Transcriptomic Changes During Stage Progression of Mycosis Fungoides
Bibliographic record
Abstract
Abstract Background Mycosis fungoides (MF) is the most common cutaneous T cell lymphoma, which in the early patch/plaque stages runs an indolent course. However, ~25% of MF patients develop skin tumors, a hallmark of progression to the advanced stage and associated with high mortality. The mechanisms involved in stage progression are poorly elucidated. Methods We performed whole-transcriptome and whole-exome sequencing of malignant MF cells from skin biopsies obtained by laser-capture microdissection. We compared three types of MF lesions: early-stage plaques (ESP, n=12), and plaques and tumors from patients in late-stage disease (late-stage plaques, LSP, n=10, and tumors, TMR, n=15). Gene Ontology (GO) and KEGG analysis were used to determine pathway changes specific for different lesions which we linked to the recurrent somatic mutations overrepresented in MF tumors. Results The key upregulated pathways during stage progression were those related to cell proliferation and survival (MEK/ERK, Akt-mTOR), Th2/Th9 signaling (IL4, STAT3, STAT5, STAT6), meiomitosis (CT45A1, CT45A3, STAG3, GTSF1, and REC8) and DNA repair (PARP1, MYCN, OGG1). Principal coordinate clustering of the transcriptome revealed extensive gene expression differences between early (ESP) and advanced-stage lesions (LSP and TMR). LSP and TMR showed remarkable similarities at the level of the transcriptome, which we interpreted as evidence of cell percolation between lesions via hematogenous self-seeding. Conclusion Stage progression in MF is associated with Th2/Th9 polarization of malignant cells, activation of proliferation, survival, as well as increased genomic instability. Global transcriptomic changes in multiple lesions are probably caused by hematogenous cell percolation between discrete skin lesions.
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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 | 0.001 |
| 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".