Implications of systemic dysfunction for the etiology of malignancy

Mathematical models of intercellular signaling in breast cancer

BACKGROUND AND OBJECTIVES

The development and regulation of healthy and cancerous breast tissue is guided by communication between cells. Diverse signals are exchanged between cancer cells and non-cancerous cells of the tumor microenvironment (TME), influencing all stages of tumor progression. Mathematical models are essential for understanding how this complex network determines cancer progression and the effectiveness of treatment.

METHODOLOGY

We reviewed the current dynamical mathematical models of intercellular signaling in breast cancer, examining models with cancer cells only, fibroblasts, endothelial cells, macrophages and the immune system as whole. We categorized the goals and complexity of these models, to highlight how they can explain many features of cancer emergence and progression.

RESULTS

We found that dynamical models of intercellular signaling can elucidate tissue-level dysregulation in cancer by explaining: i) maintenance of non-heritable intratumor phenotypic heterogeneity, ii) transitions between tumor dormancy and accelerated invasive growth, iii) stromal support of tumor vascularization and growth factor enrichment and iv) suppression of immune infiltration and cancer surveillance. These models also provide a framework to propose novel TME-targeting treatment strategies. However, most models were focused on a highly selected and small set of signaling interactions between a few cell types, and their translational applicability were severely limited by the availability of tumor-specific data for personalized model calibration.

CONCLUSIONS AND IMPLICATIONS

Mathematical models of breast cancer have many challenges and opportunities to incorporate signaling. The four key challenges are: 1) finding ways to treat signaling networks as a context-dependent language that incorporates non-linear and non-additive responses, 2) identifying the key cell phenotypes that signals control and understanding the feedbacks between signals and phenotype that determine the progression of cancer, (3) estimating parameters of specific patient tumors early in treatment, 4) linking models with novel data collection methods that have single cell and spatial resolution. As our approaches advance, it is our hope that dynamical mathematical models of inter-cellular signaling can play a central role in identifying and testing new treatment strategies as well as forecasting impacts of disease treatment.

Molecular Alterations of Colorectal Cancer with Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is an important etiologic factor in the development of colorectal cancer (CRC). The risk of CRC begins to increase 8 or 10 years after the diagnosis of IBD. This type of cancer is called colitis-associated CRC (CA-CRC). The molecular pathogenesis of inflammatory epithelium might play a critical role in the development of CA-CRC. Genetic alterations detected in CA-CRC such as genetic mutations, microsatellite instability, and DNA hypermethylation are also recognized in sporadic CRC; however, there are differences in the timing and frequency of molecular events between CA-CRC and sporadic CRC. Interaction between gene-environmental factors, including inflammation, lifestyle, psychological stress, and prior appendectomy, might be associated with the etiopathology of IBD. The mucosal inflammatory mediators, such as oxidant stress, free radicals, and chemokines, may cause the genetic alterations. Understanding the molecular mechanisms of CA-CRC might be important to develop clinical efficacies for patients with IBD. This review discusses the molecular characteristics of CA-CRC, especially ulcerative colitis-associated CRC, including clinical features, signaling pathways, and interactions between genetic alterations and environment involved in inflammatory carcinogenesis.

Outlier Analysis and Top Scoring Pair for Integrated Data Analysis and Biomarker Discovery

Pathway deregulation has been identified as a key driver of carcinogenesis, with proteins in signaling pathways serving as primary targets for drug development. Deregulation can be driven by a number of molecular events, including gene mutation, epigenetic changes in gene promoters, overexpression, and gene amplifications or deletions. We demonstrate a novel approach that identifies pathways of interest by integrating outlier analysis within and across molecular data types with gene set analysis. We use the results to seed the top-scoring pair algorithm to identify robust biomarkers associated with pathway deregulation. We demonstrate this methodology on pediatric acute myeloid leukemia (AML) data. We develop a biomarker in primary AML tumors, demonstrate robustness with an independent primary tumor data set, and show that the identified biomarkers also function well in relapsed pediatric AML tumors.