Identifying lupus Patient Subsets Through Immune Cell Deconvolution of Gene Expression Data in Two Atacicept Phase II
Matthew Studham1, Cristina Vazquez-Mateo1, Eileen Samy1
1EMD Serono, Billerica, MA, United States.
ACR Open Rheumatology
|September 15, 2023
Summary
Gene signatures identified systemic lupus erythematosus (SLE) patient subsets more likely to respond to atacicept treatment. This finding could personalize SLE therapy by targeting specific molecular profiles for better outcomes.
Area of Science:
- Immunology
- Genomics
- Pharmacology
Background:
- Systemic lupus erythematosus (SLE) is a complex autoimmune disease with variable patient responses to treatment.
- Identifying predictive biomarkers is crucial for optimizing therapeutic strategies in SLE.
Purpose of the Study:
- To identify patient subsets within SLE clinical trials likely to respond to atacicept using cell-based gene signatures.
- To stratify patients based on molecular profiles to predict treatment efficacy.
Main Methods:
- Applied immune cell deconvolution algorithms to whole blood gene expression data from APRIL-SLE and ADDRESS II trials.
- Utilized Affymetrix gene array and RNA sequencing data to define patient clusters based on cell subset signatures.
- Assessed clinical characteristics, biomarkers, and treatment response (atacicept) within identified patient clusters.
Main Results:
- Five distinct patient clusters (P1-5) were identified based on dominant immune cell signatures.
- Clusters P2, P4, and P5 showed higher flare rates and a greater treatment effect of atacicept compared to P1 and P3.
- In ADDRESS II, patients in P2, P4, and P5 exhibited reduced placebo response and enhanced atacicept efficacy.
Conclusions:
- Exploratory analysis reveals distinct molecularly defined patient subsets in SLE.
- These subsets demonstrate differential responses to atacicept, suggesting potential for personalized treatment strategies.
