No abstract

Therapeutic success of targeted therapy with BRAF inhibitors (BRAFi) for melanoma is limited by resistance development. Observations from preclinical mouse models and recent insights into the immunological effects caused by BRAFi give promise for future development of combination therapy for human melanoma. In our study, we used the transplantable D4M melanoma mouse model with the BRAFV600E mutation and concomitant PTEN loss in order to characterize alterations in tumor‐infiltrating effector immune cells when tumors become resistant to BRAFi. We found that BRAFi‐sensitive tumors displayed a pronounced inflammatory milieu characterized by high levels of cytokines and chemokines accompanied by an infiltration of T and NK cells. The tumor‐infiltrating effector cells were activated and produced high levels of IFN‐γ, TNF‐α and granzyme B. When tumors became resistant and progressively grew, they reverted to a low immunogenic state similar to untreated tumors as reflected by low mRNA levels of proinflammatory cytokines and chemokines and fewer tumor‐infiltrating T and NK cells. Moreover, these T and NK cells were functionally impaired in comparison to their counterparts in BRAFi‐sensitive tumors. Their effector cell function could be restored by additional peritumoral treatment with the TLR7 agonist imiquimod, a clinically approved agent for nonmelanoma skin cancer. Indeed, resistance to BRAFi therapy was delayed and accompanied by high numbers of activated T and NK cells in tumors. Thus, combining BRAFi with an immune stimulating agent such as a TLR ligand could be a promising alternative approach for the treatment of melanoma.

Genome-wide association studies (GWAS) in large biobanks are transforming genetic research and enable the detection of novel genotype-phenotype relationships. In the last two decades, over 60,000 genetic associations across thousands of human diseases and traits have been discovered using a GWAS approach. Due to denser genotyping and increasing sample sizes, researchers are increasingly faced with computational challenges when executing GWAS analysis. A reproducible, modular and extensible pipeline with a focus on parallelization is essential to simplify data analysis and to allow researchers to devote their time to other essential tasks such as result interpretation and downstream analysis. Here we present nf-gwas, a Nextflow pipeline to run biobank-scale GWAS analysis. The pipeline automatically performs numerous pre- and post-processing steps, integrates regression modeling from the REGENIE package and currently supports single-variant, gene-based and interaction testing. nf-gwas also includes an extensive reporting functionality that allows to inspect thousands of phenotypes and navigate interactive Manhattan plots directly in the web browser. The pipeline is extensively tested using the unit-style testing framework nf-test to ensure code maintainability, a crucial requirement in clinical and pharmaceutical settings. Furthermore, we validated the pipeline against published GWAS datasets and benchmarked the pipeline on high-performance computing and cloud infrastructures to provide cost estimations to end users. nf-gwas is free available at https://github.com/genepi/nf-gwas.