ABSTRACT
BACKGROUND: Despite the importance of tumor-infiltrating T lymphocytes (TILs) in cancer biology, the relationship between TIL phenotypes and their prognostic relevance for localized non-small-cell lung cancer (NSCLC) has not been well established. PATIENTS AND METHODS: Fresh tumor and normal adjacent tissue was prospectively collected from 150 patients with localized NSCLC. Tissue was comprehensively characterized by high-dimensional flow cytometry of TILs integrated with immunogenomic data from multiplex immunofluorescence, T-cell receptor sequencing, exome sequencing, RNA sequencing, targeted proteomics, and clinicopathologic features. RESULTS: While neither the magnitude of TIL infiltration nor specific TIL subsets were significantly prognostic alone, the integration of high-dimensional flow cytometry data identified two major immunotypes (IM1 and IM2) that were predictive of recurrence-free survival independent of clinical characteristics. IM2 was associated with poor prognosis and characterized by the presence of proliferating TILs expressing cluster of differentiation 103, programmed cell death protein 1, T-cell immunoglobulin and mucin-domain containing protein 3, and inducible T-cell costimulator. Conversely, IM1 was associated with good prognosis and differentiated by an abundance of CD8+ T cells expressing cytolytic enzymes, CD4+ T cells lacking the expression of inhibitory receptors, and increased levels of B-cell infiltrates and tertiary lymphoid structures. While increased B-cell infiltration was associated with good prognosis, the best prognosis was observed in patients with tumors exhibiting high levels of both B cells and T cells. These findings were validated in patient tumors from The Cancer Genome Atlas. CONCLUSIONS: Our study suggests that although the number of infiltrating T cells is not associated with patient survival, the nature of the infiltrating T cells, resolved in distinct TIL immunotypes, is prognostically relevant in NSCLC and may inform therapeutic approaches to clinical care.
Subject(s)
Carcinoma, Non-Small-Cell Lung , Lung Neoplasms , CD8-Positive T-Lymphocytes , Carcinoma, Non-Small-Cell Lung/pathology , Humans , Lung Neoplasms/pathology , Lymphocytes, Tumor-Infiltrating/pathology , PrognosisABSTRACT
Preclinical mouse models suggest that the gut microbiome modulates tumor response to checkpoint blockade immunotherapy; however, this has not been well-characterized in human cancer patients. Here we examined the oral and gut microbiome of melanoma patients undergoing anti-programmed cell death 1 protein (PD-1) immunotherapy (n = 112). Significant differences were observed in the diversity and composition of the patient gut microbiome of responders versus nonresponders. Analysis of patient fecal microbiome samples (n = 43, 30 responders, 13 nonresponders) showed significantly higher alpha diversity (P < 0.01) and relative abundance of bacteria of the Ruminococcaceae family (P < 0.01) in responding patients. Metagenomic studies revealed functional differences in gut bacteria in responders, including enrichment of anabolic pathways. Immune profiling suggested enhanced systemic and antitumor immunity in responding patients with a favorable gut microbiome as well as in germ-free mice receiving fecal transplants from responding patients. Together, these data have important implications for the treatment of melanoma patients with immune checkpoint inhibitors.
Subject(s)
Gastrointestinal Microbiome/immunology , Immunotherapy , Melanoma/therapy , Programmed Cell Death 1 Receptor/antagonists & inhibitors , Skin Neoplasms/therapy , Animals , Fecal Microbiota Transplantation , Gastrointestinal Microbiome/genetics , Humans , Melanoma/immunology , Metagenome , Mice , Skin Neoplasms/immunologyABSTRACT
We report the draft genome sequence of the mycorrhizal helper bacterium Pseudomonas fluorescens strain BBc6R8. This is the first genome of a mycorrhizal helper bacterium. The draft genome contains 6,952,353 bp and is predicted to encode 6,317 open reading frames. Comparative genomic analyses will help to identify helper traits.