Molecular signatures for inflammation vary across cancer types and correlate significantly with tumor stage, gender and vital status of patients

Abstract

1516 Abstract 17 Cancer affects millions of individuals worldwide. One shortcoming of traditional cancer 18 classification systems is that, even for tumors affecting a single organ, there is significant 19 molecular heterogeneity. Precise molecular classification of tumors could be beneficial in 20 personalizing patients' therapy and predicting prognosis. To this end, here we propose to 21 use molecular signatures to further refine cancer classification. Molecular signatures are 22 collections of genes characterizing particular cell types, tissues or disease. Signatures can 23 be used to interpret expression profiles from heterogeneous samples. Large collections of 24 gene signatures have previously been cataloged in the MSigDB database. We have 25 developed a web-based Signature Visualization Tool (SaVanT) to display signature 26 scores in user-generated expression data. Here we have undertaken a systematic analysis 27 of correlations between inflammatory signatures and cancer samples, to test whether 28 inflammation can differentiate cancer types. Inflammatory response signatures were 29 obtained from MsigDB and SaVanT and a signature score was computed for samples 30 associated with 7 different cancer types. We first identified types of cancers that had 31 high inflammation levels as measured by these signatures. The correlation between 32 signature scores and metadata of these patients (gender, age at initial cancer diagnosis, 33 cancer stage, and vital status) was then computed. We sought to evaluate correlations 34 between inflammation with other clinical parameters and identified four cancer types that 35 had statistically significant association (p-value < 0.05) with at least one clinical 36 characteristic: pancreas adenocarcinoma (PAAD), cholangiocarcinoma (CHOL), kidney 37 chromophobe (KICH), and uveal melanoma (UVM). These results may allow future 3 38 studies to use these approaches to further refine cancer subtyping and ultimately 39 treatment.