Direct Inference of SNP Heterozygosity Rates and Resolution of LOH Detection

Single nucleotide polymorphisms (SNPs) have been increasingly utilized to investigate somatic genetic abnormalities in premalignancy and cancer. LOH is a common alteration observed during cancer development, and SNP assays have been used to identify LOH at specific chromosomal regions. The design of such studies requires consideration of the resolution for detecting LOH throughout the genome and identification of the number and location of SNPs required to detect genetic alterations in specific genomic regions. Our study evaluated SNP distribution patterns and used probability models, Monte Carlo simulation, and real human subject genotype data to investigate the relationships between the number of SNPs, SNP HET rates, and the sensitivity (resolution) for detecting LOH. We report that variances of SNP heterozygosity rate in dbSNP are high for a large proportion of SNPs. Two statistical methods proposed for directly inferring SNP heterozygosity rates require much smaller sample sizes (intermediate sizes) and are feasible for practical use in SNP selection or verification. Using HapMap data, we showed that a region of LOH greater than 200 kb can be reliably detected, with losses smaller than 50 kb having a substantially lower detection probability when using all SNPs currently in the HapMap database. Higher densities of SNPs may exist in certain local chromosomal regions that provide some opportunities for reliably detecting LOH of segment sizes smaller than 50 kb. These results suggest that the interpretation of the results from genome-wide scans for LOH using commercial arrays need to consider the relationships among inter-SNP distance, detection probability, and sample size for a specific study. New experimental designs for LOH studies would also benefit from considering the power of detection and sample sizes required to accomplish the proposed aims

Single nucleotide polymorphism-based genome-wide chromosome copy change, loss of heterozygosity, and aneuploidy in Barrett's esophagus neoplastic progression.

Chromosome copy gain, loss, and loss of heterozygosity (LOH) involving most chromosomes have been reported in many cancers; however, less is known about chromosome instability in premalignant conditions. 17p LOH and DNA content abnormalities have been previously reported to predict progression from Barrett's esophagus (BE) to esophageal adenocarcinoma (EA). Here, we evaluated genome-wide chromosomal instability in multiple stages of BE and EA in whole biopsies. Forty-two patients were selected to represent different stages of progression from BE to EA. Whole BE or EA biopsies were minced, and aliquots were processed for flow cytometry and genotyped with a paired constitutive control for each patient using 33,423 single nucleotide polymorphisms (SNP). Copy gains, losses, and LOH increased in frequency and size between early- and late-stage BE (P 30% in early and late stages, respectively. A set of statistically significant events was unique to either early or late, or both, stages, including previously reported and novel abnormalities. The total number of SNP alterations was highly correlated with DNA content aneuploidy and was sensitive and specific to identify patients with concurrent EA (empirical receiver operating characteristic area under the curve = 0.91). With the exception of 9p LOH, most copy gains, losses, and LOH detected in early stages of BE were smaller than those detected in later stages, and few chromosomal events were common in all stages of progression. Measures of chromosomal instability can be quantified in whole biopsies using SNP-based genotyping and have potential to be an integrated platform for cancer risk stratification in BE

Cell proliferation, cell cycle abnormalities, and cancer outcome in patients with Barrett’s esophagus: A long-term prospective study

Purpose: Elevated cellular proliferation and cell cycle abnormalities, which have been associated with premalignant lesions, may be caused by inactivation of tumor suppressor genes. We measured proliferative and cell cycle fractions of biopsies from a cohort of patients with Barrett's esophagus to better understand the role of proliferation in early neoplastic progression and the association between cell cycle dysregulation and tumor suppressor gene inactivation. Experimental Design: Cell proliferative fractions (determined by Ki67/DNA multiparameter flow cytometry) and cell cycle fractions (DNA content flow cytometry) were measured in 853 diploid biopsies from 362 patients with Barrett's esophagus. The inactivation status of CDKN2A and TP53 was assessed in a subset of these biopsies in a cross-sectional study. A prospective study followed 276 of the patients without detectable aneuploidy for an average of 6.3 years with esophageal adenocarcinoma as an endpoint. Results: Diploid S and 4N (G2/tetraploid) fractions were significantly higher in biopsies with TP53 mutation and LOH. CDKN2A inactivation was not associated with higher Ki67-positive, diploid S, G1, or 4N fractions. High Ki67-positive and G1 phase fractions were not associated with the future development of esophageal adenocarcinoma (p=0.13 and p=0.15, respectively), while high diploid S phase and 4N fractions were (p=0.03 and p<0.0001, respectively). Conclusions: High Ki67-positive proliferative fractions were not associated with inactivation of CDKN2A and TP53 or future development of cancer in our cohort of patients with Barrett's esophagus. Bi-allelic inactivation of TP53 was associated with elevated 4N fractions, which have been associated with the future development of esophageal adenocarcinoma

NSAIDs Modulate Clonal Evolution in Barrett's Esophagus

Cancer is considered an outcome of decades-long clonal evolution fueled by acquisition of somatic genomic abnormalities (SGAs). Non-steroidal anti-inflammatory drugs (NSAIDs) have been shown to reduce cancer risk, including risk of progression from Barrett's esophagus (BE) to esophageal adenocarcinoma (EA). However, the cancer chemopreventive mechanisms of NSAIDs are not fully understood. We hypothesized that NSAIDs modulate clonal evolution by reducing SGA acquisition rate. We evaluated thirteen individuals with BE. Eleven had not used NSAIDs for 6.2±3.5 (mean±standard deviation) years and then began using NSAIDs for 5.6±2.7 years, whereas two had used NSAIDs for 3.3±1.4 years and then discontinued use for 7.9±0.7 years. 161 BE biopsies, collected at 5–8 time points over 6.4–19 years, were analyzed using 1Million-SNP arrays to detect SGAs. Even in the earliest biopsies there were many SGAs (284±246 in 10/13 and 1442±560 in 3/13 individuals) and in most individuals the number of SGAs changed little over time, with both increases and decreases in SGAs detected. The estimated SGA rate was 7.8 per genome per year (95% support interval [SI], 7.1–8.6) off-NSAIDs and 0.6 (95% SI 0.3–1.5) on-NSAIDs. Twelve individuals did not progress to EA. In ten we detected 279±86 SGAs affecting 53±30 Mb of the genome per biopsy per time point and in two we detected 1,463±375 SGAs affecting 180±100 Mb. In one individual who progressed to EA we detected a clone having 2,291±78 SGAs affecting 588±18 Mb of the genome at three time points in the last three of 11.4 years of follow-up. NSAIDs were associated with reduced rate of acquisition of SGAs in eleven of thirteen individuals. Barrett's cells maintained relative equilibrium level of SGAs over time with occasional punctuations by expansion of clones having massive amount of SGAs

p16 Mutation Spectrum in the Premalignant Condition Barrett's Esophagus

Background: Mutation, promoter hypermethylation and loss of heterozygosity involving the tumor suppressor gene p16 (CDKN2a/INK4a) have been detected in a wide variety of human cancers, but much less is known concerning the frequency and spectrum of p16 mutations in premalignant conditions. Methods and Findings: We have determined the p16 mutation spectrum for a cohort of 304 patients with Barrett’s esophagus, a premalignant condition that predisposes to the development of esophageal adenocarcinoma. Forty seven mutations were detected by sequencing of p16 exon 2 in 44 BE patients (14.5%) with a mutation spectrum consistent with that caused by oxidative damage and chronic inflammation. The percentage of patients with p16 mutations increased with increasing histologic grade. In addition, samples from 3 out of 19 patients (15.8%) who underwent esophagectomy were found to have mutations. Conclusions: The results of this study suggest the environment of the esophagus in BE patients can both generate an

NSAIDs Modulate CDKN2A, TP53, and DNA Content Risk for Progression to Esophageal Adenocarcinoma

BACKGROUND: Somatic genetic CDKN2A, TP53, and DNA content abnormalities are common in many human cancers and their precursors, including esophageal adenocarcinoma (EA) and Barrett's esophagus (BE), conditions for which aspirin and other nonsteroidal anti-inflammatory drugs (NSAIDs) have been proposed as possible chemopreventive agents; however, little is known about the ability of a biomarker panel to predict progression to cancer nor how NSAID use may modulate progression. We aimed to evaluate somatic genetic abnormalities with NSAIDs as predictors of EA in a prospective cohort study of patients with BE. METHODS AND FINDINGS: Esophageal biopsies from 243 patients with BE were evaluated at baseline for TP53 and CDKN2A (p16) alterations, tetraploidy, and aneuploidy using sequencing; loss of heterozygosity (LOH); methylation-specific PCR; and flow cytometry. At 10 y, all abnormalities, except CDKN2A mutation and methylation, contributed to EA risk significantly by univariate analysis, ranging from 17p LOH (relative risk [RR] = 10.6; 95% confidence interval [CI] 5.2–21.3, p < 0.001) to 9p LOH (RR = 2.6; 95% CI 1.1–6.0, p = 0.03). A panel of abnormalities including 17p LOH, DNA content tetraploidy and aneuploidy, and 9p LOH was the best predictor of EA (RR = 38.7; 95% CI 10.8–138.5, p < 0.001). Patients with no baseline abnormality had a 12% 10-y cumulative EA incidence, whereas patients with 17p LOH, DNA content abnormalities, and 9p LOH had at least a 79.1% 10-y EA incidence. In patients with zero, one, two, or three baseline panel abnormalities, there was a significant trend toward EA risk reduction among NSAID users compared to nonusers (p = 0.01). The strongest protective effect was seen in participants with multiple genetic abnormalities, with NSAID nonusers having an observed 10-y EA risk of 79%, compared to 30% for NSAID users (p < 0.001). CONCLUSIONS: A combination of 17p LOH, 9p LOH, and DNA content abnormalities provided better EA risk prediction than any single TP53, CDKN2A, or DNA content lesion alone. NSAIDs are associated with reduced EA risk, especially in patients with multiple high-risk molecular abnormalities

Application of Biomarkers in Cancer Risk Management: Evaluation from Stochastic Clonal Evolutionary and Dynamic System Optimization Points of View

Aside from primary prevention, early detection remains the most effective way to decrease mortality associated with the majority of solid cancers. Previous cancer screening models are largely based on classification of at-risk populations into three conceptually defined groups (normal, cancer without symptoms, and cancer with symptoms). Unfortunately, this approach has achieved limited successes in reducing cancer mortality. With advances in molecular biology and genomic technologies, many candidate somatic genetic and epigenetic “biomarkers” have been identified as potential predictors of cancer risk. However, none have yet been validated as robust predictors of progression to cancer or shown to reduce cancer mortality. In this Perspective, we first define the necessary and sufficient conditions for precise prediction of future cancer development and early cancer detection within a simple physical model framework. We then evaluate cancer risk prediction and early detection from a dynamic clonal evolution point of view, examining the implications of dynamic clonal evolution of biomarkers and the application of clonal evolution for cancer risk management in clinical practice. Finally, we propose a framework to guide future collaborative research between mathematical modelers and biomarker researchers to design studies to investigate and model dynamic clonal evolution. This approach will allow optimization of available resources for cancer control and intervention timing based on molecular biomarkers in predicting cancer among various risk subsets that dynamically evolve over time