For a child diagnosed with neuroblastoma—the most common childhood cancer, which occurs when early nerve cells grow out of control—the path to a cure is not straightforward. Some types of neuroblastoma go away on their own, while others require aggressive intervention. Researchers have tried matching treatments to patients based on single-gene mutations with limited success. This is because patients’ outcomes depend on their entire molecular background containing millions or even billions of features, such as DNA and RNA from tissues and blood.
It’s much more than a single gene – everything that happens in the patient’s cells matters.”
Orly Alter, Associate Professor of Biomedical Engineering, University of Utah Institute for Computing & Imaging Sciences
Current artificial intelligence and machine learning (AI/ML) approaches require a huge amount of training data and, in particular, many more patient samples than genetics. This makes them poorly suited for predicting patient outcomes in most clinical trials, which typically enroll only 20 to 100 people. For example, a recent large language model of the 30,000-nucleotide genome of the COVID-19 virus required about 110 million samples. Translating this to the human genome of 3 billion nucleotides, a conventional AI approach would need 33 trillion patients.
Using the mathematics of quantum mechanics, Alter and her colleagues developed a new AI/ML technique that can improve treatment options and drug success rates. Their work is featured in the magazine Applied Physics Letters (APL) Quantum.
Billions of molecular features
“Our quantum approach allows us to find the relevant information in each layer of data, for example, from patients’ blood in addition to their tumors,” Alter said. “Even for very few patients, we can take everything—millions to billions of molecular features—and understand them. We can therefore understand disease mechanisms and predict drug targets to improve patient outcomes. We are also experimentally validating our AI/ML target and outcome predictions, which is widely considered the holy grail of biotechnology.”
The technique develops a set of algorithms, called multi-voltage comparative spectral decompositions, which Alter based on the quantum mechanical concepts of entanglement and superposition. Like a prism that separates white light into individual colors, this approach breaks down a patient’s multiple layers of molecular data—such as the tumor and blood genome and the tumor (or the RNA messages that drive cancer growth)—into connected patterns that predict health outcomes.
Alter and her team demonstrated their technique with an open-source data analysis of neuroblastoma cases. The algorithms discovered two new predictors of patients’ life expectancy in response to treatment, and these predictors consistently outperformed standard biomarkers in tumor and blood DNA and tumor RNA. These findings held up in separate groups of children treated at different times and hospitals, meaning the method can be applied to the general population to provide a clearer roadmap for patient care and drug development.
Developing more targeted therapies
“Neural network models are black boxes, but our predictors are interpretable; they indicate disease mechanisms and suggest genes to target to sensitize tumors to therapy,” Alter said. Her team also experimentally validated their predictions of adult glioblastoma patient outcomes and drug targets in clinical trials and preclinical studies, leveraging CRISPR-Cas9, the gene-editing tool.
An expert in computational medicine, Alter holds an adjunct appointment in the U’s Department of Human Genetics and is a member of the Huntsman Cancer Institute’s Cancer Control & Population Sciences Research Program.
Her university’s spinoff company, Prism AI Therapeutics, Inc., uses algorithms and predictors to help biotech and pharmaceutical companies develop better drugs by determining which patients will benefit most from a clinical trial and which genes to target to further improve outcomes.
Looking ahead, Alter hopes that as her team continues this work, they will be able to apply it to individual patients. “This is the ultimate precision medicine,” he said. “You have just one person. Can you take the data from just that one person and find a cure for it? I think we can get there.”
Alter hopes for other challenges as well. “Algorithms are completely data agnostic and there could be endless applications outside of medicine as well,” he said, highlighting sustainable energy as a possibility.
