Decoding the Epigenetic and Transcriptional Signatures of Pathogenic B-Cells in Patients at High Risk of Transformation to Lymphoma
Bibliographic record
Abstract
Systemic Lupus Erythematosus (SLE) is a chronic, heterogenous autoimmune disorder characterized by diverse clinical manifestations and treatment responses due to various genetic, epigenetic, and immune-related factors (Kaul et al., 2016). SLE is characterized by the expansion of a rare B cell subset, double negative (DN), with the majority of these being DN2 cells (ZEB2hiITGAXhi, Jenks et al., 2018). These cells are known to be the main cause of SLE pathogenicity, and are precursors of pathogenic antibody secreting cells (Elsner & Shlomchik, 2020). SLE patients can experience several life threatening complications, including a 3-fold higher risk of developing hematological malignancies, particularly B cell lymphomas (Clarke et al., 2021). While recent studies have linked mutations involved in B cell clonal expansion to alterations in SLE pathogenesis-driving cells, there is limited direct clinical evidence on how this malignancy transformation begins (Pullabhatla et al., 2018; Singh et al., 2020; Venturutti et al., 2023). Therefore, studying DN2 cells in autoimmune disorders represents a unique opportunity to identify transcriptional, genetic and epigenetic alterations that might serve as pre-malignancy indicators in these patients. To characterize the somatic mutational landscape of active and inactive B-cells in SLE, we profiled a cohort of 14 SLE patients and 3 healthy controls (HC) using whole-exome sequencing. Three subsets of peripheral blood mononuclear cells were sorted: naïve B cells [CD19+IgD+], memory B cells [CD19+IgD-CD27+] and DN B cells [CD19+IgD-CD27-]. Using nf-core/sarek (Garcia et al., 2020), we identified several somatic alterations in the DN cell population that were previously found in lymphoid malignancies, such as EP300, a histone acetyltransferase (Huang et al., 2021), ITPKB, a kinase involved in the PI3K-AKT signaling (Tiacci et al., 2018), and DUSP22 tumor suppressor (Melart et al., 2016). These findings suggest the possibility of DN2 cells as precursors of lymphoid cancers, thus prompting the need to further characterize DN2 cellular state. To investigate the transcriptional and epigenetic alterations in DN2 cells, we conducted single-cell multiome sequencing on a pilot cohort of 4 SLE patients. In total we analyzed 34,472 cells from the SLE cohort including 2,664 DN2 cells, and 24,162 cells from the HC samples including 2,571 DN cells. Differential gene expression analysis revealed significant enrichment of Interferon (IFN) Alpha and Gamma in the SLE DN2 cells, and over-expression of CD86 compared to HC-DN cells, as previously reported (Jenks et al., 2018). However, by analyzing variations in transcriptomic profiles obtained from scRNA using consensus non-negative matrix factorization, we uncovered a unique and novel expression program in SLE DN2 cells that was absent in HC DN cells. This program was enriched in hematological malignancy-associated IFN/MHC class I and II pathways previously found in heme-related cancers (Gavish et al., 2023). This indicates an inherent bias in SLE DN2 cells towards a more malignant phenotype. Further, we discovered that highly accessible genes in SLE DN2 cells were enriched in a signature corresponding to activated B cell diffuse large B cell lymphoma (DLBCL) state 4 cells (Steen et al., 2021). These cells are identified as pre-plasmablast DLBCL cells and exhibit a more aggressive and unfavorable phenotype associated with poorer clinical outcomes. Motif enrichment analysis in open chromatin regions of SLE DN2 cells further revealed enrichment of the POU family of transcription factors (TFs), such as POU2F2 (encoding OCT2) and POU3F1 (encoding OCT-6) among others. OCT2 is known to be highly active in B cells, and a mutated form of this TF, has been found in various lymphomas with a different repertoire of targets (Hodson et al., 2016). Interestingly, the POU2F2 gene harbors a frameshift mutation in the Memory and DN subsets of an active SLE patient in our cohort. In summary, we identified distinctive transcriptional and epigenetic profiles in SLE DN2 cells, enriched in pathways associated with hematological malignancies and containing key oncogenic mutations. These findings suggest that the malignant phenotypic alterations in DN2 cells could serve as biomarkers of pre-malignancy, providing new insights into the pathogenesis and potential transformation of autoimmune diseases into malignancies.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".