Characterization of genetic heterogeneity in neoplastic progression

A65

Cancer is characterized by a large number of genetic and epigenetic alterations. This genomic instability is a key feature of nearly all neoplasms, yet the fundamental dynamics of diversity in neoplastic progression are poorly understood. The fact that measures of diversity can predict risk of progression (Maley 2006) suggests that genetic heterogeneity increases as a neoplasm progresses towards cancer. However, the model of cancer as a multi-step progression of successive clonal expansions suggests that diversity might fluctuate during progression as these selective “sweeps” of advantageous mutations occur. Computational simulations of a neoplasm can provide insight into these and other related questions. We have developed an agent-based, computational model of clonal evolution in neoplastic progression in order to develop robust and accurate methods to measure genetic diversity in neoplasms. Our simulations indicate that bootstrap estimators provide the best estimate of the number of clones present in a simulated neoplasm with only a few samples (simulated biopsies) and less than a total of 50 cells needed to provide a reasonable approximation of the number of clones. They also show that in some cases the distribution of the abundance of clones in a neoplasm fits a broken stick model. Results from these simulations provide predictions for future experiments where we will examine diversity in cell culture models and in tissues by adapting inter-simple sequence repeat (ISSR) PCR to an automated, fluorescence-based capillary electrophoresis system. This method measures insertion or deletion events that occur between microsatellite repeats, and provides a simple method for capturing a “fingerprint” of the genetic changes that have occurred. How genetic heterogeneity should be characterized, once it has been measured in neoplasms, also remains a critical question. Association with cancer outcome is the gold standard for risk assessment. Recent evidence suggests that some measures of genetic diversity in Barrett’s esophagus, specifically the number of clones, divergence, and the Shannon Index of diversity are predictors of the risk of progression to esophageal adenocarcinoma (Maley 2006). We have extended this study by applying a new set of ecological measures of diversity to data on loss of heterozygosity and shifts in 18 microsatellites in biopsies taken from 267 patients in the Seattle Barrett’s Esophagus cohort and examined which measures best correlate with risk of progression. Taxonomic and functional diversity measures, as well as measures of species distribution and evenness are used and compared to results from previously studied measures. These studies will help to determine best practices for measuring genetic heterogeneity for cancer risk stratification.