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Pan, C. ; Schoppe, O.* ; Parra-Damas, A.* ; Cai, R. ; Todorov, M.I. ; Gondi, G. ; von Neubeck, B. ; Böğürcü-Seidel, N.* ; Seidel, S.* ; Sleiman, K.* ; Veltkamp, C.* ; Förstera, B. ; Mai, H. ; Rong, Z. ; Trompak, O.* ; Ghasemigharagoz, A.* ; Reimer, M.A.* ; Cuesta, A.M.* ; Coronel, J.* ; Jeremias, I. ; Saur, D.* ; Acker-Palmer, A.* ; Acker, T.* ; Garvalov, B.K.* ; Menze, B.* ; Zeidler, R. ; Ertürk, A.

Deep learning reveals cancer metastasis and therapeutic antibody targeting in entire body.

Cell 179, 1661-1676 (2019)
Verlagsversion Postprint DOI
Open Access Green
Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long been needed to better understand and treat cancer metastasis. Here, we developed an integrated pipeline for automated quantification of cancer metastases and therapeutic antibody targeting, named DeepMACT. First, we enhanced the fluorescent signal of cancer cells more than 100-fold by applying the vDISCO method to image metastasis in transparent mice. Second, we developed deep learning algorithms for automated quantification of metastases with an accuracy matching human expert manual annotation. Deep learning-based quantification in 5 different metastatic cancer models including breast, lung, and pancreatic cancer with distinct organotropisms allowed us to systematically analyze features such as size, shape, spatial distribution, and the degree to which metastases are targeted by a therapeutic monoclonal antibody in entire mice. DeepMACT can thus considerably improve the discovery of effective antibody-based therapeutics at the preclinical stage.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Antibody ; Cancer ; Deep Learning ; Drug Targeting ; Imaging ; Light-sheet ; Metastasis ; Microscopy ; Tissue Clearing ; Vdisco; Single-cell Resolution; Pancreatic-cancer; Monoclonal-antibodies; In-vivo; Tissue; Classification; Microscopy; Medicine; Organs; Image
Sprache englisch
Veröffentlichungsjahr 2019
HGF-Berichtsjahr 2019
ISSN (print) / ISBN 0092-8674
e-ISSN 1097-4172
Zeitschrift Cell
Quellenangaben Band: 179, Heft: 7, Seiten: 1661-1676 Artikelnummer: , Supplement: ,
Verlag Cell Press
Verlagsort Cambridge, Mass.
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Tissue Engineering and Regenerative Medicine (ITERM)
Research Unit Gene Vector (AGV)
Research Unit Apoptosis in Hematopoietic Stem Cells (AHS)
POF Topic(s) 30205 - Bioengineering and Digital Health
30203 - Molecular Targets and Therapies
30204 - Cell Programming and Repair
Forschungsfeld(er) Enabling and Novel Technologies
Immune Response and Infection
Stem Cell and Neuroscience
PSP-Element(e) G-505800-001
G-501500-001
G-506600-001
G-501501-001
DOI 10.1016/j.cell.2019.11.013
WOS ID WOS:000502546200021
Scopus ID 85076264043
Erfassungsdatum 2019-12-20
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