Distinct immunological landscapes characterise inherited and sporadic mismatch repair deficient endometrial cancer

Neal Ramchander1,2, Neil Ryan3,4, Thomas Walker3, Lauren Harries5, James Bolton5, Tjalling Bosse6, David Gareth Evans4,7, Emma J Crosbie3,8

1University of Manchester Medical School, Manchester, United Kingdom. 2Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, United Kingdom. 3Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom. 4Division of Evolution and Genomic Medicine, Faculty of Biology, Medicine and Health, University of Manchester, St. Mary’s Hospital, Manchester, United Kingdom. 5Department of Histopathology, Manchester University NHS Foundation Trust, Manchester, United Kingdom. 6Department of Pathology, Leiden University Medical Center, Leiden, Netherlands. 7Manchester Centre for Genomic Medicine, St. Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Ce, Manchester, United Kingdom. 8Department of Obstetrics and Gynaecology, St Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom

Abstract

Objectives

Lynch Syndrome (LS) is an inherited cancer predisposition syndrome that primes a robust anti-cancer immunological response1. We sought to explore immunological differences between LS-associated mismatch repair (MMR) deficient, sporadic MMR-deficient, and MMR-proficient endometrial cancer (EC). This is important as immunotherapy studies treat different aetiologies of MMR deficiency as homogenous2-4.

Methods

EC from women with LS were identified (n=25). Comparator tumours were recruited prospectively following genetic and immunohistochemical analysis: sporadic MLH1 hypermethylated MMR-deficient EC (n=33), and MMR proficient EC (n=35). A fully automated immunohistochemistry panel identified CD3+, CD8+, CD45RO+, FoxP3+, and PD-1+ immune cells, and PD-L1 expression on tumour/immune cells. Mean and overall compartmental T-cell counts generated standard (binary: Low/High) and higher resolution (quaternary: 0-25%, 25-50%, 50-75%, 75-100%) immune scores, which were used as explanatory features in neural network, support vector machine, and discriminant predictive modelling.

Results

Overall T-cell counts were significantly different between the three cohorts: CD3+ (p=<0.0001), CD8+ (p=<0.0001), CD45RO+ (<0.0001), FoxP3+ (p=<0.0001) and PD1+ (p=<0.0001), with LS-associated MMR-deficient tumours having highest infiltrations. There were significant differences in CD8+ (p=0.02), CD45RO+ (p=0.007) and PD-1+ (p=0.005) T-cell counts at the invasive margin between LS-associated and sporadic MMR-deficient tumours, but not between sporadic MMR-deficient and MMR-proficient tumours. Predictive modelling could accurately determine MMR status based on CD8+ T-cell count at the tumour centre.

Conclusions

LS-associated MMR-deficient and sporadic MMR-deficient EC’s are distinct immunological entities; immunotherapy studies must examine them separately. Using our predictive model, CD8+ T-cell density at the tumour centre can accurately identify MMR status in EC.

 

Abstract references

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