Tertiary lymphoid structures harbour stem-like tumour-specific T cells
(1) Afeyan AB (2) Nagler A (3) Tu CR (4) Roberti De Oliveira G (5) Simsek B (6) Seager MD (7) El Ahmar N (8) Sax HE (9) Lin E (10) Sud A (11) Borji M (12) Forman C (13) Liu S (14) Ott PA (15) Choueiri TK (16) Abelin JG (17) Burack R (18) Li S (19) Livak KJ (20) Tyekucheva S (21) Keskin DB (22) Chen F (23) Atkins MB (24) Simon JM (25) Signoretti S (26) Oliveira G (27) Braun DA (28) Wu CJ
Afeyan, Nagler, and Tu et al. investigated how tertiary lymphoid structures (TLSs) impact phenotypic properties of tumor-specific T cells in treatment-naive renal carcinoma (RCC). Compared to TLS- tumors, TLS+ tumors were associated with increased numbers of exhausted CD8+ T cells (Tex) as well as IFN-stimulated CD8+ T cells and proliferating T cells, although CD8+ T cells exhibited greater exhaustion in TLS- tumors. 12% of tumor-specific clonotypes in TLSs exhibited a stem-like progenitor exhausted phenotype (Tpex) associated with favorable antitumor immunity. CD163+ immunosuppressive TAMs mapped to TLS boundaries with Tex cells.
Contributed by Katherine Turner
(1) Afeyan AB (2) Nagler A (3) Tu CR (4) Roberti De Oliveira G (5) Simsek B (6) Seager MD (7) El Ahmar N (8) Sax HE (9) Lin E (10) Sud A (11) Borji M (12) Forman C (13) Liu S (14) Ott PA (15) Choueiri TK (16) Abelin JG (17) Burack R (18) Li S (19) Livak KJ (20) Tyekucheva S (21) Keskin DB (22) Chen F (23) Atkins MB (24) Simon JM (25) Signoretti S (26) Oliveira G (27) Braun DA (28) Wu CJ
Afeyan, Nagler, and Tu et al. investigated how tertiary lymphoid structures (TLSs) impact phenotypic properties of tumor-specific T cells in treatment-naive renal carcinoma (RCC). Compared to TLS- tumors, TLS+ tumors were associated with increased numbers of exhausted CD8+ T cells (Tex) as well as IFN-stimulated CD8+ T cells and proliferating T cells, although CD8+ T cells exhibited greater exhaustion in TLS- tumors. 12% of tumor-specific clonotypes in TLSs exhibited a stem-like progenitor exhausted phenotype (Tpex) associated with favorable antitumor immunity. CD163+ immunosuppressive TAMs mapped to TLS boundaries with Tex cells.
Contributed by Katherine Turner
ABSTRACT: Tertiary lymphoid structures (TLSs) are associated with improved responses to immune checkpoint blockade across solid tumours(1,2), but how they impact the phenotypic properties of tumour-specific T cells remains unclear. Here we found, across 24 treatment-naive renal cell carcinoma (RCC) tumours, that TLS-containing tumours are more heavily infiltrated by exhausted CD8(+) T cells and have a reduced terminal exhaustion transcriptional program compared with TLS(-) tumours. Specificity screening of 554 T cell clonotypes expanded within the microenvironment of 6 RCC tumours revealed 82 TCRs that were reactive against tumour cells and/or RCC antigens. A subset of tumour-specific T cell clonotypes (12%) was enriched within TLSs, and these expressed an increased program of stem-like progenitor exhaustion, associated with favourable anti-tumour immunity. However, in 60 independent RCC tumours, macrophages within tumour margins of TLS-containing tumours had an inferred immunosuppressive phenotype and were colocalized with exhausted putative tumour-reactive T cells in a subgroup that was further analysed, therefore supporting this mode of immune evasion as a counterbalance to T cell immune pressure. Our data reveal that TLSs are reservoirs of tumour-specific T cells with stem-like progenitor features that could be leveraged by T cell immunotherapies.
Author Info:
(1) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Harvard Medical School, Boston, MA, USA. (2) Department of Medical Oncology, Dana-Farber Cancer I
nstitute, Boston, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. (3) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA. (4) Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA. (5) Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA. (6) Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA. (7) Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA. (8) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. (9) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. (10) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Harvard Medical School, Boston, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, UK. (11) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. (12) Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA. (13) Broad Institute of MIT and Harvard, Cambridge, MA, USA. Ragon Institute of MGB, MIT and Harvard, Cambridge, MA, USA. (14) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Harvard Medical School, Boston, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. (15) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Harvard Medical School, Boston, MA, USA. (16) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. (17) University of Rochester Medical Center, Department of Pathology and Laboratory Medicine, Rochester, NY, USA. (18) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA, USA. (19) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA, USA. (20) Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA. (21) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Harvard Medical School, Boston, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA, USA. Department of Computer Science, Metropolitan College, Boston University, Boston, MA, USA. Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Lyngby, Denmark. (22) Broad Institute of MIT and Harvard, Cambridge, MA, USA. (23) Georgetown Lombardi Comprehensive Cancer Center, Washington, DC, USA. (24) Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA. Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA. (25) Harvard Medical School, Boston, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA. Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, MA, USA. (26) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. giacomo_oliveira@dfci.harvard.edu. Harvard Medical School, Boston, MA, USA. giacomo_oliveira@dfci.harvard.edu. Broad Institute of MIT and Harvard, Cambridge, MA, USA. giacomo_oliveira@dfci.harvard.edu. (27) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. david.braun@yale.edu. Section of Medical Oncology, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA. david.braun@yale.edu. Center of Molecular and Cellular Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA. david.braun@yale.edu. (28) Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. catherine_wu@dfci.harvard.edu. Harvard Medical School, Boston, MA, USA. catherine_wu@dfci.harvard.edu. Broad Institute of MIT and Harvard, Cambridge, MA, USA. catherine_wu@dfci.harvard.edu.
