An SCC lesion from an FA patient analyzed by tCycIF imaging. The location within the oral cavity and stage of a hypopharynx tumor from a 41-year-old woman with FA is indicated (A). The hematoxylin- and eosin-stained tumor sample shows multistage carcinogenesis, ranging from low-grade dysplasia (yellow) via high-grade dysplasia (orange) to invasive carcinoma (red) (B). Multi-omic analysis of the tumor includes tissue transcriptomics, genomics, proteomics, and metagenomics for detection of pathogens inhabiting the tumor. Machine learning-based methods are applied in combination with single-cell level segmentation of the tumors and delineation of tumor neighborhoods (C). In this inset of invasive carcinoma, every circle represents an individual tumor cell, and its color indicates its stage within the multi-step tumorigenesis process. The data produced from tumor multi-omics can be processed using non-supervised machine learning algorithms, such as UMAP, for detection of commonalities and divergencies in the tumor sections from multiple patients, and information on markers expression can be extracted from every cell so as to generate graphs for comparing markers expression across the carcinogenic progression.