Next-Generation Sequencing in Diffuse Large B-Cell Lymphoma Highlights Molecular Divergence and Therapeutic Opportunities: a LYSA Study
Bibliographic record
Abstract
PURPOSE: Next-generation sequencing (NGS) has detailed the genomic characterization of diffuse large B-cell lymphoma (DLBCL) by identifying recurrent somatic mutations. We set out to design a clinically feasible NGS panel focusing on genes whose mutations hold potential therapeutic impact. Furthermore, for the first time, we evaluated the prognostic value of these mutations in prospective clinical trials. EXPERIMENTAL DESIGN: A Lymphopanel was designed to identify mutations in 34 genes, selected according to literature and a whole exome sequencing study of relapsed/refractory DLBCL patients. The tumor DNA of 215 patients with CD20(+)de novo DLBCL in the prospective, multicenter, and randomized LNH-03B LYSA clinical trials was sequenced to deep, uniform coverage with the Lymphopanel. Cell-of-origin molecular classification was obtained through gene expression profiling with HGU133+2.0 Affymetrix GeneChip arrays. RESULTS: The Lymphopanel was informative for 96% of patients. A clear depiction of DLBCL subtype molecular heterogeneity was uncovered with the Lymphopanel, confirming that activated B-cell-like (ABC), germinal center B-cell like (GCB), and primary mediastinal B-cell lymphoma (PMBL) are frequently affected by mutations in NF-κB, epigenetic, and JAK-STAT pathways, respectively. Novel truncating immunity pathway, ITPKB, MFHAS1, and XPO1 mutations were identified as highly enriched in PMBL. Notably, TNFAIP3 and GNA13 mutations in ABC patients treated with R-CHOP were associated with significantly less favorable prognoses. CONCLUSIONS: This study demonstrates the contribution of NGS with a consensus gene panel to personalized therapy in DLBCL, highlighting the molecular heterogeneity of subtypes and identifying somatic mutations with therapeutic and prognostic impact. Clin Cancer Res; 22(12); 2919-28. ©2016 AACRSee related commentary by Lim and Elenitoba-Johnson, p. 2829.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 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) | 0.000 | 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 teacher head, 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".