Gene expression analysis in Cutaneous T-Cell Lymphomas (CTCL) highlights disease heterogeneity and potential diagnostic and prognostic indicators
Bibliographic record
Abstract
Cutaneous T-Cell Lymphomas (CTCL) are rare, but potentially devastating malignancies, whose pathogenesis remains poorly elucidated. Unfortunately, currently it is not possible to predict based on the available criteria in which patients the cancer will progress and which patients will experience an indolent disease course. Furthermore, at early stages this malignancy often masquerades as psoriasis, chronic eczema or other benign inflammatory dermatoses. As a result, it takes on average 6 y to diagnose this lymphoma since its initial presentation. In this study, we performed transcription expression profiling using TruSeq targeted RNA gene expression on 181 fresh and formalin-fixed and paraffin-embedded (FFPE) skin samples from CTCL patients and patients affected by benign inflammatory dermatoses that often mimic CTCL clinically and on histology (e.g., psoriasis, chronic eczema, etc.) We also analyzed multiple longitudinal biopsies that were obtained from the same patients over time. Our results underscore significant molecular heterogeneity with respect to gene expression between different patients and even within the same patients over time. Our study also confirmed TOX, FYB, LEF1, CCR4, ITK, EED, POU2AF, IL26, STAT5, BLK, GTSF1 and PSORS1C2 genes as being differentially expressed between CTCL and benign skin biopsies. In addition, we found that differential expression for a subset of these markers (e.g., TOX, FYB, GTSF1 and CCR4) may be useful in prognosticating this disease. This research, combined with other molecular analyses, prepares the foundation for the development of personalized molecular approach toward diagnosis and management of CTCL.
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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 | 0.001 | 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".