Abstract
Cancer affects millions of individuals worldwide. One shortcoming of traditional cancer classification systems is that, even for tumors affecting a single organ, there is significant molecular heterogeneity. Precise molecular classification of tumors could be beneficial in personalizing patients’ therapy and predicting prognosis. To this end, here we propose to use molecular signatures to further refine cancer classification. Molecular signatures are collections of genes characterizing particular cell types, tissues or disease. Signatures can be used to interpret expression profiles from heterogeneous samples. Large collections of gene signatures have previously been cataloged in the MSigDB database. We have developed a web-based Signature Visualization Tool (SaVanT) to display signature scores in user-generated expression data. Here we have undertaken a systematic analysis of correlations between inflammatory signatures and cancer samples, to test whether inflammation can differentiate cancer types. Inflammatory response signatures were obtained from MsigDB and SaVanT and a signature score was computed for samples associated with 7 different cancer types. We first identified types of cancers that had high inflammation levels as measured by these signatures. The correlation between signature scores and metadata of these patients (sex, age at initial cancer diagnosis, cancer stage, and vital status) was then computed. We sought to evaluate correlations between inflammation with other clinical parameters and identified four cancer types that had statistically significant association (p-value < 0.05) with at least one clinical characteristic: pancreas adenocarcinoma (PAAD), cholangiocarcinoma (CHOL), kidney chromophobe (KICH), and uveal melanoma (UVM). These results may allow future studies to use these approaches to further refine cancer subtyping and ultimately treatment.
Highlights
Many cancers are found when there is already local invasion or even distant metastatic disease
In order to focus the evaluation of our methods, seven different tumor primary types were chosen to be utilized for analysis with clinical metadata–pancreatic adenocarcinoma (PAAD), glioblastoma multiforme (GBM), cholangiocarcinoma (CHOL), kidney renal papillary cell carcinoma (KIRP), kidney chromophobe (KICH), adrenocortical carcinoma (ACC), and uveal melanoma (UVM)
Hierarchical clustering was performed to group cancer subtypes by inflammatory signature scores, and the three subgroups were determined by the dendrogram structure resulting from the hierarchical clustering (Fig 1)
Summary
Many cancers are found when there is already local invasion or even distant metastatic disease. Among the issues complicating treatment options are the fact that there are many tumor types, whose response to therapy may differ depending on site of origin and cellular composition [1]. Even within the same organ, there are heterogeneous tumor types with different responses to therapies. Precise tumor classification is crucial; depending on the categorization of a tumor, the clinical course, prognosis, and treatment can vary dramatically [2]. The traditional histology-based method to classify cancer is based on observing the site of origin, degree of spread and cellular morphology [3,4,5]. Because tumors are heterogeneous and frequently contain abundant somatic mutations, traditional approaches for classifying tumor subtypes are often insufficient
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