is the most common oncogenic driver in lung adenocarcinoma (LUAC). We previously reported that (KL) or (KP) comutations define distinct subgroups of -mutant LUAC. Here, we examine the efficacy of PD-1 inhibitors in these subgroups. Objective response rates to PD-1 blockade differed significantly among KL (7.4%), KP (35.7%), and K-only (28.6%) subgroups ( < 0.001) in the Stand Up To Cancer (SU2C) cohort (174 patients) with -mutant LUAC and in patients treated with nivolumab in the CheckMate-057 phase III trial (0% vs. 57.1% vs. 18.2%; = 0.047). In the SU2C cohort, KL LUAC exhibited shorter progression-free ( < 0.001) and overall ( = 0.0015) survival compared with ; LUAC. Among 924 LUACs, alterations were the only marker significantly associated with PD-L1 negativity in TMB LUAC. The impact of alterations on clinical outcomes with PD-1/PD-L1 inhibitors extended to PD-L1-positive non-small cell lung cancer. In-mutant murine LUAC models, loss promoted PD-1/PD-L1 inhibitor resistance, suggesting a causal role. Our results identify alterations as a major driver of primary resistance to PD-1 blockade in -mutant LUAC. This work identifies alterations as the most prevalent genomic driver of primary resistance to PD-1 axis inhibitors in-mutant lung adenocarcinoma. Genomic profiling may enhance the predictive utility of PD-L1 expression and tumor mutation burden and facilitate establishment of personalized combination immunotherapy approaches for genomically defined LUAC subsets. .

Traditional pathology approaches have played an integral role in the delivery of diagnosis, semi-quantitative or qualitative assessment of protein expression, and classification of disease. Technological advances and the increased focus on precision medicine have recently paved the way for the development of digital pathology-based approaches for quantitative pathologic assessments, namely whole slide imaging and artificial intelligence (AI)–based solutions, allowing us to explore and extract information beyond human visual perception. Within the field of immuno-oncology, the application of such methodologies in drug development and translational research have created invaluable opportunities for deciphering complex pathophysiology and the discovery of novel biomarkers and drug targets. With an increasing number of treatment options available for any given disease, practitioners face the growing challenge of selecting the most appropriate treatment for each patient. The ever-increasing utilization of AI-based approaches substantially expands our understanding of the tumor microenvironment, with digital approaches to patient stratification and selection for diagnostic assays supporting the identification of the optimal treatment regimen based on patient profiles. This review provides an overview of the opportunities and limitations around implementing AI-based methods in biomarker discovery and patient selection and discusses how advances in digital pathology and AI should be considered in the current landscape of translational medicine, touching on challenges this technology may face if adopted in clinical settings. The traditional role of pathologists in delivering accurate diagnoses or assessing biomarkers for companion diagnostics may be enhanced in precision, reproducibility, and scale by AI-powered analysis tools.

Background: Mutations in BRAF have recently been identified in a significant percentage of primary and metastatic cutaneous malignant melanomas. As ultraviolet (UV) exposure may play a role in the development of cutaneous melanoma lesions with BRAF mutations, BRAF mutation frequency in melanomas arising in sites protected from sun exposure may be lower than those from sun-exposed areas. Thus, we determined the BRAF mutation frequency in a panel of 13 mucosal melanomas and compared those data with data from all currently published series of cutaneous melanomas. Methods: BRAF exon 15 DNA from 13 archival primary mucosal melanomas (eight vulvar, four anorectal, and one laryngeal) was sequenced using intron-based primers. As archival DNA occasionally produces poor-quality template, results were confirmed with a TspRI restriction fragment length polymorphism (RFLP) that distinguishes wild-type BRAF from the common mutant form V599E. A binomial test was used to compare the mutation frequency in the mucosal melanomas with the published mutation frequency in cutaneous melanomas. Results: None of the 13 mucosal melanomas in this series had an exon 15 BRAF mutation, as compared to 54/165 (33%) primary cutaneous melanomas with BRAF mutations in a compilation of all current published studies (p = 0.006). Discussion: These data suggest that UV exposure, plays a role in the genesis of BRAF mutations in cutaneous melanoma, despite the absence of the characteristic C.T or CC.TT mutation signature associated with UV exposure, and suggests mechanisms other than pyrimidine dimer formation are important in UV-induced mutagenesis.