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. 2021 Jul 14;6:59. doi: 10.1038/s41525-021-00223-7

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© The Author(s) 2021

Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 2 — a Barplots illustrate differences in CMS distributions between primary tumors and liver metastases using the original primary CRC classifier and the new metastases-translated classifier. Odds ratios (ORs) indicate changes in proportions of each CMS compared to the primary setting (only counting confidently classified samples). b Principal component analyses demonstrate that the transcriptomic “liver background” (here illustrated by expression of the hepatocyte-enriched F9 gene marker) represents a large source of variation in raw expression data from bulk tissue samples of liver metastases (left), but not when only considering the 1104 cancer cell-intrinsic candidate markers after “liver background-adjustment” (right). c Principal component analyses showing CMS-classified liver metastases (colored dots, n = 295) projected onto the first four principal components from primary tumors. Colored backgrounds represent the dominant subtype in the primary tumors. d Gene set analysis with sample type (black: metastases; white: primary tumors) and assigned CMS (as in a) indicated on top. Samples are ordered according to CMS and p-values from the tailored CMS classifier. Gene sets (in rows) are ordered according to CMS hallmarks (CMS1 characteristics first, followed by CMS2 and so on).