COMP-11. SINGLE CELL MASS CYTOMETRY SIGNALING PROFILES AND A NOVEL COMPUTATIONAL TOOL IDENTIFY HIGH RISK GLIOBLASTOMA CELLS. (11th November 2019)

• Record Type: Journal Article
• Authors: Leelatian, Nalin; Sinnaeve, Justine; Mistry, Akshitkumar; Barone, Sierra; Diggins, Kirsten; Greenplate, Allison; Bartkowiak, Todd; Roe, Caroline; Weaver, Kyle; Thompson, Reid; Chambless, Lola; Mobley, Bret; Irish, Jonathan; Ihrie, Rebecca
• Is Part Of: Neuro-oncology. Volume 21(2019)Supplement 6
• Page Start: vi63
• Page End: vi63
• Publication Date: 2019-11-11
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noz175.254
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: In glioblastoma, changes in signaling, gene sequence, copy number, or transcript expression can define patient subgroups, but these subgroups are not yet associated with differential outcome for most patients with high-risk, IDH wild-type disease. Single cell interrogation of phospho-protein signaling has successfully revealed novel cell types associated with patient outcomes in blood cancers, suggesting that a comparable approach could be used in brain tumors. The goal of this study was to combine a single cell phospho-protein profiling approach with novel, automated computational analysis to identify abnormal glioblastoma cells that stratify patient clinical risk. Effective tissue dissociation strategies and validated antibody panels were created for mass cytometry analyses of resected glioblastoma tissue. These panels simultaneously measured 45 determinants of neural and glioma cell identity, including transcription factors, phospho-proteins, and surface receptors. 28 glioblastoma tumors were stained and analyzed using traditional gating, existing computational tools, and a new risk assessment population identification algorithm (RAPID, https://www.biorxiv.org/content/10.1101/632208v3 ). RAPID revealed two malignant cell types closely associated with differential patient outcomes. Glioblastoma negative prognostic (GNP) cells were associated with poor survival and defined by phospho-protein signaling in cells with aberrant neural developmental phenotypes. Glioblastoma positive prognostic (GPP) cells were associated with better progression free survival and defined by increased immunogenic signaling. A Cox proportional-hazards regression model was created to assess the influence of GNP and GPP cells on OS and PFS as continuous variables while accounting for other well-known clinical predictors. Each 1% increase of GNP cells was associated with an 7% increase in annual mortality rate (HR=1.07 [95% CI 1.03–1.12], p=0.001). Tumors containing GNP cells also significantly lacked CD45+ immune cell infiltration (Pearson r=-0.8). The signaling events that define these clinically significant glioblastoma cells represent a useful molecular classification, may indicate responsiveness to immunotherapy, and are themselves important targets of opportunity for new therapeutic approaches.