ArticlePLoS genetics2013
Comprehensive analysis of transcriptome variation uncovers known and novel driver events in T-cell acute lymphoblastic leukemia.
Article in PLoS genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers.
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Who cites it
84 citing papers in PubMed, 137 citations in OpenAlex.
- Trial
- Targeting mannosylation of nicastrin N-glycans attenuates γ-secretase activity and notch-dependent leukemia progression.The Journal of clinical investigation · 2026Article
- NKX2-1 regulates cell survival, maturation, and DNAdamage responses as a cofactor of RUNX1 in T-cell acute lymphoblastic leukemia.Haematologica · 2026Article
- Myeloid/lymphoid neoplasm with FLT3 gene fusion: report of a case with a novel t(5;13)(q13;q12) SSBP2::FLT3 fusion.Journal of hematopathology · 2026Article
- RareAmerican journal of cancer research · 2026Article
- Benchmarking standard-of-care and emerging genomic approaches to enhance diagnosis in pediatric acute lymphoblastic leukemia.British journal of cancer · 2025Article
- Epigenetic alterations affecting hematopoietic regulatory networks as drivers of mixed myeloid/lymphoid leukemia.Nature communications · 2024Article
- Dissecting the multifaceted roles of autophagy in cancer initiation, growth, and metastasis: from molecular mechanisms to therapeutic applications.Medical oncology (Northwood, London, England) · 2024Review
- Breaking paradigms: Long non-coding RNAs forming gene fusions with potential implications in cancer.Genes & diseases · 2024Review
- Hyperactive STAT5 hijacks T cell receptor signaling and drives immature T cell acute lymphoblastic leukemia.The Journal of clinical investigation · 2024Article
- Metabolic Profiling as an Approach to Differentiate T-Cell Acute Lymphoblastic Leukemia Cell Lines Belonging to the Same Genetic Subgroup.International journal of molecular sciences · 2024Article
- Janus Kinase 3 (JAK3): A Critical Conserved Node in Immunity Disrupted in Immune Cell Cancer and Immunodeficiency.International journal of molecular sciences · 2024Review
- Article
- β-Catenin activity induces an RNA biosynthesis program promoting therapy resistance in T-cell acute lymphoblastic leukemia.EMBO molecular medicine · 2023Article
- Case Report: RareGenes · 2023Article
- Oncohistone interactome profiling uncovers contrasting oncogenic mechanisms and identifies potential therapeutic targets in high grade glioma.Acta neuropathologica · 2022Article
- Dynamics of broad H3K4me3 domains uncover an epigenetic switch between cell identity and cancer-related genes.Genome research · 2022Article
- Circular and Fusion RNAs in Medulloblastoma Development.Cancers · 2022Article
- Oncogenic cooperation between TCF7-SPI1 and NRAS(G12D) requires β-catenin activity to drive T-cell acute lymphoblastic leukemia.Nature communications · 2021Article
- Pediatric fibromyxoid soft tissue tumor with PLAG1 fusion: A novel entity?Genes, chromosomes & cancer · 2021Article
24 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
17 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.