Revisiting Reddy: A DLBCL Do-over
Bibliographic record
Abstract
Abstract The 2017 study by Reddy et al described the comprehensive characterization of somatic drivers of diffuse large B-cell lymphoma using whole exome sequencing. 1 After additional large studies relying on exome or whole genome sequencing were published, several oddities unique to the Reddy results have emerged. Seeking to explain the discrepancies, we reanalyzed their data using established open-source pipelines. This revealed thousands of mutations that could not be independently reproduced by these pipelines and a larger set of high-quality mutations that were not reported by Reddy. This caused an artificial under-representation of the mutation prevalence in many genes including clinically relevant hot spots affecting EZH2 and CD79B . More generally, the study had an under-representation of mutations in DLBCL genes that disproportionately affected genes known to have the highest mutation rates. The missing variants and the spurious variants can be attributed to distinct problems with the analytical approaches employed in that study. Our analysis also identified strong associations between mutations and patient outcome including TP53, KMT2D and PIM1 , which were not found in the Reddy study. Overall, we demonstrate that this combination of errors influenced many of the central novel findings from their study rendering their results largely non-replicable. The full results of our analyses are included as supplemental items as a resource for other researchers with an interest in the genetics of B-cell lymphomas.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".