RT Journal Article
T1 Deep Learning Reveals Cancer Metastasis and Therapeutic Antibody Targeting in the Entire Body
A1 Pan, Chenchen
A1 Schoppe, Oliver
A1 Parra-Damas, Arnaldo
A1 Cai, Ruiyao
A1 Todorov, Mihail Ivilinov
A1 Gondi, Gabor
A1 Neubeck, Bettina von
A1 Böğürcü-Seidel, Nuray
A1 Seidel, Sascha
A1 Sleiman, Katia
A1 Veltkamp, Christian
A1 Förstera, Benjamin
A1 Mai, Hongcheng
A1 Rong, Zhouyi
A1 Trompak, Omelyan
A1 Ghasemigharagoz, Alireza
A1 Reimer, Madita Alice
A1 Javier Coronel, 
A1 Jeremias, Irmela
A1 Saur, Dieter
A1 Acker-Palmer, Amparo
A1 Acker, Till
A1 Garvalov, Boyan K.
A1 Menze, Bjoern
A1 Zeidler, Reinhard
A1 Ertürk, Ali
A1 Cuesta Martínez, Ángel
AB 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 pre-clinical stage.
SN 0092-8674
YR 2019
FD 2019-12-01
LK https://hdl.handle.net/20.500.14352/94010
UL https://hdl.handle.net/20.500.14352/94010
LA eng
NO Pan C, Schoppe O, Parra-Damas A, et al. Deep Learning Reveals Cancer Metastasis and Therapeutic Antibody Targeting in the Entire Body. Cell. 2019;179(7):1661-1676.e19. doi:10.1016/j.cell.2019.11.013
NO Vascular Dementia Research Foundation, Synergy Excellence Cluster Munich (SyNergy)
NO Helmholtz-Center for Environment Health
NO Fritz Thyssen Stiftung and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
NO German Federal Ministry of Education and Research via the Software Campus initiative
NO European Research Council
NO Excellence Cluster Cardio-Pulmonary Institute
DS Docta Complutense
RD 6 abr 2025