Cancer is a complex constellation of diseases, each driven by a variety of different environmental and genetic influences. Understanding how cancer development is shaped by these influences is paramount to developing better treatment and prevention protocols. In pursuit of this understanding I have undertaken an investigation into the forces molding tumor development in mouse models of cancer.
To characterize the influence of the environment on tumor development as well as progression, I have utilized two independent models of chemical carcinogenesis. In the first model, we induced mouse lung tumors by either chemical or genetic means. By comparing between the two induction strategies we were able to demonstrate that chemical induction leaves an indelible mark on the tumor and is significantly different from signature of genetic induction.
In the second model chemical carcinogens were used to induce primary carcinomas that were allowed to develop into metastases. By comparing the primary and metastatic lesions we found that shared mutations were defined by the chemical specificity of the inducing carcinogen, while mutations private to the metastases were representative of genomic instability. These results show that the influence of environmental factors can wax and wane during tumor development and progression.
To gain insight on the genetic factors influencing cancer susceptibility, we investigated the genetics of body-mass index (BMI) and how this factor influences tumor development. We found that in a genetically heterogeneous mouse population elevated BMI strongly influenced cancer susceptibility. By carefully dissecting the genetics influencing BMI in this population we were able to identify a candidate gene (Panx3) linking BMI and tumorigenesis. This represents a significant step forward in our understanding of the genetics underlying these traits.
Finally, we profiled the genetic elements contributing to the development of inflammation-driven tumors. Through a combined approach involving sequence, expression, and gene coexpression network analysis we were able to implicate the S100 gene family as a major factor influencing inflammation driven tumorigenesis. We further validated these claims in a human tumor cohort and demonstrated that network expression levels are severely impacted during tumor progression.
The combined result of the work detailed in this dissertation is to illuminate the relationship between both environmental and genetic factors and cancer development through the use of mouse models. By extending these observations and methods to human data we hope to develop a better understanding of how human tumors develop, in order to improve both prevention and treatment strategies.