Department of Molecular, Cell, and Developmental Biology, University of California Los Angeles, Los Angeles, California, United States of America.
Gilead Pharmaceuticals, Foster City, California, United States of America.
PLoS One. 2020 Apr 24;15(4):e0221545. doi: 10.1371/journal.pone.0221545. eCollection 2020.
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.
癌症影响着全球数百万人。传统癌症分类系统的一个缺点是，即使对于影响单一器官的肿瘤，也存在显著的分子异质性。对肿瘤进行精确的分子分类可能有助于对患者进行个体化治疗并预测预后。为此，我们在此提议使用分子特征进一步细化癌症分类。分子特征是描述特定细胞类型、组织或疾病的基因集合。特征可用于解释来自异质样本的表达谱。以前，在 MSigDB 数据库中已经对大量基因特征集进行了编目。我们开发了一个基于网络的签名可视化工具 (SaVanT)，用于在用户生成的表达数据中显示签名分数。在这里，我们对炎症特征与癌症样本之间的相关性进行了系统分析，以测试炎症是否可以区分癌症类型。炎症反应特征从 MsigDB 和 SaVanT 中获得，并为与 7 种不同癌症类型相关的样本计算了特征得分。我们首先确定了这些特征所测量的具有高炎症水平的癌症类型。然后计算了特征得分与这些患者的元数据（性别、癌症初诊时的年龄、癌症分期和生存状态）之间的相关性。我们试图评估炎症与其他临床参数之间的相关性，并确定了与至少一个临床特征具有统计学显著关联（p 值<0.05）的四种癌症类型：胰腺腺癌（PAAD）、胆管癌（CHOL）、肾嫌色细胞癌（KICH）和葡萄膜黑色素瘤（UVM）。这些结果可能允许未来的研究使用这些方法进一步细化癌症亚型，最终改善治疗效果。
