Transcriptional Profiles Predict Disease Outcome in Patients with Cutaneous T-Cell Lymphoma
Bibliographic record
Abstract
PURPOSE: Average survival of cutaneous T-cell lymphoma (CTCL) is associated with clinical stage at diagnosis, where stage I has a favorable survival prognosis, whereas patients with more advanced stages succumb to their disease within 5 years. Although the majority of patients present with an early-stage CTCL, 15% to 20% of them will inevitably progress. Current state-of-the-art clinical criteria cannot identify individuals with stage I disease who are at risk of progression. The purpose of the current work is to gain novel molecular insight into the pathophysiology of CTCL to be able to identify patients with poor versus favorable prognosis. Our previous work used microarray analysis of skin biopsies from 62 CTCL patients to perform an unsupervised analysis of gene expression, which revealed three distinct transcription profile clusters. EXPERIMENTAL DESIGN: In the present study, we used reverse transcription-PCR to confirm gene expression levels for a subset of representative genes in each cluster. We also performed a Kaplan-Meier analysis of survival and disease progression based on the 6 years of clinical follow-up. RESULTS: Our reverse transcription-PCR results confirmed the upregulation of representative genes for each cluster, whereas clinical analysis documents that all stage I cases that progressed to stage II and beyond were in poor and intermediate prognosis clusters 1 and 3 and none were in favorable prognosis cluster 2. This analysis also identified certain genes that were preferentially expressed in favorable (e.g., WIF-1) versus poor (e.g., IL-17F) prognosis clusters. CONCLUSION: This work suggests that it may be possible to stratify CTCL patients into low-risk, intermediate-risk, and high-risk groups based on gene expression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".