Abstract 6572: A lung cancer cell line explorer

Abstract

Abstract Over 300 lung cancer cell lines have been established in the past few decades with clinical annotations. Efforts from multiple institutions comprehensively profiled these cell lines for their genetic and epigenetic variations, gene and protein expression, metabolism, and functional liabilities. These data provide an unprecedented opportunity to accelerate lung cancer research. We developed a lung cancer cell line explorer at https://lccl.shinyapps.io/LCCL/ integrating over 35 datasets. We also incorporated engineered features such as pathway enrichment scores from copy number-denoised transcriptomic data and neuroendocrine scores. Our web application allows users to 1) query and download the processed datasets, 2) assess data reproducibility across studies, 3) review genomic abnormalities, 4) perform ad hoc bivariate analyses with the option to add filtering or coloring by a third feature, 5) generate heatmaps and correlation maps, 6) perform global association test to screen for significant association between a feature of interest and all features from a dataset of interest, and 7) upload their own datasets to be analyzed in conjunction with our in-house datasets. In summary, this online tool will greatly facilitate the utilization of high-dimensional datasets that characterize lung cancer cell lines. Citation Format: Ling Cai, Luc Girard, Ralph DeBerardinis, Guanghua Xiao, John Minna, Yang Xie. A lung cancer cell line explorer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6572.