Immune Signatures Combined With BRCA1-Associated Protein 1 Mutations Predict Prognosis and Immunotherapy Efficacy in Clear Cell Renal Cell Carcinoma

BAP1 maintains HIF-dependent interferon beta induction to suppress tumor growth in clear cell renal cell carcinoma

BRCA1-associated protein 1 (BAP1) is a deubiquitinase that is mutated in 10-15% of clear cell renal cell carcinomas (ccRCC). Despite the association between BAP1 loss and poor clinical outcome, the critical tumor suppressor function(s) of BAP1 in ccRCC remains unclear. Previously, we found that hypoxia-inducible factor 2α (HIF2α) and BAP1 activate interferon-stimulated gene factor 3 (ISGF3), a transcription factor activated by type I interferons and a tumor suppressor in ccRCC xenograft models. Here, we aimed to determine the mechanism(s) through which HIF and BAP1 regulate ISGF3. We found that in ccRCC cells, loss of the von Hippel-Lindau tumor suppressor (VHL) activated interferon beta (IFN-β) expression in a HIF2α-dependent manner. IFN-β was required for ISGF3 activation and suppressed the growth of Ren-02 tumors in xenografts. BAP1 enhanced the expression of IFN-β and stimulator of interferon genes (STING), both of which activate ISGF3. Both ISGF3 overexpression and STING agonist treatment increased ISGF3 activity and suppressed BAP1-deficient tumor growth in Ren-02 xenografts. Our results indicate that BAP1 loss reduces type I interferon signaling, and reactivating this pathway may be a novel therapeutic strategy for treating ccRCC.

Predicting prognosis and immunotherapeutic response of clear cell renal cell carcinoma

Immune checkpoint inhibitors have emerged as a novel therapeutic strategy for many different tumors, including clear cell renal cell carcinoma (ccRCC). However, these drugs are only effective in some ccRCC patients, and can produce a wide range of immune-related adverse reactions. Previous studies have found that ccRCC is different from other tumors, and common biomarkers such as tumor mutational burden, HLA type, and degree of immunological infiltration cannot predict the response of ccRCC to immunotherapy. Therefore, it is necessary to further research and construct corresponding clinical prediction models to predict the efficacy of Immune checkpoint inhibitors. We integrated PBRM1 mutation data, transcriptome data, endogenous retrovirus data, and gene copy number data from 123 patients with advanced ccRCC who participated in prospective clinical trials of PD-1 inhibitors (including CheckMate 009, CheckMate 010, and CheckMate 025 trials). We used AI to optimize mutation data interpretation and established clinical prediction models for survival (for overall survival AUC: 0.931; for progression-free survival AUC: 0.795) and response (ORR AUC: 0.763) to immunotherapy of ccRCC. The models were internally validated by bootstrap. Well-fitted calibration curves were also generated for the nomogram models. Our models showed good performance in predicting survival and response to immunotherapy of ccRCC.