Tumor immune infiltration estimated from gene expression profiles predicts colorectal cancer relapse

Abstract

A substantial fraction of patients with stage I-III colorectal adenocarcinoma (CRC) experience disease relapse after surgery with curative intent. However, biomarkers for predicting the likelihood of CRC relapse have not been fully explored. Therefore, we assessed the association between tumor infiltration by a broad array of innate and adaptive immune cell types and CRC relapse risk. We implemented a discovery-validation design including a discovery dataset from Moffitt Cancer Center (MCC; Tampa, FL) and three independent validation datasets: (1) GSE41258 (2) the Molecular Epidemiology of Colorectal Cancer (MECC) study, and (3) GSE39582. Infiltration by 22 immune cell types was inferred from tumor gene expression data, and the association between immune infiltration by each cell type and relapse-free survival was assessed using Cox proportional hazards regression. Within each of the four independent cohorts, CD4+ memory activated T cell (HR: 0.93, 95% CI: 0.90-0.96; FDR = 0.0001) infiltration was associated with longer time to disease relapse, independent of stage, microsatellite instability, and adjuvant therapy. Based on our meta-analysis across the four datasets, 10 innate and adaptive immune cell types associated with disease relapse of which 2 were internally validated using multiplex immunofluorescence. Moreover, immune cell type infiltration was a better predictors of disease relapse than Consensus Molecular Subtype (CMS) and other expression-based biomarkers (Immune-AIC MCC :238.1-238.9; CMS-AIC MCC : 241.0). These data suggest that transcriptome-derived immune profiles are prognostic indicators of CRC relapse and quantification of both innate and adaptive immune cell types may serve as candidate biomarkers for predicting prognosis and guiding frequency and modality of disease surveillance.

Identification of Cancer Hub Gene Signatures Associated with Immune-Suppressive Tumor Microenvironment and Ovatodiolide as a Potential Cancer Immunotherapeutic Agent

“…Consistently, the gene expression profiles suggestive of immune infiltration appeared to be a predictor of dysfunctional T cell phenotypes and a worse prognosis of the cohort of those cancers. In agreement with our findings, previous studies indicated that NK (resting and activated) cell, monocyte, resting mast cell, neutrophil, M1 and M2 TAM, activated dendritic cell, CD4+ T cell, and CD8+ T cell infiltrations were significant predictors of disease relapse, even after accounting for known prognostic indicators, including adjuvant therapy [40].…”

“…Furthermore, the hub genes expression signatures of M2 TAMs are inversely associated with gene expression profiles suggestive of T cell exclusion phenotypes (Figure 6D), indicating that the hub genes are unlikely to play a role in macrophages dependent regulation of T cell exclusion phenotypes in the hub cancers. Conversely, the gene expression profiles suggestive of dysfunctional T cell phenotypes and high survival risk (Figure 6C) predicted in cohorts with high expression levels of the hub genes suggest the oncogenic role of Mo macrophages in these cancers, a conclusion supported by previous studies [40,42].…”

Adoptive NK Cell Transfer as a Treatment in Colorectal Cancer Patients: Analyses of Tumour Cell Determinants Correlating With Efficacy In Vitro and In Vivo

“…However, downregulation of these molecules is a common mechanism of immune evasion ( 6 ), albeit its correlation with tumour prognosis is not clear yet ( 7 ). For instance, NK cell infiltration has been associated with a lower risk of disease relapse in CRC ( 8 ). As a safeguard for T-cell function, NK cells control tumours that have reduced HLA-I expression and provide an activating balance of signals through surface receptors ( 9 ).…”

Challenges and Opportunities in the Statistical Analysis of Multiplex Immunofluorescence Data

“…mIF technologies allow for the use of multiple antibodies to achieve the simultaneous detection of several marker on single tissue sample (recently nine or more markers). This technology has been used in research and clinical settings showing the utility of this approach for studying the TIME [ 45 , 46 , 47 ]. An overview of the data generation process with mIF technology is presented in Figure 1 , focusing on cyclic-immunofluorescence and tyramide-based mIF.…”

“…This is a labor-intensive procedure and may take several days to complete. However, fully automated staining protocols for mIF have been developed, saving time and improving staining variability [ 45 , 46 , 47 , 48 , 49 ].…”