Iya Khalil

Data‐Driven Computer Simulation of Human Cancer Cell

Abstract: Using the Diagrammatic Cell Language™, Gene Network Sciences (GNS) has created a network model of interconnected signal transduction pathways and gene expression networks that control human cell proliferation and apoptosis. It includes receptor activation and mitogenic signaling, initiation of cell cycle, and passage of checkpoints and apoptosis. Time‐course experiments measuring mRNA abundance and protein activity are conducted on Caco‐2 and HCT 116 colon cell lines. These data were used to constrain unknown regulatory interactions and kinetic parameters via sensitivity analysis and parameter optimization methods contained in the DigitalCell™ computer simulation platform. FACS, RNA knockdown, cell growth, and apoptosis data are also used to constrain the model and to identify unknown pathways, and cross talk between known pathways will also be discussed. Using the cell simulation, GNS tested the efficacy of various drug targets and performed validation experiments to test computer simulation predictions. The simulation is a powerful tool that can in principle incorporate patient‐specific data on the DNA, RNA, and protein levels for assessing efficacy of therapeutics in specific patient populations and can greatly impact success of a given therapeutic strategy.

A systems biology dynamical model of mammalian G1 cell cycle progression.

The current dogma of G1 cell‐cycle progression relies on growth factor‐induced increase of cyclin D:Cdk4/6 complex activity to partially inactivate pRb by phosphorylation and to sequester p27Kip1‐triggering activation of cyclin E:Cdk2 complexes that further inactivate pRb. pRb oscillates between an active, hypophosphorylated form associated with E2F transcription factors in early G1 phase and an inactive, hyperphosphorylated form in late G1, S and G2/M phases. However, under constant growth factor stimulation, cells show constitutively active cyclin D:Cdk4/6 throughout the cell cycle and thereby exclude cyclin D:Cdk4/6 inactivation of pRb. To address this paradox, we developed a mathematical model of G1 progression using physiological expression and activity profiles from synchronized cells exposed to constant growth factors and included a metabolically responsive, activating modifier of cyclin E:Cdk2. Our mathematical model accurately simulates G1 progression, recapitulates observations from targeted gene deletion studies and serves as a foundation for development of therapeutics targeting G1 cell‐cycle progression.

Causal Modeling Using Network Ensemble Simulations of Genetic and Gene Expression Data Predicts Genes Involved in Rheumatoid Arthritis

Tumor necrosis factor α (TNF-α) is a key regulator of inflammation and rheumatoid arthritis (RA). TNF-α blocker therapies can be very effective for a substantial number of patients, but fail to work in one third of patients who show no or minimal response. It is therefore necessary to discover new molecular intervention points involved in TNF-α blocker treatment of rheumatoid arthritis patients. We describe a data analysis strategy for predicting gene expression measures that are critical for rheumatoid arthritis using a combination of comprehensive genotyping, whole blood gene expression profiles and the component clinical measures of the arthritis Disease Activity Score 28 (DAS28) score. Two separate network ensembles, each comprised of 1024 networks, were built from molecular measures from subjects before and 14 weeks after treatment with TNF-α blocker. The network ensemble built from pre-treated data captures TNF-α dependent mechanistic information, while the ensemble built from data collected under TNF-α blocker treatment captures TNF-α independent mechanisms. In silico simulations of targeted, personalized perturbations of gene expression measures from both network ensembles identify transcripts in three broad categories. Firstly, 22 transcripts are identified to have new roles in modulating the DAS28 score; secondly, there are 6 transcripts that could be alternative targets to TNF-α blocker therapies, including CD86 - a component of the signaling axis targeted by Abatacept (CTLA4-Ig), and finally, 59 transcripts that are predicted to modulate the count of tender or swollen joints but not sufficiently enough to have a significant impact on DAS28.

An integrated approach for inference and mechanistic modeling for advancing drug development

An important challenge facing researchers in drug development is how to translate multi‐omic measurements into biological insights that will help advance drugs through the clinic. Computational biology strategies are a promising approach for systematically capturing the effect of a given drug on complex molecular networks and on human physiology. This article discusses a two‐pronged strategy for inferring biological interactions from large‐scale multi‐omic measurements and accounting for known biology via mechanistic dynamical simulations of pathways, cells, and organ‐ and tissue level models. These approaches are already playing a role in driving drug development by providing a rational and systematic computational framework.

Large-scale identification of clinical and genetic predictors of motor progression in patients with newly diagnosed Parkinson's disease: a longitudinal cohort study and validation

Background Better understanding and prediction of PD progression could improve disease management and clinical trial design. We aimed to use longitudinal clinical, molecular, and genetic data to develop predictive models, compare potential biomarkers, and identify novel predictors for motor progression in PD. We also sought to assess the use of these models in the design of treatment trials in PD. Methods A Bayesian multivariate predictive inference platform was applied to data from the Parkinson’s Progression Markers Initiative (PPMI) study (NCT01141023). We used genetic data and baseline molecular and clinical variables from PD patients and healthy controls to construct an ensemble of models to predict the annualised rate of the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale parts II and III combined. We tested our overall explanatory power, as assessed by the coefficient of determination (R2), and replicated novel findings in an independent clinical cohort of PD patients from the Longitudinal and Biomarker Study in PD (LABS-PD; NCT00605163). The potential utility of these models for clinical trial design was quantified by comparing simulated randomized placebo-controlled trials within the out-of sample LABS-PD cohort. Findings A total of 117 controls and 312 PD cases were available for analysis. Our model ensemble exhibited strong performance in-cohort (5-fold cross-validated R2=41%, 95% CI: 35% – 47%) and significant, though reduced, performance out-of-cohort (R2=9%, 95% CI: 4% – 16%). Individual predictive features identified from PPMI data were confirmed in the LABS-PD cohort of 317 PD patients. These included significant replication of higher baseline motor score, male sex, and increased age, as well as a novel PD-specific epistatic interaction all indicative of faster motor progression. Genetic variation was the most useful predictive marker of motor progression (2.9%, 95%CI: 1.5–4.3%). CSF biomarkers at baseline showed a more modest (0.3%; 95%CI: 0.1–0.5%), but still significant effect on motor progression prediction. The simulations (n=5000) showed that incorporating the predicted rates of motor progression into the final models of treatment effect reduced the variability in the study outcome allowing significant differences to be detected at sample sizes up to 20% smaller than in naïve trials. Interpretation Our model ensemble confirmed established and identified novel predictors of PD motor progression. Improving existing prognostic models through machine learning approaches should benefit trial design and evaluation, as well as clinical disease monitoring and treatment. Funding Michael J. Fox Foundation for Parkinson’s Research and National Institute of Neurological Disorders and Stroke (1P20NS092529-01).

Bayesian network inference modeling identifies TRIB1 as a novel regulator of cell-cycle progression and survival in cancer cells

Molecular networks governing responses to targeted therapies in cancer cells are complex dynamic systems that demonstrate nonintuitive behaviors. We applied a novel computational strategy to infer probabilistic causal relationships between network components based on gene expression. We constructed a model comprised of an ensemble of networks using multidimensional data from cell line models of cell-cycle arrest caused by inhibition of MEK1/2. Through simulation of a reverse-engineered Bayesian network model, we generated predictions of G1-S transition. The model identified known components of the cell-cycle machinery, such as CCND1, CCNE2, and CDC25A, as well as revealed novel regulators of G1-S transition, IER2, TRIB1, TRIM27. Experimental validation of model predictions confirmed 10 of 12 predicted genes to have a role in G1-S progression. Further analysis showed that TRIB1 regulated the cyclin D1 promoter via NFκB and AP-1 sites and sensitized cells to TRAIL-induced apoptosis. In clinical specimens of breast cancer, TRIB1 levels correlated with expression of NFκB and its target genes (IL8, CSF2), and TRIB1 copy number and expression were predictive of clinical outcome. Together, our results establish a critical role of TRIB1 in cell cycle and survival that is mediated via the modulation of NFκB signaling. Cancer Res; 77(7); 1575-85. ©2017 AACR.

Systems biology and in vitro validation identifies family with sequence similarity 129 member A (FAM129A) as an asthma steroid response modulator

Background: Variation in response to the most commonly used class of asthma controller medication, inhaled corticosteroids, presents a serious challenge in asthma management, particularly for steroid‐resistant patients with little or no response to treatment. Objective: We applied a systems biology approach to primary clinical and genomic data to identify and validate genes that modulate steroid response in asthmatic children. Methods: We selected 104 inhaled corticosteroid–treated asthmatic non‐Hispanic white children and determined a steroid responsiveness endophenotype (SRE) using observations of 6 clinical measures over 4 years. We modeled each subjects cellular steroid response using data from a previously published study of immortalized lymphoblastoid cell lines under dexamethasone (DEX) and sham treatment. We integrated SRE with immortalized lymphoblastoid cell line DEX responses and genotypes to build a genome‐scale network using the Reverse Engineering, Forward Simulation modeling framework, identifying 7 genes modulating SRE. Results: Three of these genes were functionally validated by using a stable nuclear factor &kgr;‐light‐chain‐enhancer of activated B cells luciferase reporter in A549 human lung epithelial cells, IL‐1&bgr; cytokine stimulation, and DEX treatment. By using small interfering RNA transfection, knockdown of family with sequence similarity 129 member A (FAM129A) produced a reduction in steroid treatment response (P < .001). Conclusion: With this systems‐based approach, we have shown that FAM129A is associated with variation in clinical asthma steroid responsiveness and that FAM129A modulates steroid responsiveness in lung epithelial cells.

A Bayesian mathematical model of motor and cognitive outcomes in Parkinson’s disease

Background There are few established predictors of the clinical course of PD. Prognostic markers would be useful for clinical care and research. Objective To identify predictors of long-term motor and cognitive outcomes and rate of progression in PD. Methods Newly diagnosed PD participants were followed for 7 years in a prospective study, conducted at 55 centers in the United States and Canada. Analyses were conducted in 244 participants with complete demographic, clinical, genetic, and dopamine transporter imaging data. Machine learning dynamic Bayesian graphical models were used to identify and simulate predictors and outcomes. The outcomes rate of cognition changes are assessed by the Montreal Cognitive Assessment scores, and rate of motor changes are assessed by UPDRS part-III. Results The most robust and consistent longitudinal predictors of cognitive function included older age, baseline Unified Parkinson’s Disease Rating Scale (UPDRS) parts I and II, Schwab and England activities of daily living scale, striatal dopamine transporter binding, and SNP rs11724635 in the gene BST1. The most consistent predictor of UPDRS part III was baseline level of activities of daily living (part II). Key findings were replicated using long-term data from an independent cohort study. Conclusions Baseline function near the time of Parkinson’s disease diagnosis, as measured by activities of daily living, is a consistent predictor of long-term motor and cognitive outcomes. Additional predictors identified may further characterize the expected course of Parkinson’s disease and suggest mechanisms underlying disease progression. The prognostic model developed in this study can be used to simulate the effects of the prognostic variables on motor and cognitive outcomes, and can be replicated and refined with data from independent longitudinal studies.

Abstract 4025: Data-driven computational modeling to identify biomarkers of response to lenvatinib (E7080) in melanoma.

Background: Lenvatinib is an oral tyrosine kinase inhibitor targeting VEGFR1-3, FGFR1-4, RET, KIT and PDGFRβ. Anti-tumor activity has been observed in melanoma patients in Phase I studies. We applied an integrative supercomputer-driven analysis approach to identify biomarkers of lenvatinib treatment response in melanoma patients. Methods: Clinical data sets including tumor response data (RECIST), progression free survival (PFS), pharmacokinetic parameters (PK) and molecular data sets including baseline tumor gene expression (Affymetrix U133Plus2) and BRAF and NRAS mutational status, were collected from 18 patients with metastatic melanoma who received lenvatinib 10 mg orally twice daily in 28-day cycles. These clinical and molecular data sets were used to generate computational models developed using REFS (Reverse Engineering and Forward Simulation) modeling platform, which utilizes Bayesian network inference and simulations. Simulations were performed to identify biomarkers of lenvatinib treatment response. These potential predictive biomarkers were then tested for their ability to predict response to lenvatinib in preclinical models. Results: Using a model comprising gene expression, mutational status and PK data, REFS identified a panel of 18 potential predictive biomarkers of lenvatinib treatment response. Identified biomarkers were able to predict up to 89% of the observed variance in the tumor response data. A total of 32 identified genes including 6 candidate predictive biomarkers (TARBP2, CACNA1, C7ORF, RAP2A, SHMT1, IL22RA2) were further validated in a preclinical melanoma model system (n=12) and tissue bank samples with matched normal adjacent tissue (n=21). Expression of 14 genes correlated with relative tumor volume (r>0.35 or r 2). Conclusions: Potential predictive biomarkers of lenvatinib treatment response in melanoma patients were identified by computational modeling and validated in a preclinical model system and tumor tissue bank samples. The identified biomarkers will be tested for their predictive value in an ongoing Phase 2 trial. Citation Format: Tadashi Kadowaki, Yasuhiro Funahashi, Junji Matsui, Kumar Pavan, Pallavi Sachdev, Jim O9Brien, Heming Xing, Paul D. McDonagh, Iya Khalil, Razelle Kurzrock, David S. Hong, John Nemunaitis. Data-driven computational modeling to identify biomarkers of response to lenvatinib (E7080) in melanoma. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4025. doi:10.1158/1538-7445.AM2013-4025

Abstract 4933: Novel insight into hallmark switch in cancer metabolism using the Interrogative Biology™ discovery platform

Cancer metabolism is an intergrative ensemble of disrupted enzyme kinetics and dysregulated metabolite utilization leading to loss of normal cellular function that is the result of a multi-factorial yet coordinated breakdown in vascular, immune, cell cycle, apoptotic, and ECM components. In actively metabolizing cancer, the switch from mitochondrial OXPHOS to anaerobic glycolysis is very well characterized and understood. Global cellular changes in response to metabolic switch have either been overlooked or not been primary interest or relevance to cancer metabolism. We describe a novel systems biology/engineering approach encompassing cell models that are conditioned under various oncogenic perturbations or environments and then coupled with functional bioenergetic read out such as employing the XF24 Seahorse Bioscience analyzer, ATP assays, and ROS production. The OCR and ECAR measurements generated by XF24 analyzer enabled quantifying the switch from aerobic to the anerobic mode of energy metabolism. Cellular profiles were captured in the form of multi-omic (proteomic, genomic, proteomic) signatures using high-throughput mass spectrometry based protocols. Analyses were performed on oncogenic breast, prostate, liver, pancreatic, skin (melanoma, squamous cell carcinoma) and were compared to normal fibroblasts, keratinocytes, hepatocytes, kidney cells, adipocytes, and human aortic and endothelial cells. High throughput data cascades from various cancer states were integrated with the metabolic data from the XF24 analyzer using an AI-based data mining platform to generate causal network based on bayesian models (REFS™ model). The output enables the understanding of differential mechanisms that drive glycolysis and mitochondrial OXPHOS in a cancer versus normal environment. Further validation of prominent hub of activity as they partake as key drivers of metabolic end points by siRNA knockdown experiments followed by measurement using the XF24 analyzer confirmed the relevance of these hubs in cancer metabolism and their relevance as potential therapeutic targets and biomarkers for diagnostics development. The data output presented herein strongly suggest that the Interrogative Biology ® platform is a key tool in deciphering differential network analysis pertinent to disease pathophysiology and bioenergetics. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4933. doi:1538-7445.AM2012-4933