Johan M Kros1, Karin Huizer2, Aurelio Hernández-Laín2, Gianluca Marucci2, Alex Michotte2, Bianca Pollo2, Elisabeth J Rushing2, Teresa Ribalta2, Pim French2, David Jaminé2, Nawal Bekka2, Denis Lacombe2, Martin J van den Bent2, Thierry Gorlia2. 1. Johan M. Kros and Karin Huizer, Erasmus Medical Center; Pim French and Martin J. van den Bent, Dr Daniel den Hoed Cancer Center, Rotterdam, the Netherlands; Aurelio Hernández-Laín, Hospital Universitario 12 de Octubre Reseach Institute, Madrid; Teresa Ribalta, Hospital Clínic, University of Barcelona, Barcelona, Spain; Gianluca Marucci, Bellaria Hospital, University of Bologna, Bologna; Bianca Pollo, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico, Istituto Neurologico "C. Besta," Milano, Italy; Alex Michotte, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel; David Jaminé, Nawal Bekka, Denis Lacombe, and Thierry Gorlia, European Organisation for Research and Treatment of Cancer, Brussels, Belgium; and Elisabeth J. Rushing, Institute for Neuropathology, University Hospital of Zurich, Zurich, Switzerland. j.m.kros@erasmusmc.nl. 2. Johan M. Kros and Karin Huizer, Erasmus Medical Center; Pim French and Martin J. van den Bent, Dr Daniel den Hoed Cancer Center, Rotterdam, the Netherlands; Aurelio Hernández-Laín, Hospital Universitario 12 de Octubre Reseach Institute, Madrid; Teresa Ribalta, Hospital Clínic, University of Barcelona, Barcelona, Spain; Gianluca Marucci, Bellaria Hospital, University of Bologna, Bologna; Bianca Pollo, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico, Istituto Neurologico "C. Besta," Milano, Italy; Alex Michotte, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel; David Jaminé, Nawal Bekka, Denis Lacombe, and Thierry Gorlia, European Organisation for Research and Treatment of Cancer, Brussels, Belgium; and Elisabeth J. Rushing, Institute for Neuropathology, University Hospital of Zurich, Zurich, Switzerland.
Abstract
PURPOSE: With the rapid discovery of prognostic and predictive molecular parameters for glioma, the status of histopathology in the diagnostic process should be scrutinized. Our project aimed to construct a diagnostic algorithm for gliomas based on molecular and histologic parameters with independent prognostic values. METHODS: The pathology slides of 636 patients with gliomas who had been included in EORTC 26951 and 26882 trials were reviewed using virtual microscopy by a panel of six neuropathologists who independently scored 18 histologic features and provided an overall diagnosis. The molecular data for IDH1, 1p/19q loss, EGFR amplification, loss of chromosome 10 and chromosome arm 10q, gain of chromosome 7, and hypermethylation of the promoter of MGMT were available for some of the cases. The slides were divided in discovery (n = 426) and validation sets (n = 210). The diagnostic algorithm resulting from analysis of the discovery set was validated in the latter. RESULTS: In 66% of cases, consensus of overall diagnosis was present. A diagnostic algorithm consisting of two molecular markers and one consensus histologic feature was created by conditional inference tree analysis. The order of prognostic significance was: 1p/19q loss, EGFR amplification, and astrocytic morphology, which resulted in the identification of four diagnostic nodes. Validation of the nodes in the validation set confirmed the prognostic value (P < .001). CONCLUSION: We succeeded in the creation of a timely diagnostic algorithm for anaplastic glioma based on multivariable analysis of consensus histopathology and molecular parameters.
PURPOSE: With the rapid discovery of prognostic and predictive molecular parameters for glioma, the status of histopathology in the diagnostic process should be scrutinized. Our project aimed to construct a diagnostic algorithm for gliomas based on molecular and histologic parameters with independent prognostic values. METHODS: The pathology slides of 636 patients with gliomas who had been included in EORTC 26951 and 26882 trials were reviewed using virtual microscopy by a panel of six neuropathologists who independently scored 18 histologic features and provided an overall diagnosis. The molecular data for IDH1, 1p/19q loss, EGFR amplification, loss of chromosome 10 and chromosome arm 10q, gain of chromosome 7, and hypermethylation of the promoter of MGMT were available for some of the cases. The slides were divided in discovery (n = 426) and validation sets (n = 210). The diagnostic algorithm resulting from analysis of the discovery set was validated in the latter. RESULTS: In 66% of cases, consensus of overall diagnosis was present. A diagnostic algorithm consisting of two molecular markers and one consensus histologic feature was created by conditional inference tree analysis. The order of prognostic significance was: 1p/19q loss, EGFR amplification, and astrocytic morphology, which resulted in the identification of four diagnostic nodes. Validation of the nodes in the validation set confirmed the prognostic value (P < .001). CONCLUSION: We succeeded in the creation of a timely diagnostic algorithm for anaplastic glioma based on multivariable analysis of consensus histopathology and molecular parameters.
Authors: Quinn T Ostrom; Haley Gittleman; Carol Kruchko; David N Louis; Daniel J Brat; Mark R Gilbert; Valentina I Petkov; Jill S Barnholtz-Sloan Journal: J Neurooncol Date: 2016-07-14 Impact factor: 4.130
Authors: Kaspar Draaisma; Maarten M J Wijnenga; Bas Weenink; Ya Gao; Marcel Smid; P Robe; Martin J van den Bent; Pim J French Journal: Acta Neuropathol Commun Date: 2015-12-23 Impact factor: 7.801
Authors: Ken G Andersson; Maryam Oroujeni; Javad Garousi; Bogdan Mitran; Stefan Ståhl; Anna Orlova; John Löfblom; Vladimir Tolmachev Journal: Int J Oncol Date: 2016-10-05 Impact factor: 5.650