The Genomic Landscape of SMARCA4 Alterations and Associations with Outcomes in Patients with Lung Cancer.

PURPOSE: SMARCA4 mutations are among the most common recurrent alterations in non-small cell lung cancer (NSCLC), but the relationship to other genomic abnormalities and clinical impact has not been established. EXPERIMENTAL DESIGN: To characterize SMARCA4 alterations in NSCLC, we analyzed the genomic, protein expression, and clinical outcome data of patients with SMARCA4 alterations treated at Memorial Sloan Kettering. RESULTS: In 4,813 tumors from patients with NSCLC, we identified 8% (n = 407) of patients with SMARCA4-mutant lung cancer. We describe two categories of SMARCA4 mutations: class 1 mutations (truncating mutations, fusions, and homozygous deletion) and class 2 mutations (missense mutations). Protein expression loss was associated with class 1 mutation (81% vs. 0%, P < 0.001). Both classes of mutation co-occurred more frequently with KRAS, STK11, and KEAP1 mutations compared with SMARCA4 wild-type tumors (P < 0.001). In patients with metastatic NSCLC, SMARCA4 alterations were associated with shorter overall survival, with class 1 alterations associated with shortest survival times (P < 0.001). Conversely, we found that treatment with immune checkpoint inhibitors (ICI) was associated with improved outcomes in patients with SMARCA4-mutant tumors (P = 0.01), with class 1 mutations having the best response to ICIs (P = 0.027). CONCLUSIONS: SMARCA4 alterations can be divided into two clinically relevant genomic classes associated with differential protein expression as well as distinct prognostic and treatment implications. Both classes co-occur with KEAP1, STK11, and KRAS mutations, but individually represent independent predictors of poor prognosis. Despite association with poor outcomes, SMARCA4-mutant lung cancers may be more sensitive to immunotherapy.

Bionimbus: a cloud for managing, analyzing and sharing large genomics datasets.

BACKGROUND: As large genomics and phenotypic datasets are becoming more common, it is increasingly difficult for most researchers to access, manage, and analyze them. One possible approach is to provide the research community with several petabyte-scale cloud-based computing platforms containing these data, along with tools and resources to analyze it. METHODS: Bionimbus is an open source cloud-computing platform that is based primarily upon OpenStack, which manages on-demand virtual machines that provide the required computational resources, and GlusterFS, which is a high-performance clustered file system. Bionimbus also includes Tukey, which is a portal, and associated middleware that provides a single entry point and a single sign on for the various Bionimbus resources; and Yates, which automates the installation, configuration, and maintenance of the software infrastructure required. RESULTS: Bionimbus is used by a variety of projects to process genomics and phenotypic data. For example, it is used by an acute myeloid leukemia resequencing project at the University of Chicago. The project requires several computational pipelines, including pipelines for quality control, alignment, variant calling, and annotation. For each sample, the alignment step requires eight CPUs for about 12 h. BAM file sizes ranged from 5 GB to 10 GB for each sample. CONCLUSIONS: Most members of the research community have difficulty downloading large genomics datasets and obtaining sufficient storage and computer resources to manage and analyze the data. Cloud computing platforms, such as Bionimbus, with data commons that contain large genomics datasets, are one choice for broadening access to research data in genomics.