WHOLE GENOME SEQUENCING OF MATCHED PRIMARY AND RELAPSED DLBCL REVEALS DISTINCT EVOLUTIONARY DYNAMICS ASSOCIATED WITH RELAPSE TIMING
Bibliographic record
Abstract
Introduction: Diffuse large B-cell lymphoma (DLBCL) is a genetically heterogeneous disease with poor outcomes for the 40% of patients who relapse or are refractory to frontline therapy. To explore genetic changes associated with relapse, we applied fluorescence in situ hybridization (FISH), gene expression profiling, and whole genome sequencing (WGS) to two or more DLBCL biopsies from patients with relapsed or refractory disease. Methods: Archival paraffin biopsies with DLBCL morphology were selected from 109 R-CHOP-treated patients, of which 55% had a detectable low-grade lymphoma at some point in their disease course. Recurrences were defined as primary refractory (REFR, n = 24), early relapse (ER, n = 29), or late relapse (LR, n = 56) within <9, 9-24, or >24 months from the first DLBCL biopsy, respectively. Break-apart FISH (n = 101 pairs) was used to identify oncogene translocations and the NanoString DLBCL90 assay (n = 106 pairs) identified cell-of-origin (COO) subgroups. Simple somatic mutations (SSMs) were identified from WGS (n = 52 tumour pairs plus matched constitutional DNA) using an ensemble of four variant callers (Strelka2, LoFreq, Mutect2 and SAGE), and somatic copy number variations (CNVs) were detected using Battenberg. The LymphGen classifier was applied to assign genetic subgroups. Results: MYC and BCL6 translocation status was discordant between time points in 16% and 15% of tumour pairs, respectively. BCL2 translocations were concordant in all cases, consistent with their acquisition during VDJ recombination early in B-cell differentiation. Among pairs with DLBCL90 data, 9% (one ER and 6 LR) were discordant for COO, excluding cases that were unclassified at either time point. Of the 52 pairs with WGS, LymphGen classified 27 primary tumors and 31 secondary tumors, and the classification was discordant in only one of 23 pairs that were classified at both time points. The mutational repertoire between time points in REFR pairs was strikingly consistent, reflecting little evolution. Interestingly, despite shared mutations and largely concordant genetic subgroup assignments, ER and LR pairs exhibited striking genetic divergence with mutation patterns indicative of branching evolution from a shared precursor cell. In spite of this divergence, unique driver mutations and aberrant somatic hypermutation tended to affect the same genes in both tumours. Figure 1 shows a representative LR pair from a patient with no record of low-grade disease. Keywords: Genomics, Epigenomics, and Other -Omics, Tumor Biology and Heterogeneity, Aggressive B-cell non-Hodgkin lymphoma Conflicts of interests pertinent to the abstract R. D. Morin Other remuneration: Named inventor on the DLBCL90 patent. D. W. Scott Other remuneration: Named inventor on the DLBCL90 patent.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".