A Guide to the Quantitative Proteomic Profiles of the Cancer Cell Line Encyclopedia

We recently reported the quantitative proteomics of 375 samples as part of the Cancer Cell Line Encyclopedia (Nusinow et al., 2020). Mass spectrometry-based proteomics data is broadly unfamiliar to most biologists in our experience, resulting in questions from analysts about how to use the data. From the proteomics community there was interest about how we normalized the data, as the scope of this project was so much larger than what has been commonly available. This paper serves as a guide to the data set to answer these questions and acts as a supplement to the main manuscript. The first part addresses users of the data, describing the experimental design, interpretation of the values, and dealing with standard issues in proteomics like multiple protein isoforms per gene and missing values. The second part of the manuscript details how we arrived at the normalization procedure reported in the paper, including the diagnostics used to assess multiple normalization schemes.

Identification of CAV1 and DCN as potential predictive biomarkers for lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common histological form of lung cancer that is clinically diagnosed. The aim of this study is to explore the novel genes associated with LUAD tumorigenesis. Comprehensive bioinformatics analyses of the data were obtained from several publicly available databases, such as the Gene Expression Omnibus, the Human Protein Atlas project, and the Cancer Cell Line Encyclopedia. The clinical relevance of these novel genes in LUAD was further examined by immunohistochemistry. We identified the overlapping differentially expressed genes (DEGs) in five independent microarray data sets from the Gene Expression Omnibus database ( GSE75037 , GSE85716 , GSE85841 , GSE63459 , and GSE32867 ). Using the criteria of |log (fold change)| ≥ 1 and P value <0.05, 167 genes were preliminarily validated as co-DEGs. Protein-protein interaction network analysis indicated that caveolin 1 (CAV1) and decorin (DCN) levels were significantly reduced and that these genes were the most promising predictive biomarkers for the occurrence and prognosis of LUAD. A cell proliferation assay indicated that overexpressed CAV1 and DCN could significantly inhibit the proliferation rate of A549 and H157 cells. Additionally, these two downregulated candidate genes were further verified by immunohistochemistry conducted on a LUAD tissue array and comprehensive bioinformatics analyses, including those using the Oncomine platform and the Cancer Cell Line Encyclopedia. Our study demonstrates low levels of CAV1 and DCN in LUAD. An understanding of their functional roles in LUAD biology would give us important insights that would be useful in further investigations.

Genetic load makes cancer cells more sensitive to common drugs: evidence from Cancer Cell Line Encyclopedia

Genetic alterations initiate tumors and enable the evolution of drug resistance. The pro-cancer view of mutations is however incomplete, and several studies show that mutational load can reduce tumor fitness. Given its negative effect, genetic load should make tumors more sensitive to anticancer drugs. Here, we test this hypothesis across all major types of cancer from the Cancer Cell Line Encyclopedia, that provides genetic and expression data of 496 cell lines together with their response to 24 common anticancer drugs. We found that the efficacy of 9 out of 24 drugs showed significant association with genetic load in a pan-cancer analysis. The associations for some tissue-drug combinations were remarkably strong with genetic load explaining up to 83% of the variance in the drug response. Overall, the role of genetic load depended on both the drug and the tissue type with 10 tissues being particularly vulnerable to genetic load. We also identified changes in gene expression associated with increased genetic load, which included cell-cycle checkpoints, DNA damage and apoptosis. Our results show that genetic load is an important component of tumor fitness and can predict drug sensitivity. Beyond being a biomarker, genetic load might be a new, unexplored vulnerability of cancer.