Immune Classification of Clear Cell Renal Cell Carcinoma

Abstract

<jats:title>ABSTRACT</jats:title><jats:p>Since the outcome of treatments, particularly immunotherapeutic interventions, depends on the tumor immune micro-environment (TIM), several experimental and computational tools such as flow cytometry, immunohistochemistry, and digital cytometry have been developed and utilized to classify TIM variations. In this project, we identify immune pattern of clear cell renal cell carcinomas (ccRCC) by estimating the percentage of each immune cell type in 526 renal tumors using the new powerful technique of digital cytometry. The results, which are in agreement with the results of a large-scale mass cytometry analysis, show that the most frequent immune cell types in ccRCC tumors are CD8+ T-cells, macrophages, and CD4+ T-cells. Saliently, unsupervised clustering of ccRCC primary tumors based on their relative number of immune cells indicates the existence of four distinct groups of ccRCC tumors. Tumors in the first group consist of approximately the same numbers of CD8+ T-cells, CD4+ T-cells, and macrophages, while tumors in the second group have a significantly high number of macrophages compared to any other immune cell type (P-value&lt; 0.01). The third group of ccRCC tumors have a significantly higher number of CD8+ T-cells than any other immune cell type (P-value&lt; 0.01), while tumors in the group 4 have approximately the same numbers of macrophages and CD4+ T-cells and a significantly smaller number of CD8+ T-cells than CD4+ T-cells (P-value&lt; 0.01). Moreover, there is a high positive correlation between the expression levels of IFNG and PDCD1 and the percentage of CD8+ T-cells, and higher stage and grade of tumors have a substantially higher percentage of CD8+ T-cells. Furthermore, the primary tumors of patients, who are tumor free at the last time of follow up, have a significantly higher percentage of mast cells (P-value&lt; 0.01) compared to the patients with tumors for all groups of tumors except group 3.</jats:p>

Data Driven Mathematical Model of Colon Cancer Progression

“…In recent years, several tumor deconvolution methods have been developed to estimate the relative abundance of various cell types in a tumor from its gene expression profile. A review of these methods [97] and an application of CIBERSORTx on renal cancer [98] show a great performance of CIBERSORTx model. To identify the immune profiles of colonic tumors, we applied CIBERSORTx [99] on RNA-seq gene expression profiles of primary tumors of patients with colon cancer from the Cancer Genome Atlas (TCGA) project of Colon Adenocarcinoma (COAD) downloaded from University of California Santa Cruz (UCSC) Xena web portal.…”

Data driven mathematical model of colon cancer progression

“…In recent years, several tumor deconvolution methods have been developed to estimate the relative abundance of various cell types in a tumor from its gene expression profile. A review of these methods [79] and an application of CIBERSORTx on renal cancer [80] show a great performance of CIBERSORTx model. To identify the immune profiles of colonic tumors, we applied CIBERSORTx [81] on RNA-seq gene expression profiles of primary tumors of patients with colon cancer from the TCGA project of COAD downloaded from UCSC Xena web portal.…”