Dataset Information

Combining measures of immune infiltration shows additive effect on survival prediction in high-grade serous ovarian carcinoma.

ABSTRACT:

Background

In colorectal and breast cancer, the density and localisation of immune infiltrates provides strong prognostic information. We asked whether similar automated quantitation and combined analysis of immune infiltrates could refine prognostic information in high-grade serous ovarian carcinoma (HGSOC) and tested associations between patterns of immune response and genomic driver alterations.

Methods

Epithelium and stroma were semi-automatically segmented and the infiltration of CD45RO⁺, CD8⁺ and CD68⁺ cells was automatically quantified from images of 332 HGSOC patient tissue microarray cores.

Results

Epithelial CD8 [p = 0.027, hazard ratio (HR) = 0.83], stromal CD68 (p = 3 × 10^-4, HR = 0.44) and stromal CD45RO (p = 7 × 10^-4, HR = 0.76) were positively associated with survival and remained so when averaged across the tumour and stromal compartments. Using principal component analysis, we identified optimised multiparameter survival models combining information from all immune markers (p = 0.016, HR = 0.88). There was no significant association between PTEN expression, type of TP53 mutation or presence of BRCA1/BRCA2 mutations and immune infiltrate densities or principal components.

Conclusions

Combining measures of immune infiltration provided improved survival modelling and evidence for the multiple effects of different immune factors on survival. The presence of stromal CD68⁺ and CD45RO⁺ populations was associated with survival, underscoring the benefits evaluating stromal immune populations may bring for prognostic immunoscores in HGSOC.

SUBMITTER: Montfort A

PROVIDER: S-EPMC7283353 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundHigh-grade serous ovarian cancer (HGSOC) carries the highest mortality in the gynecological cancers; however, therapeutic outcomes have not significantly improved in recent decades. Macrophages play an essential role in the occurrence and development of ovarian cancer, so the mechanisms of macrophage infiltration should be elucidated.MethodWe downloaded transcriptome data of ovarian cancers from the Gene Expression Omnibus and The Cancer Genome Atlas. After rigorous screening, 1566 HGSOC were used for data analysis. CIBERSORT was used to estimate the level of macrophage infiltration and WGCNA was used to identify macrophage-related modules. We constructed a macrophage-related prognostic model using machine learning LASSO algorithm and verified it using multiple HGSOC cohorts.ResultsIn the GPL570-OV cohort, high infiltration level of M1 macrophages was associated with a good outcome, while high infiltration level of M2 macrophages was associated with poor outcomes. We used WGCNA to select genes correlated with macrophage infiltration. These genes were used to construct protein-protein interaction maps of macrophage infiltration. IFL44L, RSAD2, IFIT3, MX1, IFIH1, IFI44, and ISG15 were the hub genes in the network. We then constructed a macrophage-related prognostic model composed of CD38, ACE2, BATF2, HLA-DOB, and WARS. The model had the ability to predict the overall survival rate of HGSOC patients in GPL570-OV, GPL6480-OV, TCGA-OV, GSE50088, and GSE26712. In exploring the immune microenvironment, we found that CD4 memory T cells and activated mast cells showed that the degree of infiltration was higher in the high-risk group, while M1 macrophages were the opposite, and HLA molecules were overexpressed in the high-risk group.ConclusionWe constructed a macrophage infiltration-related protein interaction network that provides a basis for studying macrophages in HGSOC. Our macrophage-related prognostic model is robust and widely applicable. It predicts overall survival in HGSOC patients and may improve HGSOC treatment.