A comprehensive survey of clonal diversity measures in Barrett's esophagus as biomarkers of progression to esophageal adenocarcinoma

Abstract

Neoplastic progression is an evolutionary process driven by the generation of clonal diversity and natural selection on that diversity within a neoplasm. We hypothesized that clonal diversity is associated with risk of progression to cancer. We obtained molecular data from a cohort of 239 participants with Barrett's esophagus, including microsatellite shifts and loss of heterozygosity, DNA content tetraploidy and aneuploidy, methylation, and sequence mutations. Using these data, we tested all major diversity measurement methods, including genetic divergence and entropy-based measures, to determine which measures are correlated with risk of progression to esophageal adenocarcinoma. We also tested whether the use of different sets of loci and alterations to define clones (e.g., selectively advantageous versus evolutionarily neutral) improved the predictive value of the diversity indices. All diversity measures were strong and highly significant predictors of progression (Cox proportional hazards model, P < 0.001). The type of alterations evaluated had little effect on the predictive value of most of the diversity measures. In summary, diversity measures are robust predictors of progression to cancer in this cohort.

Figure 1

Example of effect of the distribution of clones on diversity indices for different Hill numbers (values of q) (27). Participants 1 and 2 have 4 clones (Diversity index=4 at q =0). In this hypothetical example, as q increases, a single dominant clone in Participants 2 and 3 has an increasingly strong negative effect on the total diversity. In other words, the rare clones in Particpants 2 and 3 have less impact on diversity as q increases. The addition of 3 more rare clones in Participant 3, relative to Participant 2, has a large impact on the q =0 diversity measure (number of clones), a small effect on the Shannon Index (q =1), and negligible effect on the q =2 (Simpson index) or q=3 diversity measures in Participant 3.

Figure 2

Map of the clones in the BE segment from a single participant from our cohort under different definitions of a clone. The pie charts below each segment represent total % of each clone in the BE segment. Defining clones in different ways alters the distribution and number of clones and the diversity indices. A. Both LOH and changes in microsatellite lengths (shifts) in all 18 microsatellites, as well as sequence mutations in CDKN2A (p16/INK4A) and TP53 (p53), and DNA content are used to define a clone. B. Only LOH lesions in the 18 microsatellites and DNA content are used to define a clone, while shifts and mutations are ignored. C. Only lesions that increase the fitness of a clone are used to define a clone. These lesions include LOH or sequence mutations in CDKN2A or TP53, or hypermethylation of the CDKN2A promoter (20). D. DNA content and lesions that have no detectable effect on the fitness of a clone were used, including shifts in any of the 19 microsatellites and LOH on the q arms of chromosomes 9 and 17 (20). This illustrative patient was chosen because the different definitions of clones lead to different diversity measures, though this was often not the case for other participants in the cohort.

Figure 3

Kaplan-Meier cancer incidence curves for mean pairwise genetic divergence based on four different definitions of clones (p<0.001 in all cases). Red=upper quartile, Black=bottom three quartiles. The (number of cancers/total number of participants) in the upper and lower 3 quartiles are given as numbers to right of the curves.

Figure 4

Kaplan-Meier cancer incidence curves for q=0 based on four different definitions of clones (p<0.001 in all cases). Red=upper quartile, Black=bottom three quartiles. The (number of cancers/total number of participants) in the upper and lower 3 quartiles are given as numbers to right of the curves.

Figure 5

Kaplan-Meier cancer incidence curves for q=2 based on four different definitions of clones (p<0.001 in all cases). ). Red=upper quartile, Black=bottom three quartiles. The (number of cancers/total number of participants) in the upper and lower 3 quartiles are given as numbers to right of the curves.