The picture reveals the SPHINKS community for the precision concentrating on of grasp kinases in glioblastoma. Credit score: Antonio Iavarone, M.D.
Findings may introduce new and correct AI-based alternatives within the scientific setting, doubtlessly resulting in customized therapies for sufferers with in any other case deadly types of most cancers.
Scientists at Sylvester Complete Most cancers Middle on the College of Miami Miller College of Medication, collaborating with worldwide researchers, have developed a classy AI algorithm that performs superior computational evaluation to determine potential therapeutic targets for glioblastoma multiforme (GBM) and different cancers.
Their analysis is described within the February 2 problem of the journal Nature Most cancers and will have profound implications for future therapy of GBM, an aggressive, normally deadly kind of mind most cancers, and sure breast, lung, and pediatric cancers.
“Our work represents translational science that gives speedy alternatives to alter the way in which glioblastoma sufferers are routinely managed within the clinic,” defined Antonio Iavarone, M.D., deputy director of Sylvester Complete Most cancers Middle and senior writer of the examine. “Our algorithm presents functions to precision most cancers medication, giving oncologists a brand new software to battle this lethal illness and different cancers as properly.”
The AI algorithm, referred to as SPHINKS – Substrate PHosphosite-based Inference for Community of KinaseS – deployed deep-machine learning to help the researchers identify and experimentally validate two protein kinases (PKCd and DNAPKcs) as the culprits associated with tumor progression in two GBM subtypes and as potential therapeutic targets for other cancers.
Protein kinases are the key targets currently used in precision cancer medicine to tailor treatment to a patient’s specific cancer properties. The most active kinases, which the researchers labeled “master kinases” in their paper, are those for which clinicians direct targeted drugs as a hallmark of current cancer treatment.
In addition to identifying the master kinases, Dr. Iavarone and colleagues used tumor organoids grown in the laboratory from patient samples – what they called “patient-derived tumor avatars” – to show that targeted drugs that interfere with the activity of master kinases can thwart tumor growth.
Previously, Dr. Iavarone and team had reported a new glioblastoma classification by capturing key tumor cell traits and grouping GBM patients based on their likelihood of survival and their tumor’s vulnerability to drugs. In the new study, these classifications were independently confirmed through several omics platforms: genomics (genes), proteomics (proteins) lipidomics (fat molecules), acetylomics (epigenetics), metabolomics (metabolites) and others.
SPHINKS leverages machine learning to refine these omics datasets and create an interactome –a complete set of biological interactions – to pinpoint the kinases that generate aberrant growth and treatment resistance in each glioblastoma subtype. These findings show that multi-omics data can generate new algorithms that predict which targeted therapies can provide the best therapeutic options based on each patient’s glioblastoma subtype.
“We can now stratify glioblastoma patients based on biological features that are common between different omics,” Dr. Iavarone said. “Reading the genome alone has not been enough. We have needed more comprehensive data to identify tumor vulnerabilities.”
Despite breakthroughs for many other cancers, glioblastoma patients face dismal prognoses – the five-year survival rate is below 10%. Although numerous drugs are being developed as potential therapy, clinicians have needed a way to identify the molecular mechanisms that drive each patient’s disease and are applicable to precision cancer medicine.
The SPHINKS algorithm and related methods can be readily incorporated into molecular pathology labs, according to the researchers. Their paper includes a clinical classifier that can help assign the appropriate glioblastoma subtype to each patient. The team has also established an online portal to access the algorithm. The authors believe this approach can produce insightful information that could benefit as many as 75% of glioblastoma patients.
“This classifier can be used in basically any lab,” said Anna Lasorella, M.D., professor of biochemistry and molecular biology at Sylvester CCC and co-senior author on the study. “By importing the omics information into the web portal, pathologists receive classification information for one tumor, ten tumors, however many they import. These classifications can be applied immediately to patient care.”
While SPHINKS was first tested on glioblastoma, the algorithm is equally applicable to several other cancers. The team found the same cancer-driving kinases in breast, lung and pediatric brain tumors. Drs. Iavarone and Lasorella and colleagues believe this finding could be the impetus for a new type of clinical trial.
“We are exploring the concept of basket trials,” Dr. Iavarone explained, “which would include patients with the same biological subtype but not necessarily the same cancer types. If patients with glioblastoma or breast or lung cancer have similar molecular features, they could be included in the same trial,” he continued. “Rather than doing multiple trials for a single agent, we could conduct one combined trial and potentially bring more effective drugs to more patients faster.”
Reference: “Integrative multi-omics networks identify PKCd and DNA-PK as master kinases of glioblastoma subtypes and guide targeted cancer therapy” 2 February 2023, Nature Cancer.
DOI: 10.1038/s43018-022-00510-x
This work was supported by National Institutes of Health grant nos. U54CA193313, R01CA239721 and R01CA268592 (to A.L.); U54CA193313, R01CA190891, R01CA268592, R01CA239698 and R35CA253183; NCI P30 Supplement GBM CARE-HOPE; the Chemotherapy Foundation (to A.I.); and the Italian Association for Cancer Research Project IDs 21846 (IG) and 21073 (5 per mille) (to M.C.). S.M. is recipient of a fellowship from the Italian Association for Cancer Research.
Drs. Lasorella and Iavarone are inventors of a biomarker technology that has been licensed to QIAGEN. Dr. Iavarone received sponsored research funding from AstraZeneca and Taiho Pharmaceutical and has served as a paid consultant/advisor to AIMEDBIO. Dr. Lasorella received sponsored research funding from Celgene. Both are inventors of a patent application based on this work. All other authors declare no competing interests.
Sylvester Comprehensive Cancer Center, part of UHealth — University of Miami Health System and the University of Miami Miller School of Medicine, is the only National Cancer Institute (NCI)-designated and nationally ranked cancer center in South Florida, according to U.S. News & World Report 2022-2023 Best Hospitals for Cancer. NCI designation is the “gold standard” for cancer centers and recognizes that Sylvester has met the most rigorous standards for cancer research, beginning in our laboratories, extending to patient care, and addressing specific needs in our community. It also offers a Phase 1 clinical trials program – the first step in evaluating how patients respond to the latest investigational treatments. Equipped with a highly qualified team of nearly 2,500 cancer-focused physicians, researchers, and support staff working together, Sylvester discovers, develops, and delivers more precision cancer care. To serve current and future patients, Sylvester has a network of 10 conveniently located outpatient treatment facilities throughout South Florida.
