Dataset Information

Identifying lupus Patient Subsets Through Immune Cell Deconvolution of Gene Expression Data in Two Atacicept Phase II Studies.

ABSTRACT:

Objective

To use cell-based gene signatures to identify patients with systemic lupus erythematous (SLE) in the phase II/III APRIL-SLE and phase IIb ADDRESS II trials most likely to respond to atacicept.

Methods

A published immune cell deconvolution algorithm based on Affymetrix gene array data was applied to whole blood gene expression from patients entering APRIL-SLE. Five distinct patient clusters were identified. Patient characteristics, biomarkers, and clinical response to atacicept were assessed per cluster. A modified immune cell deconvolution algorithm was developed based on RNA sequencing data and applied to ADDRESS II data to identify similar patient clusters and their responses.

Results

Patients in APRIL-SLE (N = 105) were segregated into the following five clusters (P1-5) characterized by dominant cell subset signatures: high neutrophils, T helper cells and natural killer (NK) cells (P1), high plasma cells and activated NK cells (P2), high B cells and neutrophils (P3), high B cells and low neutrophils (P4), or high activated dendritic cells, activated NK cells, and neutrophils (P5). Placebo- and atacicept-treated patients in clusters P2,4,5 had markedly higher British Isles Lupus Assessment Group (BILAG) A/B flare rates than those in clusters P1,3, with a greater treatment effect of atacicept on lowering flares in clusters P2,4,5. In ADDRESS II, placebo-treated patients from P2,4,5 were less likely to be SLE Responder Index (SRI)-4, SRI-6, and BILAG-Based Combined Lupus Assessment responders than those in P1,3; the response proportions again suggested lower placebo effect and a greater treatment differential for atacicept in P2,4,5.

Conclusion

This exploratory analysis indicates larger differences between placebo- and atacicept-treated patients with SLE in a molecularly defined patient subset.

SUBMITTER: Studham M

PROVIDER: S-EPMC10570667 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Publications

Identifying lupus Patient Subsets Through Immune Cell Deconvolution of Gene Expression Data in Two Atacicept Phase II Studies.

Studham Matthew M Vazquez-Mateo Cristina C Samy Eileen E Haselmayer Philipp P Aydemir Aida A Rolfe P Alexander PA Merrill Joan T JT Morand Eric F EF DeMartino Julie J Kao Amy A Townsend Robert R

ACR open rheumatology 20230914 10

<h4>Objective</h4>To use cell-based gene signatures to identify patients with systemic lupus erythematous (SLE) in the phase II/III APRIL-SLE and phase IIb ADDRESS II trials most likely to respond to atacicept.<h4>Methods</h4>A published immune cell deconvolution algorithm based on Affymetrix gene array data was applied to whole blood gene expression from patients entering APRIL-SLE. Five distinct patient clusters were identified. Patient characteristics, biomarkers, and clinical response to ata ...[more]

PMID: 37710418

Similar Datasets

Project description:BackgroundTo facilitate the investigation of the pathogenic roles played by various immune cells in complex tissues such as tumors, a few computational methods for deconvoluting bulk gene expression profiles to predict cell composition have been created. However, available methods were usually developed along with a set of reference gene expression profiles consisting of imbalanced replicates across different cell types. Therefore, the objective of this study was to create a new deconvolution method equipped with a new set of reference gene expression profiles that incorporate more microarray replicates of the immune cells that have been frequently implicated in the poor prognosis of cancers, such as T helper cells, regulatory T cells and macrophage M1/M2 cells.MethodsOur deconvolution method was developed by choosing ε-support vector regression (ε-SVR) as the core algorithm assigned with a loss function subject to the L1-norm penalty. To construct the reference gene expression signature matrix for regression, a subset of differentially expressed genes were chosen from 148 microarray-based gene expression profiles for 9 types of immune cells by using ANOVA and minimizing condition number. Agreement analyses including mean absolute percentage errors and Bland-Altman plots were carried out to compare the performances of our method and CIBERSORT.ResultsIn silico cell mixtures, simulated bulk tissues, and real human samples with known immune-cell fractions were used as the test datasets for benchmarking. Our method outperformed CIBERSORT in the benchmarks using in silico breast tissue-immune cell mixtures in the proportions of 30:70 and 50:50, and in the benchmark using 164 human PBMC samples. Our results suggest that the performance of our method was at least comparable to that of a state-of-the-art tool, CIBERSORT.ConclusionsWe developed a new cell composition deconvolution method and the implementation was entirely based on the publicly available R and Python packages. In addition, we compiled a new set of reference gene expression profiles, which might allow for a more robust prediction of the immune cell fractions from the expression profiles of cell mixtures. The source code of our method could be downloaded from https://github.com/holiday01/deconvolution-to-estimate-immune-cell-subsets.

Dataset Information

Identifying lupus Patient Subsets Through Immune Cell Deconvolution of Gene Expression Data in Two Atacicept Phase II Studies.

Objective

Methods

Results

Conclusion

Publications

Identifying lupus Patient Subsets Through Immune Cell Deconvolution of Gene Expression Data in Two Atacicept Phase II Studies.

Similar Datasets

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