"Because other analysis tools are not developed to account for the particularities of cancer genomics data, they often pick up false positives that could lead to incorrect clinical and biological interpretations," said Isidro Cortes-Ciriano, Group Leader at EMBL-EBI. "SAVANA changes this. By training the algorithm directly on long-read sequencing data from cancer samples, we created a new method that can tell the difference between true cancer-related genomic alterations and sequencing artefacts, thereby enabling us to elucidate the mutational processes underlying cancer using long-read sequencing with unprecedented resolution."
Optimised for Clinical Use
While many genomic tools are confined to the realm of academic research due to their complexity or computational demands, SAVANA was built with the clinic in mind. The researchers recognized that for an algorithm to be truly transformative, it must be fast, robust, and practical for use in a hospital setting where time is often of the essence.
"When we developed SAVANA, our focus was clear: create a tool sophisticated enough to characterise complex cancer genomes but practical enough for clinical use," explained Hillary Elrick, a former Predoctoral Fellow at EMBL-EBI and currently a Postdoctoral Fellow at the Francis Crick Institute.
The algorithm provides a comprehensive profile of a tumor’s genetic health. It is capable of accurately distinguishing somatic structural variants and copy number aberrations—changes in the number of copies of a particular gene. Furthermore, SAVANA can estimate tumor purity (the proportion of cancer cells in a sample) and ploidy (the number of sets of chromosomes in a cell). Each of these factors is a vital piece of the puzzle for oncologists attempting to understand a tumor’s biology and determine the most effective course of treatment.
Carolin Sauer, a Postdoctoral Fellow at EMBL-EBI, emphasized the clinical relevance of these features. "As a result, SAVANA can accurately distinguish somatic structural variants, copy number aberrations, tumour purity, and ploidy—all key to understanding tumour biology and guiding clinical treatment decisions," she said.
The practical utility of SAVANA was recently demonstrated in a study of osteosarcoma, a rare but highly aggressive form of bone cancer that primarily affects children and young adults. Osteosarcoma is known for its genomic instability, making it an ideal candidate for testing SAVANA’s capabilities. Using the new algorithm, researchers were able to uncover previously hidden genomic rearrangements, providing fresh insights into how this specific cancer evolves and progresses.
To further validate the tool, the team compared SAVANA’s results from long-read data against the current clinical standard: Illumina short-read sequencing analyzed through a traditional whole-genome pipeline. The findings were highly consistent, proving that SAVANA performs on par with established methods while offering the added benefit of identifying complex alterations that short-read technologies often miss.
