Anne Marie Barrette

An Integrated Mechanistic Model of Pan-Cancer Driver Pathways Predicts Stochastic Proliferation and Death

Most cancer cells harbor multiple drivers whose epistasis and interactions with expression context clouds drug sensitivity prediction. We constructed a mechanistic computational model that is context-tailored by omics data to capture regulation of stochastic proliferation and death by pan-cancer driver pathways. Simulations and experiments explore how the coordinated dynamics of RAF/MEK/ERK and PI-3K/AKT kinase activities in response to synergistic mitogen or drug combinations control cell fate in a specific cellular context. In this context, synergistic ERK and AKT inhibitor-induced death is likely mediated by BIM rather than BAD. AKT dynamics explain S-phase entry synergy between EGF and insulin, but stochastic ERK dynamics seem to drive cell-to-cell proliferation variability, which in simulations are predictable from pre-stimulus fluctuations in C-Raf/B-Raf levels. Simulations predict MEK alteration negligibly influences transformation, consistent with clinical data. Our model mechanistically interprets context-specific landscapes between driver pathways and cell fates, moving towards more rational cancer combination therapy.

Expression of Carcinoembryonic Cell Adhesion Molecule 6 and Alveolar Epithelial Cell Markers in Lungs of Human Infants with Chronic Lung Disease

The membrane protein carcinoembryonic antigen cell adhesion molecule (CEACAM6) is expressed in the epithelium of various tissues, participating in innate immune defense, cell proliferation and differentiation, with overexpression in gastrointestinal tract, pancreatic and lung tumors. It is developmentally and hormonally regulated in fetal human lung, with an apparent increased production in preterm infants with respiratory failure. To further examine the expression and cell localization of CEACAM6, we performed immunohistochemical and biochemical studies in lung specimens from infants with and without chronic lung disease. CEACAM6 protein and mRNA were increased ~4-fold in lungs from infants with chronic lung disease as compared with controls. By immunostaining, CEACAM6 expression was markedly increased in the lung parenchyma of infants and children with a variety of chronic lung disorders, localizing to hyperplastic epithelial cells with a ~7-fold elevated proliferative rate by PCNA staining. Some of these cells also co-expressed membrane markers of both type I and type II cells, which is not observed in normal postnatal lung, suggesting they are transitional epithelial cells. We suggest that CEACAM6 is both a marker of lung epithelial progenitor cells and a contributor to the proliferative response after injury due to its anti-apoptotic and cell adhesive properties.

Integrating Transcriptomic Data with Mechanistic Systems Pharmacology Models for Virtual Drug Combination Trials

Monotherapy clinical trials with mutation-targeted kinase inhibitors, despite some success in other cancers, have yet to impact glioblastoma (GBM). Besides insufficient blood-brain barrier penetration, combinations are key to overcoming obstacles such as intratumoral heterogeneity, adaptive resistance, and the epistatic nature of tumor genomics that cause mutation-targeted therapies to fail. With now hundreds of potential drugs, exploring the combination space clinically and preclinically is daunting. We are building a simulation-based approach that integrates patient-specific data with a mechanistic computational model of pan-cancer driver pathways (receptor tyrosine kinases, RAS/RAF/ERK, PI3K/AKT/mTOR, cell cycle, apoptosis, and DNA damage) to prioritize drug combinations by their simulated effects on tumor cell proliferation and death. Here we illustrate a first step, tailoring the model to 14 GBM patients from The Cancer Genome Atlas defined by an mRNA-seq transcriptome, and then simulating responses to three promiscuous FDA-approved kinase inhibitors (bosutinib, ibrutinib, and cabozantinib) with evidence for blood-brain barrier penetration. The model captures binding of the drug to primary targets and off-targets based on published affinity data and simulates responses of 100 heterogeneous tumor cells within a patient. Single drugs are marginally effective or even counterproductive. Common copy number alterations (PTEN loss, EGFR amplification, and NF1 loss) have a negligible correlation with single-drug or combination efficacy, reinforcing the importance of postgenetic approaches that account for kinase inhibitor promiscuity to match drugs to patients. Drug combinations tend to be either cytostatic or cytotoxic, but seldom both, highlighting the need for considering targeted and nontargeted therapy. Although we focus on GBM, the approach is generally applicable.

Expression of human carcinoembryonic antigen‐related cell adhesion molecule 6 and alveolar progenitor cells in normal and injured lungs of transgenic mice

Carcinoembryonic antigen‐related cell adhesion molecule 6 (CEACAM6) is expressed in the epithelium of various primate tissues, including lung airway and alveoli. In human lung, CEACAM6 is developmentally and hormonally regulated, protects surfactant function, has anti‐apoptotic activity and is dysregulated in cancers. We hypothesized that alveolar CEACAM6 expression increases in lung injury and promotes cell proliferation during repair. Studies were performed in CEABAC transgenic mice‐containing human CEACAM genes. The level of CEACAM6 in adult CEABAC lung was comparable to that in human infants; expression occurred in epithelium of airways and of some alveoli but rarely co‐localized with markers of type I or type II cells. Ten days after bleomycin instillation, both the number of CEACAM6+ cells and immunostaining intensity were elevated in injured lung areas, and there was increased co‐localization with type I and II cell markers. To specifically address type II cells, we crossed CEABAC mice with animals expressing EGFP driven by the SP‐C promoter. After bleomycin injury, partially flattened, elongated epithelial cells were observed that expressed type I cell markers and were primarily either EGFP+ or CEACAM6+. In cell cycle studies, mitosis was greater in CEACAM6+ non‐type II cells versus CEACAM6+/EGFP+ cells. CEACAM6 epithelial expression was also increased after hyperoxic exposure and LPS instillation, suggesting a generalized response to acute lung injuries. We conclude that CEACAM6 expression is comparable in human lung and the CEABAC mouse. CEACAM6 in this model appears to be a marker of a progenitor cell population that contributes to alveolar epithelial cell replenishment after lung injury.

Validating Antibodies for Quantitative Western Blot Measurements with Microwestern Array

Fluorescence-based western blots are quantitative in principal, but require determining linear range for each antibody. Here, we use microwestern array to rapidly evaluate suitable conditions for quantitative western blotting, with up to 192 antibody/dilution/replicate combinations on a single standard size gel with a seven-point, two-fold lysate dilution series (~100-fold range). Pilot experiments demonstrate a high proportion of investigated antibodies (17/24) are suitable for quantitative use; however this sample of antibodies is not yet comprehensive across companies, molecular weights, and other important antibody properties, so the ubiquity of this property cannot yet be determined. In some cases microwestern struggled with higher molecular weight membrane proteins, so the technique may not be uniformly applicable to all validation tasks. Linear range for all validated antibodies is at least 8-fold, and up to two orders of magnitude. Phospho-specific and total antibodies do not have discernable trend differences in linear range or limit of detection. Total antibodies generally required higher working concentrations, but more comprehensive antibody panels are required to better establish whether this trend is general or not. Importantly, we demonstrate that results from microwestern analyses scale to normal “macro” western for a subset of antibodies.

A mechanistic pan-cancer pathway model informed by multi-omics data interprets stochastic cell fate responses to drugs and mitogens

Most cancer cells harbor multiple drivers whose epistasis and interactions with expression context clouds drug and drug combination sensitivity prediction. We constructed a mechanistic computational model that is context-tailored by omics data to capture regulation of stochastic proliferation and death by pan-cancer driver pathways. Simulations and experiments explore how the coordinated dynamics of RAF/MEK/ERK and PI-3K/AKT kinase activities in response to synergistic mitogen or drug combinations control cell fate in a specific cellular context. In this MCF10A cell context, simulations suggest that synergistic ERK and AKT inhibitor-induced death is likely mediated by BIM rather than BAD, which is supported by prior experimental studies. AKT dynamics explain S-phase entry synergy between EGF and insulin, but simulations suggest that stochastic ERK, and not AKT, dynamics seem to drive cell-to-cell proliferation variability, which in simulations is predictable from pre-stimulus fluctuations in C-Raf/B-Raf levels. Simulations suggest MEK alteration negligibly influences transformation, consistent with clinical data. Tailoring the model to an alternate cell expression and mutation context, a glioma cell line, allows prediction of increased sensitivity of cell death to AKT inhibition. Our model mechanistically interprets context-specific landscapes between driver pathways and cell fates, providing a framework for designing more rational cancer combination therapy.

A multi-center study on factors influencing the reproducibility of in vitro drug-response studies

Evidence that some influential biomedical results cannot be recapitulated has increased calls for data that is findable, accessible, interoperable, and reproducible (FAIR). Here, we study factors influencing the reproducibility of a prototypical cell-based assay: responsiveness of cultured cell lines to anti-cancer drugs. Such assays are important for drug development, mechanism of action studies, and patient stratification. This study involved seven research centers comprising the NIH LINCS Program Consortium, which aims to systematically characterize the responses of human cells to perturbation by gene disruption, small molecule drugs, and components of the microenvironment. We found that factors influencing the measurement of drug response vary substantially with the compound being analyzed and thus, the underlying biology. For example, substitution of a surrogate assay such as CellTiter-Glo® for direct microscopy-based cell counting is acceptable in the case of neratinib or alpelisib, but not palbociclib or etoposide. Uncovering and controlling for such context sensitivity requires systematic measurement of assay robustness in the face of biological variation, which is distinct from assay precision and sensitivity. Conversely, validating assays only over a narrow range of conditions has the potential to introduce serious systematic error in a large dataset spanning many compounds and cell lines.

Abstract 1568: Predicting stochastic proliferation and death in response to drugs with mechanistic models tailored to genomic, transcriptomic, and proteomic data

Over the past decade we have seen a shift in cancer therapy from broadly cytotoxic drugs to molecular therapies targeting “driver” mutations. Although targeted therapy has seen great success for some cancers (e.g. imatinib for leukemia), it has struggled with poor efficacy in treating other cancers that can sometimes possess multiple “driver” mutations. This highlights the complex, and at times non-intuitive, interplay between multiple players in a signaling cascade, which can be highly dependent on the biological context – that is, gene expression levels and mutational architecture – of a tumor or cell line. A quantitative, mechanistic, biologically-tailored understanding of how these signaling dynamics drive proliferation and death could improve precision pharmacology approaches to treat cancer. Here, we constructed the first highly detailed, large-scale ordinary differential equation (ODE) mechanistic mathematical model depicting the most commonly mutated cancer signaling pathways across human cancers, as indicated by a pan-cancer analysis by The Cancer Genome Atlas (TCGA). The model includes the RTK/Ras/MAPK, PI3K/AKT/mTOR, CDK/RB cell cycle, p53/MDM2 DNA damage response, and BCL/Caspases apoptosis pathways. The adjustable parameters of the model can be informed by measurements from patients or cell lines, including copy number alterations, mutations, and gene expression levels. This single-cell model links stochastic gene expression processes to quantitative signaling dynamics, and once tailored to a biological context can be used to simulate the effect of various anti-cancer therapies on cell fate behavior such as proliferation and death for a population of cells. The first instance of the model integrated genomic, transcriptomic, and proteomic data from the MCF10A cell line, a non-transformed cell line with predictable phenotypic behaviors. We trained the model using western blot and flow cytometry experiments to refine various biochemical parameters and phenotypic outcomes. Many fundamental questions in signal transduction arose during this process, such as how EGF and insulin synergize to drive S-phase entry or how a specific biological context confers sensitivity or resistance to inhibitors of the ERK and AKT pathways. Simultaneously, we are tailoring the model to patient-derived genetic information from primary glioblastoma tumors and screening brain-penetrable compounds in a patient-specific manner. In conclusion, a quantitative, mechanistic, biologically-tailored mathematical model depicting the major cancer pathways allows us to probe the mechanisms that underlie how signaling dynamics drive proliferation and death in response to various perturbations, and gain insight into their dependence on the biological context of cell lines and patient tumors. Note: This abstract was not presented at the meeting. Citation Format: Mehdi Bouhaddou, Anne Marie Barrette, Rick J. Koch, Marc R. Birtwistle. Predicting stochastic proliferation and death in response to drugs with mechanistic models tailored to genomic, transcriptomic, and proteomic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1568. doi:10.1158/1538-7445.AM2017-1568