American Association for Cancer Research
00085472can200354-sup-236622_2_supp_6259938_q9mhf0.xlsx (2.57 MB)

Supplementary Data Tables S1-S15 from State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Development of Acute Myeloid Leukemia

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posted on 2023-03-31, 03:46 authored by Russell C. Rockne, Sergio Branciamore, Jing Qi, David E. Frankhouser, Denis O'Meally, Wei-Kai Hua, Guerry Cook, Emily Carnahan, Lianjun Zhang, Ayelet Marom, Herman Wu, Davide Maestrini, Xiwei Wu, Yate-Ching Yuan, Zheng Liu, Leo D. Wang, Stephen Forman, Nadia Carlesso, Ya-Huei Kuo, Guido Marcucci

Table S1. Differentially expressed genes for c_1 vs c_1^* Table S2. Differentially expressed genes for c_2 vs c_1^* Table S3. Differentially expressed genes for c_3 vs c_1^* Table S4. Differentially expressed genes for c_2 vs c_1 Table S5. Differentially expressed genes for c_3 vs c_1 Table S6. Differentially expressed genes for c_3 vs c_2 Tables S7-S10. Differentially expressed genes for early, transition, persistent, and leukemia events. Tables S11-S14. GO terms enriched for early, transition, persistent, and leukemia events. Table S15. Top 1% of eigengenes.





Temporal dynamics of gene expression inform cellular and molecular perturbations associated with disease development and evolution. Given the complexity of high-dimensional temporal genomic data, an analytic framework guided by a robust theory is needed to interpret time-sequential changes and to predict system dynamics. Here we model temporal dynamics of the transcriptome of peripheral blood mononuclear cells in a two-dimensional state-space representing states of health and leukemia using time-sequential bulk RNA-seq data from a murine model of acute myeloid leukemia (AML). The state-transition model identified critical points that accurately predict AML development and identifies stepwise transcriptomic perturbations that drive leukemia progression. The geometry of the transcriptome state-space provided a biological interpretation of gene dynamics, aligned gene signals that are not synchronized in time across mice, and allowed quantification of gene and pathway contributions to leukemia development. Our state-transition model synthesizes information from multiple cell types in the peripheral blood and identifies critical points in the transition from health to leukemia to guide interpretation of changes in the transcriptome as a whole to predict disease progression. These findings apply the theory of state transitions to model the initiation and development of acute myeloid leukemia, identifying transcriptomic perturbations that accurately predict time to disease development.See related commentary by Kuijjer, p. 3072

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