Prognostic value of immune infiltration in colorectal cancer: Development of a histopathology-related immunoscore via multiplexed immunohistochemistry.

Abstract

Our objective was to evaluate the prognostic value of immune infiltration within the intratumoral and peritumoral tissues and to establish a novel histopathology-related immunoscore associated with postoperative colorectal cancer prognosis. In the tissue microarrays, a total of 104 patients with colorectal cancer were enrolled and randomly assigned to the derivation cohort (n = 61) or the validation cohort (n = 43). Eighteen prognostic immune biomarkers in both intratumoral and peritumoral tissues were examined by the multiplexed immunohistochemistry method, with quantification performed through digital pathology. The histopathology-related immunoscore score was constructed using least absolute shrinkage and selection operator Cox analysis by selected immune features. On the basis of the Cox regression analysis, 3 predictive models were established. Harrell C-statistics were used to assess the performance of those models. The area under the curve was 0.743 (confidence interval, 0.457-1.000) in the derivation cohort and 0.739 (confidence interval, 0.538-0.940) in the validation cohort. Subsequently, the groups were classified on the basis of the optimal cutoff value, with the high-risk group exhibiting a poorer prognosis. Furthermore, 3 predictive clinical models were constructed, incorporating the significant risk factors and histopathology-related immunoscore score. The first model incorporating both histopathology-related immunoscore score and statistically significant factors identified through univariate analysis demonstrated superior predictive capability for survival across all 3 models, with an area under the curve of 0.852 and C-index of 0.837. The histopathology-related immunoscore score offers a novel means of estimating of survival in patients with colorectal cancer. These findings indicated that the immunoscore and the clinical factors might serve as complementary tools to TNM staging to improve the accuracy of patient survival prediction.