Distinct Gene Profiles for Tumor and Non-Tumor Tissue in the Head and Neck: An Analytical Approach.
In a study of genetic alterations, the Multiplex Ligation-dependent Probe Amplification (MLPA) assay was used to measure gain or loss of 113 gene-probes in tumor and non-tumor tissue samples collected from each of the 220 patients with squamous head and neck cancer (HNSCC). Conditional and marginal models were available; both models account for correlated data but have different aspects. The conditional logistic regression model was proposed to estimate the subject-specific risk of tumor based on the paired tumor and non-tumor data collection, which was in contrast with the marginal model to estimate population-average risk.The modeling process included rigorous variable selection, an initial multivariable model, a final model selection, and model validation. Genes with individual effect (p<0.01) were considered as candidates for the initial multivariable model for tumor. The final model included gene-probes with p<0.01 and estimations of odds ratios (OR) 95% Confidence Intervals (CIs) and the model's predictive ability, measured by the receiver operating characteristic curve (ROC). A 10-fold cross-validation was performed to validate the model. Of 113 gene-probes, using the conditional approach, 16 genes in 7 chromosomes, remained in the final multivariable model with p<0.01 and an ROC score of 0.94. The cross-validation showed ROC mean (SD) score of 0.96(0.04). The marginal model, in contrast, ended with 8 gene-probes and had an observed ROC of 0.81. CONCLUSION: The conditional approach appears to be the model of choice when assessing gene-probe risks of subjects with paired data collection and fewer missing covariates, compared to the marginal approach. This multiple gene model demonstrated excellent ability to discriminate tumor from non-tumor, and supports its contribution to the pathogenesis of HNSCC as well as their potential utility for further markers of early tumor detection.
Department of Public Health Sciences, Henry Ford Health System, Detroit, MI 48202, USA.
Mei
M
Lu
Josena K
JK
Stephen
Kang Mei
KM
Chen
Shaleta
S
Havard
Maria J
MJ
Worsham