(P 117) THE TUMOR IMMUNE MICROENVIRONMENT OF DEDIFFERENTIATED SARCOMAS: DIGITAL SPATIAL PROFILING SHOWS IMMUNE TRANSCRIPTIONAL DIFFERENCES BETWEEN DEDIFFERENTIATED LIPOSARCOMA AND CHONDROSARCOMA

Objective: Dedifferentiated sarcomas display the most aggressive behavior among mesenchymal malignancies. The tumor immune microenvironment (TIME) of sarcomas is poorly understood, although B-cells in tertiary lymphoid structures have been associated with improved survival in dedifferentiated liposarcoma (DDLPS). In this study we explore differences in immune cell subsets and immune related RNA transcripts with respect to DDLPS and dedifferentiated chondrosarcomas (DDCS). We hypothesize that differences in immune cell subsets and immune related RNA transcripts account for differences in patient outcome.

Methods: 28 cases of DDCS and 42 cases of DDLPS were obtained from the Department of Pathology files.
Image Analysis: Representative FFPE tissue blocks of the dedifferentiated components were selected and immunohistochemical stains for CD4, CD8 and CD20 were applied to whole sections. Automated image analysis was performed to quantitate immune cell densities per mm2 of tumor area.
Vectra Polaris: Tissue microarrays containing 14 DDCS (26 2 mm cores) and 19 DDLPS (37 2 mm cores) were created. A panel of antibodies was applied to highlight immune subsets (CD3, CD8, CD20, CD68, CD163 and CTLA4). Cell counts per core and antibody were collected for bioinformatics analysis using R package ggpubr v.0.4.0.
Digital Spatial Profiling (DSP): Tissue microarrays containing 9 DDCS (27 regions of interest) and 13 DDLPS (36 regions of interest) underwent DSP by Nanostring GEOMX. Regions of interest included areas of tumor infiltrated by immune cells and non-immune areas. RNA-seq data collected were analyzed using R package GeomxTools v.2.0.0.

Results: Image analysis and Vectra Polaris showed increased T cells in DDLPS as compared to DDCS: increased CD4 by image analysis (p=0.038) and increased CD8 by Vectra Polaris (p=0.022). B cell populations were not statistically different by both methods and were low in both tumors.
DSP revealed increased immune-related RNA transcripts in DDLPS cases both in immune infiltrated and non-immune areas of the tumors (Pink highlights increased transcripts in both areas). Immunoglobulins and some complement factors appear increased in non-immune areas (Green). The immune transcript profile of DDCS reveals an immunosuppressive TME (Table 1). Grade 2,3 DDLPS shows a significantly better overall survival as compared to DDCS (p=0.0003) (Fig. 1).

Conclusion: The tumor immune microenvironments of DDLPS and DDCS are different in that T cell subsets (CD4 and CD8) appear increased in DDLPS, while CD20 populations are low in both tumors. Immune related RNA transcripts revealed by DSP in DDLPS are more numerous and show higher expression than in DDCS, likely contributing to survival outcomes. The immunosuppressive TIME of DDCS may be related to tumor associated macrophage infiltration. Ultimately, understanding how to alter the DDCS immune environment so that it resembles that of DDLPS may improve survival in DDCS.