Subgroup analysis based on prognostic and predictive gene signatures for adjuvant chemotherapy in early-stage non-small-cell lung cancer patients.

In treating patients diagnosed with early Stage I non-small-cell lung cancer (NSCLC), doctors must choose surgery alone, Adjuvant Cisplatin-Based Chemotherapy (ACT) alone or both. For patients with resected stages IB to IIIA, clinical trials have shown a survival advantage from 4-15% with the adoption of ACT. However, due to the inherent toxicity of chemotherapy, it is necessary for doctors to identify patients whose chance of success with ACT is sufficient to justify the risks. This research seeks to use gene expression profiling in the development of a statistical decision-making algorithm to identify patients whose survival rates will improve from ACT treatment. Using the data from the National Cancer Institute, the lasso method in the Cox-Proportional-Hazards regression model is used as the main method to determine a feasible number of genes that are strongly associated with the treatment-related patient survival. Considering treatment groups separately, the patients are assigned a risk category based on the estimation of survival times. These risk categories are used to develop a Random Forests classification model to identify patients who are likely to benefit from chemotherapy treatment. This model allows the prediction of a new patient's prognosis and the likelihood of survival benefit from ACT treatment based on a feasible number of genomic biomarkers. The proposed methods are evaluated using a simulation study.