head and neck cancer that is characterized by aggressive local invasion and metastasis. Despite the leading edge (invasive front) of the tumor being a driver of OSCC pathophysiology, its biology and clinical relevance have not been fully characterized. We used spatial transcriptomics to explore signaling patterns within the leading edge and tumor core. Methods: Fresh-frozen, surgically resected OSCC samples from three HPV-negative OSCC patients were profiled using the 10x Genomics Visium Spatial Gene Expression platform. Leading edge and tumor core regions were defined by pathologist annotations and expression of previously identified edge and core gene signatures from the literature. Spatial differential gene expression (DGE) analysis and pathway analysis was performed using the Seurat package and Ingenuity Pathway Analysis (IPA), respectively. Cell-cell interaction networks were reconstructed using the CellChat package. Results: The leading edge and tumor core displayed unique transcriptional and signaling profiles that were conserved across all three OSCC patient samples. DEG analysis revealed 31 genes enriched in the leading edge and 62 genes enriched in the tumor core with a log2FC > 0.58 and adjusted p-value < 0.01. The top genes upregulated in the leading edge were FN1, COL1A1, COL1A2, IFITM3, and SPARC. Top tumor-core genes included CRCT1, LCE3D, DEFB4A, SPRR2A, and CNFN. IPA analysis of upregulated DEGs in the leading edge and tumor core predicted the activation of wound healing and GP6 signaling pathways, and activation of intrinsic prothrombin activation and MSP-RON signaling pathways, respectively. Cell communication analysis revealed that the leading edge had higher intercellular signaling than the tumor core. Upregulated leading edge cell signaling modules included collagen, CD99, CSPG4, and non-canonical WNT pathways, which have been linked to tumor invasion, metastasis, and adhesion. COL1A1 and COL1A2 ligands and CD44 and SDC1 receptors were upregulated in leading edge signaling. The tumor core was enriched for ANGPTL and PERIOSTIN cell signaling modules. Conclusions: This is the first study to characterize the tumor core and leading edge of OSCC tumors using spatial transcriptomics. Further investigation of the therapeutic potential of identified signaling pathways may improve OSCC outcomes.
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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 | 0.001 | 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 | 0.000 |
| 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".