Summary: Two landmark studies have mapped the complex cellular ecosystems of glioblastoma, the deadliest form of brain cancer, offering unprecedented insights into how its diverse cells evolve and resist treatment. Using single-cell RNA sequencing on over 430,000 cells from 59 patients, researchers identified three new glioblastoma cell states and classified tumors into three overarching cellular ecosystems.
Analysis of samples before and after treatment showed how genetic factors and therapy shape tumor progression and resistance, with some tumors becoming more aggressive or hypoxic. These findings offer a roadmap for future therapies targeting glioblastoma’s adaptive strategies and cellular diversity.
Key Facts:
- Single-Cell Precision: Over 430,000 tumor cells revealed new glioblastoma states and ecosystems.
- Evolving Resistance: Tumors adapt between diagnosis and recurrence, often becoming more aggressive.
- Treatment Implications: Findings may guide therapies targeting radiation or chemo-resistant cell states.
Source: Yale
A pair of research articles published May 9 in Nature Genetics shed new light on the cellular complexity of glioblastoma, the most aggressive type of brain cancer.
An international team of scientists, including researchers at the Yale Cancer Center, analyzed tumor samples from 59 glioblastoma patients to better understand how diverse cell types within a tumor change over time and in response to standard therapy.
Their findings identify previously unrecognized patterns of cancer cell activity and may help guide future treatment strategies for this disease.
The work described in the two articles was supervised by five senior researchers including Roel Verhaak, PhD, Harvey and Kate Cushing Professor of Neurosurgery at Yale School of Medicine.
His research has long focused on the identification and characterization of gene expression subtypes in glioblastoma. The current articles build on Verhaak’s prior research by leveraging the latest genomic technologies.
“Using high-resolution technologies that enable us to measure gene expression at the single-cell level, we are now able to specifically pinpoint characteristics of glioblastoma cells and the mechanisms behind disease progression.
“This new work applies these technologies at scale, revealing the heterogeneity and evolution of this aggressive disease,” he says.
The multilayered transcriptional architecture of glioblastoma ecosystems
The first article presents a detailed analysis of 121 primary and recurrent glioblastoma samples from the 59 patients to discover cancer cell types not previously identified in earlier, smaller studies.
The large data set included about 430,000 cells and led to the identification of three novel glioblastoma cell “states,” in addition to confirming previously identified ones, that may contribute to a glioblastoma’s ability to adapt and evade therapies.
Glioblastoma is different from patient to patient and its cellular composition is varied even within the same tumor. Despite this variability, the researchers found some common cellular programs across patients that are influenced by specific gene mutations and a tumor’s surrounding cells.
These common patterns define three overarching “ecosystems” that reflect distinct cellular communities.
“By dissecting glioblastoma at the single-cell resolution, we’re beginning to understand how individual cancer cells function collectively as an ecosystem. Mapping this cellular landscape gives us critical insights into how glioblastomas develop, evolve, and ultimately resist treatment,” says Kevin Johnson, PhD, research scientist in the Department of Neurosurgery at Yale School of Medicine and co-first author of the studies.
Deciphering the longitudinal trajectories of glioblastoma ecosystems by integrative single-cell genomics
The second article examined how the glioblastoma cellular ecosystems change between a patient’s initial diagnosis and disease recurrence.
These findings both added to, and reinforced, the existing complex picture of varied glioblastoma cells and how they evolve and develop resistance to treatment.
Most recurrent glioblastomas retain the cellular makeup associated with the primary tumor, but some do not. For example, tumors with higher levels of the gene MGMT, which is related to chemotherapy resistance, can transition to a more aggressive form when they recur.
Another subgroup, with genetic patterns linked to therapeutic radiation resistance, displayed a low-oxygen (hypoxic) profile in the recurring tumor that may assist glioblastoma cells in surviving standard radiation therapy.
Together, the insights from these articles mark a major step toward decoding glioblastoma’s notorious complexity and treatment resistance mechanisms.
The articles noted that further genetic studies are still needed to reveal additional recurrence patterns that could inform treatment decisions that improve patient outcomes.
Funding: Funding for this work was sourced worldwide and includes the GBM CARE initiative; the National Cancer Institute through the National Institutes of Health (awards R01CA237208, R21NS114873, R21CA256575, and P30CA034196); the Luxembourg National Research Fund; and Yale University. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
About this glioblastoma brain cancer research news
Author: Colleen Moriarty
Source: Yale
Contact: Colleen Moriarty – Yale
Image: The image is credited to Neuroscience News
Original Research: Open access.
“The multilayered transcriptional architecture of glioblastoma ecosystems” by Roel Verhaak et al. Nature Genetics
Open access.
“Deciphering the longitudinal trajectories of glioblastoma ecosystems by integrative single-cell genomics” by Roel Verhaak et al. Nature Genetics
Abstract
The multilayered transcriptional architecture of glioblastoma ecosystems
In isocitrate dehydrogenase wildtype glioblastoma (GBM), cellular heterogeneity across and within tumors may drive therapeutic resistance.
Here we analyzed 121 primary and recurrent GBM samples from 59 patients using single-nucleus RNA sequencing and bulk tumor DNA sequencing to characterize GBM transcriptional heterogeneity.
First, GBMs can be classified by their broad cellular composition, encompassing malignant and nonmalignant cell types.
Second, in each cell type we describe the diversity of cellular states and their pathway activation, particularly an expanded set of malignant cell states, including glial progenitor cell-like, neuronal-like and cilia-like.
Third, the remaining variation between GBMs highlights three baseline gene expression programs.
These three layers of heterogeneity are interrelated and partially associated with specific genetic aberrations, thereby defining three stereotypic GBM ecosystems.
This work provides an unparalleled view of the multilayered transcriptional architecture of GBM.
How this architecture evolves during disease progression is addressed in the companion manuscript by Spitzer et al.
Abstract
Deciphering the longitudinal trajectories of glioblastoma ecosystems by integrative single-cell genomics
The evolution of isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GBM) after standard-of-care therapy remains poorly understood.
Here we analyzed matched primary and recurrent GBMs from 59 patients using single-nucleus RNA sequencing and bulk DNA sequencing, assessing the longitudinal evolution of the GBM ecosystem across layers of cellular and molecular heterogeneity.
The most consistent change was a lower malignant cell fraction at recurrence and a reciprocal increase in glial and neuronal cell types in the tumor microenvironment (TME).
The predominant malignant cell state differed between most matched pairs, but no states were exclusive or highly enriched in either time point, nor was there a consistent longitudinal trajectory across the cohort. Nevertheless, specific trajectories were enriched in subsets of patients.
Changes in malignant state abundances mirrored changes in TME composition and baseline profiles, reflecting the co-evolution of the GBM ecosystem.
Our study provides a blueprint of GBM’s diverse longitudinal trajectories and highlights the treatment and TME modifiers that shape them.
