Gu, Zhang, Wang, and Jiang et al. performed a genome-wide CRISPR knockout library screen and FACS-sorting to identify cells that upregulate expression of MHC-I, but not PD-L1. TRAF3 was detected as a top negative MHC-I regulator, functioning by inhibiting NF-κB signaling. A TRAF3 deficiency gene signature in human datasets correlated with survival, immune infiltration, and checkpoint therapy response. SMAC mimetics were identified as drugs mimicking the transcriptional effects of TRAF3 deficiency. Birinapant upregulated MHC-I, but not PD-L1, in vitro and increased checkpoint blockade efficacy in the B16F10 melanoma mouse model.
Contributed by Maartje Wouters
ABSTRACT: Immune checkpoint blockade (ICB) therapy revolutionized cancer treatment, but many patients with impaired MHC-I expression remain refractory. Here, we combined FACS-based genome-wide CRISPR screens with a data-mining approach to identify drugs that can upregulate MHC-I without inducing PD-L1. CRISPR screening identified TRAF3, a suppressor of the NF-kB pathway, as a negative regulator of MHC-I but not PD-L1. The Traf3-knockout (Traf3-KO) gene expression signature is associated with better survival in ICB-naive cancer patients and better ICB response. We then screened for drugs with similar transcriptional effects as this signature and identified SMAC mimetics. We experimentally validated that the SMAC mimetic birinapant upregulates MHC-I, sensitizes cancer cells to T-cell-dependent killing, and adds to ICB efficacy. Our findings provide preclinical rationale for treating tumors expressing low MHC-I expression with SMAC mimetics to enhance sensitivity to immunotherapy. The approach used in this study can be generalized to identify other drugs that enhance immunotherapy efficacy.
Author Info: (1) Dana-Farber Cancer Institute. (2) Department of Bioinformatics, Tongji University. (3) Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harv
Author Info: (1) Dana-Farber Cancer Institute. (2) Department of Bioinformatics, Tongji University. (3) Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard School of Public Health. (4) Center for Cancer Research, National Cancer Institute. (5) Graduate School of Biomedical Science, Tufts University. (6) School of Life Sciences and Technology, Tongji University. (7) Dana-Farber Cancer Institute. (8) Dana-Farber Cancer Institute. (9) Medical Oncology, Dana-Farber Cancer Institute. (10) Medical Oncology, Dana-Farber Cancer Institute. (11) Medical Oncology, Dana-Farber Cancer Institute. (12) Dana-Farber Cancer Institute. (13) Pathology and Center for Immuno-Oncology, Dana-Farber Cancer Institute. (14) Dana-Farber Cancer Institute. (15) Medical Oncology, Dana-Farber Cancer Institute. (16) Dana-Farber Cancer Institute. (17) Medical Oncology, Dana-Farber Cancer Institute. (18) Dana-Farber Cancer Institute. (19) Dana-Farber Cancer Institute. (20) Dana-Farber Cancer Institute. (21) Data Sciences, Dana-Farber Cancer Institute, Harvard Medical School. (22) School of Life Science and Technology, Tongji University. (23) Data Science, Dana-Farber Cancer Institute. (24) Medical School, The University of Texas Southwestern Medical Center. (25) Brigham and Women's Hospital. (26) Medical Oncology, Dana-Farber Cancer Institute. (27) Department of Medical Oncology, Dana-Farber Cancer Institute. (28) Pathology/ Center for Immuno-Oncology, Dana-Farber Cancer Institute. (29) Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute. (30) Medicine, Infectious Disease, Brigham and Women's Hospital. (31) Medical Oncology, Dana-Farber Cancer Institute. (32) Department of Medical Oncology, Dana-Farber Cancer Institute. (33) Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard School of Public Health xsliu@jimmy.harvard.edu.
