The Use of Transcriptional Profiling to Improve Personalized Diagnosis and Management of Cutaneous T-cell Lymphoma (CTCL)
Bibliographic record
Abstract
PURPOSE: Although many patients with mycosis fungoides presenting with stage I disease enjoy an indolent disease course and normal life expectancy, about 15% to 20% of them progress to higher stages and most ultimately succumb to their disease. Currently, it is not possible to predict which patients will progress and which patients will have a stable disease. Previously, we conducted microarray analyses with RT-PCR validation of gene expression in biopsy specimens from 60 patients with stage I-IV cutaneous T-cell lymphoma (CTCL), identified three distinct clusters based upon transcription profile, and correlated our molecular findings with 6 years of clinical follow-up. EXPERIMENTAL DESIGN: We test by RT-PCR within our prediction model the expression of about 240 genes that were previously reported to play an important role in CTCL carcinogenesis. We further extend the clinical follow-up of our patients to 11 years. We compare the expression of selected genes between mycosis fungoides/Sézary syndrome and benign inflammatory dermatoses that often mimic this cancer. RESULTS: Our findings demonstrate that 52 of the about 240 genes can be classified into cluster 1-3 expression patterns and such expression is consistent with their suggested biologic roles. Moreover, we determined that 17 genes (CCL18, CCL26, FYB, T3JAM, MMP12, LEF1, LCK, ITK, GNLY, IL2RA, IL26, IL22, CCR4, GTSF1, SYCP1, STAT5A, and TOX) are able to both identify patients who are at risk of progression and also distinguish mycosis fungoides/Sézary syndrome from benign mimickers. CONCLUSIONS: This study, combined with other gene expression analyses, prepares the foundation for the development of personalized molecular approach toward diagnosis and treatment 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 | 0.001 |
| 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 | 0.001 | 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".