MODULAR GENE EXPRESSION CHANGES IN THE SLEEK PHASE 2 STUDY OF UPADACITINIB AND ABBV-599 IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS
Bibliographic record
Abstract
PV182 / #43 Poster Topic: AS20 - Precision Medicine Background/Purpose In systemic lupus erythematosus (SLE), targeting both type I interferon (IFN) and B cell pathways should be a promising therapeutic approach since each provides independent and additive contributions to pathology. Upadacitinib (UPA) is a Janus kinase (JAK) inhibitor acting at several receptors both directly and indirectly transmitting IFN signals. Elsubrutinib inhibits Bruton’s tyrosine kinase (BTK) associated with B cell signaling. A phase 2 SLE trial ( NCT03978520 ) of UPA, elsubrutinib, or combination (ABBV-599) found that both UPA and ABBV-599 met the 24-week endpoint of SLE Responder Index 4 (SRI-4). However, in the overall population, efficacy of UPA alone was comparable to ABBV-599. To characterize the mechanism of action of UPA and ABBV-599 in patients with SLE. Methods This randomized, double-blind, phase 2 trial collected blood at baseline, and weeks 2, 12, 24, and 48. RNAseq analysis was compared from 147 patients who received placebo, UPA (30 mg once daily (QD)), or ABBV-599 (elsubrutinib 60 mg + UPA 30 mg QD). Limma mixed-model analyses were used to determine differentially expressed genes between timepoints and to predict responders/nonresponders totherapy. Weighted gene co-expression network analysis (WGCNA) was used to construct gene networks and to determine changes of these networks (significance by paired Wilcoxon test). Total immunoglobulin G (IgG), immunoglobulin M (IgM), and anti-double-stranded DNA (anti-dsDNA) IgG concentrations were measured from serum using an immunoturbidimetric assay and enzyme-linked immunosorbent assay. Immune cell subsets and counts were identified using flow cytometry. Results Differentially expressed genes (FDR <.05) for all timepoints compared with baseline were detected for both UPA and ABBV-599, but not placebo. Distinct differences between UPA and ABBV-599 in the number of differentially expressed genes at all timepoints suggested unique mechanisms for the drugs. WGCNA of the 147 baseline samples formed 14 modules of highly correlated genes and 12 of these modules overlapped previously identified WGCNA-derived SLE gene modules. Module trait correlation demonstrated significant relationships between module scores and clinical traits ( P <.01; R >.3), but not with response to treatment, in agreement with baseline heterogeneity of gene module expression for responders demonstrated by hierarchical clustering (Figure 1). The change in module eigengene values demonstrated that the BTK inhibitor led to a significant increase in neutrophil module scores, and flow cytometry demonstrated a significant increase in the percentage of neutrophils in ABBV-599, but not UPA–treated patients. However, ABBV-599 did not demonstrate a unique effect on B cell modules, with comparable significant impacts of both UPA and ABBV-599 on post-baseline decrease in total IgG, anti-dsDNA antibodies and increase in B cells by flow cytometry. As expected, UPA significantly changed gene module values for type I IFN compared with placebo and also decreased the expression of basophil, cell cycle, and cytotoxic T cell gene modules. Figure 1. Baseline heterogeneity in SLE gene module expression Conclusions ABBV-599 increased neutrophil and other myeloid cell gene module scores compared to UPA, but this effect could have neutral impact related to its role in neutrophil extravasation. The BTK inhibitor in combination with UPA had little additional effect on B cells compared to UPA alone. In addition to the expected decreased expression of type I IFN gene modules, UPA treatment was associated with decreased basophil, cytotoxic T cell, and cell cycle gene modules accounting for its efficacy in SLE patients with different baseline gene expression patterns.
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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 | 0.001 | 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) | 0.000 | 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".