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@ARTICLE{Wurm:283153,
author = {A. A. Wurm$^*$ and S. Brilloff and S. Kolovich and S.
Schäfer and E. Rahimian and V. Kufrin and M. Bill$^*$ and
Z. Carrero$^*$ and S. Drukewitz$^*$ and A. Krüger$^*$ and
M. Hüther and S. Uhrig and S. Oster$^*$ and D. Westphal and
F. Meier and K. Pfütze$^*$ and D. Hübschmann$^*$ and P.
Horak$^*$ and S. Kreutzfeldt$^*$ and D. Richter$^*$ and E.
Schröck$^*$ and G. Baretton$^*$ and C. Heining$^*$ and L.
Möhrmann$^*$ and S. Fröhling$^*$ and C. Ball$^*$ and H.
Glimm$^*$},
title = {{S}ignaling-induced systematic repression of mi{RNA}s
uncovers cancer vulnerabilities and targeted therapy
sensitivity.},
journal = {Cell reports / Medicine},
volume = {4},
number = {10},
issn = {2666-3791},
address = {Maryland Heights, MO},
publisher = {Elsevier},
reportid = {DKFZ-2023-01939},
pages = {101200},
year = {2023},
note = {#LA:B280# / 2023 Oct 17;4(10):101200},
abstract = {Targeted therapies are effective in treating cancer, but
success depends on identifying cancer vulnerabilities. In
our study, we utilize small RNA sequencing to examine the
impact of pathway activation on microRNA (miRNA) expression
patterns. Interestingly, we discover that miRNAs capable of
inhibiting key members of activated pathways are frequently
diminished. Building on this observation, we develop an
approach that integrates a low-miRNA-expression signature to
identify druggable target genes in cancer. We train and
validate our approach in colorectal cancer cells and extend
it to diverse cancer models using patient-derived in vitro
and in vivo systems. Finally, we demonstrate its additional
value to support genomic and transcriptomic-based drug
prediction strategies in a pan-cancer patient cohort from
the National Center for Tumor Diseases (NCT)/German Cancer
Consortium (DKTK) Molecularly Aided Stratification for Tumor
Eradication (MASTER) precision oncology trial. In
conclusion, our strategy can predict cancer vulnerabilities
with high sensitivity and accuracy and might be suitable for
future therapy recommendations in a variety of cancer
subtypes.},
keywords = {cancer driver (Other) / drug response (Other) / miRNA
signatures (Other) / organoids (Other) / precision oncology
(Other) / spheroids (Other) / target prediction (Other)},
cin = {DD01 / HD01 / B340 / B280},
ddc = {610},
cid = {I:(DE-He78)DD01-20160331 / I:(DE-He78)HD01-20160331 /
I:(DE-He78)B340-20160331 / I:(DE-He78)B280-20160331},
pnm = {312 - Funktionelle und strukturelle Genomforschung
(POF4-312)},
pid = {G:(DE-HGF)POF4-312},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:37734378},
doi = {10.1016/j.xcrm.2023.101200},
url = {https://inrepo02.dkfz.de/record/283153},
}