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. 2023 Sep 4;55(9):1531–1541. doi: 10.1038/s41588-023-01480-1

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© The Author(s) 2023

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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Extended Data Fig. 6 — a-b, Distribution of p53-LSC scores in BeatAML (a) and TCGA (b) cohorts stratified by TP53 mutational status. c-e, Kaplan^-Meier analysis of de novo AML patients from the full TCGA AML dataset (n = 132) (c), TP53-WT AML patients from BeatAML (n = 294) (d) and TP53-WT AML patients from TCGA (n = 124) (e) stratified according to high or low p53 LSC signature score. f-g, Hazard ratio of all AML patients (n = 322) (f) or secondary AML patients (n = 49) (g) from the BeatAML cohort using LSC17 score (Ng et al, 2016), p53-all-cells score (derived from all TP53-mutant sAML cells) and p53-LSC signature score (derived from transcriptionally-defined LSCs; related to Fig. 2a). Boxes represent hazard ratios and lower and upper bounds of error bars, 95% confidence intervals. Genes used for each score are listed in Supplementary Table 4. “p” indicates log-rank test p-value.