SUPPLEMENTARY MATERIAL: Transcriptomic analysis reveals pro-inflammatory signatures associated with acute myeloid leukemia progression
Numerous studies have been performed over the last decade to exploit the complexity of genomic and transcriptomic lesions driving the initiation of acute myeloid leukemia (AML). These studies have helped improve risk classification and treatment options. Detailed molecular characterization of longitudinal AML samples is sparse, however; meanwhile, relapse and therapy resistance represent the main challenges in AML care. To this end, we performed transcriptome-wide RNA sequencing of longitudinal diagnosis, relapse, and/or primary resistant samples from 47 adult and 23 pediatric AML patients with known mutational background. Data consists of a supplemental Pdf file and an Excel file with following tables: Supplemental Table 1. Study cohort sample overview Supplemental Table 2. Study cohort sample characteristics Supplemental Table 3. Clinical information Supplemental Table 4. Characteristics of CD34+ BM-control samples Supplemental Table 5. Antibody information Supplemental Table 6. RNA-seq statistics Supplemental Table 7. SNVs and small InDels detected by RNA-seq Supplemental Table 8. Comprised metadata and RNA-seq- and WGS/WES results Supplemental Table 9. Fusion transcripts in R/PR AML Supplemental Table 10. Sample usage for generation of various analyses Supplemental Table 11. DEGs associated with short vs. long EFS Supplemental Table 12. GO-analysis of DEGs between short vs. long EFS-associated samples Supplemental Table 13. Statistics associated with survival analyses Supplemental Table 14. DEGs between patient-matched diagnosis and relapse samples Supplemental Table 15. GO-analysis of DEGs between patient-matched diagnosis and relapse samples Supplemental Table 16. Machine learning model rules for diagnosis and relapse in adult AML Supplemental Table 17. Machine learning model rules for diagnosis and relapse in pediatric AML Supplemental Table 18. Machine learning model rules for diagnosis and relapse in pediatric AML (features merged with TARGET) Supplemental Table 19. Machine learning model rules for diagnosis and relapse in the TARGET cohort (features merged with Local Pediatric) Supplemental Table 20. Verification of transcriptomic fusion events and associated primer information The dataset was originally published in DiVA and moved to SND in 2024.
Stratmann, S., Yones, S. A., Garbulowski, M., Sun, J., Skaftason, A., Mayrhofer, M., … Holmfeldt, L. (2022). Transcriptomic analysis reveals proinflammatory signatures associated with acute myeloid leukemia progression. Blood Advances, 6(1), 152–164. https://doi.org/10.1182/bloodadvances.2021004962
Department of Immunology, Genetics and Pathology, Experimental and Clinical Oncology / Science for Life Laboratory
Department of Cell and Molecular Biology / Science for Life Laboratory
Department of Cell and Molecular Biology, Computational Biology and Bioinformatics
Department of Molecular Medicine and Surgery
Department of Molecular Biology, National Bioinformatics Infrastructure Sweden / Science for Life Laboratory
Department of Clinical Medicine / Department of Pediatrics and Adolescent Medicine
Department of Immunology, Genetics and Pathology, Clinical and experimental pathology / Science for Life Laboratory
Department of Medical Sciences, Haematology
Department of Medical Sciences, Haematology / Department of Medical Sciences
Department of Women's and Children's Health, Neuropediatrics, Paediatric oncology / Science for Life Laboratory
Sahlgrenska Academy at University of Gothenburg
Department of Pediatrics, Institute of Clinical Sciences
University of Helsinki and Helsinki University Central Hospital
Division of Pediatric and Adolescent Medicine
Karolinska Institutet and Karolinska University hospital
Department of Immunology, Genetics and Pathology / Science for Life Laboratory