Integrated mathematical and experimental analysis of immune system signaling in breast cancer
- Integrated mathematical and experimental analysis of immune system signaling in breast cancer
Our project seeks, via an integrated experimental–computational approach, to unravel the complexity of immune signaling networks in healthy individuals and patients with estrogen receptor-positive breast cancer (ER+ BC). Our focus is on the identification and characterization, at a systems level, of the dynamical flow of information through immune signaling networks in healthy individuals and ER+ BC patients, leading to a deeper understanding of how immune signaling defects at diagnosis reflect patients’ immune responses and eventuate into different clinical outcomes. The systems biology and dynamic modeling tools and methods that are being developed are, of course, generalizable within a broader immune-oncological context.
Approximately 40% of patients with estrogen receptor-positive breast cancer (ER+ BC, of any stage) harbor immune signaling defects in their blood at diagnosis. Importantly, these defects can predict cancer relapse years later and thus may be developed into a prognostic blood test. Furthermore, rational modulation of the immune system to correct signaling defects offers a novel approach to improving outcomes for ER+ BC patients. However, the underlying mechanisms and downstream consequences of these immune signaling defects remain poorly understood at a systems (network) level. There is also a gap in our understanding of how multiple signaling elements of the immune system and tumor cells act in concert. To integrate key signaling pathways in immune responses to cancer and model their interactions as a system—a complexity compounded by the intrinsic diversity of the immune system—we are pursuing a multidisciplinary and systems biology computational research strategy.
Specifically, our approach is to experimentally capture a rich data set spanning multiple heterogeneous biological inputs (molecular, genetic, and cellular data), then to filter and integrate these data using Bayesian Networks (BNs) to build probabilistic relationships between the disease state and various nodes of interest within the BNs reflecting key multimodal biological variables (e.g. gene expression, protein modifications). These are also being augmented by the dynamical modeling approaches. Our early results revealed major cytokine signaling abnormalities within helper T cells, regulatory T cells, and monocytes from the peripheral blood of ER+ BC patients that reflect immune activity within tumors and can predict clinical outcome. Furthermore, our early data showed that cytokines interact in unexpected ways. Application of BNs allows us to distill the complexity of immune signaling and its defects, which involve temporal and spatial dynamics composed of multiple cellular and molecular components, into comprehensive and accurate predictive models that can be further studied in an iterative process between experimental and mathematical approaches.
We believe that information contained in the immune signaling networks in patients with ER+ BC is a critical predictor of clinical outcomes. Accordingly, we are pursuing three research directions:
1.Building data-driven models of the immune signaling network in a control group of healthy individuals using heterogeneous biological data inputs. We are generating experimental data from peripheral blood mononuclear cells (PBMCs) stimulated with multiple cytokines alone or in combination and developing BNs / mathematical models to establish signaling patterns and dynamics in the normal immune system. Biological data inputs include next-gen 30-color flow cytometry, single-cell RNA-sequencing, and functional assays.
2.Building data-driven models of the immune signaling network in ER+ BC patients using heterogeneous biological data inputs. To determine how cancer distorts information flow in immune signaling dynamics, we are using experimental data acquired from patient PBMCs at time of diagnosis and subsequent time points to build networks (using the same approach as above) to determine changes in immune signaling over time and after hormonal treatment. After quantifying intra-and inter-network variability of healthy and ER+ BC patients, we can characterize and model the temporal dynamics of information flow resulting from defects in immune signaling networks.
3.Computational analysis of intratumoral immune infiltration and integration with peripheral blood immune signaling and clinical outcome. We are carrying out high-dimensional histology and spatial image analysis of archival and prospective PBMC-paired human ER+ breast tumors. Subsequently, these data are used to train computational models that predict clinical outcome, then validate this signature and correlate PBMC immune signaling defects with features of paired tumors.
In summary, our work aims to establish a computational framework by which to characterize and study immune signaling as a network and provide new knowledge of that network in healthy and ER+ BC subjects. This work will lead to a comprehensive, multivariate predictor of clinical outcome from peripheral blood, and an improved understanding of defects in cytokine signaling as a novel immune dysfunction of information flow in cancer. An additional deliverable is a computational systems biology data analysis toolkit to construct, interrogate, and dissect signaling networks based on the integration of network and dynamic modeling. These tools are, by design, generalizable to the broader immune-oncology and systems biology applications.
Dr. Lee’s research focuses on understanding how the tumor microenvironment (TME) impacts host immune responses in cancer patients, with the goal of developing novel treatments to modulate the TME and restore/enhance immune function in cancer patients. Dr. Lee seeks to rationally integrate immunotherapies into combinations to achieve proper treatment sequencing and maximum clinical efficacy. Towards these ends, he utilizes state-of-the-art technologies – including high-dimensional flow cytometry, quantitative spatial image analysis, and next-generation genomics – to dissect the complex interplay between immune/stromal cells and cancer cells within tumors, tumor-draining lymph nodes (TDLNs), and blood. Dr. Lee’s group also utilizes computational modeling and network analysis to understand the population dynamics of cancer, stroma, and immune responses. Dr. Lee’s team is highly interdisciplinary, combining immunology, pathology, genomics, bioinformatics, and computational modeling.
Dr. Lee trained in clinical immunology at UCSF and hematology at Stanford University. He was a tenured faculty at Stanford before joining City of Hope in 2011, where he is now Billy and Audrey L. Wilder Endowed Professor in Cancer Immunotherapeutics and Chair of Immuno-Oncology. Dr. Lee has over 100 peer-reviewed publications and trained over 20 pre- and postdoctoral fellows. He is an elected member of the American Society for Clinical Investigation (ASCI), and recipient of Damon Runyon Scholar Award (Connie and Bob Lurie Scholar), American Cancer Society Research Scholar Award, Dept. of Defense Era of Hope Scholar Award for Breast Cancer Research, Dept. of Defense Multi-Team Award for Breast Cancer Research, and Stand Up To Cancer (SU2C)/Breast Cancer Research Foundation (BCRF) Convergence Team leader.
Dr. Rockne earned a double Bachelor’s degree in mathematics and fine art at the University of Colorado at Boulder, followed by a Master’s degree and Ph.D. in applied mathematics from the University of Washington, Seattle. Dr. Rockne entered the field of Mathematical Oncology following his Ph.D. advisor Dr. Kristin R. Swanson, building patient-specific mathematical models of brain tumor growth and response to treatment, with a particular emphasis on multi-modal imaging data (MRI, PET) and radiation therapy. Following a postdoctoral period at Northwestern University in Chicago Illinois, Dr. Rockne was recruited to the Beckman Research Institute at City of Hope (COH) National Medical Center in 2015 and established the Division of Mathematical Oncology within the Department of Computational and Quantitative Medicine. Since arriving at COH, Dr. Rockne has expanded his research into hematologic malignancies, studies of immune cell communication, quantitative systems pharmacology, and radioimmunotherapy. Dr. Rockne is a PI or mPI on 3 active NIH research grants and is an enthusiastic and active co-investigator on several other NIH awards. 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 several clinician scientists at COH to achieve this goal, including Drs. Lee and Rodin on this CSBC U01 research project to study immune cell signaling defects in ER+ breast cancer.
Dr. Rodin’s research focuses on developing and maintaining systems biology / computational biology data analysis methodology and software (with emphasis on mathematical rigour and adaptability to heterogeneous data types) directly applicable to the large-scale heterogeneous data being routinely generated within the current biomedical research pipelines. Dr. Rodin actively collaborates with both internal (City of Hope) and external investigators in applying such methodology to the big datasets ranging from genomic to epigenomic to transcriptomic to single cell. Dr. Rodin’s group has developed and is maintaining Bayesian network modeling software (BNOmics, https://bitbucket.org/uthsph/bnomics/) specifically tailored to diverse large-scale multimodal biological data. Dr. Rodin also has a long-standing interest in developing mathematical models and flexible analysis techniques in the context of molecular evolution research. Dr. Rodin is especially interested in refining evolutionary mathematical models combining epigenetic factors, transposons, tRNAs and other non-canonical (i.e. not just primary genetic sequences) molecular evolution data. Dr. Rodin trained in Human and Molecular Genetics at University of Texas / MD Andreson Cancer Center and was a tenured faculty at University of Texas before joining City of Hope in 2013, where he is now Susumu Ohno Endowed Associate Professor in Theoretical Biology.