Full author information is available at the end of the article Xuefeng Zang and Qian Wang contributed equally to this work. Hua Zhou, Sanhong Liu and Xinying Xue contributed equally to this work. The members of the COVID-19 Early Prone Position Study Group are listed in the Acknowledgements.

To characterise the genetics of splenic marginal zone lymphoma (SMZL), we performed whole exome sequencing of 16 cases and identified novel recurrent inactivating mutations in Kruppel-like factor 2 (KLF2), a gene whose deficiency was previously shown to cause splenic marginal zone hyperplasia in mice. KLF2 mutation was found in 40 (42%) of 96 SMZLs, but rarely in other B-cell lymphomas. The majority of KLF2 mutations were frameshift indels or nonsense changes, with missense mutations clustered in the C-terminal zinc finger domains. Functional assays showed that these mutations inactivated the ability of KLF2 to suppress NF-κB activation by TLR, BCR, BAFFR and TNFR signalling. Further extensive investigations revealed common and distinct genetic changes between SMZL with and without KLF2 mutation. IGHV1-2 rearrangement and 7q deletion were primarily seen in SMZL with KLF2 mutation, while MYD88 and TP53 mutations were nearly exclusively found in those without KLF2 mutation. NOTCH2, TRAF3, TNFAIP3 and CARD11 mutations were observed in SMZL both with and without KLF2 mutation. Taken together, KLF2 mutation is the most common genetic change in SMZL and identifies a subset with a distinct genotype characterised by multi-genetic changes. These different genetic changes may deregulate various signalling pathways and generate cooperative oncogenic properties, thereby contributing to lymphomagenesis.

Immune-related adverse events (irAEs), caused by anti-PD-1/PD-L1 antibodies, can lead to fulminant and even fatal consequences and thus require early detection and aggressive management. However, a comprehensive approach to identify biomarkers of irAE is lacking. Here, we utilize a strategy that combines pharmacovigilance data and omics data, and evaluate associations between multi-omics factors and irAE reporting odds ratio across different cancer types. We identify a bivariate regression model of LCP1 and ADPGK that can accurately predict irAE. We further validate LCP1 and ADPGK as biomarkers in an independent patient-level cohort. Our approach provides a method for identifying potential biomarkers of irAE in cancer immunotherapy using both pharmacovigilance data and multi-omics data.