Journal Article

DZNE-2022-01569

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Decoding mechanism of action and sensitivity to drug candidates from integrated transcriptome and chromatin state.

Carraro, C.Extern* ; Bonaguro, L.DZNE* ; Schulte-Schrepping, J.DZNE* ; Horne, A.DZNE* ; Oestreich, M.DZNE* ; Warnat-Herresthal, S.DZNE* ; Helbing, T. ; De Franco, M. ; Händler, K.DZNE* ; Mukherjee, S.DZNE* ; Ulas, T.DZNE* ; Gandin, V. ; Goettlich, R. ; Aschenbrenner, A. C.DZNE* ; Schultze, J. L.DZNE* ; Gatto, B.

2022
eLife Sciences Publications Cambridge

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Please use a persistent id in citations: doi:10.7554/eLife.78012

Abstract: Omics-based technologies are driving major advances in precision medicine, but efforts are still required to consolidate their use in drug discovery. In this work, we exemplify the use of multi-omics to support the development of 3-chloropiperidines, a new class of candidate anticancer agents. Combined analyses of transcriptome and chromatin accessibility elucidated the mechanisms underlying sensitivity to test agents. Furthermore, we implemented a new versatile strategy for the integration of RNA- and ATAC-seq (Assay for Transposase-Accessible Chromatin) data, able to accelerate and extend the standalone analyses of distinct omic layers. This platform guided the construction of a perturbation-informed basal signature predicting cancer cell lines' sensitivity and to further direct compound development against specific tumor types. Overall, this approach offers a scalable pipeline to support the early phases of drug discovery, understanding of mechanisms, and potentially inform the positioning of therapeutics in the clinic.

Keyword(s): Chromatin (MeSH) ; Precision Medicine (MeSH) ; RNA (MeSH) ; Transcriptome (MeSH) ; Transposases: metabolism (MeSH) ; chromatin accessibility ; computational biology ; drug candidate ; human ; mechanism of action ; multi-omics ; sensitivity ML prediction ; systems biology ; transcriptome ; Chromatin ; RNA ; Transposases

Note: CC BY: https://creativecommons.org/licenses/by/4.0/

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Contributing Institute(s):

1. United epigenomic platform (AG Schultze)
2. Platform for Single Cell Genomics and Epigenomics at DZNE & University of Bonn (R&D PRECISE)
3. Statistics and machine learning (AG Mukherjee)

Research Program(s):

1. 352 - Disease Mechanisms (POF4-352) (POF4-352)
2. 354 - Disease Prevention and Healthy Aging (POF4-354) (POF4-354)

Appears in the scientific report 2022

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