SF3B1 mutation accelerates the development of CLL via activation of the mTOR pathway

RNA splicing factor SF3B1 is among the most recurrently mutated genes in chronic lymphocytic leukemia (CLL); these mutations frequently co-occur with chromosome 13q deletion (del(13q)). The presence of SF3B1 mutation and del(13q) is predictive of poorer prognosis in CLL, suggesting that these two lesions impact the aggressiveness of CLL. While del(13q) in murine B cells (Mdr mice), but not expression of Sf3b1-K700E, drives the initiation of CLL, we hypothesize that SF3B1 mutation accelerates CLL progression. In this study, we crossed mice with a B-cell-specific Sf3b1-K700E allele with Mdr mice to determine the impact of Sf3b1 mutation on CLL progression. We found that the co-occurrence of these two lesions in murine B cells indeed causes acceleration of CLL. We showed that Sf3b1-K700E impacts alternative RNA splicing of Nfatc1 and activates mTOR signaling and the MYC pathway, contributing to CLL acceleration. Moreover, concurrent inhibition of RNA splicing and mTOR pathways leads to cell death in vitro and in vivo in murine CLL cells with SF3B1 mutation and del(13q). Our results thus suggest that SF3B1 mutation contributes to the aggressiveness of CLL through the activation of the mTOR pathway, likely mediated by alternative splicing of Nfatc1, providing a rationale for targeting mTOR and RNA splicing in the subset of CLL patients with both SF3B1 mutations and del(13q). Normal splenic B cells or CLL B cells were first enriched by a pan-B cell selection kit (Miltenyi Biotec, Germany), and total RNA was isolated from these cells using a Nucleospin RNA plus kit (Machery Nagel, Allentown, PA). Libraries for RNA-seq were constructed using the Stranded Total RNA Prep with Ribo-Zero Plus Kit (Illumina) and sequenced on the Novaseq S4 platform using paired-end 150 bp mode. The fastq sequence files exported by the sequencer were checked using FastQC (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc). Adaptors and low-quality bases were removed from the sequencing reads using Trimmomatic. The remaining reads were aligned to the mouse reference genome (mm10) using STAR(20) with default parameters. Adaptor trimming and mapping quality reports were generated using MultiQC. The DESeq2 R package performed differential expression mRNA analyses. mRNAs with absolute log2FC more than 1 and FDR less than 0.05 were identified as significantly dysregulated genes. RNA splicing analysis was performed using our previously established pipeline (24, 46). In brief, we integrated StringTie, LeafCutter , and rMATs to maximally improve the power of detection of splicing dysregulation. We assembled de novo transcripts using StringTie with default parameters. LeafCutter was used to detect additional novel exon boundaries. Together with the isoform annotation file downloaded from GENCODE (release 26), we merged all isoform information to generate a comprehensive isoform annotation file using a custom R script as a reference file for rMATs. Percent spliced-in (PSI) value was calculated using rMATs. For differential splicing analysis, we adopted the differential splicing analysis statistical model from rMATs with an absolute IncLevelDifference value of more than 0.1 and FDR less than 0.05 as significant cutoff.