The <scp>MCL</scp>35 gene expression proliferation assay predicts high‐risk <scp>MCL</scp> patients in a Norwegian cohort of younger patients given intensive first line therapy
Bibliographic record
Abstract
Patients with mantle cell lymphoma (MCL) generally have a dismal prognosis. Intensified induction treatment with rituximab and high dose cytarabine (R_HDAC), and consolidation with high-dose therapy with autologous stem cell support has resulted in 10-year overall survival (OS) higher than 60%. However, the clinical course varies. Diagnostic tools capable of stratifying patients include the MCL International Prognostic Index (MIPI), gene expression-based proliferation signature, Ki-67 proliferation index or tumour cell morphology. Here, we tested the performance of a newly developed Nanostring-based RNA expression-based proliferation assay (MCL35) on formalin-fixed paraffin-embedded tumour tissue from younger patients recruited in or treated according to Nordic MCL protocols compared to the prognosticators listed above. Seventy-four patients were included and the assay performed well in all cases except four, which had inadequate RNA quality. The patients were evenly distributed in the MCL35 low-, intermediate- and high-risk categories. MCL35 low- and intermediate- risk groups had overlapping progression-free survival (PFS), while patients in the high-risk category had significantly inferior PFS. Combining MCL35 with MIPI or the MIPI-C (MIPI with the addition of binary Ki67 score +/-30%) showed a better discrimination than either assessment alone. In conclusion, the MCL35 assay alone or combined with MIPI or MIPI-C scores can identify patients who still have a dismal outcome despite intensified treatment.
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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".