TAMI-51. IDENTIFYING NEW TUMOR MICROENVIRONMENT (TME) CONTEXTS OF VULNERABILITY IN GLIOBLASTOMA. (9th November 2020)

• Record Type: Journal Article
• Authors: White, Kieron; Meylan, Maxime; Bougoüin, Antoine; Connor, Kate; Salvucci, Manuela; Bielle, Franck; Prehn, Jochen H M; Verreault, Maite; Idbaih, Ahmed; Sautès-Fridman, Catherine; Fridman, Wolf H; Byrne, Annette
• Is Part Of: Neuro-oncology. Volume 22(2020)Supplement 2
• Page Start: ii224
• Page End: ii224
• Publication Date: 2020-11-09
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noaa215.938
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: Glioblastoma (GBM) is the most frequent and aggressive adult brain tumor with 85% of patients dying within two years. New effective precision medicine therapies are urgently required, especially for isocitrate dehydrogenase wild-type (IDHwt) disease. Despite efforts to subtype patients based on molecular profiles, this approach has yet failed to direct treatment strategies. Further interrogation of the tumor microenvironment (TME) across molecular subtypes and identification of new TME specific subtypes may guide new directions for future therapies. Here, we analysed transcriptomic data from selected GLIOTRAIN(www.gliotrain.eu )(n=120) and TCGA(n=69) IDHwt patients. Firstly, the microenvironment cell population (MCP)-counter method (a gene-expression-based TME deconvolution tool) was validated for use in the brain tumor setting using quantitative multiplex immunohistochemistry. In this context, immune markers (CD20/CD3/CD68/CD8) were significantly correlated with MCP-counter scores. We are currently optimizing and validating a vessel-density and microglial RNA-signature to provide a more robust representation of brain TME. Next, using MCP-counter, the TME composition of IDHwt tumors was assessed within proneural (24%), classical (38%) and mesenchymal (38%) subtypes. We initially classified the GLIOTRAIN cohort into 3 novel clusters characterised by differences in TME composition and validated our findings in the TCGA cohort. A TME-high group (37%) is characterized by elevated presence of lymphocytes and myeloid cells, and presents a high level of immune checkpoint genes: PDCD1(PD1) and CTLA4. In addition, the presence of tertiary lymphoid structures (TLS) is a feature of TME high /mesenchymal + patients. This finding has been validated by IHC and RNA-signature. TME-med (38%) displayed heterogenous immune populations and the TME-low (25%) represented an 'immune-desert' group. There was no significant difference in OS based on these TME subtypes(p=0.50). We hypothesise that PD1/CTLA4 blockade might be an effective treatment strategy in TME-high patients. These hypotheses will be tested (in the adjuvant setting) using appropriate syngeneic disease models which incorporate surgical resection.