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Supplementary Tables S1-S10. Supplementary Figures S1-S5. from Prognostic Significance of Immune Cell Populations Identified by Machine Learning in Colorectal Cancer Using Routine Hematoxylin and Eosin–Stained Sections

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posted on 2023-03-31, 22:01 authored by Juha P. Väyrynen, Mai Chan Lau, Koichiro Haruki, Sara A. Väyrynen, Andressa Dias Costa, Jennifer Borowsky, Melissa Zhao, Kenji Fujiyoshi, Kota Arima, Tyler S. Twombly, Junko Kishikawa, Simeng Gu, Saina Aminmozaffari, Shanshan Shi, Yoshifumi Baba, Naohiko Akimoto, Tomotaka Ugai, Annacarolina Da Silva, Mingyang Song, Kana Wu, Andrew T. Chan, Reiko Nishihara, Charles S. Fuchs, Jeffrey A. Meyerhardt, Marios Giannakis, Shuji Ogino, Jonathan A. Nowak

Table S1. Analyses of MSI, DNA methylation, KRAS, BRAF, and PIK3CA mutations, and neoantigen load. Table S2. Clinical and pathological characteristics of colorectal cancer cases according to tumor stromal immune cell densities in TCGA dataset. Table S3. Steps of immune cell detection and quantification and tissue category classification from images of H&E stained sections using QuPath. Table S4. Statistical methods for survival analysis. Table S5. Immune cell densities in tumor intraepithelial and stromal regions and colorectal cancer-specific survival with inverse probability weighting (IPW). Table S6. Densities of stromal lymphocytes, plasma cells, and eosinophils and patient survival in strata of MSI status with inverse probability weighting (IPW). Table S7. Comparison of the prognostic power of immune cell densities in colorectal cancer-specific survival analyses. Table S8. Combination of spatial G-cross measurement and density measurements of lymphocytes, plasma cells, neutrophils, and eosinophils in relation to colorectal cancer-specific survival. Table S9. Densities of intraepithelial and stromal immune cells and patient survival in TCGA dataset. Table S10. Tumor:Immune cell G-cross function area under the curve and patient survival in TCGA dataset. Figure S1. Reproducibility of the immune cell counting. Figure S2. Boxplots of the densities of intraepithelial and stromal lymphocytes, plasma cells, neutrophils, and eosinophils across 10 TMAs. Figure S3. UMAP analysis of colorectal cancer cases according to intraepithelial and stromal densities of lymphocytes, plasma cells, neutrophils, and eosinophils. Figure S4. Relationships of intraepithelial and stromal immune cell densities with LINE-1 methylation level, BRAF mutation, KRAS mutation, and PIK3CA mutation. Figure S5. Inverse probability weighting-adjusted Kaplan-Meier curves of colorectal cancer-specific survival according to ordinal categories (C1-C4) of intraepithelial lymphocyte (A), plasma cell (B), neutrophil (C), and eosinophil (D) densities.

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NIH

Cancer Research UK

Stand Up To Cancer

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ARTICLE ABSTRACT

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. 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 GTumor: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. 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 [Ptrend < 0.001; multivariable HR (4th vs 1st quartile of eosinophils), 0.49; 95% confidence interval, 0.34–0.71]. High GTumor:Lymphocyte area under the curve (AUC0,20μm; Ptrend = 0.002) and high GTumor:Eosinophil AUC0,20μm (Ptrend < 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 (Ptrend < 0.001). These findings highlight the potential for machine learning assessment of H&E-stained sections to provide robust, quantitative tumor-immune biomarkers for precision medicine.

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    Clinical Cancer Research

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    BiomarkersPrognostic biomarkersGastrointestinal CancersColorectal cancerGynecological CancersUterine cancerImmunologyImmune responses to cancerTumor MicroenvironmentHost-tumor interactionsImmune cells and the microenvironment

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