MAY 19, 2020 11:15 AM EDT | APAC MAY 20, 2020 2:15 PM CST

Digital Spatial Mapping of the Immune Landscape of Triple Negative Breast Cancer Reveals Novel Features of Immune-tumor Cell Interaction

Speaker
  • Professor of Cancer Biology, Mayo Clinic Comprehensive Cancer Center
    Biography
      Dr. Thompson's primary focus is clinical and translational research. His team uses genomic technology and functional genomic tools to elucidate the mechanisms that underlie malignant transformation and may ultimately identify new therapeutic targets or biomarkers of therapeutic response. The team's primary focus is on breast cancer, although the tools and technologies the team has developed are also applied to lung, thyroid, renal and pancreatic cancers.
      The research interests of E. Aubrey Thompson, Ph.D., involve gene structure and function and control of gene expression as it applies to regulation of cell proliferation, differentiation and death.

    Abstract

    Background: Growing evidence supports the critical role of preexisting immune response in triple negative breast cancer (TNBC). However, there are limitations with current evaluation approaches: inability to functionally assess the type of immune infiltration with traditional pathologic evaluation and loss of spatial distribution in conventional high-plex gene or protein expression analyses from the whole tumor section. Here, we report the initial analysis of immune protein expression as a function of spatial distribution and clinical outcomes in TNBC samples.

    Methods: NanoString GeoMx® Digital Spatial Profiling (DSP) was used to quantify 39 immunerelated proteins in stromal and tumor segments from 44 TNBC samples from the FinXX trial. Samples were matched for patient characteristics, treatment arm (capecitabine vs. 5- fluorouracil), and outcome based on recurrence-free survival (RFS) with 22 samples from patients who recurred and 22 samples from patients with durable RFS. Regions of interest (ROIs) were selected based on expression of cytokeratin (tumor), CD45 (leukocytes), or CD68 (macrophages). Each ROI was segmented into tumor (pancytokeratin-positive area) and stroma (pancytokeratin-negative/nuclear SYTO13-positive area). The general linear model was used for statistical analysis of differential expression with RFS as a categorical variable (recur yes or no).

    Results: A total of 950 tumor and stroma segments were included in this initial analysis. In both tumor and stroma segments, over-expression of T cell activation markers (CD137, GITR) was associated with better outcome, whereas T cell markers (CD3, CD4, CD8) were not significantly associated with outcome. In tumor segments alone, improved outcome was significantly associated with increased protein expression [> 2-fold change (FC) at p2.0, p2.0, p
    In CD45-enriched stroma, improved outcome was associated with elevated expression (FC>2.0, p2.0, p
    whereas durable RFS was associated with elevated PD-L2 and IDO1 expression in CD68-enriched stroma. In contrast, macrophage/dendritic cell markers CD68 and CD11c were not associated with outcome. In tumor cells adjacent to CD45-enriched stroma, durable RFS was associated with increased abundance (FC>2.0, p
    Among 39 immune function proteins, only elevated CTLA4 expression in CD68-enriched stroma was associated with recurrence (FC=0.55, p whereas tumor-segment CTLA4 was associated with RFS.

    Conclusions: Using an in-depth analysis to precisely quantify the abundance of multiple immune function proteins in a spatially defined manner, we observed that PD-L2, IDO1, and T cell activation markers were robustly associated with durable RFS in both tumor and stromal segments. In contrast, MHC components (HLA-DR, beta-2-microglobulin), B cell markers (CD20), and NK cell markers (CD56) were strongly associated with favorable outcome in tumor but not in stromal segments. Our study highlights the conclusion that the immune landscape of TNBC is far too complex to be encompassed by any single molecular marker, and more detailed analyses of the DSP data reported here are ongoing with a view towards using quantitative multiplex analyses to refine our understanding of how therapeutic outcome is influenced by interactions among immune cells and between immune and tumor cells.


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