Comprehensive analysis of transcriptome variation uncovers known and novel driver events in T-cell acute lymphoblastic leukemia

Abstract

RNA-seq is a promising technology to re-sequence protein coding genes for the identification of single nucleotide variants (SNV), while simultaneously obtaining information on structural variations and gene expression perturbations. We asked whether RNA-seq is suitable for the detection of driver mutations in T-cell acute lymphoblastic leukemia (T-ALL). These leukemias are caused by a combination of gene fusions, over-expression of transcription factors and cooperative point mutations in oncogenes and tumor suppressor genes. We analyzed 31 T-ALL patient samples and 18 T-ALL cell lines by high-coverage paired-end RNA-seq. First, we optimized the detection of SNVs in RNA-seq data by comparing the results with exome re-sequencing data. We identified known driver genes with recurrent protein altering variations, as well as several new candidates including H3F3A, PTK2B, and STAT5B. Next, we determined accurate gene expression levels from the RNA-seq data through normalizations and batch effect removal, and used these to classify patients into T-ALL subtypes. Finally, we detected gene fusions, of which several can explain the over-expression of key driver genes such as TLX1, PLAG1, LMO1, or NKX2-1; and others result in novel fusion transcripts encoding activated kinases (SSBP2-FER and TPM3-JAK2) or involving MLLT10. In conclusion, we present novel analysis pipelines for variant calling, variant filtering, and expression normalization on RNA-seq data, and successfully applied these for the detection of translocations, point mutations, INDELs, exon-skipping events, and expression perturbations in T-ALL.

Figure 1. RNA-seq data analysis pipelines for (A) variant calling and filtering to detect point mutations, (B) fusion detection and annotation, (C) gene expression analysis.

Figure 2. Comparison between RNA-seq and exome-seq.

Variant Allele Frequency plots for evaluating two RNA-seq mapping strategies for two example samples, namely the RPMI8402 cell line (A, B) and the TLE79 patient sample (C, D). On the left are the results of mapping with TopHat 1.3.3. (A,C), while on the right are the results of mapping with TopHat 2.0.5 with forced re-mapping of all reads to the genome. The SNVs that have at least 20 reads in exome-seq and RNA-seq are plotted. Red and green dots represent the SNVs that are detected only in RNA-seq and only in exome-seq, respectively, while black dots represent the SNVs that are called in both. Venn diagrams are produced from the points represented in the graphs.

Figure 3. Point mutations and gene fusions organized into functional categories.

Protein altering mutations and INDELs, alternative splicing events and validated fusions are shown. Red boxes indicate protein-altering mutations (i.e. nonsense, missense and splice site mutations); purple boxes indicate frame-shift INDELs whereas blue, green and orange boxes represent fusion events resulting in over-expression of the partner gene, inactivation of the partner gene or generation of a chimeric protein, respectively, and finally black boxes indicating alternative splicing events.

Figure 4. Validation and discovery using gene expression data, and SUZ12 ATE.

(A) Classification of the samples using the TFs that are known to be overexpressed in T-ALL. Using the expression patterns of TAL1, TLX1, TLX3, NKX2-5, LYL1 and LMO2 we could discriminate the samples in to six distinct clusters. The heatmap is plotted with the normalized log2(count) values. Gene set enrichment analysis curves are displayed for (B) enrichment of TAL1 associated clusters 2, 6 and 3 in TAL1 based ranking, (C) enrichment of TLX associated clusters 7 and 8 in TLX based ranking, and (D) enrichment of LYL1 associated clusters 10 and 11 in LYL1 based ranking of the genes. (E) Expression of JAK3 and PTK2B across samples is significantly correlated (with PTM p-value = 1E-05). (F) Normalized expression values of TAL1 and TLX1 with translocations affecting these genes indicated. The samples with a translocation have elevated expression of the affected gene, showing the driver potential of the fusion event. There are additional samples with high expression of TLX1 and TAL1 without the indicated fusions, pointing to other mechanisms of activating these genes. (G) Predicted SUZ12 transcript aligned with the known SUZ12 isoforms. Dotted red box indicates the location of the exon-skipping event. (H) The sashimi plot shows the junction (in black) supporting the exon-skipping event in patient sample R5 with respect to Thymus. (I) Agarose gel electrophoresis of the RT-PCR products for validation of SUZ12 exon skipping event. The two isoforms are clearly detected in R5 and to a minor extent in the other T-ALL samples while Thymus shows only the canonical transcript.

Figure 5. SSBP2-FER and TPM3-JAK2 fusions transform lymphoid cells and show constitutive activity.

(A) Schematic representations of the predicted SSBP2-FER and TPM3-JAK2 fusion joining the dimerization units of SSBP2 (LisH domain) or TPM3 (coiled-coil domains) to the TK domain of FER or JAK2, respectively. (B) Proliferation curve of mouse Ba/F3 cells in the absence of the cytokine interleukin 3 (IL3) (upper graph) and in the presence of ruxolitinib (lower graph). In the absence of IL3, cells expressing empty vector died whereas cells expressing the SSBP2-FER or TPM3-JAK2 fusion protein were transformed and could proliferate. Ba/F3 cells expressing the oncogenic JAK1 A634D mutant were used as positive control for transformation . The graph shows mean +/− st. dev. The lower graph illustrates the effects of the JAK kinase inhibitor ruxolitinib on Ba/F3 cell proliferation after 24 hours of treatment. The graph represents mean +/− st. dev. of triplicate measurements. (C) Western blot analysis of Ba/F3 cells transformed by the indicated kinases. The 2 upper panels show phosphorylation of the JAK and FER kinases, the panels below illustrate phosphorylation of downstream targets STAT5, STAT3, SRC and ERK1/2. (D) TCR gene fusions result in overexpression of a flanking gene in RIC3-TRBC2 and SFTA3-TRDC fusions. The barplot is drawn for relative (to Thymus) expression values for the upstream and downstream flanking genes around RIC3 and SFTA3 for R4 and TLE90 samples, respectively. In both cases, the nearest downstream neighbor shows increased expression. (E) The heatmap illustrates the expression patterns of RIC3 and SFTA3, together with their immediately upstream and downstream flanking genes in the genome, showing strong over-expression (red) of LMO1 near the RIC3 fusion, and of NKX2-1 near the SFTA3 fusion.