National Cancer Institute
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City of Hope PS-OP

Duarte, California

Overview | Investigators

Overview

City of Hope

Project Name

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

Project Website

https://www.cityofhope.org/mathematical-oncology

List of Collaborating Institutions

City of Hope National Medical Center

Project Description

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. 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 project 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 will pursue the following Specific 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 in a controlled AML mouse model. Specific Aim 3. Characterize the information contained in the transcriptome, miRome, and epigenome state-spaces in both mouse and human samples.

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 state-transition points.

Figure 1

Figure 1. A) The CM-AML mouse model will be used to collect time-sequential peripheral blood samples for sequencing. B) Critical points in state-transition will be estimated for mRNA, miRNA, and DNA methylation trajectories. C) A mathematical model will be used to simulate state-transition dynamics using a stochastic equation of motion and by evaluating the spatial-temporal evolution of probability density given by the Fokker-Plank equation, with critical points and parameters estimated from the experimental data. The model simulations will then be used to drive new experiments, which will guide the design of new therapies targeting pathways at critical state-transition points.

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Investigators

Russell Rockne, Ph.D.

Russell Rockne, Ph.D.
Dr. Rockne is Assistant Professor and Director of the Division of Mathematical Oncology in the Department of Computational and Quantitative Medicine at City of Hope. Dr. Rockne’s research lies at the interface of precision medicine, data science, and mathematical modeling. Dr. Rockne’s research approach uses mathematical models to connect data from multiple modalities and scales to predict cancer growth and response to therapy. Dr. Rockne has made contributions to mathematical model parameterization and applications, with patented algorithms as well as first and senior author level peer reviewed publications in the field of Mathematical Oncology. The aim of Dr. Rockne’s research is to bring mathematics into the clinic to help improve outcomes for patients with cancer. Dr. Rockne partners with scientists and clinicians at City of Hope to achieve this goal, including Drs. Kuo and Marcucci on this PSON U01 research project to study Information flow and state transitions at the system and multi-dimensional scales in leukemia progression.

 

Ya-Huei Kuo, Ph.D.

Ya-Huei Kuo, Ph.D.
Dr. Kuo is an Associate Professor in the Department of Hematologic Malignancies Translational Science. Dr. Kuo’s long-term research goals are to unravel the mechanisms driving malignant cell transformation and maintenance, and to develop mechanism-based targeted therapies. Dr. Kuo has established a conditional Cbfb-MYH11 knock-in mouse model that mimics somatic acquisition of a common acute myeloid leukemia (AML)-associated chromosomal aberration, inv(16)(p13.1q22) or t(16;16)(p13.1;q22). Dr. Kuo demonstrated that this model represents a pertinent experimental system to dissect pre-malignant perturbations, genetic modifiers, disease dynamics and evaluation of therapeutic response relevant to human disease. Leukemia stem cell (LSC) are recognized as the critical leukemia-initiating cells which are resistant to chemotherapy and contribute to refractory disease and subsequent relapse. Dr. Kuo has made contributions to uncover novel mechanisms underlying LSC transformation and persistence and identify therapeutic targets aimed at eliminating LSC and improving patient outcome.

 

Guido Marucci, M.D.

Guido Marcucci, M.D.
Dr. Marcucci is Chair and Professor, Department of Hematologic Malignancies Translational Science; Director, Gehr Family Center for Leukemia Research, and Professor in the Department of Hematology & Hematopoietic Cell Transplantation. In addition to being a practicing clinician in medical oncology and stem cell transplantation, Dr. Marcucci has extensive research experience in basic, translational and clinical research in leukemia, specifically acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Over the years, Dr. Marcucci has focused on drug development and discovery of basic mechanisms of leukemogenesis, novel therapeutic targets and epigenetic and genetic prognostic biomarkers in AML. Dr. Marcucci has chaired several Phase I, II and III single and multi-institutional clinical trials within the Oncology Cooperative Group Alliance, and currently serves as the vice-chair of the Leukemia Committee and a member of the Leukemia Correlative Science Committee. Dr. Marcucci is also the director of the City of Hope Hematopoietic Tissue Bank.

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