Germline Neurofibromin 1 mutation enhances the anti-tumour immune response and decreases juvenile myelomonocytic leukaemia tumourigenicity

NF1 mutations as biomarker of response to immune checkpoint blockades for lung adenocarcinoma patients

Little is known about immune checkpoint inhibitors (ICI) response of NF1-mutated lung adenocarcinomas. 341/4,181 (8.2%) TCGA lung adenocarcinomas samples have a somatic NF1 mutation. NF1-mutated tumors have higher TMB (p < 0.0001), higher expression of immune genes ("hot phenotype") and higher CD8 + T cell (p = 0.03) and macrophage (p = 0.02) infiltrations compared to NF1 wild-type tumors. NF1 mutation status appears as a candidate predictive biomarker for ICI response in lung adenocarcinoma patients.

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.