Reverse Sensitivity Analysis for Identifying Predictive Proteomics Signatures of Cancer
- Reverse Sensitivity Analysis for Identifying Predictive Proteomics Signatures of Cancer
A prominent driver of cancer cell growth is signaling pathway deregulation from mutations in key regulatory nodes and loss/gain in gene copy number (CNV). A major aim of cancer systems biology is to build models that can predict the impact of these genetic disruptions to guide therapeutic interventions. Recent work by our group discovered that the abundances of most signaling pathway proteins are highly conserved and that pathway activity is controlled by only a few, low abundance key nodes. The regulation of these nodes appears to depend on their low abundance and modulation by phosphorylation. However, some nodes, such as Grb2 and Shc, appear preferentially amplified in many cancers. We hypothesize that CNV and genetic mutations dysregulate signaling pathways in cancer by shifting control from tightly regulated nodes to poorly regulated ones.
Unfortunately, current mathematical modeling approaches do not adequately capture the impact of CNV on signaling pathway topology and feedback. We propose to address this critical gap by implementing a new approach for identifying the functional topology of signaling networks. This method, termed Reverse Sensitivity Analysis, uses targeted CRISPR libraries to modulate the abundance of pathway components together with flow cytometry and highly sensitive and quantitative targeted proteomics and phosphoproteomics to measure the subsequent impact. These data will be used with new modeling approaches to generate models that should recapitulate the impact of CNV on cancer cell signaling behavior and suggest pathway nodes that can be targeted for therapeutic interventions.
We will initially use reverse sensitivity analysis to identify key differences in the regulation of signaling pathways between normal and cancer cells with alterations in the ERK and AKT pathways. This work will develop and validate a general platform that can identify proteomics signatures of altered signaling pathways in cancer, build predictive models of these altered pathways, and explore how these alterations contribute to mechanisms of drug resistance.
H. Steven Wiley, Ph.D. (co-PI)
Steven Wiley is a Laboratory Fellow at the Environmental Molecular Sciences Division at Pacific Northwest National Laboratory (PNNL). He has been a pioneer in computational biology since the early 1980s, focusing on the dynamic regulation of cell signaling networks using the EGFR system as a model system. Using a combination of receptor biochemistry and modeling, he focused on the role of altered receptor abundance or structure in uncontrolled signaling, being the first to show that overexpression of the EGFR altered its internalization. He was the first to demonstrate that overexpression of HER2 caused constitutive activation of the EGFR and in collaboration with Web Cavenee’s group showed that the VIII EGFR was transforming in part due to defective internalization and down regulation. Since moving to PNNL in 2000, he has focused on building a team with both experimental and modeling expertise to identify key differences in the composition of different cell types. This required incorporating new advances in proteomics, live-cell reporters and CRISPR technologies to develop the tools needed to precisely alter and measure the impact of protein abundance and localization on signaling pathway activity.
Herbert M Sauro Ph.D. (co-PI)
Herbert Sauro is a Professor in the Department of Bioengineering at the University of Washington in Seattle. His interests include anything to do with mechanistic modeling of cellular networks. Early in his career he helped develop metabolic control analysis and wrote one of the first popular pathway simulators for PCs. He was a founding member of the team that developed the systems biology exchange language, SBML, and later initiated the development of the synthetic biology open language (SBOL). He is an active member of COMBINE as well as the multiscale modeling consortium IMAG/MSM. He is director of the NIH center for reproducible modeling that aims to encourage authors to publish reproducible and reusable models in biomedical research. His current interest in CSBC is to develop new approaches to modeling that couples machine learning with novel experimental techniques in order to generate highly predictive mechanistic models of signaling pathways involved in cancer. He has also written a number of textbooks on pathway modeling, enzyme kinetics and control theory.
Wei-Jun Qian, Ph.D. (co-PI)
Wei-Jun Qian is a Laboratory Fellow in the Biological Sciences Division at Pacific Northwest National Laboratory (PNNL). He also currently serves as the Team Leader of Proteomics within the Integrative Omics group. He was trained as an analytical biochemist and has devoted much of his career in developing and applying novel analytical biochemistry principles for achieving accurate global and targeted quantitative measurements of low abundance proteins and PTMs, particularly thiol-based redox modifications and phosphorylation, and applying them to biomedical research. He was recognized by a 2009 NIH Director’s New Innovator Award and a 2011 Presidential Early Career Award for Scientists and Engineers. His latest research involves redox proteomics and redox biology, nanoscale proteomics, targeted mass spectrometry-based measurements of proteins and PTMs, and biomedical applications related to type 1 diabetes, metabolism, and oxidative stress, and systems biology.
Tujin Shi, Ph.D. (co-investigator)
Tujin Shi is a bioanalytical chemist in the Biological Sciences Division at PNNL. His research efforts over the past nine years have centered on developing new proteomics technologies for ultrasensitive high throughput analysis of proteins and protein modifications in complex biological and clinical samples. He has been a leader in developing a highly sensitive targeted proteomics platform for enabling reliable quantification of extremely low-abundance proteins (e.g., 50-100 pg/mL level in human blood and single-digit protein copies per cell) without the use of any affinity reagents. This new proteomics capability has been broadly applied in many biomedical projects for preclinical verification of biomarkers as well as absolute quantification of low abundant signaling proteins or modifications when high quality antibodies are not available. His recent interest is the development of an ultrasensitive MS system for quantitative proteomics analysis of single cells as well as mass-limited human specimens.
Jin Xu, Ph.D. (co-investigator)
Jin Xu is a Senior Fellow in the Department of Bioengineering at University of Washington in Seattle. Her research interests focus on complex system modeling and biomedical data science. She is currently working on signaling pathway modeling and its application to cancer research, focusing on building mechanistic computational models to understand the underlying biological process. Experimental data is either applied as part of the model for prediction or comparison with the theoretical or computational work. Examples of past applications include pancreatic islet system for diabetes and immune system for infectious disease.