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In The Journal of molecular diagnostics : JMD

Across multiple tumor types, immune checkpoint inhibitors (ICIs) have demonstrated clinical benefit to patients with cancer, yet there is a need to identify predictive biomarkers of response to these therapies. A multiparameter gene expression profiling (GEP)-based tumor inflammation assay may offer robust characterization of the tumor microenvironment (TME), thereby extending the utility of single-gene analysis or immunohistochemistry (IHC) in predicting response to ICIs. We interrogated 1778 commercially procured, formalin-fixed, paraffin-embedded samples using GEP and pathology-assisted digital CD8 IHC. A machine-learning approach was used to develop gene expression signatures that predicted CD8+ immune cell abundance as surrogates for tumor inflammation in melanoma and squamous cell carcinoma of the head and neck samples. An assay for a 16-gene CD8 signature was developed and analytically validated across 12 tumor types. CD8 signature scores correlated with CD8 IHC in a platform-independent manner, and inflammation prevalence was similar between assay methods for all tumor types except prostate cancer and small cell lung cancer. In retrospective analyses, CD8 signature scores associated with progression-free survival and overall survival with nivolumab in patients with urothelial carcinoma from CheckMate 275. This study demonstrated that the CD8 signature assay can be used to accurately quantify CD8+ immune cell abundance in the TME and has potential clinical utility for determining patients with cancer who are likely to respond to ICIs.

Szabo Peter M, Pant Saumya, Ely Scott, Desai Keyur, Anguiano Esperanza, Wang Lisu, Edwards Robin, Green George, Zhang Nancy