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@ARTICLE{Aybey:302809,
author = {B. Aybey and B. Brors$^*$ and E. Staub},
title = {{E}xpression signatures with specificity for type {I} and
{II} {IFN} response and relevance for autoimmune diseases
and cancer.},
journal = {Journal of translational medicine},
volume = {23},
number = {1},
issn = {1479-5876},
address = {London},
publisher = {BioMed Central},
reportid = {DKFZ-2025-01349},
pages = {740},
year = {2025},
abstract = {Aberrant interferon signaling is a key element of various
diseases, but resolving gene expression signatures that stem
from different types of IFNs in tissue samples is still a
challenge. Most published IFN signatures comprise genes that
are activated by different IFNs: they cannot discriminate
type-I (IFN-I) and type-II (IFN-II) IFN stimulation. Most
often such signatures were obtained from a single expression
dataset that had been obtained in a specific cellular
context, and their translatability to other experimental
contexts has not been demonstrated.We leveraged multiple
RNA-seq datasets of IFN stimulation in a network
meta-analysis workflow to obtain IFN gene signatures
separating IFN-I and IFN-II. We validated our signatures in
bulk and single cell RNA-seq datasets of various cellular
contexts demonstrating similar or higher coherence than
previously published signatures. Our IFN-II signature is
broader applicable than other published signatures as it
demonstrates strong performance in detecting IFN-II response
in more cell types. In three SLE microarray datasets our
IFN-I signature was highly coherent and correlated with
disease severity better than most published signatures. In
TCGA, our IFN-II signature produced distinct profiles
compared to published IFN-I signatures and correlated
strongly with published CD8+ T cell signatures. In cohorts
of three different cancer types, we observed higher
signature scores of our IFN-II signature in responders than
in non-responders to immune checkpoint inhibitor (ICI)
therapy.Our IFN-I and IFN-II response-specific gene
expression signatures can inform on complex IFN responses in
a more fine-grained way than previously possible. They can
be used to assess type I versus II IFN response in gene
expression data produced by different technologies, for
different diseases and even different cell types in single
cell studies. The association of high scores of our IFN-II
signature with anti-tumor response to ICIs suggests a role
as a biomarker to predict ICI response.},
keywords = {Humans / Neoplasms: genetics / Interferon Type I: genetics
/ Interferon Type I: metabolism / Autoimmune Diseases:
genetics / Gene Expression Profiling / Reproducibility of
Results / Transcriptome / Gene expression signature
discovery (Other) / Immunology (Other) / Interferon (Other)
/ Oncology (Other) / SLE-systemic lupus nephritis (Other) /
Transcriptomics (Other) / Interferon Type I (NLM Chemicals)},
cin = {B330 / HD01},
ddc = {610},
cid = {I:(DE-He78)B330-20160331 / I:(DE-He78)HD01-20160331},
pnm = {312 - Funktionelle und strukturelle Genomforschung
(POF4-312)},
pid = {G:(DE-HGF)POF4-312},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:40611262},
pmc = {pmc:PMC12231911},
doi = {10.1186/s12967-025-06628-7},
url = {https://inrepo02.dkfz.de/record/302809},
}