Jaiswal et al. applied a systems biology approach to multiple single-cell and bulk RNAseq datasets to examine CD8+ TIL differentiation status, kinetic trajectories, and relatedness to patient survival and ICB response. A key conclusion was that current reliance on a small number of transcripts to determine activation status has masked complexities of TIL differentiation that are ICB-response relevant. For example, immune checkpoints conflate “exhausted” and “early activated” cells, and it is the latter which negatively correlate with response. Generation of a memory phenotype, including resident memory, is critical to positive outcomes.
Contributed by Margot O’Toole
ABSTRACT: There is a need for better classification and understanding of tumor-infiltrating lymphocytes (TILs). Here, we applied advanced functional genomics to interrogate 9,000 human tumors and multiple single-cell sequencing sets using benchmarked T cell states, comprehensive T cell differentiation trajectories, human and mouse vaccine responses, and other human TILs. Compared with other T cell states, enrichment of T memory/resident memory programs was observed across solid tumors. Trajectory analysis of single-cell melanoma CD8(+) TILs also identified a high fraction of memory/resident memory-scoring TILs in anti-PD-1 responders, which expanded post therapy. In contrast, TILs scoring highly for early T cell activation, but not exhaustion, associated with non-response. Late/persistent, but not early activation signatures, prognosticate melanoma survival, and co-express with dendritic cell and IFN-_ response programs. These data identify an activation-like state associated to poor response and suggest successful memory conversion, above resuscitation of exhaustion, is an under-appreciated aspect of successful anti-tumoral immunity.
Author Info: (1) Department of Dermatology, Weill Cornell Medicine, New York, NY 10026, USA; Immunology and Microbial Pathogenesis Program, Weill Cornell Medicine, New York, NY 10026, USA. (2)
Author Info: (1) Department of Dermatology, Weill Cornell Medicine, New York, NY 10026, USA; Immunology and Microbial Pathogenesis Program, Weill Cornell Medicine, New York, NY 10026, USA. (2) Institute for Computational Biomedicine, Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA. (3) Department of Computer Science and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA. (4) Division of Surgical Oncology - Breast and Melanoma Surgery, Department of Surgery, Human Immune Therapy Center, Cancer Center, University of Virginia, Charlottesville, VA 22908, USA; Carter Immunology Center, Department of Microbiology, Immunology, and Cancer Biology, University of Virginia School of Medicine, Charlottesville, VA 22908, USA. (5) Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel. (6) Department of Medicine, Division of Hematology/Oncology, Herbert Irving Comprehensive Cancer Center, Columbia Center for Translational Immunology and Program for Mathematical Genomics, Columbia University, New York, NY 10032, USA. (7) Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul, South Korea. (8) Department of Dermatology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA. (9) Department of Dermatology, Weill Cornell Medicine, New York, NY 10026, USA. (10) Division of Surgical Oncology - Breast and Melanoma Surgery, Department of Surgery, Human Immune Therapy Center, Cancer Center, University of Virginia, Charlottesville, VA 22908, USA. (11) Division of Surgical Oncology - Breast and Melanoma Surgery, Department of Surgery, Human Immune Therapy Center, Cancer Center, University of Virginia, Charlottesville, VA 22908, USA; Carter Immunology Center, Department of Microbiology, Immunology, and Cancer Biology, University of Virginia School of Medicine, Charlottesville, VA 22908, USA. (12) Carter Immunology Center, Department of Microbiology, Immunology, and Cancer Biology, University of Virginia School of Medicine, Charlottesville, VA 22908, USA. (13) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02115, USA; Center for Cancer for Cancer Precision Medicine, Boston, MA 02115, USA; Brigham and Women's Hospital, Boston, MA 02115, USA. (14) Broad Institute of MIT and Harvard, Cambridge, MA, USA. (15) Department of Dermatology, Weill Cornell Medicine, New York, NY 10026, USA. (16) Brigham and Women's Hospital, Department of Surgical Oncology Harvard Medical School, Boston, MA 02115, USA. (17) Department of Computer Science and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA; Simons Center for Data Analysis, Simons Foundation, New York, NY 10010, USA. (18) Institute for Computational Biomedicine, Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA. (19) Department of Genetics and Genomic Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (20) Department of Dermatology, Weill Cornell Medicine, New York, NY 10026, USA; Immunology and Microbial Pathogenesis Program, Weill Cornell Medicine, New York, NY 10026, USA; Institute for Computational Biomedicine, Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA; Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10026, USA; Department of Microbiology and Immunology, Weill Cornell Medicine, New York, NY 10026, USA. Electronic address: nia9069@med.cornell.edu.
