Pan, C., Schoppe, O., Parra-Damas, A., Cai, R., Todorov, M. I., Gondi, G., von Neubeck, B., Bogurcu-Seidel, N., Seidel, S., Sleiman, K., Veltkamp, C., Forstera, 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., and Erturk, A. (2019). Cell 179, 1661-1676 e1619. doi: 10.1016/j.cell.2019.11.013

Abstract: 

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.