👤 Isabelle Aerts

The wide application of RNA sequencing in clinical practice has allowed the discovery of novel fusion genes, which have contributed to a refined molecular classification of rhabdomyosarcoma (RMS). Most fusions in RMS result in aberrant transcription factors, such as PAX3/7::FOXO1 in alveolar RMS (ARMS) and fusions involving VGLL2 or NCOA2 in infantile spindle cell RMS. However, recurrent fusions driving oncogenic kinase activation have not been reported in RMS. Triggered by an index case of an unclassified RMS (overlapping features between ARMS and sclerosing RMS) with a novel FGFR1::ANK1 fusion, we reviewed our molecular files for cases harboring FGFR1-related fusions. One additional case with an FGFR1::TACC1 fusion was identified in a tumor resembling embryonal RMS (ERMS) with anaplasia, but with no pathogenic variants in TP53 or DICER1 on germline testing. Both cases occurred in males, aged 7 and 24, and in the pelvis. The 2nd case also harbored additional alterations, including somatic TP53 and TET2 mutations. Two additional RMS cases (one unclassified, one ERMS) with FGFR1 overexpression but lacking FGFR1 fusions were identified by RNA sequencing. These two cases and the FGFR1::TACC1-positive case clustered together with the ERMS group by RNAseq. This is the first report of RMS harboring recurrent FGFR1 fusions. However, it remains unclear if FGFR1 fusions define a novel subset of RMS or alternatively, whether this alteration can sporadically drive the pathogenesis of known RMS subtypes, such as ERMS. Additional larger series with integrated genomic and epigenetic datasets are needed for better subclassification, as the resulting oncogenic kinase activation underscores the potential for targeted therapy. Show less

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. Show less