This is how cancerous cells deceive healthy brain cells to evade the immune system and develop resistance to treatment, according to a new study.
Some cancers are more challenging to treat because they include cells that are very good at passing for healthy cells and avoiding therapies or our immune systems.
Take glioblastoma, an incurable form of brain cancer, for instance. It is characterized by cells with a remarkable ability to mimic human neurons, even developing axons and establishing active connections with normal brain neurons.
This cancer is particularly deadly, with an average survival time slightly exceeding one year from diagnosis. Its deadly nature stems from frequent recurrence post-initial treatment, and these recurrent tumors consistently exhibit resistance to therapy.
However, a recent study led by researchers at the University of Miami Miller School of Medicine’s Sylvester Comprehensive Cancer Center and other institutions sheds light on this neuron mimicry and potential therapies to combat treatment resistance.
Their findings were published today in the journal Cancer Cell.
“Our findings were made possible by a unique approach to studying glioblastoma,” explained lead author Antonio Iavarone.
Iavarone said that they used a platform made to examine the whole proteome—the collection of proteins found in glioblastoma cells—as well as particular changes to those proteins that signify the activity of enzymes inside the cells.
“These platforms can provide a landscape of alterations in individual tumors that you cannot get from genetics alone,” he said.
The largest dataset available to date
The study team compiled the biggest dataset of its type, which included matched tumor samples from 123 glioblastoma patients at diagnosis and recurrence after the first treatment. Researchers were able to uncover key alterations not previously found in prior cancer studies that evaluated the tumors’ genomes or transcriptomes, the collection of RNA molecules in cancer cells, by investigating the proteomes and protein modifications in the samples.
According to the researchers, this is the first time scientists have utilized proteomics to analyze the change of glioblastoma from curable to treatment-resistant. By examining cancer proteins and their changes, including phosphorylation, they discovered that glioblastoma cells were in a proliferative stage before therapy, where the cells expended energy duplicating themselves.
Because cancer cells grow faster than healthy cells, several chemotherapies target cell activities in self-replication. When glioblastoma tumors recurred months later, the cells appeared drastically different – and more like healthy neurons.
The team says that this replication-to-neuronal shift enables cancer cells avoid being destroyed by the first glioblastoma treatment, which is often a mix of chemotherapy, radiation, and surgery.
“The tumor cells actually resemble normal brain cells,” remarked Dr. Simona Migliozzi – 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.”
Identifying glioblastoma weaknesses
The team then utilized this new information to find possible medicines that may kill these resistant tumors, concentrating on enzymes called kinases that phosphorylate other proteins. Migliozzi and colleagues used a machine-learning technique they had previously developed to identify the most active kinases in neuron-like glioblastomas. Kinases are essential for a variety of cellular activities and are major targets for several FDA-approved cancer treatments.
Among the kinases scrutinized, one emerged prominently: BRAF. While the gene responsible for encoding this kinase often undergoes mutations in certain cancers, such as melanoma, an intriguing distinction surfaced in glioblastoma—BRAF protein levels escalated without concurrent alterations in the corresponding gene.
They next tried vemurafenib, an existing BRAF inhibitor, on treatment-resistant glioblastoma cells in a petri dish and a patient-derived xenograft tumor in mice. In both instances, the treatment, when used with the chemotherapeutic medicine temozolomide, killed the previously resistant malignancies. In the mouse model, the BRAF inhibitor improved survival over chemotherapy alone.
What’s Next
Iavarone thinks their artificial intelligence method for predicting the most active kinase in glioblastoma may be extended to other cancer types. He and his colleagues are working on a clinical test that would utilize AI to detect therapeutic vulnerabilities in a range of tumors by identifying the highest active kinase in each tumor and treating it with an existing kinase inhibitor.
Iavarone and colleagues are now planning a clinical study for glioblastoma with vemurafenib or another BRAF inhibitor medication. They want to treat trial participants with the inhibitor from the start to prevent the tumors from becoming resistant.
“Proteomics give us a much more direct prediction of protein activity,” Iavarone added. “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.”
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