Biological and Clinical Relevance of Associated Genomic Alterations in MYD88 L265P and non-L265P–Mutated Diffuse Large B-Cell Lymphoma: Analysis of 361 Cases
Bibliographic record
Abstract
Abstract Purpose: MYD88 mutations, notably the recurrent gain-of-function L265P variant, are a distinguishing feature of activated B-cell like (ABC) diffuse large B-cell lymphoma (DLBCL), leading to constitutive NFκB pathway activation. The aim of this study was to examine the distinct genomic profiles of MYD88-mutant DLBCL, notably according to the presence of the L265P or other non-L265P MYD88 variants. Experimental Design: A cohort of 361 DLBCL cases (94 MYD88 mutant and 267 MYD88 wild-type) was submitted to next-generation sequencing (NGS) focusing on 34 genes to analyze associated mutations and copy number variations, as well as gene expression profiling, and clinical and prognostic analyses. Results: Importantly, we highlighted different genomic profiles for MYD88 L265P and MYD88 non-L265P–mutant DLBCL, shedding light on their divergent backgrounds. Clustering analysis also segregated subgroups according to associated genetic alterations among patients with the same MYD88 mutation. We showed that associated CD79B and MYD88 L265P mutations act synergistically to increase NFκB pathway activation, although the majority of MYD88 L265P–mutant cases harbors downstream NFκB alterations, which can predict BTK inhibitor resistance. Finally, although the MYD88 L265P variant was not an independent prognostic factor in ABC DLBCL, associated CD79B mutations significantly improved the survival of MYD88 L265P–mutant ABC DLBCL in our cohort. Conclusions: This study highlights the relative heterogeneity of MYD88-mutant DLBCL, adding to the field's knowledge of the theranostic importance of MYD88 mutations, but also of associated alterations, emphasizing the usefulness of genomic profiling to best stratify patients for targeted therapy. Clin Cancer Res; 23(9); 2232–44. ©2016 AACR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".