Clinical Significance and Inflammatory Landscape of aNovel Recurrence-Associated Immune Signature in Stage II/III Colorectal Cancer

Abstract

Background: A considerable number of patients with stage II/III colorectal cancer (CRC) will relapse within 5 years after surgery, which is a leading cause of death in early-stage CRC. The current TNM stage system is limited due to the heterogeneous clinical outcomes displayed in patients of same stage. Therefore, searching for a novel tool to identify patients at high recurrence-risk for improving post-operative individual management is an urgent need.

Methods: Using four independent public cohorts and qRT-PCR data from 66 tissues, we developed and validated a recurrence-associated immune signature (RAIS) based on global immune genes. The clinical and molecular features, tumor immune microenvironment landscape, and immune checkpoints profiles of RAIS were also investigated.

Results: In five independent cohorts, this novel scoring system was proven to be an independent recurrent factor and displayed excellent discrimination and calibration in predicting the recurrence-risk at 1~5 years. Further analysis revealed that the high-risk group displayed high mutation rate of TP53, while the low-risk group had more abundance of activated CD4+/CD8+ T cells and high expression of PD-1/PD-L1.

Conclusions: The RAIS model is highly predictive of recurrence in patients with stage II/III CRC, which might serve as a powerful tool to further optimize decision-making in adjuvant chemotherapy and immunotherapy, as well as tailor surveillance protocol for individual patients.

Keywords: adjuvant chemotherapy; immune checkpoints; immune signature; immunotherapy; recurrence; stage II/III colorectal cancer.

Figure 1

The flowchart of this study.

Figure 2

The development of the RAIS model based on the LASSO algorithm. (A) Ten-fold cross-validations to tune the parameter selection in the LASSO model. The two dotted vertical lines are drawn at the optimal values by minimum criteria (left) and 1−SE (standard error) criteria (right). (B) LASSO coefficient profiles of the candidate genes for RAIS construction. (C) The distribution of risk score, recurrence status, and gene expression panel in four cohort.

Figure 3

Survival significance of RAIS in four cohorts. (A) Kaplan-Meier curves of RFS according to the RAIS. (B) Univariate and multivariate Cox regression analysis of the risk score. The bold values mean P <0.05.

Figure 4

Evaluation of the RAIS model in four cohorts. (A) Time-dependent ROC analysis for predicting RFS at 1~5 years. (B) The Harrell’s C-index of RAIS. (C) Calibration plots for comparing the actual probabilities and the predicted probabilities of RFS at 1~5 years. (D) Comparison of recurrence rate between the high-risk and low-risk groups. (E) ROC analysis of the RAIS model for predicting the recurrence event of patients.

Figure 5

TIME landscape and immune checkpoints profiles of RAIS in four cohorts. (A) The correlation analysis between RAIS and 28 immune cells infiltration abundance. (B) The distribution difference of activated CD4+/CD8+ T cells infiltration between the high-risk and low-risk groups. (C) Four heatmaps of 27 immune checkpoints profiles in high-risk and low-risk groups. *P < 0.05, **P < 0.01, ***P < 0.001.

Figure 6

Validation of our discovery in a clinical in-house cohort. (A) Kaplan-Meier curves of RFS according to the RAIS. (B) Univariate and multivariate Cox regression analysis of the risk score. (C) Time-dependent ROC analysis for predicting RFS at 1~5 years.