Characteristics: PD-1 expression and CD69 on T cells, an increase in macrophages compared to the poorly infiltrated tumors, and neutrophil infiltration with IL-17 expression. TME and single cell phenotypes. Glucagon (19-29), human Abstract Providing effective personalized immunotherapy for triple negative breast cancer (TNBC) patients requires a detailed understanding of the composition of the tumor microenvironment. Both the tumor cell and non-tumor components of TNBC can exhibit tremendous heterogeneity in individual patients and change over time. Delineating cellular phenotypes and spatial topographies associated with distinct immunological states and the impact of chemotherapy will be necessary to optimally time immunotherapy. The clinical successes in immunotherapy have intensified research on the tumor microenvironment, aided by a plethora of high-dimensional technologies to define cellular phenotypes. These high-dimensional technologies include, but are not limited to, single cell RNA sequencing, spatial transcriptomics, T cell repertoire analyses, advanced flow cytometry, imaging mass cytometry, and their integration. In this review, we discuss the cellular phenotypes and spatial patterns of the lymphoid-, myeloid-, and stromal cells in the TNBC microenvironment and the potential value of Ntrk3 mapping these features onto tumor cell genotypes. Keywords: single cell, immune profiling, breast cancer, spatial profiling, tumor evolution 1. Introduction Tumor heterogeneity is associated with therapy resistance and poor prognosis in a variety of solid tumors [1,2]. Triple negative breast cancer (TNBC), in particular, shows a substantial level of genomic, cellular, and phenotypic heterogeneity [3,4,5,6]. While genomic heterogeneity and subclonal diversity are prevalent in this subgroup of tumors, and accompanied by high-levels of genomic instability, a growing body of evidence indicates that the disease course depends on the interaction between cancer cells and the tumor micro-environment (TME). The TME is not static and can change over time, owing to differences in cell numbers, phenotypes, and spatial relationships. Immune cells, especially cytotoxic T cells, have been the center of attention in view of the rise of immune checkpoint blockade and their potential to kill the tumor cells [7,8,9]. In breast cancer, the endogenous anti-cancer immune response is often expressed as the level of tumor infiltrating lymphocytes (TILs) and is tightly associated with prognosis and response to (immuno-)therapy in TNBC [10,11,12,13,14,15,16,17,18]. However, a low level of TILs does not equate to disease progression. As the response rates to anti-PD-(L)1 therapy in metastatic TNBC and the combination of anti-PD-(L)1 and chemotherapy in primary TNBC have been modest [14,15,16,17,18], there is a clinical need to understand Glucagon (19-29), human why the majority of the patients remain without an effective response. Thus, further characterization of the TME may provide a biological rationale for novel immunomodulatory strategies. Efforts to further elucidate the TME have been aided by a plethora of new technologies that study tumors in a high-dimensional manner. These high-dimensional technologies enable comprehensive analysis of cell phenotypes at the single cell level or the spatial relationships of tumor and immune cells. High-dimensional phenotyping of the breast TME has been successfully achieved by technologies like flow cytometry, single cell mass cytometry, and single cell RNA sequencing (scRNAseq) [19,20,21,22,23] (Table 1). Technologies that preserve the spatial relationships between cells in TNBC include multiplex immunofluorescence, imaging mass cytometry (IMC) and multiplex ion beam imaging (MIBI) [24,25,26,27] (Table 1). Most studies do not capture the dynamics of the TME yet, as it requires sequential tissue biopsies which are difficult to obtain in patients. Nevertheless, information on the evolutionary path of breast tumor cells in the context of their TME can potentially guide the design of synergistic immunotherapy combinations for relatively cold tumors like breast cancer. Table 1 Methods to generate high-dimensional phenotypic data.
Single cell RNA sequencing Single cell transcriptome sequencing to assess gene expression patterns for each cell individuallyRNANoSingle cell[6,21,23,28,29] Spatial transcriptomics Spatial information is obtained by integrating imaging and positional barcoding.RNAYes~100s of cells[30,31,32] TCR sequencing Single T cell receptor sequencing to profile the repertoire of T cell receptorsTCR sequence (clonotype)NoSingle cell Flow cytometry Single cell labeling with fluorescent-tagged antibodies (~4 to 5 plex)ProteinNoSingle cell[20,33,34] CyTOF Single cell labeling with metal-tagged antibodies (~40-plex) measured using laser ablation and mass spectrometry-based time-of-flightProteinNoSingle cell Nanostring Digital Spatial Profiling Photocleavable oligonucleotide barcodes covalently linked to in-situ affinity reagents (antibodies/RNA probes)Protein/RNAYes~100s to 1000s of cells[35,36,37] Multiplex immune-fluorescence Immunofluorescence with Glucagon (19-29), human multiple antibodies (~4 to 5) to assess marker relationships in tissueProteinYesSingle cell[25,27,38] Imaging Mass Cytometry (IMC) Immunohistochemistry staining using metal metal-tagged antibodies (~40-plex).