For children diagnosed with neuroblastoma, the most common infant cancer that occurs when early nerve cells grow out of control, the path to treatment is not easy. Some types of neuroblastoma may heal on their own, while others require aggressive intervention. Researchers have tried to tailor treatments to patients based on a single gene mutation, but with limited success. This is because patient outcomes depend on the entire molecular background, which includes millions or even billions of features, such as DNA and RNA from tissues and blood.
“It’s not just one gene; everything that’s going on inside a patient’s cells is important,” said Orie Alter, associate professor of biomedical engineering at the University of Utah and researcher in Scientific Computing and Imaging and the Huntsman Cancer Institute’s Cancer Control and Population Sciences Program.
Current artificial intelligence and machine learning (AI/ML) approaches require large amounts of training data, especially patient samples that are much larger than genetic features. For this reason, most clinical trials, which typically enroll only 20 to 100 people, are not very good at predicting patient outcomes. For example, a recent large-scale language model of the 30,000-nucleotide genome of the coronavirus required approximately 110 million samples. Translating this into the 3 billion nucleotide human genome, traditional AI approaches would require 33 trillion patients.
By using the mathematics of quantum mechanics, Alter and his collaborators have developed new AI/ML techniques that can improve treatment selection and drug success rates.
Features of billions of molecules
“Our quantum approach allows us to find relevant information from all layers of data, for example in the patient’s blood as well as the tumor,” Alter said. “We can take in and understand all of the millions to billions of molecular signatures of even a small number of patients. Therefore, we can understand disease mechanisms and predict drug targets to improve patient outcomes. We are also experimentally validating AI/ML predictions of targets and outcomes, widely considered the holy grail of biotechnology.”
The technique deploys a set of algorithms called multitensor comparative spectral decomposition, which Alter built on the quantum mechanical concepts of entanglement and superposition. Like a prism that splits white light into individual colors, this approach breaks down multiple layers of a patient’s molecular data, including the tumor, blood genome, and tumor transcriptome (or the RNA messages that drive cancer growth) into linked patterns that predict health outcomes.
Alter and her team demonstrated their technology by analyzing open-source data from neuroblastoma cases. The algorithm discovered two new predictors of a patient’s life expectancy in response to treatment. These predictors consistently outperformed standard biomarkers across tumors, blood DNA, and tumor RNA. These findings hold true across 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 treatments
“While neural network models are black boxes, our predictors are interpretable. They point to disease mechanisms and suggest genes to target to increase tumor treatment sensitivity,” Alter said. Her team also experimentally tested predictions of outcome and drug targets for adult glioblastoma patients in clinical trials and preclinical studies using the gene editing tool CRISPR-Cas9.
Alter, an expert in computational medicine, holds a part-time position in the Department of Human Genetics, part of the US College of Medicine. Her university-independent company, Prism AI Therapeutics, Inc., helps biotech and pharmaceutical companies better develop drugs by using algorithms and predictors to identify which patients will benefit most from clinical trials and which genes can be targeted to further improve outcomes.
Looking to the future, Alter hopes that as the team continues this research, it will be possible to apply it to individual patients. “This is the ultimate in precision medicine,” she said. “You have one person. Can you take data from that one person and come up with a treatment for that person? I think we can accomplish that.”
Alter also hopes to use quantum approaches to solve other challenges. “Algorithms are completely data agnostic and could have endless applications beyond medicine,” she said, highlighting sustainable energy as one possibility.
reference: Alter O, Newman E, Ponnapalli SP, Tsai JW. Quantum mechanics-based multitensor AI/ML can uniquely discover, validate, and interpret predictive variables from noisy, high-dimensional multi-omics data in small cohorts. APL quantum. 2026;3(2):026116. doi: 10.1063/5.0305656
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