Uncovering Spatiotemporal Heterogeneity of High-Grade Gliomas: From Disease Biology to Therapeutic Implications

Andrea Comba, Syed M. Faisal, Maria Luisa Varela, Todd Hollon, Wajd N. Al-Holou, Yoshie Umemura, Felipe J. Nunez, Sebastien Motsch, Maria G. Castro, Pedro R. Lowenstein

Research output: Contribution to journalReview articlepeer-review

Abstract

Glioblastomas (GBM) are the most common and aggressive tumors of the central nervous system. Rapid tumor growth and diffuse infiltration into healthy brain tissue, along with high intratumoral heterogeneity, challenge therapeutic efficacy and prognosis. A better understanding of spatiotemporal tumor heterogeneity at the histological, cellular, molecular, and dynamic levels would accelerate the development of novel treatments for this devastating brain cancer. Histologically, GBM is characterized by nuclear atypia, cellular pleomorphism, necrosis, microvascular proliferation, and pseudopalisades. At the cellular level, the glioma microenvironment comprises a heterogeneous landscape of cell populations, including tumor cells, non-transformed/reactive glial and neural cells, immune cells, mesenchymal cells, and stem cells, which support tumor growth and invasion through complex network crosstalk. Genomic and transcriptomic analyses of gliomas have revealed significant inter and intratumoral heterogeneity and insights into their molecular pathogenesis. Moreover, recent evidence suggests that diverse dynamics of collective motion patterns exist in glioma tumors, which correlate with histological features. We hypothesize that glioma heterogeneity is not stochastic, but rather arises from organized and dynamic attributes, which favor glioma malignancy and influences treatment regimens. This review highlights the importance of an integrative approach of glioma histopathological features, single-cell and spatially resolved transcriptomic and cellular dynamics to understand tumor heterogeneity and maximize therapeutic effects.

Original languageEnglish (US)
Article number703764
JournalFrontiers in Oncology
Volume11
DOIs
StatePublished - Aug 5 2021
Externally publishedYes

Keywords

  • deep learning
  • dynamic
  • glioblastoma multiforme
  • heterogeneity
  • precision oncology
  • spatial resolution
  • tumor microenvironment

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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