Cancer is one of the leading causes of death worldwide. More than 90% of cancer patients die of distal metastases rather than as a direct result of the primary tumor. Cancer metastases usually develop from single disseminated cancer cells, which evade the body's immune surveillance system. Up to now, comprehensive detection of these cells within the entire body has not been possible, owing to the limited resolution of imaging techniques such as bioluminescence and MRI. This has resulted in a relative lack of knowledge of the specific dissemination mechanisms of diverse cancer types, which is a prerequisite for effective therapy. It has also hampered efforts to assess the efficacy of new drug candidates for tumor therapy.
Transcending human detection capabilities with deep learning
In order to develop new techniques to overcome these hurdles, the team led by Dr. Ali Ertürk, Director of the Institute for Tissue Engineering and Regenerative Medicine at Helmholtz Zentrum München, had previously developed vDISCO - a method of tissue clearing and fixation which transforms mouse bodies into a transparent state allowing the imaging of single cells. Using laser-scanning microscopes, the researchers were able to detect the smallest metastases down to individual cancer cells in cleared the tissue of the mouse bodies.
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However, manually analyzing such high-resolution imaging data would be a very time-consuming process. Given the limited reliability and processing speed of currently available algorithms for this kind of data analysis, the teams have developed a novel deep-learning based algorithm called DeepMACT. The researchers have now been able to detect and analyze cancer metastases and map the distribution of therapeutic antibodies in vDISCO preparations automatically. The DeepMACT algorithm matched the performance of human experts in detecting the metastases - but did so more than 300 times faster. "With a few clicks only, DeepMACT can do the manual detection work of months in less than an hour. We are now able to conduct high-throughput metastasis analysis down to single disseminated tumor cells as a daily routine", says Oliver Schoppe, co-first-author of the study and Ph.D. student in the group of Prof. Dr. Bjoern Menze at TranslaTUM, the Center for Translational Cancer Research at TUM.
Detecting cells, gathering data, learning about cancer
Using DeepMACT, the researchers have gained new insights into the unique metastatic profiles of different tumor models. Characterization of the dissemination patterns of diverse cancer types could enable tailored drug targeting for different metastatic cancers. By analyzing the progression of breast-cancer metastases in mice, DeepMACT has uncovered a substantial increase in small metastases throughout the mouse body over time. "None of these features could be detected by conventional bioluminescence imaging before. DeepMACT is the first method to enable the quantitative analysis of metastatic process at a full-body scale", adds Dr. Chenchen Pan, a postdoctoral fellow at Helmholtz Zentrum München and also joint first author of the study. "Our method also allows us to analyze the targeting of tumor antibody therapies in more detail."