• DNA methylation and copy number variation profiling of T-cell lymphoblastic leukemia and lymphoma 

      Haider, Zahra; Landfors, Mattias; Golovleva, Irina; Erlanson, Martin; Schmiegelow, Kjeld; Flægstad, Trond; Kanerva, Jukka; Norén-Nyström, Ulrika; Hultdin, Magnus; Degerman, Sofie (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-04-28)
      Despite having common overlapping immunophenotypic and morphological features, T-cell lymphoblastic leukemia (T-ALL) and lymphoma (T-LBL) have distinct clinical manifestations, which may represent separate diseases. We investigated and compared the epigenetic and genetic landscape of adult and pediatric T-ALL (<i>n</i> = 77) and T-LBL (<i>n</i> = 15) patient samples by high-resolution genome-wide ...
    • Multimodal classification of molecular subtypes in pediatric acute lymphoblastic leukemia 

      Krali, Olga; Marincevic-Zuniga, Yanara; Arvidsson, Gustav; Enblad, Anna Pia; Lundmark, Anders; Sayyab, Shumaila; Zachariadis, Vasilios; Heinäniemi, Merja; Suhonen, Janne; Oksa, Laura; Vepsäläinen, Kaisa; Öfverholm, Ingegerd; Barbany, Gisela; Nordgren, Ann; Lilljebjörn, Henrik; Fioretos, Thoas; Madsen, Hans O.; Marquart, Hanne Vibeke; Flægstad, Trond; Forestier, Erik; Jónsson, Ólafur G.; Kanerva, Jukka; Lohi, Olli; Norén-Nyström, Ulrika; Schmiegelow, Kjeld; Harila, Arja; Heyman, Mats; Lönnerholm, Gudmar; Syvänen, Ann-Christine; Nordlund, Jessica (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-12-08)
      Genomic analyses have redefined the molecular subgrouping of pediatric acute lymphoblastic leukemia (ALL). Molecular subgroups guide risk-stratification and targeted therapies, but outcomes of recently identified subtypes are often unclear, owing to limited cases with comprehensive profiling and cross-protocol studies. We developed a machine learning tool (ALLIUM) for the molecular subclassification ...