Browsing Department of Clinical Medicine by Author "Ohnstad, Hege Oma"

Now showing items 1-2 of 2

    • Impact of Prosigna test on adjuvant treatment decision in lymph node-negative early breast cancer—a prospective national multicentre study (EMIT-1) 

      Ohnstad, Hege Oma; Blix, Egil Støre; Akslen, Lars Andreas; Gilje, Bjørnar; Raj, Sunil Xavier; Skjerven, Helle; Borgen, Elin; Janssen, Emiel; Mortensen, Elin Synnøve; Brekke, Marianne B.; Falk, Ragnhild Sørum; Schlichting, Ellen; Boge, Beate; Songe-Møller, Silje; Olsson, Pernilla Marie A.; Heie, Anette; Mannsåker, Bård; Vestlid, Magdalena Aas; Kursetgjerde, Torgunn; Gravdehaug, Berit; Suhrke, Pål; Sánchez, E.; Bublevic, J.; Røe, Oluf Dimitri; Geitvik, Gry; Halset, Eline Holli; Rypdal, Maria Christine; Langerød, Anita; Lømo, Jon; Garred, Øystein; Porojnicu, Alina Carmen; Engebraaten, O.; Geisler, Jürgen; Lyngra, Marianne; Hansen, M. H.; Søiland, Håvard; Nakken, T.; Asphaug, Lars; Kristensen, Vessela N.; Sørlie, Therese; Sørlie, T.; Nygård, Jan Franz; Kiserud, Cecilie E.; Reinertsen, Kristin Valborg; Russnes, Hege Elisabeth Giercksky; Naume, Bjørn (Journal article; Peer reviewed, 2024)
      Background EMIT-1 is a national, observational, single-arm trial designed to assess the value of the Prosigna, Prediction Analysis of Microarray using the 50 gene classifier (PAM50)/Risk of Recurrence (ROR), test as a ...

    • An independent poor-prognosis subtype of breast cancer defined by a distinct tumor immune microenvironment 

      Tekpli, Xavier; Lien, Tonje Gulbrandsen; Røssevold, Andreas Hagen; Nebdal, Daniel J.H.; Borgen, Elin; Ohnstad, Hege Oma; Kyte, Jon A; Vallon-Christersson, Johan; Fongaard, Marie; Due, Eldri Undlien; Svartdal, Lisa Gregusson; Sveli, My Anh Tu; Garred, Øystein; Frigessi Di Rattalma, Arnoldo; Sahlberg, Kristine Kleivi; Sørlie, Therese; Russnes, Hege Elisabeth Giercksky; Naume, Bjørn; Kristensen, Vessela N. (Peer reviewed; Journal article, 2019)
      How mixtures of immune cells associate with cancer cell phenotype and affect pathogenesis is still unclear. In 15 breast cancer gene expression datasets, we invariably identify three clusters of patients with gradual levels ...