Targeted Next-Generation Sequencing Reveals Exceptionally High Rates of Molecular Driver Mutations in Never-Smokers With Lung Adenocarcinoma.

Introduction: Historically, high rates of actionable driver mutations have been reported in never-smokers with lung adenocarcinoma (ADC). In the era of modern, comprehensive cancer mutation sequencing, this relationship necessitates a more detailed analysis.

Methods: All Mount Sinai patients between January 1, 2015, and June 1, 2020, with a diagnosis of ADC of any stage with known smoking status who received genomic testing were included.

Identifying modules of cooperating cancer drivers.

Identifying cooperating modules of driver alterations can provide insights into cancer etiology and advance the development of effective personalized treatments. We present Cancer Rule Set Optimization (CRSO) for inferring the combinations of alterations that cooperate to drive tumor formation in individual patients. Application to 19 TCGA cancer types revealed a mean of 11 core driver combinations per cancer, comprising 2-6 alterations per combination and accounting for a mean of 70% of samples per cancer type.

GRAPE: a pathway template method to characterize tissue-specific functionality from gene expression profiles.

Background: Personalizing treatment regimes based on gene expression profiles of individual tumors will facilitate management of cancer. Although many methods have been developed to identify pathways perturbed in tumors, the results are often not generalizable across independent datasets due to the presence of platform/batch effects. There is a need to develop methods that are robust to platform/batch effects and able to identify perturbed pathways in individual samples.