"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," explained 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."
This ability to filter out the noise allows researchers to elucidate the mutational processes underlying cancer with an unprecedented level of resolution. The algorithm was rigorously developed and tested across 99 human tumor samples, ensuring its robustness across different types of malignancies and varying levels of genetic complexity.
Optimised for Clinical Use
While many algorithms remain confined to research laboratories, the developers of SAVANA were adamant that the tool be practical for the fast-paced environment of a hospital. Clinical diagnostics require tools that are not only accurate but also rapid and robust enough to handle the variability of real-world patient samples.
Hillary Elrick, a former Predoctoral Fellow at EMBL-EBI and currently a Postdoctoral Fellow at the Francis Crick Institute, emphasized this focus during the development phase. "When we developed SAVANA, our focus was clear: create a tool sophisticated enough to characterise complex cancer genomes but practical enough for clinical use," she said.
The practical utility of SAVANA lies in its comprehensive output. It does more than just identify mutations; it provides a detailed profile of the tumor’s biology. Carolin Sauer, a Postdoctoral Fellow at EMBL-EBI, noted that the algorithm can accurately distinguish somatic structural variants, copy number aberrations, tumor purity, and ploidy. Purity refers to the proportion of cancer cells versus healthy cells in a sample, while ploidy refers to the number of sets of chromosomes in a cell—both of which are critical factors in determining the aggressiveness of a tumor and the most effective course of treatment.
The real-world efficacy of the algorithm was recently demonstrated in a study of osteosarcoma, a rare and particularly aggressive form of bone cancer that predominantly affects children and young adults. Because osteosarcoma genomes are notoriously rearranged and complex, they served as the ultimate testing ground for SAVANA. The algorithm helped researchers uncover previously unknown genomic rearrangements, providing novel insights into how this specific cancer evolves and progresses.
Furthermore, the team conducted a head-to-head comparison between SAVANA’s long-read analysis and the current clinical standard: Illumina short-read sequencing. The results were highly consistent across both technologies. Crucially, while SAVANA matched the performance of current standards, it also revealed additional cancer-relevant alterations that the standard pipelines had missed, proving its potential to add significant value to clinical reporting.
