Proteomics Highlights Potential New Targets for Glioblastoma Treatment


The brain cancer glioblastoma (GBM) is characterized by the presence of cells that can mimic human neurons, even growing axons and making active connections with healthy brain neurons. This cancer almost always recurs after initial treatment, and recurrent tumors are always resistant to therapy.

Using a platform that allows researchers to analyze glioblastoma cells’ proteome and protein modifications as the cancers evolve. Researchers headed by a team at the Sylvester Comprehensive Cancer Center at the University of Miami Miller School of Medicine have now provided new insights into this neuron mimicry and identified a potential therapeutic approach to prevent recurrence and treatment resistance. Their in vivo studies showed that using temozolomide chemotherapy in combination with the BRAF kinase inhibitor vemurafenib significantly improved survival in mice carrying patient-derived glioblastoma xenografts.

“Our findings were made possible by a unique approach to studying glioblastoma,” said Antonio Iavarone, MD, deputy director at Sylvester who led the study with Jong Bae Park, PhD, of the National Cancer Center in Korea. “These platforms can provide a landscape of alterations in individual tumors that you cannot get from genetics alone … Proteomics give us a much more direct prediction of protein activity. We hope this analysis can be seamlessly translated into the clinic as a next-generation precision therapy for this very challenging disease and other resistant cancers as well.”

Glioblastoma cells can evade treatment by mimicking healthy neural cells.
Glioblastoma cells can evade treatment by mimicking healthy neural cells. [Cancer Cell]

Iavarone and colleagues reported on their findings in Cancer Cell, in a paper titled “Integrated proteogenomic characterization of glioblastoma evolution,” in which they concluded, “Our work uncovers the proteomic and phosphoproteomic changes leading to GBM recurrence and provides insights that can be used for diagnostics and precision therapeutics to prevent and/or delay drug resistance.”

Glioblastoma is the most common, and lethal brain cancer in adults, with a median survival time of less than 15 months, the authors wrote. Tumor relapse, which occurs in more than 90% of patients, represents what the team describe as a major roadblock that contributes to such a “dismal prognosis.” And while the standard of care for primary treatment has been established, they continued, “… recurrent GBM remains therapeutically unresolved due to the complexity of tumor heterogeneity and limited understanding of the evolutionary dynamics after therapy.”

Current knowledge of how GBM evolves has been generated through genomic and transcriptomic studies, but such studies haven’t been able to identify the evolutionary pathways leading to tumor resistance to therapy that is invariably associated with recurrent disease. To help provide new insights in glioblastoma tumor evolution and drug resistance, Iavarone and colleagues turned to proteomics, alongside genomics and transcriptomics for the evaluation of tumors. This study represents the first time scientists have used proteomics to study glioblastoma’s transition from treatable to treatment-resistant, according to the researchers. “When combined with genomics and transcriptomics, proteomics provides another paradigm for investigating a hidden dimension of cancer biology that has gone largely undetected,” they wrote.

The team assembled what they say became the largest dataset of its kind, featuring matched tumor samples from 123 glioblastoma patients both at diagnosis, and then recurrence after initial therapy. By studying the tumor proteomes and protein modifications in the samples, researchers were able to detect important changes not previously seen in similar cancer studies that examined the tumors’ genomes or transcriptomes (the set of RNA molecules in cancer cells). “… we performed comprehensive proteogenomic analyses of 123 matched primary and recurrent GBMs and adopted the integration of genomics and deep proteomics characterization to extract the significant changes that drive GBM evolution under therapy,” they wrote. “The primary innovation of the study is the application of proteomics and phospho-proteomics to investigate the evolutionary trajectory of GBM.”

“Proteomics give us a much more direct prediction of protein activity,” said Antonio Iavarone, MD, deputy director at Sylvester and the study’s lead author. “We hope this analysis can be seamlessly translated into the clinic as a next-generation precision therapy for this very challenging disease and other resistant cancers as well.”
“Proteomics give us a much more direct prediction of protein activity,” said Antonio Iavarone, MD, deputy director at Sylvester and the study’s lead author. “We hope this analysis can be seamlessly translated into the clinic as a next-generation precision therapy for this very challenging disease and other resistant cancers as well.” [Photo by Sylvester]

By looking at cancer proteins and protein phosphorylation, the investigators demonstrated that before treatment, glioblastoma cells were in a proliferative state where the cells expend energy toward replicating themselves. Many chemotherapies work by targeting the cell functions in self-replication, as cancer cells typically grow faster than healthy cells.

 

However, the team found that when tumors recurred in glioblastoma patients months later, the cells looked very different—and more like healthy neurons. “The proteogenomic inspection of the longitudinal GBM cohort identified the activation of neuronal programs as the primary mechanism of evolution driving tumor recurrence after therapy,” they stated, reasoning that there is something about this replication-to-neuronal transition that helps cancer cells evade being killed by the initial glioblastoma treatment, which is usually a combination of chemotherapy, radiation, and surgery.

“The tumor cells actually resemble normal brain cells,” said Simona Migliozzi, PhD, an assistant scientist at Sylvester and one of the study’s lead authors. “Why? Because tumor cells want to survive, they want to live, and they’re able to acquire therapy resistance by mimicking the normal brain.”

Migliozzi and colleagues then used the new dataset to identify potential therapies that could target and kill these resistant cancers, and focused on kinase enzymes that are responsible for phosphorylating other proteins. Kinases are important for many different cellular functions and are key targets for many FDA-approved cancer drugs.

To do this, the team deployed a machine-learning approach they had developed previously to find the most active kinases in the neuron-like glioblastomas. One kinase, BRAF, stood out. The gene encoding this kinase is commonly mutated in some cancers, including melanoma, but in glioblastoma, the team found that BRAF protein levels increase without corresponding gene changes. This discovery wouldn’t have been made without examining the cancer proteome. “The proteogenomic characterization, at multi-omics levels, of longitudinal GBM presented here has allowed us to address biological questions on this lethal disease that remained unexplored …” they stated.

Writing in their paper, the team speculated that the physiological functions of BRAF are hijacked by glioma cells promoting axon growth and synaptogenesis, and so driving glioma invasion and connectivity with normal brain cells and affecting patient outcome. “Our results support the notion that BRAF blockade may reverse these changes, thus introducing BRAF inhibitors as a potential therapeutic option for recurrent GBM,” they further noted.

The researchers tested an existing BRAF inhibitor, vemurafenib, on treatment-resistant glioblastoma cells in vitro, and also in a patient-derived xenograft tumor in mice. In both cases the drug, when used in combination with temozolomide chemotherapy, knocked down the formerly resistant tumors. In the mouse model, the BRAF inhibitor extended the animals’ survival when compared with chemotherapy alone.

Iavarone believes that the artificial-intelligence algorithm to predict glioblastoma’s most active kinase could be applied to other cancer types. The team is now working to develop a clinical test that would use AI to identify therapeutic weaknesses in a variety of cancers by finding each tumor’s most active kinase and treating it with an existing kinase inhibitor.

Presently, Iavarone and colleagues are discussing plans for a clinical trial testing vemurafenib or another BRAF inhibitor drug for glioblastoma. They plan to treat trial patients with the inhibitor from the start to prevent the cancers from transitioning to the resistant state.





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