Chen, Dung‐Tsa

e21016 Background: BRAF mutations are grouped (class I, II or III) according to kinase activity, dependence on RAS, and dimerization. BRAF class I mutations are targeted with BRAF/MEK inhibitors. The management of class II/III mutations has not been fully elucidated. We aimed to further characterize BRAF-mutant NSCLC. Methods: Moffitt patients with BRAF-mutant NSCLC were identified through retrospective review of a clinical molecular database and electronic health record from 1/1/2012 - 03/31/2020. Demographics, PD-L1 expression, responses to chemotherapy/immunotherapy, and overall survival were collected. The correlation between BRAF mutation classes and these variables was determined using a pairwise comparison of p-values and corrected for testing using the Benjamini-Hochberg method. For survival analysis, we used the Kaplan-Meier method with log-rank test. Results: 153 patients were included. Table summarizes the demographic data. 14 evaluable patients with class I mutations received anti-BRAF therapy. The DCR was 79% (11/14) and 83% when PD-L1 ≥1% (5/6). 36 response-evaluable patients received immunotherapy. The DCR was 79% (11/14) for patients with class I, 69% (9/13) for class II, and 78% (7/9) for class III mutations (p=0.89). The DCR was 73% in those with PD-L1 ≥1% (22/30) and 44% in those with PD-L1 <1% (7/16; p= 0.02). 43 response-evaluable patients received chemotherapy. The DCR was 86% (18/21) for patients with class I, 69% (9/13) for class II, and 78% (7/9) for class III mutations (p=0.49). The mOS was 26.2 months for all patients. The mOS was 31.2 months for class I, 15.6 months for class II, 28.8 months for class III, 34.8 months for AMP, and 24 months for VUS (p=0.6). Conclusions: To our knowledge, this is the largest retrospective study of patients with BRAF-mutant NSCLC. BRAFmutations were more common in patients with adenocarcinoma and those with a smoking history. PD-L1 levels ≥1% were seen more frequently in all BRAF-mutant patients. Class I mutations were significantly more common than other mutations and responded well to BRAF-targeted therapy, particularly when PD-L1 ≥1%. All BRAF-mutant classes responded similarly to immunotherapy and better responses were seen among those with PD-L1 ≥1%. Responses to chemotherapy were similar across all classes. Improved OS in class I-mutant disease was seen. These findings were not statistically significant. Additional research is needed to better characterize these patients to individualize management and improve outcomes.[Table: see text]

AbstractMotivationThe use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP–SNP interactions.ResultsWe tested two candidate approaches: the ‘Five-Full’ and ‘AA9int’ method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP–SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP–SNP interactions in large-scale studies.Availability and implementationThe ‘AA9int’ and ‘parAA9int’ functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/.Supplementary informationSupplementary data are available at Bioinformatics online.