Gene Expression Profiling of Mycosis Fungoides in Early and Tumor Stage—A Proof-of-Concept Study Using Laser Capture/Single Cell Microdissection and NanoString Analysis
Bibliographic record
Abstract
A subset of patients with mycosis fungoides (MF) progress to the tumor stage, which correlates with a worse clinical outcome. The molecular events driving this progression are not well-understood. To identify the key molecular drivers, we performed gene expression profiling (GEP) using NanoString. Ten formalin-fixed/paraffin-embedded skin biopsies from six patients (six non-tumor and four tumor MF) were included; non-tumor and tumor samples were available in three patients. Laser capture/single cell microdissection of epidermotropic MF cells was used for non-tumor cases. We found that the RNA extracted from 700-800 single cells was consistently sufficient for GEP, provided that multiplexed target enrichment amplification was used. An un-supervised/hierarchical analysis revealed clustering of non-tumor and tumor cases. Many of the most upregulated or downregulated genes are implicated in the PI3K, RAS, cell cycle/apoptosis and MAPK pathways. Two of the targets, HMGA1 and PTPN11 (encodes SHP2), were validated using immunohistochemistry. HMGA1 was positive in six out of six non-tumor MF samples and negative in five out of five tumor MF samples. An opposite pattern was seen with SHP2. Our study has provided a proof-of-concept that single-cell microdissection/GEP can be applied to archival tissues. Some of our identified gene targets might be key drivers of the disease progression of MF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".