A Physical Mechanism and Global Quantification of Breast Cancer

Data mining and mathematical models in cancer prognosis and prediction

Cancer is a fetal and complex disease. Individual differences of the same cancer type or the same patient at different stages of cancer development may require distinct treatments. Pathological differences are reflected in tissues, cells and gene levels etc. The interactions between the cancer cells and nearby microenvironments can also influence the cancer progression and metastasis. It is a huge challenge to understand all of these mechanistically and quantitatively. Researchers applied pattern recognition algorithms such as machine learning or data mining to predict cancer types or classifications. With the rapidly growing and available computing powers, researchers begin to integrate huge data sets, multi-dimensional data types and information. The cells are controlled by the gene expressions determined by the promoter sequences and transcription regulators. For example, the changes in the gene expression through these underlying mechanisms can modify cell progressing in the cell-cycle. Such molecular activities can be governed by the gene regulations through the underlying gene regulatory networks, which are essential for cancer study when the information and gene regulations are clear and available. In this review, we briefly introduce several machine learning methods of cancer prediction and classification which include Artificial Neural Networks (ANNs), Decision Trees (DTs), Support Vector Machine (SVM) and naive Bayes. Then we describe a few typical models for building up gene regulatory networks such as Correlation, Regression and Bayes methods based on available data. These methods can help on cancer diagnosis such as susceptibility, recurrence, survival etc. At last, we summarize and compare the modeling methods to analyze the development and progression of cancer through gene regulatory networks. These models can provide possible physical strategies to analyze cancer progression in a systematic and quantitative way.

Quantification of the Landscape for Revealing the Underlying Mechanism of Intestinal-Type Gastric Cancer

Gastric cancer is a daunting disease with a tragic impact on global health. It is the fourth most common cancer and has become the second most frequent cause of cancer death in recent times. According to the Lauren classification, gastric cancer can be classified into two types: intestinal and diffuse. Intestinal-type gastric cancer (IGC) is more common in elderly people, and atrophic gastritis (AG) and intestinal metaplasia (IM) have been proven to be the main premalignant causes of intestinal-type gastric cancer. In turn, Helicobacter pylori infection has been identified as the most significant cause of AG and IM. In this study, we determine the mechanism of IGC progression and how H. pylori infection induces IGC. Through researching the relevant literature, we identified the key genes associated with gastric cancer and the specific genes associated with IGC. We then use hese genes to build up a gene regulatory network for IGC. Based on this gene regulatory network, we quantify the IGC landscape. Within this landscape, there are three stable states, which are classified as the normal, AG, and gastric cancer states. Through landscape topography, we can determine the biological features and progression process of IGC. To investigate the influence of H. pylori infection on IGC, we simulated different degrees of H. pylori infection. As the H. pylori infection becomes more serious, the landscape topography changes accordingly. A fourth state, named the intestinal metaplasia (IM) state, emerges on the landscape and is associated with a very high risk of developing gastric cancer. The emergence of this state is due to the interactions/regulations among genes. Through variations in the landscape topography, we can determine the influence of H. pylori infection on IGC. Finally, we use global sensitivity analysis to research the regulations most sensitive to IGC prevention or therapies. This study presents a new approach and a novel model with which to explore the mechanism of IGC. The simulations of different degrees of H. pylori infection can provide us with a systematic view of IGC progression. The key regulations found can give us some insight and guidance for clinical trials and experimental studies.

Perspectives on the landscape and flux theory for describing emergent behaviors of the biological systems

We give a review on the landscape theory of the equilibrium biological systems and landscape-flux theory of the nonequilibrium biological systems as the global driving force. The emergences of the behaviors, the associated thermodynamics in terms of the entropy and free energy and dynamics in terms of the rate and paths have been quantitatively demonstrated. The hierarchical organization structures have been discussed. The biological applications ranging from protein folding, biomolecular recognition, specificity, biomolecular evolution and design for equilibrium systems as well as cell cycle, differentiation and development, cancer, neural networks and brain function, and evolution for nonequilibrium systems, cross-scale studies of genome structural dynamics and experimental quantifications/verifications of the landscape and flux are illustrated. Together, this gives an overall global physical and quantitative picture in terms of the landscape and flux for the behaviors, dynamics and functions of biological systems.

Physics of biomolecular recognition and conformational dynamics

Biomolecular recognition usually leads to the formation of binding complexes, often accompanied by large-scale conformational changes. This process is fundamental to biological functions at the molecular and cellular levels. Uncovering the physical mechanisms of biomolecular recognition and quantifying the key biomolecular interactions are vital to understand these functions. The recently developed energy landscape theory has been successful in quantifying recognition processes and revealing the underlying mechanisms. Recent studies have shown that in addition to affinity, specificity is also crucial for biomolecular recognition. The proposed physical concept of intrinsic specificity based on the underlying energy landscape theory provides a practical way to quantify the specificity. Optimization of affinity and specificity can be adopted as a principle to guide the evolution and design of molecular recognition. This approach can also be used in practice for drug discovery using multidimensional screening to identify lead compounds. The energy landscape topography of molecular recognition is important for revealing the underlying flexible binding or binding-folding mechanisms. In this review, we first introduce the energy landscape theory for molecular recognition and then address four critical issues related to biomolecular recognition and conformational dynamics: (1) specificity quantification of molecular recognition; (2) evolution and design in molecular recognition; (3) flexible molecular recognition; (4) chromosome structural dynamics. The results described here and the discussions of the insights gained from the energy landscape topography can provide valuable guidance for further computational and experimental investigations of biomolecular recognition and conformational dynamics.

Data-Driven Modeling of Breast Cancer Tumors Using Boolean Networks

Cancer is a genomic disease involving various intertwined pathways with complex cross-communication links. Conceptually, this complex interconnected system forms a network, which allows one to model the dynamic behavior of the elements that characterize it to describe the entire system’s development in its various evolutionary stages of carcinogenesis. Knowing the activation or inhibition status of the genes that make up the network during its temporal evolution is necessary for the rational intervention on the critical factors for controlling the system’s dynamic evolution. In this report, we proposed a methodology for building data-driven boolean networks that model breast cancer tumors. We defined the network components and topology based on gene expression data from RNA-seq of breast cancer cell lines. We used a Boolean logic formalism to describe the network dynamics. The combination of single-cell RNA-seq and interactome data enabled us to study the dynamics of malignant subnetworks of up-regulated genes. First, we used the same Boolean function construction scheme for each network node, based on canalyzing functions. Using single-cell breast cancer datasets from The Cancer Genome Atlas, we applied a binarization algorithm. The binarized version of scRNA-seq data allowed identifying attractors specific to patients and critical genes related to each breast cancer subtype. The model proposed in this report may serve as a basis for a methodology to detect critical genes involved in malignant attractor stability, whose inhibition could have potential applications in cancer theranostics.

A global and physical mechanism of gastric cancer formation and progression

Gastric cancer is regarded as a major health issue for human being nowadays. The Helicobacter pylori (H. pylori) infection has been found to accelerate the development of gastritis and gastric cancer. Significant efforts have been made towards the understanding of the biology of gastric cancer on both genetic and epigenetic levels. However the physical mechanism behind the gastric cancer formation is still elusive. In this study, we constructed a model for investigating gastric cancer formation by explored the gastric cancer landscape and the flow flux. We uncovered three stable state attractors on the landscape: normal, gastritis and gastric cancer. The definition of each attractor is based on the biological function and gene expression levels. The global stabilities and the switching processes were quantified through the barrier heights and dominant kinetic paths. To investigate the underlying mechanism of the process from normal through the gastritis to the gastric cancer caused by genetic or epigenetic factors, we simulate the oncogenesis of gastric cancer through changes of several gene regulation strengths and H. pylori infection. The simulated results can illustrate the developmental and metastasis process of gastric cancer. Different H. pylori infection degrees accelerating the process from gastritis to gastric cancer can be quantified. Then we applied global sensitivity analysis, one key gene and four key regulations were found. These results are consist with the experimental results and can be used to design the polygenic anti-cancer agents through multiple key genes or regulations. The landscape approach provides a physical and simple strategy for analyzing gastric cancer in a systematic and quantitative way. It also offers new insight into treatment strategy for gastric cancer by adjusting relevant polygenic genes and regulations.

Uncovering the Underlying Mechanisms of Cancer Metabolism through the Landscapes and Probability Flux Quantifications

Cancer metabolism is critical for understanding the mechanism of tumorigenesis, yet the understanding is still challenging. We studied gene-metabolism regulatory interactions and quantified the global driving forces for cancer-metabolism dynamics as the underlying landscape and probability flux. We uncovered four steady-state attractors: a normal state attractor, a cancer OXPHOS state attractor, a cancer glycolysis state attractor, and an intermediate cancer state attractor. We identified the key regulatory interactions through global sensitivity analysis based on the landscape topography. Different landscape topographies of glycolysis switch between normal cells and cancer cells were identified. We uncovered that the normal state to cancer state transformation is associated with the peaks of the probability flux and the thermodynamic dissipation, giving dynamical and thermodynamic origin of cancer formation. We found that cancer metabolism oscillations consume more energy to support cancer malignancy. This study provides a quantitative understanding of cancer metabolism and suggests a metabolic therapeutic strategy.

Quantification of the Underlying Mechanisms and Relationships Among Cancer, Metastasis, and Differentiation and Development

Recurrence and metastasis have been regarded as two of the greatest obstacles to cancer therapy. Cancer stem cells (CSCs) contribute to cancer development, with the distinctive features of recurrence and resistance to popular treatments such as drugs and chemotherapy. In addition, recent discoveries suggest that the epithelial mesenchymal transition (EMT) is an essential process in normal embryogenesis and tissue repair, as well as being a required step in cancer metastasis. Although there are many indications of the connections between metastasis and stem cells, these have often been studied separately or at most bi-laterally, not in an integrated way. In this study, we aimed to explore the global mechanisms and interrelationships among cancer, development, and metastasis, which are currently poorly understood. First, we constructed a core gene regulatory network containing specific genes and microRNAs of CSCs, EMT, and cancer. We uncovered seven distinct states emerging from the underlying landscape, denoted normal, premalignant, cancer, stem cell, CSC, lesion, and hyperplasia. Given the biological definition of each state, we also discuss the metastasis ability of each state. We show how and which types of cells can be transformed to a cancer state, and the connections among cancer, CSCs, and EMT. The barrier height and flux of the kinetic paths are explored to quantify how and which cells switch stochastically between the states. Our landscape model provides a quantitative approach to reveal the global mechanisms of cancer, development, and metastasis.

Landscape perspectives of tumor, EMT, and development

A tumor is rarely fatal until becoming metastatic. Recent discoveries suggest that epithelial mesenchymal transition(EMT) is an important process which contributes to not only cancer metastasis but also increased stemness. Cancer cells with stem cell characteristics are called cancer stem cells (CSCs). We review recent efforts to quantify and delineate the relationship among EMT, CSC and tumor development. When the gene regulatory network is tightly regulated through the effectively fast regulatory binding, Cancer, Premalignant, Normal, CSC, stem cell (SC), Lesion and Hyperplasia states emerged. The corresponding landscape topography for all of these states can be quantified to a global way for uncovering the relationship among the tumor, metastasis, and development. On the other hand, phenotypic and functional heterogeneity is regarded as one of the greatest challenge in cancer treatment. Cancer and CSCs are heterogeneous and give rise to tumorigenic and non-tumorigenic cells during self-renewal, differentiation and epigenetic diversification. Further, if the gene regulatory network is weakly regulated through the effective slow regulatory binding (by DNA methylation or histone modification etc), multiple meta-stable states can emerge. This model can provide an epigenetic and physical rather than genetic and fixed origin of heterogeneity. Elucidating the origin of and dynamic nature of tumor cells will likely help better understand the cellular basis of therapeutic response, resistance, and relapse.

Identifying the optimal anticancer targets from the landscape of a cancer-immunity interaction network

Cancer immunotherapy, an approach of targeting immune cells to attack tumor cells, has been suggested to be a promising way for cancer treatment recently. However, the successful application of this approach warrants a deeper understanding of the intricate interplay between cancer cells and the immune system. Especially, the mechanisms of immunotherapy remain elusive. In this work, we constructed a cancer-immunity interplay network by incorporating interactions among cancer cells and some representative immune cells, and uncovered the potential landscape of the cancer-immunity network. Three attractors emerge on the landscape, representing the cancer state, the immune state, and the hybrid state, which can correspond to escape, elimination, and equilibrium phases in the immunoediting theory, respectively. We quantified the transition processes between the cancer state and the immune state by calculating transition actions and identifying the corresponding minimum action paths (MAPs) between these two attractors. The transition actions, directly calculated from the high dimensional system, are correlated with the barrier heights from the landscape, but provide a more precise description of the dynamics of a system. By optimizing the transition actions from the cancer state to the immune state, we identified some optimal combinations of anticancer targets. Our combined approach of the landscape and optimization of transition actions offers a framework to study the stochastic dynamics and identify the optimal combination of targets for the cancer-immunity interplay, and can be applied to other cell communication networks or gene regulatory networks.

Mesenchymal stem cells over-expressing cxcl12 enhance the radioresistance of the small intestine

The chemokine C-X-C motif chemokine 12 (CXCL12) greatly impacts various biological processes in mammals, including cell survival, growth and migration. Mesenchymal stem cells (MSCs) are promising tools for carrying foreign genes to treat radiation-induced injuries in the intestinal epithelium. In this study, human adipose-derived MSCs were constructed to over-express the mouse cxcl12 gene to treat such injuries. In vitro, because of the high levels of mouse CXCL12 in conditioned medium produced by mouse cxcl12 gene-modified cells, phosphorylation of Akt at Ser473 and Erk1/2 at Thr202/Thr204 was increased within crypt cells of irradiated organoids compared with unmodified controls. Moreover, intracellular stabilization of β-catenin was achieved after treatment of mouse cxcl12 gene-modified cells with conditioned medium. As a result, survival of crypt cells was maintained and their proliferation was promoted. When delivering mouse cxcl12 gene-modified cells into irradiated BALB/c nude mice, mice were rescued despite the clearance of cells from the host within 1 week. Irradiated mice that received mouse cxcl12 gene-modified MSCs exhibited reduced serum levels of interleukin-1α (IL-1α) and IL-6 as well as elevated levels of CXCL12. Additionally, epithelial recovery from radiation stress was accelerated compared with the irradiated-alone controls. Moreover, mouse cxcl12 gene-modified MSCs were superior to unmodified cells at strengthening host repair responses to radiation stress as well as presenting increased serum CXCL12 levels and decreased serum IL-1α levels. Furthermore, the number of crypt cells that were positive for phosphorylated Akt at Ser473 and phosphorylated Erk1/2 at Thr202/Thr204 increased following treatment with mouse cxcl12 gene-modified MSCs. Thus, cxcl12 gene-modified MSCs confer radioresistance to the intestinal epithelium.

Modeling the response of a tumor-suppressive network to mitogenic and oncogenic signals

Intrinsic tumor-suppressive mechanisms protect normal cells against aberrant proliferation. Although cellular signaling pathways engaged in tumor repression have been largely identified, how they are orchestrated to fulfill their function still remains elusive. Here, we built a tumor-suppressive network model composed of three modules responsible for the regulation of cell proliferation, activation of p53, and induction of apoptosis. Numerical simulations show a rich repertoire of network dynamics when normal cells are subject to serum stimulation and adenovirus E1A overexpression. We showed that oncogenic signaling induces ARF and that ARF further promotes p53 activation to inhibit proliferation. Mitogenic signaling activates E2F activators and promotes Akt activation. p53 and E2F1 cooperate to induce apoptosis, whereas Akt phosphorylates p21 to repress caspase activation. These prosurvival and proapoptotic signals compete to dictate the cell fate of proliferation, cell-cycle arrest, or apoptosis. The cellular outcome is also impacted by the kinetic mode (ultrasensitivity or bistability) of p53. When cells are exposed to serum deprivation and recovery under fixed E1A, the shortest starvation time required for apoptosis induction depends on the terminal serum concentration, which was interpreted in terms of the dynamics of caspase-3 activation and cytochrome c release. We discovered that caspase-3 can be maintained active at high serum concentrations and that E1A overexpression sensitizes serum-starved cells to apoptosis. This work elucidates the roles of tumor repressors and prosurvival factors in tumor repression based on a dynamic network analysis and provides a framework for quantitatively exploring tumor-suppressive mechanisms.

Multiplex Gene Expression Profiling of 16 Target Genes in Neoplastic and Non-Neoplastic Canine Mammary Tissues Using Branched-DNA Assay

Mammary gland tumors are one of the most common neoplasms in female dogs, and certain breeds are prone to develop the disease. The use of biomarkers in canines is still restricted to research purposes. Therefore, the necessity to analyze gene profiles in different mammary entities in large sample sets is evident in order to evaluate the strength of potential markers serving as future prognostic factors. The aim of the present study was to analyze the gene expression of 16 target genes (BRCA1, BRCA2, FOXO3, GATA4, HER2, HMGA1, HMGA2, HMGB1, MAPK1, MAPK3, MCL1, MYC, PFDN5, PIK3CA, PTEN, and TP53) known to be involved in human and canine mammary neoplasm development. Expression was analyzed in 111 fresh frozen (FF) and in 170 formalin-fixed, paraffin-embedded (FFPE) specimens of neoplastic and non-neoplastic canine mammary tissues using a multiplexed branched-DNA (b-DNA) assay. TP53, FOXO3, PTEN, and PFDN5 expression revealed consistent results with significant low expression in malignant tumors. The possibility of utilizing them as predictive factors as well as for assisting in the choice of an adequate gene therapy may help in the development of new and improved approaches in canine mammary tumors.