PATH-01. IDENTIFICATION OF PROGNOSTIC VARIABLES BASED ON MOLECULAR PROFILING OF LONG-TERM AND SHORT-TERM SURVIVING GLIOBLASTOMA PATIENTS. (6th November 2017) - Michaelsen, Signe Regner; Urup, Thomas; Olsen, Lars Rønn; Gillberg, Linn; Broholm, Helle; Grunnet, Kirsten; Grønbæk, Kirsten; Hamerlik, Petra; Lassen, Ulrik; Poulsen, Hans Skovgaard
Abstract: Glioblastoma is a devastating disease and despite extensive treatment, overall survival (OS) for these patients remains poor. Yet, a small proportion of glioblastoma patients present relatively long survival over 3 years, but the underlying molecular background separating these long-term survivors (LTS) from short-term survivors (STS) are still insufficiently understood. The purpose of this study was to identify independent prognostic variables for survival by examining molecular profiles of LTS and STS in a clinically well characterized cohort of glioblastoma patients. The cohort consisted of 93 patients diagnosed with primary glioblastoma and treated with radiation therapy plus concomitant and adjuvant chemotherapy as well as bevacizumab administered in the first-line setting or at time of recurrence. Among these, 14 STS (OS<12 months) and 6 LTS (OS>36 months) were identified, which were all confirmed being IDHwt. For all patients, RNA had previously been purified from microdissected tumor tissue of the diagnostic specimen and analyzed for expression levels by a customized NanoString platform. This covered 800 genes related to glioblastoma cancer hallmarks, including regulation of angiogenesis and immune response. When comparing expression of these genes in LTS vs. STS using a Welsh's t-test, 14 candidate genes ended up significant (P<0.05). These candidate genes are being tested in multivariate analysis together with known clinical prognostic factors such as age, performance status, corticosteroid use and MGMT promoter methylation, to test if they represent independent prognostic variables. Dependent on results, prognostic models will be generated able to predict survival probability for individual patients and thereby identify patients likely to become LTS. Updated results will be presented.