Histological phenotypes generally play an essential role in diagnostics, but in particular in oncology there is a growing need for selecting the right patients for the right treatments based on information derived from tissue slides. Oncology is a rapidly evolving field with many new treatment methods being discovered. To finally cope with the complexity of cancer the treatments of cancer patients is becoming increasingly more complex through the availability of those methods and their combinations. As a consequence diagnostics and the decision, which of those treatments is best for a specific patient at what stage of his or her disease, is certainly not trivial and also requires the analysis of big data.
Tissue Phenomics represents a novel technique to discover relevant patterns in tissue slides that predict drug response or other clinical outcome. This method is based on digital pathology, image analysis and data mining, and is more and more evolving into a comprehensive big data approach. The benefit of Tissue Phenomics is most obvious for oncology and more specifically – with respect to companion diagnostics – for immunotherapy especially checkpoint inhibitors. Quantified morphological structures combined with the local co-existence of certain cell types carry biological meaning. For immunotherapy the characterization of the tumor with its defense mechanisms as well as that of the state and the configuration of the immune cells turns out to be most important. Profiling the individual tumor in a quantitative manner including a big data approach leads to a meaningful response prediction.