Genome-wide multi-omics profiling of colorectal cancer identifies immune determinants strongly associated with relapse

Abstract

The use and benefit of adjuvant chemotherapy to treat stage II colorectal cancer (CRC) patients is not well understood since the majority of these patients are cured by surgery alone. Identification of biological markers of relapse is a critical challenge to effectively target treatments to the ~20% of patients destined to relapse. We have integrated molecular profiling results of several “omics” data types to determine the most reliable prognostic biomarkers for relapse in CRC using data from 40 stage I and II CRC patients. We identified 31 multi-omics features that highly correlate with relapse. The data types were integrated using multi-step analytical approach with consecutive elimination of redundant molecular features. For each data type a systems biology analysis was performed to identify pathways biological processes and disease categories most affected in relapse. The biomarkers detected in tumors urine and blood of patients indicated a strong association with immune processes including aberrant regulation of T-cell and B-cell activation that could lead to overall differences in lymphocyte recruitment for tumor infiltration and markers indicating likelihood of future relapse. The immune response was the biologically most coherent signature that emerged from our analyses among several other biological processes and corroborates other studies showing a strong immune response in patients less likely to relapse.

Immune Gene Expression Is Associated with Genomic Aberrations in Breast Cancer

“…In a pooled analysis of solid tumors in The Cancer Genome Atlas (TCGA) database, the total number of somatic mutations and the number of new antigen epitopes (i.e., neoantigen load) correlated with immune infiltration (9)(10)(11). In hepatocellular, squamous cell lung cancer, and colorectal carcinomas greater number of copy number alterations were associated with higher immunogenicity (12)(13)(14). On the basis of these observations one can hypothesize that the more genomic alterations a cancer has, the greater the immune infiltration is, due to more immunogenic neoantigens in these cancers.…”

In-silico analysis of MHC genes in hereditary colorectal cancer shows identical by state SNP sharing affecting HLA-DQB1 binding groove

“…Nonetheless, antigen presentation pathway was found to be central in colorectal cancer pathogenesis and showed enrichment in relapsed tumors [30]. Systems Biology analysis also featured a central role for HLA proteins [30]. In this study, identical by state sharing of HLA SNPs between tumor samples in HLA-DQB1 was noted (figure (2)).…”

“…A previous study reported that HLA-DQA1* 0201 was less common in 80 Italian patients with colonic carcinoma than controls (8% vs. 18%, P-value with Bonferroni correction = 0.027; OR = 0.44) [6]. Nonetheless, antigen presentation pathway was found to be central in colorectal cancer pathogenesis and showed enrichment in relapsed tumors [30]. Systems Biology analysis also featured a central role for HLA proteins [30].…”

Data mining and machine learning approaches for the integration of genome-wide association and methylation data: methodology and main conclusions from GAW20

“…Taking this limitation into consideration, Madhavan et al combined multiple machine learning approaches to integrate gene expression, micro-RNA expression, copy number variant, and serum and urine metabolomics data. They first prefiltered using univariate analyses followed by support vector machines to avoid overfitting [15]. Top features were then integrated using random forest and network analysis.…”