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Additional file 1: Table S1. Title: Genomic Regions and fusions analyzed with the 24 and 50 gene panels. Additional file 2: Table S2. Title: Gene alterations detected by the 161 gene panel. Description: The frequency of the different mutation types (SNVs, indels, CNV, fusions) for each gene is reported. Additional file 3: Table S3. Title: Alterations identified by the 161 gene panel in the 610 patients analyzed. Description: Genomic alterations and level of evidence of the variants detected in the 610 patients analyzed. The tumor type, age of diagnosis and gender are also reported. Additional file 4: Table S4. Title: Biomarker's summary in the 610 patients included in the study. Description: Patients' categorization based on TIER classification of their most clinically significant variant and immunotherapy biomarkers’ results are reported. Additional file 5: Figure S1. Title and description: Pancreatic cancer patients' categorization based on TIER classification of their most clinically significant variant. A. Pancreatic cancer patients' categorization based on TIER classification of their most clinically significant variant. Patients were categorized in the following categories: No Biomarker: Patients with no biomarker available, 1B: Patients harboring biomarkers with strong evidence of correlation to treatment, 2C.1 KRAS: Patients with a single finding in the KRAS gene, 2C.1: Patients with biomarkers related to off-label treatment. B. Percentage of patients with On-label and off-label mutations identified and the type of alterations detected. Genes of the homologous recombination complex are labeled in blue. Additional file 6: Figure S2. Title and description: Lung cancer patients' categorization based on TIER classification. A. Lung cancer patients' categorization based on TIER classification of their most clinically significant variant. The following categories were used:, 1A.1: Patients with biomarkers related to on-label treatment, 1B: Patients harboring biomarkers with strong evidence of correlation to treatment, 2C.1: Patients with biomarkers related to off-label treatment, 1A.2R; 2C.1: Patients harboring a KRAS mutation related to resistance to treatment plus an off-label, 1A.2R: Patients harboring a KRAS mutation related to EGFR TKIs resistance, 2.C.2: Patients B. % of patients with On-label and off-label mutations identified and the type of alterations detected. Genes of the homologous recombination complex are labeled in blue. Additional file 7: Figure S3. Title and description: Breast cancer patients' categorization based on the TIER classification. A. Patients' categorization based on TIER classification of their most clinically significant variant. The following categories were used: No Biomarker: Patients with no biomarker available, 1A.1: Patients with biomarkers related to on-label treatment, 2C.1: Patients with biomarkers related to off-label treatment, 2C.2: Patients with biomarkers related to clinical trials, 2D: Patients with biomarkers with preclinical evidence. B. Percentage of patients with On-label and off-label mutations identified and the type of alterations detected. Genes of the homologous recombination complex are labeled in blue. Additional file 8: Figure S4. Title and description: Colorectal cancer patients' categorization based on TIER classification. A. Patients' categorization based on TIER classification of their most clinically significant variant. Patients were categorized in the following categories: No Mutation, 1A.1: Patients with no mutation in KRAS/NRAS genes with or without other variations, 1A.2: Patients with biomarkers included in professional guidelines, 1A.1R: Patients harboring either KRAS or NRAS mutations related to resistance to treatment. B. % of patients with on-label and off-label mutations identified and the type of alterations detected. Additional file 9: Table S5. Title and description: Alterations that would have been detected if two hotspot panels of 24 and 50 genes respectively, had been used in the 610 patients analyzed. Additional file 10: Table S6. Title and description: Simulation results of the alterations that would have been identified if the gene set of the 161 gene NGS panel was used in the PCAWG samples. Additional file 11: Table S7. Title and description: Simulation results of the alterations that would have been identified if the gene set of the 514 gene NGS panel was used in the PCAWG samples. Additional file 12: Table S8. Title and Description: TMB, PD-L1 and MSI results in the 395 patients with at least one immunotherapy biomarker requested. Additional file 13: Table S9. Title and Description: Patients with suspicious hereditary pathogenic findings detected in tumor tissue.

Additional file 1: Table S1. Full list of genes on the PCR array.

A major contributing factor to glioma development and progression is its ability to evade the immune system. Nano-meter sized vesicles, exosomes, secreted by glioma-stem cells (GSC) can act as mediators of intercellular communication to promote tumor immune escape. Here, we investigated the immunomodulatory properties of GCS-derived exosomes on different peripheral immune cell populations. Healthy donor peripheral blood mononuclear cells (PBMCs) stimulated with anti-CD3, anti-CD28 and IL-2, were treated with GSC-derived exosomes. Phenotypic characterization, cell proliferation, Th1/Th2 cytokine secretion and intracellular cytokine production were analysed by distinguishing among effector T cells, regulatory T cells and monocytes. In unfractionated PBMCs, GSC-derived exosomes inhibited T cell activation (CD25 and CD69 expression), proliferation and Th1 cytokine production, and did not affect cell viability or regulatory T-cell suppression ability. Furthermore, exosomes were able to enhance proliferation of purified CD4+ T cells. In PBMCs culture, glioma-derived exosomes directly promoted IL-10 and arginase-1 production and downregulation of HLA-DR by unstimulated CD14+ monocytic cells, that displayed an immunophenotype resembling that of monocytic myeloid-derived suppressor cells (Mo-MDSCs). Importantly, the removal of CD14+ monocytic cell fraction from PBMCs restored T-cell proliferation. The same results were observed with exosomes purified from plasma of glioblastoma patients. Our results indicate that glioma-derived exosomes suppress T-cell immune response by acting on monocyte maturation rather than on direct interaction with T cells. Selective targeting of Mo-MDSC to treat glioma should be considered with regard to how immune cells allow the acquirement of effector functions and therefore counteracting tumor progression.