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. 2022 Apr 29;14:45. doi: 10.1186/s13073-022-01050-w

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© The Author(s) 2022

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 6 — Exploration of potential treatment targets from Stem.Sig using CRISPR screening data. A Ranking of genes based on their knockout effects on anti-tumor immunity across 17 CRISPR datasets. Negative (positive) z scores indicated better (worse) immune response after knockout of a specific gene. Genes were ranked according to their mean z scores. Top-ranking genes were associated with immune resistance. Blank squares in the heatmap referred to missing values of gene data from the corresponding cohort. B Radar plot comparing the percentage of top-ranked genes for Stem.Sig and other predictive signatures. C Heatmap depicting z scores of 20 Stem.Sig genes in the 3% top-ranked genes across different CRISPR datasets