Takahiro Karasaki1, Kazuhiro Nagayama1, Hideki Kuwano1, Jun-Ichi Nitadori1, Masaaki Sato1, Masaki Anraku1, Akihiro Hosoi2, Hirokazu Matsushita3, Yasuyuki Morishita4, Kosuke Kashiwabara5, Masaki Takazawa6, Osamu Ohara6, Kazuhiro Kakimi7, Jun Nakajima1. 1. Department of Thoracic Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. 2. Department of Immunotherapeutics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Medinet Co. Ltd., Yokohama, Japan. 3. Department of Immunotherapeutics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. 4. Department of Molecular Pathology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. 5. Department of Biostatistics, School of Public Health, The University of Tokyo, Tokyo, Japan. 6. Department of Technology Development, Kazusa DNA Research Institute, Kisarazu, Japan. 7. Department of Immunotherapeutics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. Electronic address: kakimi@m.u-tokyo.ac.jp.
Abstract
INTRODUCTION: The interaction of immune cells and cancer cells shapes the immunosuppressive tumor microenvironment. For successful cancer immunotherapy, comprehensive knowledge of antitumor immunity as a dynamic spatiotemporal process is required for each individual patient. To this end, we developed an immunogram for the cancer-immunity cycle by using next-generation sequencing. METHODS: Whole exome sequencing and RNA sequencing were performed in 20 patients with NSCLC (12 with adenocarcinoma, seven with squamous cell carcinoma, and one with large cell neuroendocrine carcinoma). Mutated neoantigens and cancer germline antigens expressed in the tumor were assessed for predicted binding to patients' human leukocyte antigen molecules. The expression of genes related to cancer immunity was assessed and normalized to construct a radar chart composed of eight axes reflecting seven steps in the cancer-immunity cycle. RESULTS: Three immunogram patterns were observed in patients with lung cancer: T-cell-rich, T-cell-poor, and intermediate. The T-cell-rich pattern was characterized by gene signatures of abundant T cells, regulatory T cells, myeloid-derived suppressor cells, checkpoint molecules, and immune-inhibitory molecules in the tumor, suggesting the presence of antitumor immunity dampened by an immunosuppressive microenvironment. The T-cell-poor phenotype reflected lack of antitumor immunity, inadequate dendritic cell activation, and insufficient antigen presentation in the tumor. Immunograms for both the patients with adenocarcinoma and the patients with nonadenocarcinoma tumors included both T-cell-rich and T-cell-poor phenotypes, suggesting that histologic type does not necessarily reflect the cancer immunity status of the tumor. CONCLUSIONS: The patient-specific landscape of the tumor microenvironment can be appreciated by using immunograms as integrated biomarkers, which may thus become a valuable resource for optimal personalized immunotherapy.
INTRODUCTION: The interaction of immune cells and cancer cells shapes the immunosuppressive tumor microenvironment. For successful cancer immunotherapy, comprehensive knowledge of antitumor immunity as a dynamic spatiotemporal process is required for each individual patient. To this end, we developed an immunogram for the cancer-immunity cycle by using next-generation sequencing. METHODS: Whole exome sequencing and RNA sequencing were performed in 20 patients with NSCLC (12 with adenocarcinoma, seven with squamous cell carcinoma, and one with large cell neuroendocrine carcinoma). Mutated neoantigens and cancer germline antigens expressed in the tumor were assessed for predicted binding to patients' human leukocyte antigen molecules. The expression of genes related to cancer immunity was assessed and normalized to construct a radar chart composed of eight axes reflecting seven steps in the cancer-immunity cycle. RESULTS: Three immunogram patterns were observed in patients with lung cancer: T-cell-rich, T-cell-poor, and intermediate. The T-cell-rich pattern was characterized by gene signatures of abundant T cells, regulatory T cells, myeloid-derived suppressor cells, checkpoint molecules, and immune-inhibitory molecules in the tumor, suggesting the presence of antitumor immunity dampened by an immunosuppressive microenvironment. The T-cell-poor phenotype reflected lack of antitumor immunity, inadequate dendritic cell activation, and insufficient antigen presentation in the tumor. Immunograms for both the patients with adenocarcinoma and the patients with nonadenocarcinoma tumors included both T-cell-rich and T-cell-poor phenotypes, suggesting that histologic type does not necessarily reflect the cancer immunity status of the tumor. CONCLUSIONS: The patient-specific landscape of the tumor microenvironment can be appreciated by using immunograms as integrated biomarkers, which may thus become a valuable resource for optimal personalized immunotherapy.
Authors: Cesare Gridelli; Andrea Ardizzoni; Massimo Barberis; Federico Cappuzzo; Francesca Casaluce; Romano Danesi; Giancarlo Troncone; Filippo De Marinis Journal: Transl Lung Cancer Res Date: 2017-06
Authors: Nick van Dijk; Samuel A Funt; Christian U Blank; Thomas Powles; Jonathan E Rosenberg; Michiel S van der Heijden Journal: Eur Urol Date: 2018-09-28 Impact factor: 20.096