In silico analyses of the tumor microenvironment highlight tumoral inflammation, a Th2 cytokine shift and a mesenchymal stem cell-like phenotype in advanced in basal cell carcinomas
Bibliographic record
Abstract
Basal Cell Carcinoma (BCC) represents the most common form of all cancers. BCC is characteristically surrounded by a fibromyxoid stroma. Previous studies have suggested a shift towards a Th2 response, an increase in T regulatory lymphocytes and the presence of cancer-associated fibroblasts in the BCC tumor microenvironment. In this study, we aimed to further characterize the BCC tumor microenvironment in detail by analyzing BCC RNA-Sequencing data and correlating it with clinically-relevant features via in silico RNA deconvolution. Using immune cell type deconvolution by CIBERSORT, we have identified a brisk lymphocytic infiltration, and more abundant macrophages in BCC tumors compared to normal skin. Using cell type enrichment by xCell, we confirmed the observed immune infiltration in BCC tumors and compared them to normal skin. We observed a shift towards Th2 immunity in advanced and vismodegib-resistant tumors. Tumoral inflammation induced by macrophage activity was associated with advanced BCCs, while lymphocytic infiltration was most significant in non-advanced tumors, likely related to an adaptive anti-tumoral response. In advanced and vismodegib-resistant BCCs, mesenchymal stem cell-like properties were observed. Particularly in vismodegib-resistant BCCs, fibroblasts and adipocytes were found at high number, which ultimately may contribute to the decreased drug delivery to the tumor. In conclusion, this study has revealed notable BCC tumor microenvironment findings associated with important clinical features. Microenvironment-altering agents may be used locally for "routine" BCCs and systematically for advanced or resistant BCCs.
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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 | 0.001 |
| 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".