Development of adverse event (AE) derived biomarkers for predicting clinical outcomes in lung cancer

PROJECT SUMMARY/ABSTRACT Adverse event (AE) is a critical component of clinical trial for safety data to protect patients from unnecessary risk and to develop safety profile of the drug for benefit-risk assessment. Meanwhile, various studies showed association of AE with clinical outcomes in lung cancer, such as hypertension and immune-related adverse event with improved survival outcomes and high neutrophil-to-lymphocyte ratio correlated with worse survival. However, due to the complexity, AE data has been underutilized with sub-optimal AE reporting (e.g., the worst grade method) to inefficiently assess a patient safety and efficacy profile. While existing approaches have tried to improve characterization of AE profile, limitations have prevented their board application due to either limiting to single AE analysis, lack of full utilization of AE parameters, difficulty of interpretation, or uncertainty of clinical relevance. This highly translational study aims to change the paradigm of AE data usage from descriptive summary into modern informative AE biomarkers to fulfill precision medicine. We hypothesize that AE-derived biomarkers have clinically predictive values in lung cancer because of its ability to extensively assess grade, treatment relatedness, occurrence, frequency, and duration. We also believe that comprehensive AE data analysis by modern strategy from overall AEs, toxicity category AEs, to individual AEs, will facilitate development of integrative AE biomarker signatures in predicting clinical outcomes. Two innovative research Aims are proposed to achieve the overall study objective. Aim 1 is to evaluate AE biomarkers for predictive values of clinical outcomes in lung cancer. Given the promising AE preliminary data, we will first test our new framework for identifying AE’s association with treatment response and survival outcomes in a unique set of new study cohorts in non-small cell lung cancer (NSCLC) treated with immunotherapy (Aim 1.1). We will further evaluate AE biomarkers for clinical association in NSCLC treated with chemotherapy or precision therapy (Aim 1.2). Aim 2 is to build a modern AE analysis tool to facilitate identification of clinical-associated AE biomarkers and generation of automated informative AE report. We will develop new clinically relevant AE analysis functions to enhance data analysis and AE report (Aim 2.1). We will then integrate the developed AE analysis functions into a user-friendly tool for streamline data analysis and automated report generation (Aim 2.2). If successful, the AE-derived biomarkers could help clinicians early determine who should stay in the trial for treatment benefit (e.g., treatment-related low-grade AEs) and who should be discontinued due to ineffective treatment (e.g., higher-grade AEs). The informative AE analysis tool could be leveraged by clinical and research scientists to discover novel AE biomarkers and facilitate vast meaningful AE research hypotheses.