PL02.3. A Phenotypic heterogeneity and plasticity as resistance mechanisms in Glioblastoma. (9th September 2021)

• Record Type: Journal Article
• Authors: Yabo, Y Y; Oudin, A; Skupin, A; Nazarov, P V; Niclou, S P; Golebiewska, A
• Is Part Of: Neuro-oncology. Volume 23: Supplement 2 (2021)
• Page Start: ii1
• Page End: ii1
• Publication Date: 2021-09-09
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noab180.000
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: BACKGROUND: Glioblastomas are among the most heterogeneous tumors, which hampers patient stratification and development of effective therapies. Glioblastomas create a dynamic ecosystem, where heterogeneous tumor cells interact with the tumor microenvironment to establish different niches. Upon tumor growth, Glioblastoma cells manifest remarkable plasticity and respond flexibly to selective pressures by transiting towards states favorable to the new tumor microenvitonment. How this phenotypic plasticity contributes to treatment resistance is currently less clear. The exact nature of treatment resistant, tolerant and sensitive Glioblastoma cells remains unresolved. Further studies at the single cell level are needed to reveal transient and long-term signatures of the resistant states. MATERIAL AND METHODS: To investigate long-term phenotypic changes upon treatment at the single cell level we performed single cell RNA-seq (scRNA-seq) on the longitudinal patient-derived xenograft (PDOX) models derived from Glioblastoma patient tumors prior and after the standard-of-care treatment. In addition, direct treatment of PDOXs with temozolomide combined with scRNA-seq allowed revealing short-term transcriptomic changes both in tumor cells and in the mouse-derived cells forming tumor microenvironment. Advanced computational algorithms, including reference-free deconvolution methods, were applied to reveal treatment resistance signatures and master regulators of the identified treatment-resistant subpopulations. RESULTS: We show that PDOX models recapitulate all the major cell types and transcriptional programs reported in Glioblastoma patient samples, providing clinically relevant models for investigating treatment resistance signatures of tumor cells and associated tumor microenvironment. Analysis of treatment naïve and treated Glioblastomas at the single cell level revealed presence of pre-existing treatment resistant states as well as newly established resistant subpopulatons. Certain transcriptomic changes are preserved long term, regardless of the lack of genetic evolution of the tumor cells. CONCLUSION: Phenotypic plasticity is an important factor contributing to resistance mechanisms in Glioblastoma. Key molecular regulators of tumor cell plasticity towards treatment resistance states represent novel targets for future combinatory treatments.