Elastic Net Models Based on DNA Copy Number Variations Predicts Clinical Features, Expression Signatures, and Mutations in Lung Adenocarcinoma

In the precision medicine of lung adenocarcinoma, the identification and prediction of tumor phenotypes for specific biomolecular events are still not studied in depth. Various earlier researches sheds light on the close correlation between genetic expression signatures and DNA copy number variations (CNVs), for which analysis of CNVs provides valuable information about molecular and phenotypic changes in tumorigenesis. In this study, we propose a comprehensive analysis combining genome-wide association analysis and an Elastic Net Regression predictive model, focus on predicting the levels of many gene expression signatures in lung adenocarcinoma, based upon DNA copy number features alone. Additionally, we predicted many other key phenotypes, including clinical features (pathological stage), gene mutations, and protein expressions. These Elastic Net prediction methods can also be applied to other gene sets, thereby facilitating their use as biomarkers in monitoring therapy.

A Genetic Risk Score for Glioblastoma Multiforme Based on Copy Number Variations

Glioblastoma multiforme is the most common form of brain cancer. Several lines of evidence suggest that glioblastoma multiforme has a genetic basis. A genetic test that could identify people who are at high risk of developing glioblastoma multiforme could improve our understanding of this form of brain cancer.Using the Cancer Genome Atlas (TCGA) dataset, we found common germ line DNA copy number variations in the TCGA population. We tested whether different sets of these germ line DNA copy number variations could effectively distinguish patients with glioblastoma multiforme from others in the TCGA dataset. We used a gradient boosting machine, a machine learning classification algorithm, to classify TCGA patients solely based on a set of germline DNA copy number variations.We found that this machine learning algorithm could classify TCGA glioblastoma multiforme patients from the other TCGA patients with an area under the curve (AUC) of the receiver operating characteristic curve (AUC=0.875). Grouped into quintiles, the highest ranked quintile by the machine learning algorithm had an odds ratio of 3.78 (95% CI 3.25-4.40) higher than the average odds ratio and about 40 (95% CI 20-70) times higher than the lowest quintile.The identification of an effective germ line genetic test to stratify risk of developing glioblastoma multiforme should lead to a better understanding of how this cancer forms. This result might ultimately lead to better treatments of glioblastoma multiforme.