Quantitative Real-Time Reverse Transcription-PCR Assay for Cyclin D1 Expression: Utility in the Diagnosis of Mantle Cell Lymphoma
Bibliographic record
Abstract
BACKGROUND: The t(11;14)(q13;q32) translocation present in the majority of mantle cell lymphomas (MCLs) places the cyclin D1 gene under the control of immunoglobulin transcriptional regulatory elements, causing overexpression of cyclin D1. Quantification of cyclin D1 expression can distinguish MCL from other lymphomas. METHODS: A quantitative real-time reverse transcription (RT)-PCR assay was developed for cyclin D1 mRNA suitable for use with RNA extracted from fresh and formalin-fixed, paraffin-embedded tissues. Specimens were amplified in an Applied Biosystems Model 7700 Sequence Detection System in reactions containing primers and probes for cyclin D1 and a control gene, beta(2)-microglobulin. Relative expression of the two genes was standardized against a control MCL cell line, M02058. RESULTS: The range of cyclin D1 expression among 20 MCLs was substantially higher than that in other lymphomas and reactive lymph nodes. By choosing an optimal cutoff point for assessing overexpression, the sensitivity and specificity of the assay for the diagnosis of MCL in lymph node specimens both approached 100%: Overexpression was detected in 20 of 20 MCLs, but in none of 21 non-mantle-cell lymphomas or 10 reactive lymph nodes. CONCLUSIONS: Quantitative real-time RT-PCR for cyclin D1 overexpression provides a rapid diagnostic test with clinical utility in the diagnosis of MCL.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".