Molecular and clinical diversity in primary central nervous system lymphoma.

Primary central nervous system lymphoma (PCNSL) is a rare and distinct entity within diffuse large B-cell lymphoma presenting with variable response rates probably to underlying molecular heterogeneity. To identify and characterize PCNSL heterogeneity and facilitate clinical translation, we carried out a comprehensive multi-omic analysis [whole-exome sequencing, RNA sequencing (RNA-seq), methylation sequencing, and clinical features] in a discovery cohort of 147 fresh-frozen (FF) immunocompetent PCNSLs and a validation cohort of formalin-fixed, paraffin-embedded (FFPE) 93 PCNSLs with RNA-seq and clinico-radiological data. Consensus clustering of multi-omic data uncovered concordant classification of four robust, non-overlapping, prognostically significant clusters (CS). The CS1 and CS2 groups presented an immune-cold hypermethylated profile but a distinct clinical behavior. The 'immune-hot' CS4 group, enriched with mutations increasing the Janus kinase (JAK)–signal transducer and activator of transcription (STAT) and nuclear factor-κB activity, had the most favorable clinical outcome, while the heterogeneous-immune CS3 group had the worse prognosis probably due to its association with meningeal infiltration and enriched HIST1H1E mutations. CS1 was characterized by high Polycomb repressive complex 2 activity and CDKN2A/B loss leading to higher proliferation activity. Integrated analysis on proposed targets suggests potential use of immune checkpoint inhibitors/JAK1 inhibitors for CS4, cyclin D-Cdk4,6 plus phosphoinositide 3-kinase (PI3K) inhibitors for CS1, lenalidomide/demethylating drugs for CS2, and enhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2) inhibitors for CS3. We developed an algorithm to identify the PCNSL subtypes using RNA-seq data from either FFPE or FF tissue. The integration of genome-wide data from multi-omic data revealed four molecular patterns in PCNSL with a distinctive prognostic impact that provides a basis for future clinical stratification and subtype-based targeted interventions. • Multi-omic data consensus clustering reveals four molecular subtypes of PCNSL with prognostic relevance. • RNA-seq data alone and a publicly accessible algorithm can be used to assign the multi-omic defined PCNSL subtypes. • PCNSL molecular subtyping can improve future clinical stratification and suggest rational subtype-based targeted therapies. [ABSTRACT FROM AUTHOR]