Multiomic Human Glioblastoma Cell Atlas Could Act as Compass to Guide Future Treatment Approaches

Aggressive glioblastoma (GB) brain tumors follow predictable rules, not chaotic randomness, according to new research reported by scientists at the Wellcome Sanger Institute, the European Molecular Biology Laboratory (EMBL), the German Cancer Research Center (DKFZ), the Cancer Research UK Cambridge Institute, and their collaborators.

For their study, reported in Nature (“A spatiotemporal cancer cell trajectory underlies glioblastoma heterogeneity,”) co-senior author Omer Bayraktar, PhD, at the Wellcome Sanger Institute and Professor at the UCL Cancer Institute, and colleagues mapped over one million cells across glioblastoma tumors, categorizing cancer cells at different stages of their journey within the different environments in glioblastoma tumors. They looked at different time stages and used lab-grown mini tumors to mimic this development. The study findings showed that the cancer cells organize themselves according to a consistent, predictable structural pattern across different patients.

Along with challenging previously held beliefs about brain tumors, the newly discovered roadmap opens new paths for developing targeted treatments. As the cancer cells move along in a predictable way during tumor progression, future drugs could specifically target the early developmental-like cells before they transform into harder-to-reach, resistant states.

The reported work has built a new foundational resource for studies to explore the mechanisms and biology of cancerous brain cells. The researchers have made the resulting GBM-Space atlas is freely accessible.

Using the same single-cell multiomic data, investigators also decoded the gene regulators that drive and restrict glioblastoma cell state transitions, highlighting potential targets that could help avoid treatment resistance. The team stated, “In a companion study, we leveraged the joint transcriptomic and epigenetic data generated here to define and perturb gene regulatory networks controlling GB plasticity. Beyond identifying master regulators of GB states, this orthogonal approach suggested hierarchical transitions across GB cancer states and recapitulated the GB trajectory identified here.” These results are published by co-senior author Bayraktar, and colleagues in their companion paper in Nature, titled “Decoding the regulatory programs governing glioblastoma plasticity.” Together the two papers unify understanding of the architecture of glioblastomas with insights about cancer cell plasticity, giving a holistic view of this cancer type.

Bayraktar said, “Glioblastoma has evaded understanding for years, despite being one of the most studied types of cancer tumor. By building the largest, multi-modal atlas of these tumors to date, we have shown that these tumors are not as chaotic as previously accepted. Instead, they follow a set of rules that can be used to rewrite our understanding of glioblastoma and act as a compass to guide much needed new treatments in the future.”

Glioblastoma is an aggressive form of brain cancer, and the most common type of brain cancer in adults, with an average survival time of 12 to 18 months. The most common glioblastoma treatment, when possible, is surgery followed by a combination of chemotherapy and radiotherapy. But as the authors pointed out, “The extensive tumor heterogeneity of glioblastoma (GB), an incurable adult brain cancer, presents a major obstacle to treatment.”

These tumors are difficult to treat due to their location, and as many tumors invade multiple parts of the brain, which can make surgery difficult or impossible in some cases. The tumors are also made up of multiple, highly dynamic cancer cell states that had previously not seemed to follow any consistent rules or pattern. This means that treatment might not destroy every cell, leading to drug resistance and cancer re-emergence.

“Nearly two decades of molecular profiling has found that malignant cells exist across a spectrum of cellular states within each tumor, resembling developmental neural progenitors as well as mesenchymal-like and injury response states,” the authors noted. “… we still do not understand how cancer cells diversify into distinct states and give rise to GB heterogeneity. In particular, whether malignant cells follow stereotyped or variable cellular trajectories across GB tumors is unknown.”

For their newly reported study the Sanger Institute researchers and their collaborators used state of the art multiomic technologies and tailored machine learning computational tools to map multiple sections of brain tumor samples from 12 glioblastoma patients^. combining single-cell and spatial atlasing, imaging, and whole-genome sequencing data. “Here we applied a holistic multimodal mapping approach to jointly characterize the genetic, cellular and tissue architecture of GB tumors,” they wrote.

The range of information generated offers up a complete understanding of each cancer cell’s molecular state, where it is in the tumor, how it is likely to change over time, how it interacts with the environment around it, and its genetic information. Additionally, the team used samples from multiple regions of the brain tumors, to give a full picture of the cells in each tumor section and how they related to each other.

This allowed the team to see that glioblastoma cells move through the same structural pattern across each patient, following the same rules as the tumor grows. “We identify that glioblastoma heterogeneity is driven by spatially patterned transitions of cancer cells from developmental-like states towards those defined by a glial injury response and hypoxia,” they wrote. The results found that cancer cells in early stages resemble brain stem cells. These developmental-like cells are found at the outer, leading edge of the tumor, where the glioblastoma cells start to invade the surrounding healthy brain tissue. These cells act as a pool giving rise to other cancer cell states.

Then, as the tumor expands, these cells transform into injury response cell states, mimicking cell pathways that are involved in brain tissue repair and wound healing. These injury response cells create an inner structural layer in the tumors. And in the later stages of brain tumor development the core of the tumor is cut off from oxygen, meaning that all cell types transition from injury response into severely hypoxic (oxygen deprived) states.

As well as mapping brain tumor samples, the team developed brain organoids to validate the tumor roadmap. This research also showed that each cancer cell progression stage requires specific immune microenvironments in the tumors.

“This cellular trajectory regionalizes tumors into distinct tissue microenvironments and manifests in a molecularly conserved manner across tumors as well as genetically distinct tumor subclones,” they stated in their paper. “Our findings define a stereotyped trajectory of cancer cells in glioblastoma and unify glioblastoma tumor heterogeneity into a tractable cellular and tissue framework.”

The team suggests that finding ways to interrupt or disrupt these environments could lead to new ways to treat aggressive brain tumors. In the future, new therapeutics could also focus on the gene regulation networks, potentially stopping cell progression, or target the early developmental cells that cannot be easily removed by surgery and often lead to cancer recurrence.

Richard Mair, MBBS, PhD, FRCS (SN), co-author at the Cancer Research UK Cambridge Institute and Cambridge University Hospitals NHS Foundation Trust, said: “For too long, we have approached glioblastoma treatment knowing that each patient’s tumor contains a complex mixture of cancer cells, but without a clear understanding of how best to target them all. This research gives us an opportunity to think differently. If we can anticipate the different biological states a tumor is likely to adopt, we can begin to develop treatments that target multiple aspects of the disease from the very beginning. This could fundamentally change how we design new therapies for glioblastoma, moving towards treatments that are informed not just by what a tumor looks like today, but by what it is likely to become.”

Oliver Stegle, PhD, co-senior author of both studies at the European Molecular Biology Laboratory (EMBL) and the German Cancer Research Center (DKFZ), added, “Combining unbiased single-cell and multiomics techniques with tailored computational tools allowed us to go back to the drawing board and challenge long-held assumptions about glioblastoma. We demonstrate that complex but predictable molecular networks govern cancer cell development. This work shows how computational biology can turn massive datasets into meaningful discoveries for patient care.”

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