Katharina Buczek
Aside from identifying these master kinases, though, the researchers also went one step further. By using “patient-derived tumor avatars”– or tumor organoids from patient samples that were grown in the laboratory– the team found that targeted drugs that interfere with master kinase activity can prevent tumor growth.
In the past, Dr. Iavarone and his team also reported a new classification of glioblastomas. This was completed by identifying critical tumor cell traits, as well as grouping GBM patients together based on the likelihood of survival and their tumor’s drug vulnerability.
So, during this new study, the researchers independently confirmed the classifications via numerous omics platforms– including proteomics (proteins), genomics (genes), lipidomics (fat), metabolomics (metabolites), and more.
SPHINKS is also able to leverage machine learning and refine these datasets, creating an interactome– or a full biological interaction data set.
Through this interactome, kinases that prompt abnormal growth and treatment resistance in every glioblastoma subtype can be pinpointed.
So, multi-omics data can essentially generate novel algorithms that are able to predict what targeted therapies would provide optimal therapeutic options depending on a patient’s glioblastoma subtype.
“We can now stratify glioblastoma patients based on biological features that are common between different omics. Reading the genome alone has not been enough. We have needed more comprehensive data to identify tumor vulnerabilities,” explained Dr. Lavarone.
This breakthrough is monumental– since the five-year survival rate for glioblastoma patients is below 10%.
And while various new drugs are currently being developed for potential therapies, being able to identify the molecular mechanisms that drive each patient’s disease will help improve treatment offerings.
According to the researchers, the SPHINKS algorithm is capable of just that and can be readily integrated into molecular pathology labs. Afterward, the team believes this approach could benefit up to 75% of glioblastoma patients.
And despite SPHINKS being first tested on glioblastoma, the AI algorithm is also applicable to various other cancers. More specifically, the team found the same kinases that drive cancer in lung, breast, and pediatric brain tumors.
