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Additional file 2: Supplementary Figure 2. Gating strategies. A: T cells: tumor infiltrating leukocytes were gated based on SSC-A versus FSC-A and singlets were selected from the FSC-A versus FSC-H dot plot. Dead cells were excluded with Fixable Viability Dye (FVD). On viable cells, CD45+ cells were selected and within this population CD3+T cells. From CD3+ cells CD8+ and CD4+ were differentiated. B: tumor infiltrating leukocytes were gated based on SSC-A versus FSC-A and singlets were selected from the FSC-A versus FSC-H dot plot. Dead cells were excluded with Fixable Viability Dye (FVD). On viable cells, CD45+ cells were selected and among CD45+ cells we studied CD11b+ cells. Representative dot plot of MDSC cell subset: monocytic- (Ly6C+Ly6G-) or granulocytic- (Ly6ClowLy6G+). C: dot plot of myeloid sub population selected on singlet, viable, CD45+ cells as I-Ab-CD11c-/+. Monocytes: Ly6C+Ly6G-, neutrophils: Ly6G+Ly6C+ and macrophages: Ly6C-Ly6G-. On macrophage cell population we analyzed M1/M2 polarization (M1: CD38+Egr2-, M2: CD38-Egr2+).

Additional file 3: Supplementary Figure 3. Gating strategies. A: spleen: leukocytes were gated based on SSC-A versus FSC-A and singlets were selected from the FSC-A versus FSC-H dot plot. Dead cells were excluded with Fixable Viability Dye (FVD). CD45+ from spleen cells were separated in CD3+ cells (CD4+ and CD8+) and in CD3-NKp46+ NK cells. B: lymph nodes: leukocytes were gated based on SSC-A versus FSC-A and singlets were selected from the FSC-A versus FSC-H dot plot. Dead cells were excluded with Fixable Viability Dye (FVD). CD45+ cells from tumor draining lymph nodes were separated in CD3+ cells (CD4+ and CD8+) and in CD3-NKp46+ NK cells. Dot plots of CD3+, CD4+, CD8+, NK cells are given.

Additional file 1: Supplementary Figure 1. A: analysis of viable CD45+, CD3+, CD4+ and CD8+ cells (referred to live CD45+cells) in tumors from mice receiving different in vivo treatments. No significative differences in percentages of cells were detected among different group of treatments. B: analysis of CD8+CD28-CD39+ regulatory cells percentages in tumors from mice receiving different in vivo treatments. *p

Additional file 5: Supplementary Figure 5. mIF staining of lungs from mice injected IV with B16F10 and treated with guadecitabine, guadecitabine/ICBs, ICBs or ctrl. A: Representative nine-colour multispectral images of a lung sample slide. Original magnification ×20. Immune markers and colour codes are indicated in the legend. B: Representative image of cell-cell distance analysis. CD206+ TAM-M2 cells (light green dots) within a 30 µm radius from CD8+ T cells (red dots) are represented. The distance of 200µm is represented as a reference scale.

Additional file 1: Supplementary Table S1. Automatic definition of cell types by NanoString nSolver Software. Supplementary Table S2. Automatic definition of immune pathways by NanoString nSolver Software. Supplementary Fig. S1. Representative images of cell-to-cell distance analyses. (A) For mean distance between different cell subtypes, the nearest neighbors analysis was used. The mean distance between tumor cells (light blue dots) and the nearest CD8+ cells (red dots) is represented in the figure as an example. (B) The count within analysis was employed to calculate the percentage of reference cells, among the total number of reference cells, which are present within a 20 μm radius from at least one cell of a different phenotype. The percentage of tumor cells (light blue dots) within a 20 μm radius from a CD8+ T lymphocyte (red dots) is represented in the figure as an example. Original magnification X20. Supplementary Table S3. Differentially expressed genes (DEGs) between HPV-positive and HPV-negative (used as baseline) OPSCC patients. Supplementary Fig. S2. Differential expression of immune-related pathways and cell type genes in HPV-positive and HPV-negative OPSCC. Trend plots depicting differential expression of predefined (A) pathway genes and (B) gene expression-based cell types in HPV-positive and HPV-negative OPSCC. Supplementary Table S4. Correlation analysis between immune cell populations in HPV-positive and HPV-negative primary tumors and metastases. Supplementary Fig. S3. Immune cells in primary tumors and related metastases. Density (number of cells/mm2) of different immune cell populations in HPV-positive and HPV-negative primary tumors and metastases. Supplementary Fig. S4. The immune cell contexture of metastases correlates with patient outcome. (A-C) Kaplan-Meier survival curves for disease-free survival according to the immune cell composition of (A) the entire cohort (n = 39), (B) HPV-positive (n = 24) and (C) HPV-negative (n = 15) lymph node metastases. The median cut-off of each immune cell subset density was used to separate high and low infiltrated groups. Log-rank p values, hazard ratios (HR) and 95% confidence intervals (CI) are reported in each graph. Supplementary Fig. S5. The density of immune cells differs between females and males with HPV-positive lesions. Density (number of cells/mm2) of different immune cell populations in females and males with HPV-positive (A) primary tumors and (B) metastases.

The identification of universal tumor-specific antigens (TSA) shared between multiple patients and/or multiple tumors is of great importance to overcome the practical limitations of personalized cancer immunotherapy. Recent studies support the involvement of DEPDC1 in many aspects of cancer traits, such as cell proliferation, anti-apoptosis and cell invasion, suggesting that it may play key roles in the oncogenic process. In this study, we report that DEPDC1 expression is up-regulated in several types of human tumors, and closely linked to a poorer prognosis; therefore, it might be regarded as a novel universal oncoantigen potentially suitable for targeting many different cancers. In this regard, we report the identification of an immunogenic DEPDC1-derived epitope restricted for the HLA-A*0201 molecule, which is able to induce cytotoxic T lymphocytes (CTL) exerting a strong and specific functional response in vitro in response not only to peptide-loaded cells but also to triple negative breast cancer (TNBC) cells endogenously expressing the DEPDC1 protein. Such CTL are also therapeutically active against human TNBC xenografts in vivo upon adoptive transfer in immunodeficient mice. Overall, these data provide evidences that this DEPDC1-derived antigenic epitope can be exploited as a new tool for the development of immunotherapeutic strategies for HLA-A*0201 patients with TNBC, and potentially many other cancers. Moreover, we plan to employ an approach of multiplexing digital pathology to study the intimate relationships that adoptively transferred lymphocytes can establish with TNBC cells in tumor-bearing mice, as further advances in immunotherapy approaches require a detailed understanding of cell dynamics within the tumor microenvironment. The benefits of multispectral immunohistochemistry, combined with the development of software for quantitation, are making this methodology an increasingly powerful tool in the analysis and characterization of tissue and cellular processes, supporting diagnostic potential in order to improve therapies.