Tumor budding in cervical cancer as a prognostic factor and its possible role as an additional intermediate-risk factor.

OBJECTIVE: To evaluate the prognostic value and its possible role as an additional intermediate-risk factor of tumor budding (TB) in cervical cancer following radical hysterectomy. METHODS: In total, 136 patients with cervical cancer who underwent radical hysterectomy with pelvic and/or paraaortic lymphadenectomy were included. We assessed the status of TB in available hematoxylin and eosin-stained specimens. Univariate and multivariate analyses for predicting tumor recurrence and death were performed using TB and other clinicopathologic parameters. To evaluate additional intermediate-risk factors of TB, patients who had at least one high-risk factor were excluded, and a total of 81 patients were included. We added TB to three conventional intermediate-risk models and compared their performance with new and conventional models using the log-rank test and receiver operating characteristic analysis. RESULTS: High TB was defined as ≥5 per high-power field for disease-free survival and ≥ 8 per high-power field for overall survival. Multivariate analysis revealed that high TB was an independent prognostic factor for predicting overall survival (hazard ratio, 4.96; 95% confidence intervals, 1.06-23.29; p = .0423). The addition of TB to the conventional intermediate-risk models improved the accuracy of recurrence prediction. Among the risk models, the new model using at least two risk factors, including tumor size (≥ 4 cm), deep stromal invasion (outer one-third of entire cervical thickness), lymphovascular invasion, and high TB, was the most accurate for predicting tumor recurrence (area under the curve, 0.708, hazard ratio, 4.25; p = .0231). CONCLUSION: High TB may be a prognostic biomarker of cervical cancer. Moreover, the addition of TB to the conventional intermediate-risk models improves the stratification of tumor recurrence.

Methods for adjusting population structure and familial relatedness in association test for collective effect of multiple rare variants on quantitative traits.

Because of the low frequency of rare genetic variants in observed data, the statistical power of detecting their associations with target traits is usually low. The collapsing test of collective effect of multiple rare variants is an important and useful strategy to increase the power; in addition, family data may be enriched with causal rare variants and therefore provide extra power. However, when family data are used, both population structure and familial relatedness need to be adjusted for the possible inflation of false positives. Using a unified mixed linear model and family data, we compared six methods to detect the association between multiple rare variants and quantitative traits. Through the analysis of 200 replications of the quantitative trait Q2 from the Genetic Analysis Workshop 17 data set simulated for 697 subjects from 8 extended families, and based on quantile-quantile plots under the null and receiver operating characteristic curves, we compared the false-positive rate and power of these methods. We observed that adjusting for pedigree-based kinship gives the best control for false-positive rate, whereas adjusting for marker-based identity by state slightly outperforms in terms of power. An adjustment based on a principal components analysis slightly improves the false-positive rate and power. Taking into account type-1 error, power, and computational efficiency, we find that adjusting for pedigree-based kinship seems to be a good choice for the collective test of association between multiple rare variants and quantitative traits using family data.

Distance-based phenotypic association analysis of DNA sequence data.

As the cost of sequencing decreases, the demand for association tests that use exhaustive DNA sequence information increases. One such association test is multivariate distance matrix regression (MDMR). We explore some of the features of MDMR using Genetic Analysis Workshop 17 simulated data in search of potential improvements in distance measures. We used genotype data from 697 unrelated individuals, in 200 replications, to test the power of MDMR to detect 13 trait Q2 causative genes based on the Euclidean distance metric. We also estimated the false-positive rate of MDMR using 508 control genes. In addition, we compared MDMR with Mantel's test and collapsing analysis for rare variants. MDMR performed comparably well even with the Euclidean distance measure.