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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.Significance: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

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.

Figure S1. Details of animal model and fusion gene CM, Figure S2. Ex vivo flow cytometry analysis of bone marrow, Figure S3. Details of principal component analysis, Figure S4. Comparison of state-space construction with different dimension reduction methods, Figure S5. Hierarchical clustering of time-series RNA-seq data and relation to state-space, Figure S6. Details of critical point estimation, construction of quasi-potential, and state-space dynamics, Figure S7. Correlation of state-space geometry with Kit and CM gene expression. Sensitivity of state-space geometry to inclusion of Kit and CM, Figure S8. Volcano plots of critical-point based differential gene expression analysis, Figure S9. Heatmaps of selected GO pathways in early, transition, persistent, and leukemic events, Figure S10. Computation of eigengene angle in state-space, Figure S11. Details of principal component analysis of validation cohorts and state-space dynamics, Figure S12. Mean-squared displacement analysis of particle trajectories in state-space and calibration of Fokker-Planck diffusion coefficient with training cohort, Figure S13. Sensitivity analysis of state-space construction to sample and normalization thresholds, Figure S14. Bootstrap cross validation of state-space construction.

In this supplement we provide additional data and methods to support our results. In particular, we show: details of our mouse model of AML; demonstrate the robustness of our state-space construction to normalization methods and gene selection criteria; compare dimension reduction methods; show bootstrap cross-validation of our predictions; compare hierarchical clustering with our state-space approach; and finally, provide full lists of differentially expressed genes and gene ontology (GO) term enrichment.

MicroRNAs (miRNAs) have been shown to hold prognostic value in acute myeloid leukemia (AML); however, the temporal dynamics of miRNA expression in AML are poorly understood. Using serial samples from a mouse model of AML to generate time-series miRNA sequencing data, we are the first to show that the miRNA transcriptome undergoes state-transition during AML initiation and progression. We modeled AML state-transition as a particle undergoing Brownian motion in a quasi-potential and validated the AML state-space and state-transition model to accurately predict time to AML in an independent cohort of mice. The critical points of the model provided a framework to align samples from mice that developed AML at different rates. Our mathematical approach allowed discovery of dynamic processes involved during AML development and, if translated to humans, has the potential to predict an individual’s disease trajectory.

MiR-126 and miR-155 are key microRNAs (miRNAs) that regulate, respectively, hematopoietic cell quiescence and proliferation. Herein we showed that in acute myeloid leukemia (AML), the biogenesis of these two miRNAs is interconnected through a network of regulatory loops driven by the FMS-like tyrosine kinase 3-internal tandem duplication (FLT3-ITD). In fact, FLT3-ITD induces the expression of miR-155 through a noncanonical mechanism of miRNA biogenesis that implicates cytoplasmic Drosha ribonuclease III (DROSHA). In turn, miR-155 down-regulates SH2-containing inositol phosphatase 1 (SHIP1), thereby increasing phosphor-protein kinase B (AKT) that in turn serine-phosphorylates, stabilizes, and activates Sprouty related EVH1 domain containing 1 (SPRED1). Activated SPRED1 inhibits the RAN/XPO5 complex and blocks the nucleus-to-cytoplasm transport of pre-miR-126, which cannot then complete the last steps of biogenesis. The net result is aberrantly low levels of mature miR-126 that allow quiescent leukemia blasts to be recruited into the cell cycle and proliferate. Thus, miR-126 down-regulation in proliferating AML blasts is downstream of FLT3-ITD–dependent miR-155 expression that initiates a complex circuit of concatenated regulatory feedback (i.e., miR-126/SPRED1, miR-155/human dead-box protein 3 [DDX3X]) and feed-forward (i.e., miR-155/SHIP1/AKT/miR-126) regulatory loops that eventually converge into an output signal for leukemic growth.

Previous studies have shown that approximately 40% of estrogen receptor positive (ER+) breast cancer (BC) patients harbor immune signaling defects in their blood at diagnosis that predicts overall survival. The aim of this work is to apply communication theory and signal processing methods to model cytokine-mediated signaling errors in immune cells in the peripheral blood. Our approach uses communication theory concepts to develop a communication system model consisting of a transmitter, receiver, and channel, applied to the JAK/STAT signaling pathway and tested using multi-color flow cytometry analysis of peripheral blood mononuclear cells stimulated with IFNγ, IL-2, IL-6, IL-4, IL-10, IL-12, or TGFβ in samples from 2 BC patients and 4 healthy controls. We use our model to estimate the detection error rate when one of the 7 cytokines is being transduced through the JAK/STAT signaling pathway, but its signal is being received on multiple phosphorylated signal transducers and activators of transcription (pSTAT) molecules. In addition, we extend the measured error rate value to an error rate curve by varying the signal-to-noise ratio available to the receiver in our system model. Our results show a trend towards higher detection error rates in immune cells from BC patients as compared to healthy controls, which may indicate altered immune signaling and incorrect cell decisions in these patients. Also, considering different cell subtypes, our results show two orders of magnitude increase in error rate in CD4+ memory T cells compared to CD4+ naïve T cells in BC patients. With further development, we believe that our communication theory model and quantification of optimal signal detection in cells will provide a better understanding into how immune cells can accurately “decode” information to respond properly to various signals and how the immune signaling is altered in BC patients, causing better or worse outcomes. The benefit of our novel approach is that it integrates all the transmitted (cytokines) and received signals (pSTATs) into a single communications framework that can be efficiently analyzed together. Citation Format: Adina Matache, Aleksandra Karolak, Sergio Branciamore, Andrei Rodin, Peter P. Lee, Russell C. Rockne. A communication theory framework for modeling cytokine-mediated signaling in healthy and breast cancer derived peripheral blood immune cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2741.