Abstract
The poor prognosis of patients with soft-tissue sarcoma as not changed in the past several decades, highlighting the necessity for new therapeutic approaches. T-cell based immunotherapies are a promising alternative to traditional cancer treatments due to their ability to target only malignant cells, leaving benign cells unharmed. The development of successful immunotherapy requires the identification and characterization of targetable immunogenic tumor antigens. Cancer-testis antigens (CTA) are a group of highly immunogenic tumor-associated proteins that have emerged as potential targets for CD8+ T-cell recognition. In addition to identifying a targetable antigen, it is crucial to understand the tumor immune microenvironment. The level of immune infiltration and mechanisms of immune suppression within the tumor play important roles in the outcome of immunotherapy.
The goal of this study is to identify targetable immunogenic antigens for T-cell based immunotherapy and to characterize the tumor immune microenvironment in human dedifferentiated liposarcoma (DDLS) by Nanostring and IHC.
To assess the complexity of the human DDLS tumor immune microenvironment and to identify target antigens we used the nCounter NanoString platform to generate a gene expression profile for hundreds of genes from RNA obtained from 29 DDLS and 10 control fat FFPE samples. To classify inflammatory status of DDLS tumors, we performed hierarchical clustering based on expression levels of selected tumor inflammatory signature genes (CCL5, CD27, CD274, CD276, CD8A, CMKLR1, CXCL9, CXCR6, HLA-DQA1, HLA-E, IDO1, LAG3, PDCDILG2, PSMB10, STAT1, TIGIT). To confirm protein expression and distribution of identified antigens, we performed immunohistochemistry on human tissue micro-arrays encompassing DDLPS tumor tissues and matched normal control tissue from 63 patients. IHC for the cancer testis antigens PBK, SPA17, MAGE-A3, NY-ESO-1 and SSX2 was performed, and the staining results were scored by two authors based on maximal staining intensity on a scale of zero to three (absent=0, weak=1, moderate=2, or strong=3) and the percentage of tumor cells that stained.
Hierarchical clustering of DDLS tumors based on expression of tumor inflammation signature genes revealed two distinct groups, consisting of 15 inflamed tumor and 14 non-inflamed tumors, demonstrating tumor heterogeneity within the DDLS sarcoma subtype. All antigens were found to be expressed in DDLS at an mRNA level. SPA17 was expressed at the highest levels in DDLS, however, this antigen was expressed at high levels in normal fat. Notably, antigens PBK and TTK had the largest fold change increase in expression in DDLS compared to normal fat controls.
Immunohistochemical analysis of selected antigens revealed that PBK was found to be expressed in 96% (52/54) of DDLS samples at high levels. Other antigens were absent or expressed at low levels in DDLS; MAGEA3 in 15.87% (10/63) NY-ESO-1 in 6.35% (4/62) and SSX2 in 12.7% (8/63) and SPA17 in 5.5% (3/54).
This data shows considerable inter-tumoral heterogeneity of inflammation, which should be taken into consideration when designing an immunotherapy for DDLS. To date, these results show promising expression of PBK antigen in DDLS, which may be used as a target in the future development of an immunotherapy for sarcoma.
