Mayo Clinic unveils breakthrough tool for hidden cancer DNA analysis
Mayo Clinic researchers have developed BACDAC, a computational tool that exposes elusive genomic patterns in cancer samples with low DNA coverage, with the aim of transforming how clinicians predict tumour behaviour and guide personalised treatment decisions through enhanced detection of chromosomal instability markers.
The most dangerous genetic alterations in cancer often lurk in the shadows, evading conventional detection methods. These structural DNA changes deep within tumour genomes can fuel aggressive growth and treatment resistance, particularly when tissue samples are small or degraded. Mayo Clinic’s innovative BACDAC (Binomial distribution statistics of common SNPs to calculate Allelic Content, Discretization Algorithm, and Constellation Plot) tool now illuminates these previously invisible genomic landscapes.
Published in Genome Biology on 20 June 2025, the research demonstrates how BACDAC successfully identifies signs of genomic instability using whole genome sequencing that reads entire genomes, even in low-purity or low-coverage samples requiring minimal tissue.
“This tool lets us see a layer of the genome that’s been invisible until now,” explains Dr George Vasmatzis, lead author and co-director of Mayo Clinic’s Biomarker Discovery Program. “We’ve spent decades studying the biology of genomic instability. This is the first time we’ve been able to translate that knowledge into a tool that works at scale.”
Detecting whole genome doubling events
At BACDAC’s core lies the concept of ploidy – the number of complete chromosome sets in cells. Whilst normal human cells contain two sets (46 chromosomes total), cancer cells frequently exhibit large-scale gains or losses that disrupt this balance, enabling unchecked growth.
The research team analysed more than 650 tumours across 12 cancer types, demonstrating BACDAC’s ability to detect whole genome doubling events where tumours duplicate their entire DNA complement. This abnormal ploidy pattern often correlates with aggressive behaviour and treatment resistance.
The tool’s effectiveness stems from its novel approach to allelic content assessment. As the authors note in their paper: “BACDAC is a process that combines novel algorithmic methods with a graphical interpretation to report tumour ploidy relative to the haploid genome and tumour purity from low-pass whole genome sequencing.”
Constellation plot visualisation breakthrough
BACDAC’s most striking innovation is the Constellation Plot – a custom visualisation that provides an intuitive view of chromosomal stability or disruption. This two-dimensional representation plots heterozygosity scores against copy numbers for genomic segments, creating distinctive cloud patterns that reveal allelic content and subclonal populations.
The authors emphasise that “the Constellation Plot highlights the presence or absence of subclonal cell populations” and serves as a validation tool for tumour ploidy predictions. When clouds align with expected theoretical positions (marked as stars), the solution gains confidence; misaligned patterns indicate potential analytical issues or complex genomic scenarios.
Validation across multiple platforms
BACDAC’s accuracy underwent rigorous validation using 63 high-coverage whole genome sequencing datasets from The Cancer Genome Atlas (TCGA). Despite extreme coverage differences between high-coverage (68X) and simulated low-coverage (5X) executions, BACDAC maintained consistent results with near-perfect concordance (correlation coefficient = 0.98).
Comparative analysis against established methods including ASCAT, ABSOLUTE, FACETS, and HATCHet2 revealed BACDAC’s superior performance, particularly in low-coverage scenarios where other methods struggled. The tool achieved 88% agreement with experimental validation methods including karyotyping and flow cytometry.
Clinical applications and tissue-specific insights
Applied to 653 low-pass whole genome sequencing samples spanning over 12 primary tissues, BACDAC revealed significant variations in high-ploidy frequency across cancer types. Breast and ovarian tumours showed the highest percentages (60% and 61% respectively) of high-ploidy samples, whilst haematolymphoid samples, primarily multiple myeloma, demonstrated the lowest incidence (4%).
The tool successfully operates with effective tumour coverage as low as 1.2X – equivalent to samples with 3X coverage and 40% tumour purity, or 6X coverage and 20% tumour purity. This capability proves crucial for clinical scenarios involving limited tissue availability.
Future clinical deployment
The Mayo Clinic team plans to further validate BACDAC and develop it into a clinically deployable diagnostic tool. The method’s ability to distinguish high-ploidy from near-diploid tumours using combined ploidy and loss of heterozygosity metrics represents a significant advancement over single-parameter approaches.
As the authors conclude: “We have presented a method that can report allele-specific copy number, tumour ploidy and purity in samples with as low as 3×coverage, without the need for a matched normal sample.” This capability addresses a critical unmet need in cancer genomics, where paired normal samples are often unavailable.
The research, supported by Mayo Clinic’s Center for Individualized Medicine and Center for Digital Health, promises to enhance treatment decision-making by providing clearer views of tumour structural changes previously hidden from clinical detection.
Reference
Johnson, S. H., Smadbeck, J. B., Zenka, R. M., et. al. (2025). Tumor ploidy determination in low-pass whole genome sequencing and allelic copy number visualization using the Constellation Plot. Genome Biology, 26, 132. https://doi.org/10.1186/s13059-025-03599-2