Epigenetic Profiling of Primary DLBCLs Reveals Novel DNA Methylation-Based Clusters and New Underlying Mechanisms of Lymphomagenesis
Bibliographic record
Abstract
Abstract Abstract 556 Epigenetic profiling of primary DLBCLs reveals novel DNA methylation-based clusters and new underlying mechanisms of lymphomagenesis. Nyasha Chambwe, Matthias Kormaksson, Subhajyoti De, Franziska Michor, Nathalie Johnson, David W. Scott, Randy D. Gascoyne, Ari Melnick, Fabien Campagne and Rita Shaknovich. Diffuse Large B Cell Lymphomas (DLBCLs) are a heterogeneous group of diseases from the clinical, molecular and genetic standpoints. While gene expression profiling has identified clinically and biologically relevant DLBCL subtypes, there is still considerable heterogeneity beyond what has been resolved through transcriptional and genetic profiling. It is increasingly clear that lesions in epigenetic regulatory proteins and transcription factors are a hallmark of DLBCL, which suggests that aberrant epigenetic programming is likely to be a significant factor in these tumors. Our recent preliminary data suggests that aberrant DNA methylation is also widespread in DLBCL and contributes to the abnormal expression patterns of GCB and ABC DLBCLs. Moreover recent data in the setting of AML show that DNA methylation profiles delineate disease subtypes not captured through transcriptional or genetic profiling. We hypothesized that DNA methylation profiles would allow us to identify new, biologically significant DLBCL subtypes. We therefore examined the DNA methylation of over 140 patients with DLBCL using the HELP assay covering multiple CpGs at over 14,000 gene loci. We next performed an unbiased (unsupervised) analysis of probesets that display significant variability (n=3,005), using K-means consensus clustering. This procedure identified four robust DLBCL subtypes based on epigenetic profiles. To identify the genes that define these four clusters we next performed supervised analysis of the DLBCL subtypes including the normal counterpart germinal center B-cells as a normal control, using three independent statistical methods. 46 genes defined cluster A, 236 genes defined cluster B, 376 genes defined cluster C and 1271 genes defined cluster D (selected genes displayed change in methylation of at least 30% at BH corrected p-value < 0.05). Each of these epigenetically defined DLBCL subtypes featured aberrant DNA methylation of genes and pathways with potential relevance to pathogenesis. For example cluster A was notable for aberrant DNA methylation of REL, STAT3, CD30; cluster B for aberrant methylation of the TNFa and IFN1 networks; cluster C of IDH2, FOXG1 genes; and cluster D of CDKN2A, ATF3, FOXL3 genes. Other defining characteristics of Cluster D was enrichment for ABC DLBCLs (Fisher exact test, p=0.007) and most remarkably, marked intra-tumor and inter-individual heterogeneity of DNA methylation patterning. This latter feature is suggestive of potential epigenetic clonal complexity and failure to properly control the boundaries of methylated regions of the genome in these tumors. Clusters A and B revealed enrichment for GC features and cluster B had increased expression of MUM1 (Fisher exact test, p=0.007 and p=0.002). Furthermore, we noted that higher expression of DNMT3B and DNMT3L were associated with hypermethylation in DLBCL samples, while higher expression level of AICDA was associated with aberrant hypomethylation in DLBCLs as compared to normal GC B-cells. Higher expression levels of epigenetic modifiers EZH2 and MBD4 was associated with greater heterogeneity of DNA methylation patterning compared to the normal methylation pattern in germinal center B cells. Collectively, the data indicate that DLBCLs are composed of entities defined by specific DNA methylation profiles that only partially overlap with the ABC and GCB classification. The DLBCL subtypes display perturbation of genes likely to play significant biological roles, and aberrant methylation patterning can be traced in part to aberrant expression of epigenetic regulators including DNA methyltransferases, AICDA and EZH2. Disclosures: No relevant conflicts of interest to declare.
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 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 | 0.000 |
| 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) | 0.001 | 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".