AI System Aims to Speed Personalized Cancer Care for Children With $200,000 Cloud Award

An artificial intelligence platform designed to help clinicians navigate the overwhelming complexity of pediatric cancer data has received a major boost. Sanford Burnham Prebys Medical Discovery Institute announced that it has been selected as a winner of the 2026 Amazon Web Services Imagine Grant in the Children’s Health Innovation Award category, a grant opportunity created to accelerate innovation that improves children’s health outcomes through the use of advanced cloud services. The award will provide 100,000 dollars in funding and 100,000 dollars in AWS Promotional Credits to support a project led by Lukas Chavez, PhD, an associate professor in the institute’s Cancer Genome and Epigenetics Program, to develop an AI-powered system that helps clinicians analyze complex data from children with cancer and identify personalized treatment options more quickly.

Chavez is also a member of the NCI-Designated Cancer Center and the Center for Data Science and Artificial Intelligence at Sanford Burnham Prebys, positions that place him at the intersection of cancer biology and computational science. He was named a recipient in the Children’s Health Innovation Award category, which recognizes highly innovative, mission-critical projects that accelerate children’s health innovation using advanced cloud services. Beyond the funding and computing credits, the award includes implementation support from technical specialists, a component that can prove decisive when translating a research prototype into a system that clinicians can actually rely on at the bedside. Proposals were evaluated on several criteria, including the innovative and unique nature of the project, its impact on mission-critical goals, and the presence of clearly defined outcomes and milestones.

The award arrives at a moment when the volume of data generated for a single pediatric cancer patient has grown far beyond what any individual clinician can synthesize. Modern molecular diagnostics can sequence a child’s tumor, profile its epigenetic landscape, measure gene expression, and test drug responses in laboratory models, all alongside conventional clinical records, imaging reports, and treatment histories. Each of these data streams carries clues about what might work for that specific child, but the information typically lives in separate systems, uses different technical vocabularies, and demands different kinds of expertise to interpret. Molecular tumor boards, the multidisciplinary committees that convene to weigh this evidence, do remarkable work, yet assembling and reviewing the full picture can take time that children with aggressive cancers may not have.

Chavez and his team plan to address this bottleneck with a system called Leitstern, a German word for a star that provides orientation. The name captures the project’s ambition: rather than replacing clinical judgment, the system is intended to serve as a guiding reference point amid a sea of data. Leitstern will bring together molecular, clinical, and functional data from individual patients and connect that information with knowledge drawn from scientific literature, drug databases, and clinical trial registries. In practice, this means the system could link a specific mutation found in a child’s tumor to published studies of targeted drugs, to records of how similar tumors have responded, and to open clinical trials that might be appropriate to consider.

What distinguishes the technical approach is its use of specialized AI agents, software components designed to analyze different types of data and generate evidence-based treatment hypotheses for clinicians to review. This agent-based architecture reflects a broader shift in applied artificial intelligence, away from a single monolithic model and toward coordinated systems in which each component handles the data type it is best suited to interpret. One agent may focus on genomic alterations, another on functional screening results, another on matching findings to trial eligibility criteria. Their outputs converge into treatment hypotheses that remain, crucially, advisory: the final decisions rest with the clinicians who review them.

Transparency and privacy are built into the design from the start. Patient data will be de-identified before it enters the system, and Leitstern is being engineered so that every recommendation can be traced back to its underlying sources. In a clinical domain where a single suggestion can alter the course of a child’s treatment, this kind of traceability is not a luxury but a requirement. Clinicians need to see not only what the system proposes but why, which evidence supports the proposal, and how strong that evidence is. By making the chain of reasoning inspectable, the team aims to build the trust that any clinical decision-support tool must earn before it can change practice.

The project also comes with unusually concrete evaluation goals, developed by Chavez together with graduate student Christopher Brown. The team will compare the system’s recommendations with decisions previously made by molecular tumor boards, providing a direct benchmark against expert human judgment. They will assess the reproducibility of its results, an essential quality for any tool intended for clinical settings, and measure how quickly it can produce reports, with a target of completing analyses in less than 24 hours. The team further aims for 100 percent traceability of the system’s predictions back to their supporting evidence. Eventually, the researchers plan to test the system prospectively, analyzing new cases before they are reviewed by a tumor board and then comparing the system’s findings with the board’s recommendations, a study design that offers a rigorous, real-time test of the technology’s value.

For Chavez, the motivation is fundamentally about time. Children with cancer can generate an enormous amount of complex clinical, molecular, and functional data, and the goal, he explained, is to bring that information together in a way that helps clinicians identify potential treatment options more quickly. For children with aggressive cancers, he noted, time is critical, and reducing delays in identifying treatment options can make a meaningful difference. With Leitstern, the team wants to use AI to help make sense of that complexity while ensuring that every recommendation can be traced back to the evidence that supports it. That framing, acceleration without sacrificing accountability, captures the central tension in bringing artificial intelligence into clinical medicine.

The recognition also reflects a deliberate strategy by AWS to direct cloud resources toward pediatric health. By focusing on children’s health, the company seeks to bring more attention to the wide variety of causes and care organizations dedicated to helping children live longer, healthier lives through the strategic use of cloud technology. Rick Buettner, Global Director of Nonprofits at AWS, said that this year’s Children’s Health Innovation Award recipients are doing extraordinary work to improve the lives of children, from the everyday moments to the life-changing ones. He pointed to projects ranging from using cloud technology to improve bus routes for special needs students to leveraging AI to answer pediatric health questions without subjecting minors to lengthy clinical studies, describing these organizations as reimagining what is possible for children and saying AWS is honored to support nonprofits whose work is creating lasting, meaningful change.

The Imagine Grant program, launched in 2018, has since awarded more than 21 million dollars in unrestricted funding, AWS cloud computing credits, and technical expertise to more than 170 nonprofit organizations worldwide. For Sanford Burnham Prebys, an independent not-for-profit biomedical research institute whose research spans six centers, including its NCI-designated Cancer Center, with expertise in cardiovascular, neurologic, and metabolic diseases, data science and artificial intelligence, and therapeutic discovery, the award extends an existing strength in computational biology into one of its most consequential applications. If Leitstern meets its milestones, delivering reproducible, fully traceable treatment hypotheses in under a day, the project could offer a template for how cloud-based AI systems support precision oncology for children, where the stakes are highest and every hour of delay matters.

Subject of Research: AI-powered clinical decision support for personalized pediatric cancer treatment

Article Title: Lukas Chavez from Sanford Burnham Prebys named recipient of 2026 AWS Children’s Health Innovation Award

Article References: Lukas Chavez from Sanford Burnham Prebys named recipient of 2026 AWS Children’s Health Innovation Award. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: pediatric cancer, artificial intelligence, precision oncology, AWS Imagine Grant, Sanford Burnham Prebys, molecular tumor boards, cloud computing, clinical decision support, Lukas Chavez, Leitstern, data science, children's health