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Information flow and state transitions at the system and multi-dimensional scales in leukemia progression

白血病进展中系统和多维尺度的信息流和状态转换

基本信息

批准号：
10625292
负责人：
YA-HUEI KUO
金额：
$ 70.06万
依托单位：
BECKMAN RESEARCH INSTITUTE/CITY OF HOPE
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10625292
关键词：
1q22 Acute Myelocytic Leukemia Biological Biological Process CBFbeta-MYH11 fusion protein Cancer Biology Cells Chromosomal Rearrangement Clinic Collection Communities Complex DNA DNA Methylation DNA Sequence Alteration Data Development Dimensions Disease Disease Progression Epigenetic Process Event Evolution Experimental Models Explosion Gene Expression Profile Genes Genome Genomics Goals Health Hematopoiesis Human Immune system Institution Leukemic Cell Malignant - descriptor Malignant Neoplasms Maps Mathematics Measures Messenger RNA Methylation MicroRNAs Modeling Modification Mutation Oncogenic Onset of illness Pathogenesis Pathway interactions Patients Potential Energy Prediction of Response to Therapy Probability Process Published Comment RNA Relapse Research Rest Sampling Series System Systems Biology Time Transgenes Untranslated RNA Work behavior prediction biobank cancer cell cancer therapy clinical phenotype combinatorial design epigenome experimental study fusion gene genome-wide genomic data geometric methodologies high dimensionality information model insight leukemia leukemia treatment mathematical methods mathematical model mathematical theory mouse model new therapeutic target novel peripheral blood personalized medicine predicting response targeted treatment theories tool transcriptome treatment response tumor progression

项目摘要

PROJECT SUMMARY Cancer begins as a disease of the genome, with DNA mutations initiating a cascade of events that lead to cancer progression. As single or small collection of cells undergo state transitions to become cancer cells and ultimately evolve into a malignant neoplasm, the immune system is activated and new vasculature is formed, involving non-cancerous cells in the system. This process involves the flow and transfer of information across multiple scales in time and space. Information is encoded within and transferred between cells and across multiple genomic scales may be detected at the system’s level. Our hypothesis is that information contained in one or multiple genomic landscapes can be used to detect oncogenic perturbations and predict response to therapy. It has been shown that mutations associated with AML can be detected years before the onset of disease, however, they do not predict when the disease will manifest or response to treatment. Nevertheless, these sets of mutations can be characterized by distinct gene expression signatures collectively representing perturbations underlying the observed clinical phenotypes. Thus, there is an urgent need for novel and insightful interrogations and predictions of high-dimensional genomic data sets on a system level. Our approach aims to 1) make use of the maximum amount of relevant information in the system 2) be simple and parsimonious with the data, and 3) provide insight and predictions. We propose to validate a mathematical model and approach that considers genome-wide gene activity as state transition from a healthy state to a cancer state from the perspectives of messenger RNAs (mRNAs; transcriptome), non-coding microRNA (miRNAs; the miRome), and DNA methylation (epigenome). The theory and mathematics of state transitions is well known in the systems biology community and is a powerful tool for interpreting and predicting the behavior of complex systems such as genomics and cancer biology. The central hypothesis of this proposal is that information produced during a biological process such as cancer, can be detected from different viewpoints (i.e., transcriptome, miRome, epigenome) such that information contained in one viewpoint of the genomic landscape can be mapped into another, and that disease development and progression can be interpreted and predicted with mathematical models of information flow in a multidimensional genomic space. We propose the following aims: Specific Aim 1. Parameterize a mathematical model of multi-dimensional state transition. Specific Aim 2. Quantify the impact of treatment on state transition dynamics and develop a model of therapy response and relapse. We will quantify and model therapy response in controlled AML mouse model. Specific Aim 3. Characterize the information contained in the transcriptome, miRome, and epigenome state-spaces. Impact. Through an iterative dialog between biological experiments and mathematical modeling, this work will provide insight into perturbations contributing to leukemia initiation and progression, which will guide the design of new therapies targeting pathways at critical transition points.

项目总结