Prevalence and cellular distribution of novel immune checkpoint targets across longitudinal specimens in treatment-naïve melanoma: implications for clinical trials.

CLINICAL CANCER RESEARCH(2019)

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摘要
Purpose: Immunotherapies targeting costimulating and coinhibitory checkpoint receptors beyond PD-1 and CTLA-4 have entered clinical trials. Little is known about the relative abundance, coexpression, and immune cells enriched for each specific drug target, limiting understanding of the biological basis of potential treatment outcomes and development of predictive biomarkers for personalized immunotherapy. We sought to assess the abundance of checkpoint receptors during melanoma disease progression and identify immune cells enriched for them. Experimental Design: Multiplex immunofluorescence staining for immune checkpoint receptors (ICOS, GITR, OX40, PD-1, TIM-3, VISTA) was performed on 96 melanoma biopsies from 41 treatment-naive patients, including patient-matched primary tumors, nodal metastases, and distant metastases. Mass cytometry was conducted on tumor dissociates from 18 treatment-naive melanoma metastases to explore immune subsets enriched for checkpoint receptors. Results: A small subset of tumor-infiltrating leukocytes expressed checkpoint receptors at any stage of melanoma disease. GITR and OX40 were the least abundant checkpoint receptors, with <1% of intratumoral T cells expressing either marker. ICOS, PD-1, TIM-3, and VISTA were most abundant, with TIM-3 and VISTA mostly expressed on non-T cells, and TIM-3 enriched on dendritic cells. Tumor-resident T cells (CD69(+)/CD103(+)/CD8(+)) were enriched for TIGIT (>70%) and other coinhibitory but not costimulatory receptors. The proportion of GITR(+) T cells decreased from primary melanoma (>5%) to lymph node (<1%, P = 0.04) and distant metastases (<1%, P = 0.0005). Conclusions: This study provides the first comprehensive assessment of immune checkpoint receptor expression in any cancer and provides important data for rational selection of targets for trials and predictive biomarker development.
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