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)
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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Publication type
Article: Journal article
Document type
Scientific Article
Thesis type
Editors
Keywords
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
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Language
english
Publication Year
2019
Prepublished in Year
HGF-reported in Year
2019
ISSN (print) / ISBN
0092-8674
e-ISSN
1097-4172
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Volume: 179,
Issue: 7,
Pages: 1661-1676
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Cell Press
Publishing Place
Cambridge, Mass.
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0000-00-00
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Reviewing status
Peer reviewed
Institute(s)
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
Research field(s)
Enabling and Novel Technologies
Immune Response and Infection
Stem Cell and Neuroscience
PSP Element(s)
G-505800-001
G-501500-001
G-506600-001
G-501501-001
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Copyright
Erfassungsdatum
2019-12-20