The Ectopic Expression of Meiosis Regulatory Genes in Cutaneous T-Cell Lymphomas (CTCL)
Bibliographic record
Abstract
Cancer testis (CT) antigens, under normal circumstances are uniquely expressed in testicular germ cells. Recent research has shown that meiosis-specific CT (meiCT) antigens are ectopically expressed in cutaneous T-cell lymphoma (CTCL) and may contribute to increased genomic instability. The aberrant activation of meiosis genes in a mitotic cell is now recognized as a distinctive process, “meiomitosis”. We have previously demonstrated the ectopic expression of several meiCT antigens in 9 patient-derived CTCL cell lines and in expanded peripheral T lymphocytes isolated from Sézary Syndrome patients. In this study we analyzed the transcriptional expression of meiCT genes in Sézary Syndrome patients and healthy controls using publicly-available RNA sequencing (RNA-Seq) data. We corroborated our in silico analysis by examining the expression of 5 meiCT proteins in formalin-fixed, paraffin-embedded (FFPE) lesional samples from CTCL patients. Our results show significant differential gene expression of STAG3, SGO2, SYCP3 and DMC1 in a cohort of Sézary Syndrome patients when compared to healthy controls. Additionally, our study demonstrates a heterogenous expression of meiCT genes involved in initiation (STRA8), sister chromatin cohesion (STAG3, SGO2), homologous chromosome synapsis (SYCP3) and homologous recombination (DMC1) in atypical lymphocytes in FFPE samples. Our results further confirm the ectopic expression of meiCT genes in CTCL which indicates that CTCL malignant cells likely undergo the process of cancer meiomitosis, as opposed to a typical mitotic division. The ectopic expression of meiCT genes together with investigations into the functional mechanisms of cancer meiomitosis will help provide a foundation to develop novel diagnostic tests to distinguish CTCL from benign inflammatory dermatoses and may enable us to develop additional targeted therapies for patients with this malignancy.
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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 | 0.000 |
| 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".