Strand-Specific Total RNA Sequencing Establishes the Complete Transcriptome and Alternative Splicing Repertoire in Diffuse Large B Cell Lymphoma

Background: Diffuse Large B Cell Lymphoma (DLBCL) is the most common form of lymphoma in adults. Gene expression profiling has demonstrated that DLBCL can be classified into two distinct subgroups – activated B-cell-like (ABC) and germinal center B-cell-like (GCB) DLBCL. These subgroups arise through distinct normal cells of origin, activate different oncogenic pathways and display markedly different clinical outcomes. Deregulation of the transcriptome is believed to play a key role in the malignant transformation of B cells that culminates in the development of either ABC or GCB DLBCL. Here we describe global differences in RNA expression, mutation and splicing in relation to the pathogenesis of these subgroups of DLBCL.

Methods: RNA sequencing (RNAseq) has emerged as a powerful tool for defining the cancer transcriptome. While mRNA sequencing is the most widely applied method for RNAseq, it overlooks non-coding RNAs, requires high-quality RNA and lacks strand-specificity. To overcome these limitations, we developed a method for strand-specific total RNA sequencing (ssRNAseq) to characterize the transcriptomes of 112 DLBCL tumors.

Results: Through this work, we defined the entire spectrum of coding and non-coding RNAs expressed in DLBCLs including hundreds of lincRNAs, snoRNAs and microRNAs in addition to mRNAs. We found that the strand-specificity of our method was greater than 95% in all cases. This strand-specific sequencing strategy allowed us to maintain the orientation of the transcript to enable more accurate transcript annotation and better prediction of novel transcripts. Furthermore, we showed that our method had equal efficacy on frozen and FFPE tumor specimens from the sample patient in 24 cases. In addition, through simultaneous measurement of expression of diverse RNA types combined with mutations in MYD88, GNA13, EZH2, and BCL2, we demonstrated that we could distinguish the clinically important subgroups of DLBCL. Finally, we applied ssRNAseq to distinct training and validation sets of DLBCL cases (N=86 and N=112) to define alternative splicing events in DLBCL and found 1,021 genes that were preferentially spliced in a subgroup-specific manner. These alternatively spliced genes were selectively enriched in a number of different pathways important in lymphomas including those related to immune function, cell cycle progression and focal adhesion pathways, suggesting that alternative splicing regulates a number of important oncogenic processes in DLCBL.

Conclusions: Strand-specific total RNA sequencing is a powerful method for defining the transcriptome and alternative splicing events in DLBCL. Here we define a complete coding and non-coding transcriptome of DLBCL and report the first characterization of subgroup-specific alternative splicing in DLBCL using high throughput sequencing. Our data demonstrate the power of our ssRNAseq method in defining the molecular patterns underlying DLBCLs and provide a starting point for defining the role of alternative splicing in this complex and heterogeneous disease.