<p id="P3">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.
</p>