Prognostic Significance of Immune Cell Populations Identified by Machine Learning in Colorectal Cancer Using Routine Hematoxylin and Eosin-Stained Sections

Abstract

Purpose: Although high T-cell density is a well-established favorable prognostic factor in colorectal cancer, the prognostic significance of tumor-associated plasma cells, neutrophils, and eosinophils is less well-defined.

Experimental design: We computationally processed digital images of hematoxylin and eosin (H&E)-stained sections to identify lymphocytes, plasma cells, neutrophils, and eosinophils in tumor intraepithelial and stromal areas of 934 colorectal cancers in two prospective cohort studies. Multivariable Cox proportional hazards regression was used to compute mortality HR according to cell density quartiles. The spatial patterns of immune cell infiltration were studied using the G_{Tumor:Immune cell} function, which estimates the likelihood of any tumor cell in a sample having at least one neighboring immune cell of the specified type within a certain radius. Validation studies were performed on an independent cohort of 570 colorectal cancers.

Results: Immune cell densities measured by the automated classifier demonstrated high correlation with densities both from manual counts and those obtained from an independently trained automated classifier (Spearman's ρ 0.71-0.96). High densities of stromal lymphocytes and eosinophils were associated with better cancer-specific survival [P _trend < 0.001; multivariable HR (4th vs 1st quartile of eosinophils), 0.49; 95% confidence interval, 0.34-0.71]. High G_{Tumor:Lymphocyte} area under the curve (AUC_0,20μm; P _trend = 0.002) and high G_{Tumor:Eosinophil} AUC_0,20μm (P _trend < 0.001) also showed associations with better cancer-specific survival. High stromal eosinophil density was also associated with better cancer-specific survival in the validation cohort (P _trend < 0.001).

Conclusions: These findings highlight the potential for machine learning assessment of H&E-stained sections to provide robust, quantitative tumor-immune biomarkers for precision medicine.

Figure 1.

Immune cell detection and quantification and area segmentation in H&E-stained colorectal cancer tissue microarrays using automated image analysis.

Figure 2.

Relationships between densities of intraepithelial and stromal immune cells and clinicopathologic features. (A) Boxplots of the distribution of intraepithelial (IEL) and stromal (S) immune cell densities. (B) Correlation matrix of Spearman correlation coefficients between the densities of intraepithelial and stromal immune cells. (C) Heatmap of the relationships between clinicopathologic features and the densities of lymphocytes, plasma cells, neutrophils, and eosinophils. P values are based on the correlation analysis of immune cell densities and continuous or ordinal variables (AJCC Stage, Neoantigen load) by Spearman rank correlation test or the comparison of immune cell densities across categorical variable categories (Tumor location, Tumor differentiation, MSI status, CIMP status) by the Kruskal-Wallis test or Wilcoxon rank-sum test.

Figure 3.

Inverse probability weighting-adjusted Kaplan-Meier curves of colorectal cancer-specific survival according to ordinal quartile categories (C1-C4) of stromal lymphocyte (A), plasma cell (B), neutrophil (C), and eosinophil (D) densities.

Figure 4.

Spatial analysis of tumor immune infiltrates with Tumor:Immune cell G-cross function (G_{Tumor:Immune cell}). (A)-(D) Example lymphocyte infiltration patterns and corresponding G_{Tumor:Lymphocyte}(r) plots, estimating the probability of any tumor cell having at least one neighboring lymphocyte within an r μm radius. High immune cell infiltrate localizing near tumor cells (D) results in higher area under the curve (AUC) than a low immune cell infiltrate mainly located away from tumor cells (A). The G-cross function was summarized as AUC within a 20 μm radius (AUC_0,20μm). (E)-(H) G_{Tumor:Immune cell} AUC_0,20μm quartiles (C1-C4) in relation to cancer-specific survival. The multivariable Cox regression models initially included sex, age, year of diagnosis, family history of colorectal cancer, tumor location, tumor differentiation, disease stage, microsatellite instability, CpG island methylator phenotype, KRAS, BRAF, and PIK3CA mutations, and long-interspersed nucleotide element-1 methylation level. A backward elimination with a threshold P of 0.05 was used to select variables for the final models.