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Purpose: Co-occurring mutations in KEAP1 and STK11/KRAS have emerged as determinants of survival outcomes in patients with non-small cell lung cancer (NSCLC) treated with immunotherapy. However, these mutational contexts identify a fraction of nonresponders to immune checkpoint inhibitors. We hypothesized that KEAP1 wild-type tumors recapitulate the transcriptional footprint of KEAP1 mutations and that this KEAPness phenotype can determine immune responsiveness with higher precision compared to mutation-based models., Experimental Design: The Cancer Genome Atlas was used to infer the KEAPness phenotype and explore its immunological correlates at the pan-cancer level. The association between KEAPness and survival outcomes was tested in two independent cohorts of patients with advanced NSCLC treated with immunotherapy and profiled by RNA sequencing (SU2C n = 153; OAK/POPLAR n = 439). The NSCLC TRACERx421 multiregion sequencing study (tumor regions, n = 947) was used to investigate evolutionary trajectories., Results: KEAPness-dominant tumors represented 50% of all NSCLCs and were associated with shorter progression-free survival (PFS) and overall survival (OS) compared to KEAPness-free cases in independent cohorts of patients with NSCLC treated with immunotherapy (SU2C PFS P = 0.042, OS P = 0.008; OAK/POPLAR PFS P = 0.0014, OS P < 0.001). Patients with KEAPness tumors had survival outcomes comparable to those with KEAP1-mutant tumors. In the TRACERx421, KEAPness exhibited limited transcriptional intratumoral heterogeneity and immune exclusion, resembling the KEAP1-mutant disease. This phenotypic state occurred across genetically divergent tumors, exhibiting shared and private cancer genes under positive selection when compared to KEAP1-mutant tumors., Conclusions: We identified a KEAPness phenotype across evolutionary divergent tumors. KEAPness outperforms mutation-based classifiers as a biomarker of inferior survival outcomes in patients with NSCLC treated with immunotherapy., (©2024 American Association for Cancer Research.)

Background : this study aims to investigate the accuracy and completeness of ChatGPT in answering questions and solving clinical scenarios of interceptive orthodontics. Materials and Methods : ten specialized orthodontists from ten Italian postgraduate orthodontics schools developed 21 clinical open-ended questions encompassing all of the subspecialities of interceptive orthodontics and 7 comprehensive clinical cases. Questions and scenarios were inputted into ChatGPT4, and the resulting answers were evaluated by the researchers using predefined accuracy (range 1-6) and completeness (range 1-3) Likert scales. Results : For the open-ended questions, the overall median score was 4.9/6 for the accuracy and 2.4/3 for completeness. In addition, the reviewers rated the accuracy of open-ended answers as entirely correct (score 6 on Likert scale) in 40.5% of cases and completeness as entirely correct (score 3 n Likert scale) in 50.5% of cases. As for the clinical cases, the overall median score was 4.9/6 for accuracy and 2.5/3 for completeness. Overall, the reviewers rated the accuracy of clinical case answers as entirely correct in 46% of cases and the completeness of clinical case answers as entirely correct in 54.3% of cases. Conclusions : The results showed a high level of accuracy and completeness in AI responses and a great ability to solve difficult clinical cases, but the answers were not 100% accurate and complete. ChatGPT is not yet sophisticated enough to replace the intellectual work of human beings.