The multi-hit hypothesis in basal-like breast cancer

Rethinking the Relationship between Insulin and Cancer

In addition to being a major metabolic hormone, insulin is also a growth factor with a mitogenic effect on all cells, more marked in malignant cells that often overexpress the insulin receptor. In patients with metabolic diseases characterized by hyperinsulinemia (obesity, type 2 diabetes, and metabolic syndrome), the incidence of several types of cancer is increased, as is cancer-related mortality. Because of the worldwide growing prevalence of metabolic diseases and the diffuse use of insulin and its analogs for treating diabetes, the relationship between insulin and cancer has become a clinically relevant issue. Clinical studies have not clarified the degree to which hyperinsulinemia can influence cancer occurrence and prognosis. To better understand this issue, an improved scientific approach is required, with more careful consideration of the mechanisms related to hyperinsulinemia and carcinogenesis.

Modeling genotypes in their microenvironment to predict single- and multi-cellular behavior

A cell's phenotype is the set of observable characteristics resulting from the interaction of the genotype with the surrounding environment, determining cell behavior. Deciphering genotype-phenotype relationships has been crucial to understanding normal and disease biology. Analysis of molecular pathways has provided an invaluable tool to such understanding; however, typically it does not consider the physical microenvironment, which is a key determinant of phenotype. In this study, we present a novel modeling framework that enables the study of the link between genotype, signaling networks, and cell behavior in a three-dimensional microenvironment. To achieve this, we bring together Agent-Based Modeling, a powerful computational modeling technique, and gene networks. This combination allows biological hypotheses to be tested in a controlled stepwise fashion, and it lends itself naturally to model a heterogeneous population of cells acting and evolving in a dynamic microenvironment, which is needed to predict the evolution of complex multi-cellular dynamics. Importantly, this enables modeling co-occurring intrinsic perturbations, such as mutations, and extrinsic perturbations, such as nutrient availability, and their interactions. Using cancer as a model system, we illustrate how this framework delivers a unique opportunity to identify determinants of single-cell behavior, while uncovering emerging properties of multi-cellular growth. This framework is freely available at http://www.microc.org.