The landscape of cancer genomics is undergoing a profound transformation as researchers move beyond traditional sequencing methods to embrace technologies capable of capturing the full complexity of the human genome. At the forefront of this shift is long-read sequencing, a method that analyzes extensive, continuous stretches of DNA, providing a far more comprehensive view of genetic architecture than previously possible. However, the sheer complexity of cancer genomes—characterized by chaotic rearrangements and structural instability—has historically outpaced the analytical tools available to interpret this data.
To bridge this critical gap, an international consortium of researchers has developed SAVANA, a sophisticated machine learning algorithm designed specifically to navigate the intricacies of cancer-related genetic data. Recently detailed in the journal Nature Methods, SAVANA represents a significant leap forward in our ability to detect large-scale genomic alterations with precision. By providing a clearer, more reliable window into the mutational processes that drive tumor growth, the tool promises to enhance how clinicians diagnose disease, predict patient outcomes, and tailor personalized treatment strategies.
Overcoming the Limitations of Conventional Analysis
For years, the gold standard in genomics has been short-read sequencing, which breaks DNA into tiny fragments before reassembling them like a massive jigsaw puzzle. While effective for many applications, this approach often struggles to detect "structural variants"—large-scale changes such as insertions, deletions, duplications, or complex rearrangements of genetic material. These variants are frequently the primary drivers of cancer, yet they are often too large or too complex to be accurately captured by short-read technologies.
Long-read sequencing was developed to solve this problem by reading much longer strands of DNA at once. Yet, even this advanced technology requires specialized software to interpret the results. Existing analysis tools, many of which were originally designed for healthy genomes or general genetic research, frequently falter when applied to the highly mutated environment of a tumor. In many cases, these tools produce false-positive results or fail to distinguish between genuine mutations and sequencing artifacts—the digital "noise" inherent in the technology.
These misleading interpretations are more than just a technical hurdle; they have real-world consequences for patient care. If a genomic analysis incorrectly identifies a mutation, it can lead to an inaccurate understanding of how a tumor is evolving or how it might respond to a specific therapy. Conversely, missing a critical structural variant could mean a patient is denied a life-saving targeted treatment.
The researchers behind SAVANA used a poignant analogy to describe the necessity of their new tool: while it is technically possible to eat soup with a fork, the process is inefficient and the results are unsatisfying. Similarly, using general-purpose analysis tools for cancer genomics is a mismatch of equipment and task. SAVANA was engineered to be the "spoon"—a tool specifically tailored to handle the fluidity and complexity of cancer data, ensuring that every relevant piece of information is captured efficiently and accurately.
A Collaborative Effort in Computational Biology
The development of SAVANA was a massive undertaking, involving a diverse team of experts across several world-leading institutions. The project was led by researchers at EMBL’s European Bioinformatics Institute (EMBL-EBI) and the Research and Development laboratory of Genomics England. The algorithm was not developed in a vacuum; it was rigorously tested across 99 human tumor samples, drawing on the clinical expertise of partners at University College London (UCL), the Royal National Orthopaedic Hospital (RNOH), the Instituto de Medicina Molecular João Lobo Antunes in Portugal, and Boston Children’s Hospital in the United States.
This collaborative approach allowed the team to train the SAVANA algorithm directly on long-read sequencing data derived from actual cancer samples. By using machine learning to recognize the specific patterns of cancer-related alterations, the team created a system that can effectively filter out the errors that plague other methods.
"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.
"The capability to accurately detect structural variants is transformative for clinical diagnostics," said Adrienne Flanagan, Professor at UCL and Consultant Histopathologist at RNOH. "SAVANA could help us confidently identify genomic alterations relevant for diagnosis and prognosis. Ultimately, this means we would be better placed to deliver personalised treatments for cancer patients."
Supporting the UK’s Genomic Vision
The development of SAVANA aligns with a broader national effort in the United Kingdom to integrate advanced genomics into the heart of the healthcare system. Through the NHS Genomic Medicine Service, the UK has become the first country in the world to offer whole-genome sequencing as a routine part of clinical care. This initiative aims to move away from a "one-size-fits-all" approach to medicine, instead using a patient’s unique genetic code to guide their treatment.
However, the success of such an ambitious program depends entirely on the accuracy of the data interpretation. As the NHS and Genomics England explore the potential of long-read sequencing to support earlier and faster cancer diagnoses, tools like SAVANA become indispensable.
"Using SAVANA will ensure clinicians receive accurate and reliable genomic data, enabling them to confidently integrate advanced genomic sequencing methods such as long-read sequencing into routine patient care," said Greg Elgar, Director of Sequencing R&D at Genomics England.
The impact of SAVANA is already being felt across several major initiatives. It is currently being deployed as part of the UK Stratified Medicine Paediatrics project. Funded by Cancer Research UK and Children With Cancer UK, and co-led by Cortes-Ciriano, this project seeks to develop more effective and less toxic treatments for childhood cancers. By using SAVANA to better understand the biology of pediatric tumors and monitor for disease recurrence, researchers hope to improve survival rates and long-term quality of life for young patients.
Furthermore, SAVANA is playing a key role in the SAMBAI project (Societal, Ancestry, Molecular and Biological Analyses of Inequalities). This initiative, funded by Cancer Grand Challenges, is dedicated to addressing the stark disparities in cancer outcomes observed in populations of recent African heritage. By applying high-resolution genomic analysis to diverse populations, the project aims to uncover the biological and environmental factors that contribute to these inequalities, ensuring that the benefits of the genomic revolution are shared by all.
As long-read sequencing technology continues to mature, the role of specialized analytical tools like SAVANA will only grow in importance. By turning raw genetic data into actionable clinical insights, SAVANA is helping to pave the way for a future where cancer treatment is more precise, more effective, and more personalized than ever before.
