DLBCLone: A unified framework for neighbourhood-based genetic subtyping of lymphomas
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Genetic subtyping of diffuse large B-cell lymphoma (DLBCL) has been slow to gain clinical adoption. Available classifiers either leave many tumours unclassified or depend on exome-wide features and copy-number profiles, which are not always available in routine practice. We introduce DLBCLone, a neighbourhood-based framework that enables panel-aware genetic subtyping compatible with existing taxonomies. DLBCLone learns a 2-D reference map of mutation profiles (UMAP) from a labeled training cohort, freezes this map, and deterministically projects new cases into the same latent space. Class labels are then inferred by weighted K-nearest neighbours, limiting over-assignment by considering the local density of unclassified neighbours. By default, classification thresholds optimize per-class balanced accuracy, but can be adjusted to suit study needs. The framework is intended to emulate (or “clone”) existing schemas such as LymphGen or DLBClass. Trained on a harmonized cohort of 2,130 DLBCLs, DLBCLone classifiers for different gene panels achieved consistently improve classification rates relative to fixed-threshold baselines while maintaining a reasonable per-class performance. On an in-house cohort of 323 patients, it assigned an additional 98 samples without compromising accuracy relative to LymphGen. On an external exome-sequenced subset from a 1,001-patient cohort, DLBCLone achieved a 51% classification rate (vs 36% for LymphGen) at an overall accuracy of 0.70. Compared with another LymphGen approximator (LymphPlex), DLBCLone reached a 74% classification rate (vs 55%). In general, the DLBCLone-reclassified tumours had molecular features consistent with their new labels. DLBCLone provides a deterministic, reproducible, and extensible approach to genetic subtyping under real-world constraints, facilitating prospective studies that rely on either targeted panels or more comprehensive sequencing strategies. DLBCLone is open source and available in the GAMBLR.predict package ( https://github.com/morinlab/gamblr.predict ).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it