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. 2022 Feb 16;12:806386. doi: 10.3389/fgene.2021.806386

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Copyright © 2022 Loeffler, Gaisa, Muti, van Treeck, Echle, Ghaffari Laleh, Trautwein, Heij, Grabsch, Ortiz Bruechle and Kather.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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(A) Biological hypothesis of this study. TME: tumor microenvironment. (B) Workflow for selection of data and Deep Learning methods. (1) Tumors from TCGA were analyzed. (2) Genes were selected based on the MSKCC cohort and OnkoKB platforms. (3) Alterations were grouped based on different sources. (4) Genes were grouped into pathways (see Supplementary Table S1). (5) Processing of images and training of the network for genes alone and grouped into pathways. (images from https://smart.servier.com, and Twitter Twemoji under a CC-BY license). TME: tumor microenvironment, TCGA: The Cancer Genome Atlas, MSKCC, OnkoKB, WT: wild type, MUT: mutation present.