Expression of TCF3 target genes defines a subclass of diffuse large B-cell lymphoma characterized by up-regulation of MYC target genes and poor clinical outcome following R-CHOP therapy
Bibliographic record
Abstract
TCF3 is a lymphopoietic transcription factor that acquires somatic driver mutations in diffuse large B-cell lymphoma (DLBCL). Hypothesizing that expression patterns of TCF3-regulated genes can inform clinical management, we found that unsupervised clustering analysis with 15 TCF3-regulated genes and eight additional ones resolved local DLBCL cases into two main clusters, denoted Groups A and B, of which Group A manifested inferior overall survival (OS, p = 0.0005). We trained a machine learning model to classify samples into the Groups based on expression of the 23 transcripts in an independent validation cohort of 569 R-CHOP-treated DLBCL cases. Group A overlapped with the ABC cell-of-origin subgroup but its prognostic power was superior. GSEA analysis demonstrated asymmetric expression of 30 gene sets between the Groups, pointing to biological differences. We present, validate and make available a novel method to assign DLBCL cases into biologically-distinct groups with divergent OS following R-CHOP therapy.
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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 | 0.001 | 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) | 0.000 | 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".