Genomic and transcriptomic profiling reveals distinct molecular subsets associated with outcomes in mantle cell lymphoma.

Mantle cell lymphoma (MCL) is a phenotypically and genetically heterogeneous malignancy in which the genetic alterations determining clinical indications are not fully understood. Here, we performed a comprehensive whole-exome sequencing analysis of 152 primary samples derived from 134 MCL patients, including longitudinal samples from 16 patients and matched RNA-Seq data from 48 samples. We classified MCL into 4 robust clusters (C1-C4). C1 featured mutated immunoglobulin heavy variable (IGHV), CCND1 mutation, amp(11q13), and active B cell receptor (BCR) signaling. C2 was enriched with del(11q)/ATM mutations and upregulation of NF-κB and DNA repair pathways. C3 was characterized by mutations in SP140, NOTCH1, and NSD2, with downregulation of BCR signaling and MYC targets. C4 harbored del(17p)/TP53 mutations, del(13q), and del(9p), and active MYC pathway and hyperproliferation signatures. Patients in these 4 clusters had distinct outcomes (5-year overall survival [OS] rates for C1-C4 were 100%, 56.7%, 48.7%, and 14.2%, respectively). We also inferred the temporal order of genetic events and studied clonal evolution of 16 patients before treatment and at progression/relapse. Eleven of these samples showed drastic clonal evolution that was associated with inferior survival, while the other samples showed modest or no evolution. Our study thus identifies genetic subsets that clinically define this malignancy and delineates clonal evolution patterns and their impact on clinical outcomes.

RNA-seq detects pharmacological inhibition of Epstein-Barr virus late transcription during spontaneous reactivation.

The stepwise and sequential expression of viral genes underlies progression of the infectious life cycle. The Epstein-Barr virus (EBV) is both a tractable model for elucidating principles of transcription as well as a global health threat. We describe an experimental protocol and bioinformatics pipeline for functional identification of EBV true late genes, the last step of transcription prior to virion packaging and egress. All data have been uploaded to the Gene Expression Omnibus under accession code GSE96689. The key improvement over previous approaches is leveraging the sensitivity of RNA-seq to detect gene expression changes during spontaneous reactivation.

Epstein-Barr virus latency type and spontaneous reactivation predict lytic induction levels.

The human Epstein-Barr virus (EBV) evades the immune system by entering a transcriptionally latent phase in B cells. EBV in tumor cells expresses distinct patterns of genes referred to as latency types. Viruses in tumor cells also display varying levels of lytic transcription resulting from spontaneous reactivation out of latency. We measured this dynamic range of lytic transcription with RNA deep sequencing and observed no correlation with EBV latency types among genetically different viruses, but type I cell lines reveal more spontaneous reactivation than isogenic type III cultures. We further determined that latency type and spontaneous reactivation levels predict the relative amount of induced reactivation generated by cytotoxic chemotherapy drugs. Our work has potential implications for personalizing medicine against EBV-transformed malignancies. Identifying latency type or measuring spontaneous reactivation may provide predictive power in treatment contexts where viral production should be either avoided or coerced.