We are embarking on a mix of projects, most combining experiments with theory, but some purely theoretical. Here are descriptions of a few:
Predicting the Heterogeneity of Cell Fate Decisions (NIH R01GM104184 and T32GM062754)
Alan Stern, Gregory Smith
In cancer, genetic heterogeneity is the focus of many investigations as it plays important roles in tumor progression and drug resistance by driving phenotypic diversity. Here, we consider another type of heterogeneity, one where natural cell-to-cell variability in protein levels in genetically-identical mammalian cells causes the same stimuli to yield different cell fates, such as life or death. We term this phenomenon “natural phenotypic divergence” (NPD). NPD can manifest as, for example, a persistent anticancer drug resistant subpopulation of cells, and understanding it is important for predicting cancer treatment efficacy. However, means to predict NPD from cell-based experiments have not been developed and are the subject of this project. We hypothesize that NPD can be predicted by characterizing how multivariate, endogenous protein expression noise is propagated non-linearly through signaling networks to regulate cell fate. It is the endogenous expression and degradation noise in the levels of multiple proteins within a signaling network that collectively manifest as NPD. We will test this hypothesis by combining experimental and computational approaches to examine NPD-based proliferation. First, experimentally, we will use live-cell imaging approaches to measure real-time signaling network dynamics and proliferation simultaneously. Although we can only measure one pathway at a time, our subsequent use of computational, dynamic modular response analysis theory allows us to reconstruct how these pathways dynamically interact in a stimulus-specific fashion to control stochastic proliferation fates. Second, we will build a chemical kinetics-based, stochastic computational model that simulates how the protein expression variability underlying NPD propagates into signaling dynamics heterogeneity. Analysis of this model will suggest sets of key proteins whose collective, multivariate fluctuations have a large influence on NPD-based proliferation. Finally, we will measure fluctuations in the levels of these key proteins in single live cells, use our computational models to predict whether these cells should proliferate or not in response to defined perturbations, and test the predictions by observing the actual proliferation decision in those same cells. If successful, this would be the first demonstration that the stochastic fates of individual live cells could be predicted based on biomarkers present prior to perturbation. This would be an important step towards identifying biomarker sets for individual patients and fashioning personalized therapeutic strategies.
Disease-centric Modeling of Glioblastoma Multiforme Signaling Pathways (NIH P50GM071558 -Systems Biology Center New York and T32GM062754)
The past 15 years has led to a wide variety of differential equation-based chemical kinetics models, including ours on receptor tyrosine kinase and proliferation/growth signaling, that describe the chemical reaction mechanisms comprising signaling pathways. These models are built by representing the mechanisms of biochemistry as physical principle-based rate laws that describe how quickly reactions proceed based on concentrations of cellular entities. The rationale for building such models has been that because such pathways are often deregulated in cancer, then a detailed predictive understanding of how they respond to drugs would improve our ability to personalize cancer treatments. However, The Cancer Genome Atlas (TCGA) data has made it clear that cancer “does not care” about single pathways; rather, it uses a variety of pathways simultaneously for malignant progression. Thus, this pathway-centric modeling of the near past can now be used as a base on which to build disease-centric models, which capture a much larger breadth of relevant pathophysiology. We have are building such a model for glioblastoma multiforme (GBM), which includes, as guided by TCGA data, pathways for EGFR, cMET, PDGFR, PI-3K, PTEN, NF1, BRAF, CDKN1A/B, RB, CDK4, CDK6, and P53. As a first step, we are training this model based on publicly available data in the Cancer Cell Line Encyclopedia, which contains data for 43 glioma lines for their mutations and response to 24 different chemotherapeutics and the cancer genome project. We are also applying the model to a single cell stochastic setting and training it based on live-cell imaging data, global proteomic and mRNA profiles from U87 glioma cells. We aim to use this model as a vehicle for preclinical testing of new putative drugs based on a variety of genomic alteration contexts, as well as for proposing new potentially effective treatment strategies.
Drug Toxicity Signature Generation Center—Mount Sinai LINCS (NIH U54HG008098)
Rick Koch; Collaborators: Ravi Iyengar (Mount Sinai), Eric Sobie (Mount Sinai), James Gallo (Mount Sinai)
The overall goal of the Drug Toxicity Signature Generation Center (DTSGC) is to develop robust cellular signatures for drug-induced toxicity and toxicity mitigation. We build these signatures by integrating genomic and proteomic high-throughput measurements in multiple cell types with network analyses and simulations using dynamical models. Signatures are obtained from induced pluripotent stem cell lines derived from individual human subjects. Focus is on cardiotoxicity, hepatotoxicity and peripheral neuropathy. Rick Koch performs microwestern arrays—a mesoscale western blot—to probe drug dose response and dynamics. (research.mssm.edu/pst/DToxS/index.html)
Stochastic Control of Apoptosis Fates (IBM Faculty Award and Stolovitzky Lab)
Luis Santos; Collaborators: Pablo Meyer (IBM), Gustavo Stolovitzky (IBM/Mount Sinai), Jerry Chipuk (Mount Sinai)
When exposed to a chemotherapeutic drug, genetically-identical cells can exhibit a diverse set of responses, such as life or death. A major determinant of these divergent responses is the ubiquitous cell-to-cell variability in protein expression levels, which arises from stochastic chemical kinetics and low gene copy number. For example, cell-to-cell differences in the levels of multiple proteins within the apoptotic pathways give rise to large variability in the time to and probability of cell death. However, a critical piece of cell biology that has not yet been fully considered is that of mitochondria, which plays a central role in apoptosis. We hypothesize that cell-to-cell variability in mitochondria number can have a significant impact on apoptosis variability. The goal of the proposed project is to test this hypothesis with flow cytometry, CRISPR genome editing and live-cell imaging assays.
Fluorescence Multiplexing with Combinatorial Probes (NIH R21CA196418)
Hadassa Holzapfel; Collaborators: Mihaela Skobe (Mount Sinai), Michael Donovan (Mount Sinai)
Advances in multiplexing technologies such as deep sequencing have transformed the way we can probe tumor biopsy samples for biomarkers indicative of prognosis and treatment response. Routine yet arguably more clinically relevant staining analyses of tumor sections reveal important in situ information not easily obtainable by such highly multiplexed methods, but staining analyses are not highly multiplexed and typically remain limited to ~4-5 analytes, or 7 with multi-spectral imaging. There is a significant need for technologies that multiplex measurements in tumor sections but are widely accessible and cost-effective. This project focuses on addressing this need with a readily-adoptable but novel multi-spectral fluorescence-based method. It is based on the hypothesis that the power of combinatorics can be harnessed to vastly increase the number of quantifiable analytes in a mixture by permuting the wide array of available fluorophores in new ways. We term our approach combinatorial fluorescence with spectral imaging (CoFSI). CoFSI probes are individual fluorophores (e.g. CFP and YFP) or covalently-bound combinations of these individual fluorophores (e.g. a CFP-YFP fusion) attached to, for example, an antibody. A major advantage of this technology is that it does not require specialized, expensive equipment and uses existing fluorophores in novel ways to greatly increase multiplexing, and could therefore be easily and rapidly translated to the clinic and applied in research. This could greatly impact preclinical and clinical data acquisition (e.g. allow rapid and precise diagnostics while requiring very small sample size), give critical insight into tumor heterogeneity, and facilitate clinical decision making. CoFSI is also feasibly compatible with other difficult-to-multiplex technologies such as high content screening, live-cell imaging, and in vivo rodent imaging, and thus may have broad impact.