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        <identifier>oai:researchdata.se:2024-338/1</identifier>
        <datestamp>2024-08-21</datestamp>
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        <setSpec>principal:slug:uppsala-university</setSpec>
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        <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
          <dc:type>info:eu-repo/semantics/other</dc:type>
          <dc:type>http://purl.org/dc/dcmitype/Dataset</dc:type>
          <dc:identifier>https://doi.org/10.57804/xryk-dn78</dc:identifier>
          <dc:title xml:lang="en">SUPPLEMENTARY MATERIAL: Transcriptomic analysis reveals pro-inflammatory signatures associated with acute myeloid leukemia progression</dc:title>
          <dc:title xml:lang="sv">SUPPLEMENTARY MATERIAL: Transcriptomic analysis reveals pro-inflammatory signatures associated with acute myeloid leukemia progression</dc:title>
          <dc:creator>https://orcid.org/0000-0002-7438-9093</dc:creator>
          <dc:creator>Sara A. Yones</dc:creator>
          <dc:creator>https://orcid.org/0000-0002-2497-194X</dc:creator>
          <dc:creator>Jitong Sun</dc:creator>
          <dc:creator>Aron Skaftason</dc:creator>
          <dc:creator>https://orcid.org/0000-0003-3403-0083</dc:creator>
          <dc:creator>Nina Norgren</dc:creator>
          <dc:creator>Morten Krogh Herlin</dc:creator>
          <dc:creator>https://orcid.org/0000-0002-8160-5647</dc:creator>
          <dc:creator>https://orcid.org/0000-0002-8853-1863</dc:creator>
          <dc:creator>https://orcid.org/0000-0003-2468-0226</dc:creator>
          <dc:creator>https://orcid.org/0000-0001-7431-2865</dc:creator>
          <dc:creator>Jonas Abrahamsson</dc:creator>
          <dc:creator>Kirsi Jahnukainen</dc:creator>
          <dc:creator>Monica Cheng Munthe-Kaas</dc:creator>
          <dc:creator>Bernward Zeller</dc:creator>
          <dc:creator>Katja Pokrovskaja Tamm</dc:creator>
          <dc:creator>Lucia Cavelier</dc:creator>
          <dc:subject xml:lang="en">Cancer and Oncology</dc:subject>
          <dc:subject xml:lang="sv">Cancer och onkologi</dc:subject>
          <dc:description xml:lang="en">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.</dc:description>
          <dc:description xml:lang="sv">Studie beskriven i den engelska versionen av denna katalogpost.

Data består av en kompletterande Pdf-fil och en Excel-fil med följande tabeller:

Kompletterande tabell 1. Översikt över urval av studiekohort
Kompletterande tabell 2. Egenskaper för urvalet av studiekohorten
Kompletterande tabell 3. Klinisk information
Kompletterande tabell 4. Egenskaper hos CD34+ BM-kontrollprover
Kompletterande tabell 5. Information om antikroppar
Kompletterande tabell 6. Statistik för RNA-seq
Kompletterande tabell 7. SNV:er och små InDels som upptäckts med RNA-seq
Kompletterande tabell 8. Samlade metadata och RNA-seq- och WGS/WES-resultat
Kompletterande tabell 9. Fusionstranskript i R/PR AML
Kompletterande tabell 10. Provanvändning för generering av olika analyser
Kompletterande tabell 11. DEG associerade med kort kontra lång EFS
Kompletterande tabell 12. GO-analys av DEG mellan korta och långa EFS-associerade prover
Kompletterande tabell 13. Statistik i samband med överlevnadsanalyser
Kompletterande tabell 14. DEG mellan patientmatchade diagnos- och återfallsprover
Kompletterande tabell 15. GO-analys av DEGs mellan patientmatchade diagnos- och återfallsprover
Kompletterande tabell 16. Regler för maskininlärningsmodeller för diagnos och återfall i AML hos vuxna
Kompletterande tabell 17. Regler för maskininlärningsmodeller för diagnos och återfall i AML hos barn
Kompletterande tabell 18. Modellregler för maskininlärning för diagnos och återfall i pediatrisk AML (funktioner sammanslagna med TARGET)
Kompletterande tabell 19. Modellregler för maskininlärning för diagnos och återfall i TARGET-kohorten (funktioner sammanslagna med Local Pediatric)
Kompletterande tabell 20. Verifiering av transkriptomiska fusionshändelser och tillhörande primerinformation

Datasetet har ursprungligen publicerats i DiVA och flyttades över till SND 2024.</dc:description>
          <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
          <dc:publisher xml:lang="en">Uppsala University</dc:publisher>
          <dc:publisher xml:lang="sv">Uppsala universitet</dc:publisher>
          <dc:date>2021-10-05T00:00:00Z</dc:date>
          <dc:language>eng</dc:language>
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