A single-cell multiomics analysis of IRF4 in mediating treatment resistance in ibrutinib-treated chronic lymphocytic leukemia (CLL).
Bibliographic record
Abstract
7051 Background: The Bruton tyrosine kinase (BTK) inhibitor ibrutinib (ibr) has revolutionized the treatment of CLL, showing efficacy in the majority of patients. Nevertheless, resistance occurs in a subset of patients, often with dismal clinical outcomes. Ibr resistance occurs through several mechanisms, including mutations affecting BTK or PLCG2, or upregulation of alternative survival pathways. We investigated at the single-cell level the molecular mechanisms that underlie ibr resistance in CLL cells and their interactions with non-malignant tumor microenvironment (TME) cell populations. Methods: We retrospectively identified 7 CLL patients with ibr resistance (as second line therapy). Samples were obtained before ibr treatment and at progression. Chromium Single Cell Multiome ATAC + Gene Expression (GEX) (10x Genomics) was applied to a mixture of CD19+CD5+ and CD19- cells to jointly analyze DNA accessibility and gene expression in the same single nuclei of CLL B and TME cells. An average of 4,800 cells (range 2,600-15,000) per sample was retained after QC filtering. Principal component analysis (PCA) was performed on GEX counts. ATAC peaks were called with MACS2, and latent semantic indexing was used for dimensionality reduction. We then computed a weighted nearest neighbour graph, identified clusters using the SLM algorithm and performed cell type assignment with established marker genes. Results: We identified 28 distinct cell clusters, representing CLL B-cells or cell types that constitute the TME, including CD4+ and CD8+ T cells, monocytes, dendritic and NK cells. CLL B-cell clusters were patient and timepoint-specific, while TME clusters were occupied by cells from multiple patients. Five patients showed multiple distinct CLL B-cell clusters pre-treatment or at progression, reflecting cellular heterogeneity within the tumor compartment. Interestingly, some clusters that were dominated by post-treatment CLL B-cells also included cells from pre-treatment samples, suggesting pre-existence of the clone giving rise to the relapse. For each patient, we performed pairwise differential expression between CLL B-cell clusters. Gene ontology analysis revealed increased activation of IRF4 and MHC-related pathways in CLL B-cells at progression compared to pre-treatment in 6 and 4 out of 7 patients, respectively. The elevated IRF4 pathway activity was underpinned by increased chromatin accessibility in IRF4 signature genes. Conclusions: Differential blockade of B-cell receptor signaling by ibr has been reported to downregulate activity of IRF4, a key transcription factor in B-cell activation. Our results suggest that CLL B-cells that are resistant to ibrutinib have re-activated IRF4 activity beyond pre-treatment levels, implying a potential role for IRF4 in ibrutinib resistance.
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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 | 0.000 |
| 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 | 0.000 | 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".