Iman Tavassoly

Dynamic modelling of oestrogen signalling and cell fate in breast cancer cells

Cancers of the breast and other tissues arise from aberrant decision-making by cells regarding their survival or death, proliferation or quiescence, damage repair or bypass. These decisions are made by molecular signalling networks that process information from outside and from within the breast cancer cell and initiate responses that determine the cells survival and reproduction. Because the molecular logic of these circuits is difficult to comprehend by intuitive reasoning alone, we present some preliminary mathematical models of the basic decision circuits in breast cancer cells that may aid our understanding of their susceptibility or resistance to endocrine therapy.

Endoplasmic Reticulum Stress, the Unfolded Protein Response, Autophagy, and the Integrated Regulation of Breast Cancer Cell Fate

How breast cancer cells respond to the stress of endocrine therapies determines whether they will acquire a resistant phenotype or execute a cell-death pathway. After a survival signal is successfully executed, a cell must decide whether it should replicate. How these cell-fate decisions are regulated is unclear, but evidence suggests that the signals that determine these outcomes are highly integrated. Central to the final cell-fate decision is signaling from the unfolded protein response, which can be activated following the sensing of stress within the endoplasmic reticulum. The duration of the response to stress is partly mediated by the duration of inositol-requiring enzyme-1 activation following its release from heat shock protein A5. The resulting signals appear to use several B-cell lymphoma-2 family members to both suppress apoptosis and activate autophagy. Changes in metabolism induced by cellular stress are key components of this regulatory system, and further adaptation of the metabolome is affected in response to stress. Here we describe the unfolded protein response, autophagy, and apoptosis, and how the regulation of these processes is integrated. Central topologic features of the signaling network that integrate cell-fate regulation and decision execution are discussed.

Dynamic Modeling of the Interaction Between Autophagy and Apoptosis in Mammalian Cells

Autophagy is a conserved biological stress response in mammalian cells that is responsible for clearing damaged proteins and organelles from the cytoplasm and recycling their contents via the lysosomal pathway. In cases of mild stress, autophagy acts as a survival mechanism, while in cases of severe stress cells may switch to programmed cell death. Understanding the decision process that moves a cell from autophagy to apoptosis is important since abnormal regulation of autophagy occurs in many diseases, including cancer. To integrate existing knowledge about this decision process into a rigorous, analytical framework, we built a mathematical model of cell fate decisions mediated by autophagy. Our dynamical model is consistent with existing quantitative measurements of autophagy and apoptosis in rat kidney proximal tubular cells responding to cisplatin‐induced stress.

Modelling the effect of GRP78 on anti-oestrogen sensitivity and resistance in breast cancer

Understanding the origins of resistance to anti-oestrogen drugs is of critical importance to many breast cancer patients. Recent experiments show that knockdown of GRP78, a key gene in the unfolded protein response (UPR), can re-sensitize resistant cells to anti-oestrogens, and overexpression of GRP78 in sensitive cells can cause them to become resistant. These results appear to arise from the operation and interaction of three cellular systems: the UPR, autophagy and apoptosis. To determine whether our current mechanistic understanding of these systems is sufficient to explain the experimental results, we built a mathematical model of the three systems and their interactions. We show that the model is capable of reproducing previously published experimental results and some new data gathered specifically for this paper. The model provides us with a tool to better understand the interactions that bring about anti-oestrogen resistance and the effects of GRP78 on both sensitive and resistant breast cancer cells.

A biomimetic gelatin-based platform elicits a pro-differentiation effect on podocytes through mechanotransduction

Using a gelatin microbial transglutaminase (gelatin-mTG) cell culture platform tuned to exhibit stiffness spanning that of healthy and diseased glomeruli, we demonstrate that kidney podocytes show marked stiffness sensitivity. Podocyte-specific markers that are critical in the formation of the renal filtration barrier are found to be regulated in association with stiffness-mediated cellular behaviors. While podocytes typically de-differentiate in culture and show diminished physiological function in nephropathies characterized by altered tissue stiffness, we show that gelatin-mTG substrates with Young’s modulus near that of healthy glomeruli elicit a pro-differentiation and maturation response in podocytes better than substrates either softer or stiffer. The pro-differentiation phenotype is characterized by upregulation of gene and protein expression associated with podocyte function, which is observed for podocytes cultured on gelatin-mTG gels of physiological stiffness independent of extracellular matrix coating type and density. Signaling pathways involved in stiffness-mediated podocyte behaviors are identified, revealing the interdependence of podocyte mechanotransduction and maintenance of their physiological function. This study also highlights the utility of the gelatin-mTG platform as an in vitro system with tunable stiffness over a range relevant for recapitulating mechanical properties of soft tissues, suggesting its potential impact on a wide range of research in cellular biophysics.

Systems therapeutics analyses identify genomic signatures defining responsiveness to allopurinol and combination therapy for lung cancer

The ability to predict responsiveness to drugs in individual patients is limited. We hypothesized that integrating molecular information from databases would yield predictions that could be experimentally tested to develop genomic signatures for sensitivity or resistance to specific drugs. We analyzed TCGA data for lung adenocarcinoma (LUAD) patients and identified a subset where xanthine dehydrogenase expression correlated with decreased survival. We tested allopurinol, a FDA approved drug that inhibits xanthine dehydrogenase on a library of human Non Small Cell Lung Cancer (NSCLC) cell lines from CCLE and identified sensitive and resistant cell lines. We utilized the gene expression profiles of these cell lines to identify six-gene signatures for allopurinol sensitive and resistant cell lines. Network building and analyses identified JAK2 as an additional target in allopurinol-resistant lines. Treatment of resistant cell lines with allopurinol and CEP-33779 (a JAK2 inhibitor) resulted in cell death. The effectiveness of allopurinol alone or allopurinol and CEP-33779 were verified in vivo using tumor formation in NCR-nude mice. We utilized the six-gene signatures to predict five additional allopurinol-sensitive NSCLC lines, and four allopurinol-resistant lines susceptible to combination therapy. We found that drug treatment of all cell lines yielded responses as predicted by the genomic signatures. We searched the library of patient derived NSCLC tumors from Jackson Laboratory to identify tumors that would be predicted to be sensitive or resistant to allopurinol treatment. Both patient derived tumors predicted to be allopurinol sensitive showed the predicted sensitivity, and the predicted resistant tumor was sensitive to combination therapy. These data indicate that we can use integrated molecular information from cancer databases to predict drug responsiveness in individual patients and thus enable precision medicine.

Abstract 5554: Metabolic reprogramming in non-small cell lung cancer: a precision oncology approach

Lung cancers are among the most common invasive cancers worldwide and annually lead to high mortality and morbidity. Genomic alterations have been known to control the evolution of hallmarks of cancer in a dynamic way. These molecular alterations combined with epigenomic and post-genomic modifications contribute to formation of these neoplasms. Multiplicity of these changes has made development of personalized therapeutic regimens for these cancers a complex problem. Metabolic reprogramming is one of the main mechanisms in progression of cancers. There have been efforts to model the metabolic reprogramming in cancer using metabolic networks of cancer cells, but there has been no computational framework to model these metabolic transitions in cancer for precision and personalized medicine. We have combined computational, mathematical and experimental methodologies to develop a platform for precision oncology in non-small cell lung cancer (NSCLC) by in silico models of metabolic switches. Our integrative analysis of genomic data from NSCLC has led to discovery of genomic signatures controlling metabolic reprogramming in NSCLC with KRAS mutations. This discovery was proved in vivo and in vitro using drugs blocking different metabolic pathways. We have shown that NSCLC cells and tumors which carry KRAS mutations and have these genomic signatures are addicted to the pentose phosphate pathway (PPP). We have verified and proved the predictive value of these genomic signatures using Patient Derived Xenograft (PDX) tumor models of NSCLC. We are developing a mathematical and computational framework to model these metabolic switches. Our platform is capable of using genomic data from a cell line or tumor to determine the metabolic dependency of them quantitatively and predict the optimized personalized treatments for modulating metabolic pathways aiming to control cancer progression. Citation Format: Iman Tavassoly, Ravi Iyengar. Metabolic reprogramming in non-small cell lung cancer: a precision oncology approach [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 5554. doi:10.1158/1538-7445.AM2017-5554

Source Code for Dynamic Model of Interplay Between Autophagy and Apoptosis

The Matlab code for solving ODEs used for modeling autophagy and apoptosis pathways is presented in this chapter.

Mathematical Modeling of the Interplay of Autophagy and Apoptosis

In this chapter, we have proposed a theoretical framework for analysis of dynamics of interplay of autophagy and apoptosis in mammalian cells including cancer cells. Because quantitative experimental data on time course of interplay of autophagy and apoptosis is very limited, we have used the observations of interaction of autophagy and apoptosis in different mammalian cell types to design our primary hypothesis. Using ordinary differential equations (ODEs), we have analyzed network dynamics of molecular signaling pathways controlling cell fate at crosstalk of autophagy and apoptosis. We have used time course of autophagy level and cell fates described by Periyasamy-Thandavan et al. [32] to collectively fit the parameters of the ODE system. The mathematical model presented in this chapter can be extended and by estimating more accurate parameter sets from quantitative experimental data, it can be an integrative in silico model of cell fate decision mediated by interplay of autophagy and apoptosis.

An Experimental Framework to Study the Dynamics of Autophagic Response

In this chapter we present an experimental quantitative framework for measuring kinetic parameters such as autophagy flux, time course of autophagic response, and stress/response dynamics in single cancer cells including endocrine-resistant breast cancer cells. Some primary data are presented in this chapter.