Phenotype Control techniques for Boolean gene regulatory networks.

Modeling cell signal transduction pathways via Boolean networks (BNs) has become an established method for analyzing intracellular communications over the last few decades. What's more, BNs provide a course-grained approach, not only to understanding molecular communications, but also for targeting pathway components that alter the long-term outcomes of the system. This has come to be known as phenotype control theory. In this review we study the interplay of various approaches for controlling gene regulatory networks such as: algebraic methods, control kernel, feedback vertex set, and stable motifs. The study will also include comparative discussion between the methods, using an established cancer model of T-Cell Large Granular Lymphocyte Leukemia. Further, we explore possible options for making the control search more efficient using reduction and modularity. Finally, we will include challenges presented such as the complexity and the availability of software for implementing each of these control techniques.

Uncovering potential interventions for pancreatic cancer patients via mathematical modeling.

Pancreatic Ductal Adenocarcinoma (PDAC) is widely known for its poor prognosis because it is often diagnosed when the cancer is in a later stage. We built a Boolean model to analyze the microenvironment of pancreatic cancer in order to better understand the interplay between pancreatic cancer, stellate cells, and their signaling cytokines. Specifically, we have used our model to study the impact of inducing four common mutations: KRAS, TP53, SMAD4, and CDKN2A. After implementing the various mutation combinations, we used our stochastic simulator to derive aggressiveness scores based on simulated attractor probabilities and long-term trajectory approximations. These aggression scores were then corroborated with clinical data. Moreover, we found sets of control targets that are effective among common mutations. These control sets contain nodes within both the pancreatic cancer cell and the pancreatic stellate cell, including PIP3, RAF, PIK3 and BAX in pancreatic cancer cell as well as ERK and PIK3 in the pancreatic stellate cell. Many of these nodes were found to be differentially expressed among pancreatic cancer patients in the TCGA database. Furthermore, literature suggests that many of these nodes can be targeted by drugs currently in circulation. The results herein help provide a proof of concept in the path towards personalized medicine through a means of mathematical systems biology. All data and code used for running simulations, statistical analysis, and plotting is available on a GitHub repository athttps://github.com/drplaugher/PCC_Mutations.

Modeling the Pancreatic Cancer Microenvironment in Search of Control Targets.

Pancreatic ductal adenocarcinoma is among the leading causes of cancer-related deaths globally due to its extreme difficulty to detect and treat. Recently, research focus has shifted to analyzing the microenvironment of pancreatic cancer to better understand its key molecular mechanisms. This microenvironment can be represented with a multi-scale model consisting of pancreatic cancer cells (PCCs) and pancreatic stellate cells (PSCs), as well as cytokines and growth factors which are responsible for intercellular communication between the PCCs and PSCs. We have built a stochastic Boolean network (BN) model, validated by literature and clinical data, in which we probed for intervention strategies that force this gene regulatory network (GRN) from a diseased state to a healthy state. To do so, we implemented methods from phenotype control theory to determine a procedure for regulating specific genes within the microenvironment. We identified target genes and molecules, such that the application of their control drives the GRN to the desired state by suppression (or expression) and disruption of specific signaling pathways that may eventually lead to the eradication of the cancer cells. After applying well-studied control methods such as stable motifs, feedback vertex sets, and computational algebra, we discovered that each produces a different set of control targets that are not necessarily minimal nor unique. Yet, we were able to gain more insight about the performance of each process and the overlap of targets discovered. Nearly every control set contains cytokines, KRas, and HER2/neu, which suggests they are key players in the system's dynamics. To that end, this model can be used to produce further insight into the complex biological system of pancreatic cancer with hopes of finding new potential targets.

Pancreatic cancer mutationscape: revealing the link between modular restructuring and intervention efficacy amidst common mutations.

There is increasing evidence that biological systems are modular in both structure and function. Complex biological signaling networks such as gene regulatory networks (GRNs) are proving to be composed of subcategories that are interconnected and hierarchically ranked. These networks contain highly dynamic processes that ultimately dictate cellular function over time, as well as influence phenotypic fate transitions. In this work, we use a stochastic multicellular signaling network of pancreatic cancer (PC) to show that the variance in topological rankings of the most phenotypically influential modules implies a strong relationship between structure and function. We further show that induction of mutations alters the modular structure, which analogously influences the aggression and controllability of the disease in silico. We finally present evidence that the impact and location of mutations with respect to PC modular structure directly corresponds to the efficacy of single agent treatments in silico, because topologically deep mutations require deep targets for control.

Cancer mutationscape: revealing the link between modular restructuring and intervention efficacy among mutations.

There is increasing evidence that biological systems are modular in both structure and function. Complex biological signaling networks such as gene regulatory networks (GRNs) are proving to be composed of subcategories that are interconnected and hierarchically ranked. These networks contain highly dynamic processes that ultimately dictate cellular function over time, as well as influence phenotypic fate transitions. In this work, we use a stochastic multicellular signaling network of pancreatic cancer (PC) to show that the variance in topological rankings of the most phenotypically influential modules implies a strong relationship between structure and function. We further show that induction of mutations alters the modular structure, which analogously influences the aggression and controllability of the disease in silico. We finally present evidence that the impact and location of mutations with respect to PC modular structure directly corresponds to the efficacy of single agent treatments in silico, because topologically deep mutations require deep targets for control.

Therapeutic potential of tumor-infiltrating lymphocytes in non-small cell lung cancer.

Lung cancer is the leading cause of cancer-related death worldwide, with poor outcomes even for those diagnosed at early stages. Current standard-of-care for most non-small cell lung cancer (NSCLC) patients involves an array of chemotherapy, radiotherapy, immunotherapy, targeted therapy, and surgical resection depending on the stage and location of the cancer. While patient outcomes have certainly improved, advances in highly personalized care remain limited. However, there is growing excitement around harnessing the power of tumor-infiltrating lymphocytes (TILs) through the use of adoptive cell transfer (ACT) therapy. These TILs are naturally occurring, may already recognize tumor-specific antigens, and can have direct anti-cancer effect. In this review, we highlight comparisons of various ACTs, including a brief TIL history, show current advances and successes of TIL therapy in NSCLC, discuss the potential roles for epigenetics in T cell expansion, and highlight challenges and future directions of the field to combat NSCLC in a personalized manner.

EZH2 Inhibition Promotes Tumor Immunogenicity in Lung Squamous Cell Carcinomas.

Two important factors that contribute to resistance to immune checkpoint inhibitors (ICI) are an immune-suppressive microenvironment and limited antigen presentation by tumor cells. In this study, we examine whether inhibition of the methyltransferase enhancer of zeste 2 (EZH2) can increase ICI response in lung squamous cell carcinomas (LSCC). Our in vitro experiments using two-dimensional human cancer cell lines as well as three-dimensional murine and patient-derived organoids treated with two inhibitors of the EZH2 plus IFNγ showed that EZH2 inhibition leads to expression of both MHC class I and II (MHCI/II) expression at both the mRNA and protein levels. Chromatin immunoprecipitation sequencing confirmed loss of EZH2-mediated histone marks and gain of activating histone marks at key loci. Furthermore, we demonstrate strong tumor control in models of both autochthonous and syngeneic LSCC treated with anti-PD1 immunotherapy with EZH2 inhibition. Single-cell RNA sequencing and immune cell profiling demonstrated phenotypic changes toward more tumor suppressive phenotypes in EZH2 inhibitor-treated tumors. These results indicate that EZH2 inhibitors could increase ICI responses in patients undergoing treatment for LSCC. SIGNIFICANCE: The data described here show that inhibition of the epigenetic enzyme EZH2 allows derepression of multiple immunogenicity factors in LSCC, and that EZH2 inhibition alters myeloid cells in vivo. These data support clinical translation of this combination therapy for treatment of this deadly tumor type.

Methionine Restriction Reduces Lung Cancer Progression and Increases Chemotherapy Response.

Targeting tumor metabolism through dietary interventions is an area of growing interest, and may help to improve the significant mortality of aggressive cancers, including non-small cell lung cancer (NSCLC). Here we show that the restriction of methionine in the aggressive KRAS/Lkb1-mutant NSCLC autochthonous mouse model drives decreased tumor progression and increased carboplatin treatment efficacy. Importantly, methionine restriction during early stages of tumorigenesis prevents the lineage switching known to occur in the model, and alters the tumor immune microenvironment (TIME) to have fewer tumor-infiltrating neutrophils. Mechanistically, mutations in LKB1 are linked to anti-oxidant production through changes to cystathionine-ß-synthase (CBS) expression. Human cell lines with rescued LKB1 show increased CBS levels and resistance to carboplatin, which can be partially rescued by methionine restriction. Furthermore, LKB1 rescued cells, but not mutant cells, show less G2-M arrest and apoptosis in high methionine conditions. Knock-down of CBS sensitized both LKB1 mutant and non-mutated lines to carboplatin, again rescuing the carboplatin resistance of the LKB1 rescued lines. Given that immunotherapy is commonly combined with chemotherapy for NSCLC, we next wanted to understand if T cells are impaired by MR. Therefore, we examined the ability of T cells from MR and control tumor bearing mice to proliferate in culture and found that T cells from MR treated mice had no defects in proliferation, even though we continued the MR conditions ex vivo. We also identified that CBS is most highly correlated with smoking, adenocarcinomas with alveolar and bronchiolar features, and adenosquamous cell carcinomas, implicating its roles in oxidative stress response and lineage fate in human tumors. Taken together, we have shown the importance of MR as a dietary intervention to slow tumor growth and improve treatment outcomes for NSCLC.

Phenotype control techniques for Boolean gene regulatory networks.

Modeling cell signal transduction pathways via Boolean networks (BNs) has become an established method for analyzing intracellular communications over the last few decades. What’s more, BNs provide a course-grained approach, not only to understanding molecular communications, but also for targeting pathway components that alter the long-term outcomes of the system. This has come to be known as phenotype control theory . In this review we study the interplay of various approaches for controlling gene regulatory networks such as: algebraic methods, control kernel, feedback vertex set, and stable motifs. The study will also include comparative discussion between the methods, using an established cancer model of T-Cell Large Granular Lymphocyte (T-LGL) Leukemia. Further, we explore possible options for making the control search more efficient using reduction and modularity. Finally, we will include challenges presented such as the complexity and the availability of software for implementing each of these control techniques.

EZH2 inhibition promotes tumor immunogenicity in lung squamous cell carcinomas.

Two important factors that contribute to resistance to immune checkpoint inhibitors (ICIs) are an immune-suppressive microenvironment and limited antigen presentation by tumor cells. In this study, we examine if inhibition of the methyltransferase EZH2 can increase ICI response in lung squamous cell carcinomas (LSCCs). Our in vitro experiments using 2D human cancer cell lines as well as 3D murine and patient derived organoids treated with two inhibitors of the EZH2 plus interferon-γ (IFNγ) showed that EZH2 inhibition leads to expression of both major histocompatibility complex class I and II (MHCI/II) expression at both the mRNA and protein levels. ChIP-sequencing confirmed loss of EZH2-mediated histone marks and gain of activating histone marks at key loci. Further, we demonstrate strong tumor control in models of both autochthonous and syngeneic LSCC treated with anti-PD1 immunotherapy with EZH2 inhibition. Single-cell RNA sequencing and immune cell profiling demonstrated phenotypic changes towards more tumor suppressive phenotypes in EZH2 inhibitor treated tumors. These results indicate that this therapeutic modality could increase ICI responses in patients undergoing treatment for LSCC.