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Record W4405040132 · doi:10.1182/blood-2024-202826

Decoding the Epigenetic and Transcriptional Signatures of Pathogenic B-Cells in Patients at High Risk of Transformation to Lymphoma

2024· article· en· W4405040132 on OpenAlexaff
Arghavan Ashouri, Pathum Kossinna, Xuyao Li, Jianfan Nie, Mohamed Ghaith Majjani, Joanna Kokalovski, Leandro Venturutti, Zahi Touma, Federico Gaiti

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreSpinal Cord Injury BCUniversity of British ColumbiaUniversity Health Network
Fundersnot available
KeywordsEpigeneticsLymphomaBiologyGeneticsTransformation (genetics)Cancer researchMedicineImmunologyGene

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.239
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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