State-Transition Analysis of Time-Sequential microRNA Expression Predicts Development of Acute Myeloid Leukemia
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE173785 | GEO | 2021/09/08
REPOSITORIES: GEO
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Project description:State-Transition Analysis of Time-Sequential microRNA Expression Predicts Development of Acute Myeloid Leukemia
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Project description:Temporal dynamics of gene expression are informative of changes associated with disease development and evolution. Given the complexity of high-dimensionaltemporal datasets, an analytical framework guided by a robust theory is needed to interpret time-sequential changes and to predict system dynamics. Herein, we use acute myeloid leukemia as a proof-of-principle to model gene expression dynamics in a transcriptome state-space constructed based on time-sequential RNA-sequencing data. We describe the construction of a state-transition model to identify state-transition critical points which accurately predicts leukemia development. We show an analytical approach based on state-transition critical points identifies step-wise transcriptomic perturbations driving leukemia progression. Furthermore, the gene(s) trajectory and geometry of the transcriptome state-space provides biologically-relevant gene expression signals that are not synchronized in time, and allows quantification of gene(s) contribution to leukemia development. Therefore, our state-transition model can synthesize information, identify critical points to guide interpretation of transcriptome trajectories and predict disease development.
2020-04-23 | GSE133642 | GEO
Project description:Chronic myeloid leukemia (CML) is initiated and initially maintained solely by the fusion gene BCR-ABL, encoding a mutant protein targeted in the clinic with tyrosine kinase inhibitors (TKIs) While TKI treatment is effective in inducing long-term remission, frequently is not curative. For these reasons, CML is an ideal disease to test our hypothesis that that transcriptome-based state-transition models accurately predict cancer evolution and treatment response. We hypothesized that transcriptome-based state-transition models accurately predict cancer evolution and treatment response.
2023-11-02 | GSE244990 | GEO
Project description: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 GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/15/3157/F1.large.jpg.
| S-EPMC7416495 | biostudies-literature
Project description:Chronic myeloid leukemia (CML) is initiated and maintained by BCR::ABL which is clinically targeted using tyrosine kinase inhibitors (TKIs). TKIs can induce long-term remission but are also not curative. Thus, CML is an ideal system to test our hypothesis that transcriptome-based state-transition models accurately predict cancer evolution and treatment response. We collected time-sequential blood samples from tetracycline-off (Tet-Off) BCR::ABL-inducible transgenic mice and wild-type controls. From the transcriptome, we constructed a CML state-space and a three-well leukemogenic potential landscape. The potential's stable critical points defined observable disease states. Early states were characterized by anti-CML genes opposing leukemia; late states were characterized by pro-CML genes. Genes with expression patterns shaped similarly to the potential landscape were identified as drivers of disease transition. Re-introduction of tetracycline to silence the BCR::ABL gene returned diseased mice transcriptomes to a near healthy state, without reaching it, suggesting parts of the transition are irreversible. TKI only reverted the transcriptome to an intermediate disease state, without approaching a state of health; disease relapse occurred soon after treatment. Using only the earliest time-point as initial conditions, our state-transition models accurately predicted both disease progression and treatment response, supporting this as a potentially valuable approach to time clinical intervention, before phenotypic changes become detectable.
| S-EPMC10997512 | biostudies-literature
Project description:Chronic myeloid leukemia (CML) is initiated and maintained by BCR::ABL which is clinically targeted using tyrosine kinase inhibitors (TKIs). TKIs can induce long-term remission but are also not curative. Thus, CML is an ideal system to test our hypothesis that transcriptome-based state-transition models accurately predict cancer evolution and treatment response. We collected time-sequential blood samples from tetracycline-off (Tet-Off) BCR::ABL-inducible transgenic mice and wild-type controls. From the transcriptome, we constructed a CML state-space and a three-well leukemogenic potential landscape. The potential's stable critical points defined observable disease states. Early states were characterized by anti-CML genes opposing leukemia; late states were characterized by pro-CML genes. Genes with expression patterns shaped similarly to the potential landscape were identified as drivers of disease transition. Re-introduction of tetracycline to silence the BCR::ABL gene returned diseased mice transcriptomes to a near healthy state, without reaching it, suggesting parts of the transition are irreversible. TKI only reverted the transcriptome to an intermediate disease state, without approaching a state of health; disease relapse occurred soon after treatment. Using only the earliest time-point as initial conditions, our state-transition models accurately predicted both disease progression and treatment response, supporting this as a potentially valuable approach to time clinical intervention even before phenotypic changes become detectable.
| S-EPMC10592732 | biostudies-literature
Project description: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.
| S-EPMC9032952 | biostudies-literature
Project description:State-transition Modeling of Blood Transcriptome Predicts Disease Evolution and Treatment Response in Chronic Myeloid Leukemia (CML)
| PRJNA1026584 | ENA
Project description:BackgroundAcute myeloid leukemia (AML) is a highly heterogenous disease with varying clinical outcomes among patients. Epithelial-mesenchymal transition (EMT) is an important mechanism underlying cancer metastasis and chemotherapy resistance. However, few EMT-based signatures have been established to predict AML prognosis and treatment efficacy.MethodsBy conducting comparative RNA-seq analysis, we discovered the differential expression of EMT genes between AML patients with relapse and those without relapse. Based on the prognostic analysis of the differentially expressed EMT genes, a metastasis-related EMT signature (MEMTs) was constructed. An analysis was conducted on both TARGET and TCGA cohorts to explore the possible association between MEMTs and prognosis in AML. Three separate chemotherapy treatment cohorts were utilized to assess the predictive efficacy of MEMTs for chemotherapy response. In addition, the potential correlation between MEMTs and the tumor microenvironment was also investigated. Finally, random forest analysis and functional experiments were conducted to verify the key MEMTs gene associated with AML metastasis.ResultsBased on expression and prognostic analysis, we constructed MEMTs that include three EMT genes (CDH2, LOX, and COL3A1). Our findings suggested that the MEMTs could act as a prognostic factor for AML patients, and furthermore, it proved to be a predictor of their response to chemotherapy. Specifically, high MEMTs was associated with worse prognosis and poor response to chemotherapy, while low MEMTs was linked to better prognosis and higher response rates. Random forest and functional experiments demonstrate that CDH2 is a key gene promoting leukemia cell metastasis among the three MEMTs genes.ConclusionThe identification of MEMTs could potentially act as a predictor for the prognosis and the response to chemotherapy in AML patients. Individual tumor evaluation based on MEMTs could provide personalized treatment options for AML patients in the future.
| S-EPMC10257403 | biostudies-literature
Project description:This SuperSeries is composed of the following subset Series: GSE23907: Histone H3K27ac separates active from poised enhancers and predicts developmental state (gene expression data) GSE24164: ChIP-Seq of chromatin marks at distal enhancers in Mouse Embryonic Stem Cells and adult tissues. Refer to individual Series
2010-10-31 | E-GEOD-24165 | biostudies-arrayexpress