By carefully combining information from previous studies with the results from these two recent clinical trials, the researchers showed that the therapies tested in the recent trials now show stronger evidence of meaningfully improving outcomes in children with high-risk Group 3 medulloblastoma.

High-risk Group 3 medulloblastoma is a fast-growing and notoriously hard-to-treat childhood brain cancer. Because so few children are diagnosed each year, even large national studies can enroll only a handful of patients. In one of the trials, only 10 children with this tumor type received the new therapy. In another, only 43 children were treated, despite the fact that the trial was open in dozens of cancer centers in the United States.

Unfortunately, these numbers are too low to thoroughly evaluate a therapy’s effectiveness using traditional analysis methods.

“These small numbers make it extremely difficult for traditional statistical methods to show with certainty whether the therapies truly work,” said , a neurosurgeon and scientist at UofL and who led the reanalysis study. “As a result, promising treatments for these children can remain in limbo – not because they fail, but because the evidence isn’t strong enough using traditional approaches.”

To overcome this challenge, the UofL team used a novel statistical approach called dynamic borrowing via Bayesian models, which carefully “borrows” information from previous studies to strengthen the results of new trials. The idea is to let the model learn how similar the past and present data are, and to borrow more past data that match and less when they differ. The researchers ran 10,000 computer simulations using this process, ensuring that the findings remained both reliable and not artificially inflated.

Using this method, they reanalyzed data from two recent national trials and found a greater than 90% probability that the therapies tested in the clinical trials truly do provide benefit for children with high-risk Group 3 medulloblastoma. The therapies that had limited statistical power under traditional analyses now appear strongly promising under the new approach and as a result, may warrant renewed consideration as effective treatment options.

For children and families facing the devastating diagnosis of this aggressive cancer, these findings bring renewed hope that these treatments are not only worth trying but also are likely to be effective.

The research team in September.

“This work is part of a larger effort at UofL to modernize how we design, conduct and analyze clinical trial data, helping scientists and physicians learn as much as possible from the small, precious data that take years to collect in rare diseases,” Mistry said. “Our goal is to make the most of every patient’s experience – past and present – to improve the care of future patients. It is our way of honoring every child and adult who participates in clinical trials by ensuring their contributions continue to shape the treatments of tomorrow.”

Mistry, who was profiled in the ��also led a team that recently published the , showing the composition of tumors at the genomic level, combined with clinical information such as patient age, tumor location and survival outcomes. This�� is a free, publicly available tool that promises to speed up the discovery of treatments for brain and nerve tumors, especially rare ones that have had limited research attention, like Group 3 medulloblastomas.

This project was supported by the Kentucky Pediatric Cancer Research Trust Fund and the Kentucky Department for Public Health. Mistry’s work also is supported by the Louisville Clinical and Translational Research Center at UofL and by a UofL Presidential Scholars award.

A team led by Akshitkumar Mistry, a neurosurgeon and scientist at UofL and UofL Health, has created the��. It is the largest publicly available dataset of its kind and includes more than 7,000 tumor samples from the brain, spinal cord and nerves, which were drawn from biorepositories around the world. For each sample, the atlas combines gene activity data with clinical information such as patient age, tumor location and survival outcomes.

“Brain and nervous system tumors are incredibly diverse,” Mistry said. “This tool helps researchers quickly understand how these tumors behave at the genomic level, which can point the way to new or repurposed treatments that might be tested in clinical trials.”

The team built the atlas by applying advanced machine learning techniques to tumor genomic data. It includes many extremely rare tumor types for which data have previously been sparse or unavailable.

Kentucky ranks among the highest in the nation for . Although brain cancers are rare, they are especially devastating and can affect people at any age. They are more common in children under 15 and adults over 64, and their causes remain poorly understood, leaving few options for prevention.

The atlas project was supported by the Kentucky Pediatric Cancer Research Trust Fund and the Kentucky Department for Public Health. Mistry’s work also is supported by the at UofL and by a UofL Presidential Scholars award.

A new tool to identify therapies

While the atlas is not meant to provide direct treatment recommendations, emphasized that it will enable researchers to classify tumors and identify therapies based on how their genes behave, going beyond traditional DNA mutations or histology. This deeper classification can reveal biological similarities to other cancers that already have approved therapies.

In his article about the atlas, published in the journal , Mistry and colleagues showed how the atlas identified new subtypes of��pheochromocytoma and paraganglioma, rare tumors of the nervous system. These subtypes expressed certain genes, such as��GHR��and��SST, at high levels. Drugs that target these genes already exist for other cancers and these subtypes can be easily identified by genetic analysis. Without examining the gene activity, clinicians would have no reason to test these drugs in patients with these rare tumors. Yet this valuable information is available in the atlas.

“These are therapies that are already out there, but no one had thought to try them in these tumors,” Mistry said. “Now by using the atlas, we have the data to support testing them in clinical trials.”

Eyas Hattab, chair of UofL’s Department of Pathology and Laboratory Medicine and co-author of the article on the atlas, said it supports the development of personalized and targeted therapies for neurological cancers.

“Over the past two decades, brain tumor diagnostics have undergone transformative advances, driven by the integration of cutting-edge molecular and genetic technologies,” Hattab said. “The work by Dr. Mistry and colleagues builds on this progress by mapping RNA transcripts across a spectrum of CNS tumors, significantly enhancing diagnostic precision.”

UofL Health – Brown Cancer Center recently became the first facility in the U.S. to offer its patients access to an . The Illumina TruSight Oncology (TSO) Comprehensive test essentially scans a patient’s tumor sample for cancer mutations, evaluating both the DNA and RNA of a patient’s tumor to ensure they are matched with the most effective therapies or available clinical trials.

“The new atlas complements technologies such as the TSO Comprehensive by providing a broader view of how tumors function at the RNA level — essentially, what the tumor is��doing��rather than just what it��is,” Mistry said.

Adding to the impact of their work, the methodology that Mistry and the research team used to build the atlas can also be applied to other rare diseases, giving researchers a powerful new framework to reuse and harmonize public data for maximum impact.