EMI Unveils Real-Time Genetic Mutation Tracking to Revolutionize Cancer Prevention
Researchers at the Ellison Medical Institute (EMI) have unveiled a new technique that enables real-time tracking of genetic mutations using blood samples. Unlike prior approaches that captured mutation data at single timepoints, EMI’s methodology allows for the monitoring of genetic changes across multiple intervals. This longitudinal approach provides a dynamic understanding of how mutations accumulate over time, offering a window into an individual's evolving biological landscape.
The study, recently published in Scientific Reports, a journal from Nature, represents a major advancement in the field of personalized medicine. It lays the groundwork for earlier cancer detection and more targeted preventative strategies.
"Rather than waiting for symptoms to emerge, we're using technology to identify who is at higher potential of developing DNA changes, and hence cancer – this could help us intervene sooner with preventive strategies, tailor care more precisely, and improve patient outcomes in impactful ways," said Dr. David Agus, EMI Founding CEO and senior author of the study. "By integrating advanced sequencing with clinical insight, we're expanding our knowledge of cancer risk assessment and creating new possibilities for personalized care that starts before a diagnosis."
To carry out the research, the team at EMI gathered blood samples from over 100 participants—both healthy individuals and cancer patients—every three to six months over a span of nine to 18 months. They employed long-read sequencing to examine changes in the participants’ germline DNA found in peripheral blood cells, with the aim of identifying newly developed somatic mutations, which are non-inherited and arise naturally over time.
Findings revealed a clear correlation between aging and mutation load, with approximately 27 new mutations acquired per decade. Researchers were also able to differentiate the source of mutations—whether from the aging process, tobacco exposure, or replication errors in DNA. Some of these changes occurred in mere months, underscoring the benefit of repeated sampling over time.
"To enable this research, we built a secure, cloud-based infrastructure and a custom bioinformatics pipeline that applies deep learning to rapidly extract valuable insights from large datasets," explained Xingyao Chen, EMI’s Manager of Data Science and lead author. "This system not only supports reliable scaling but also paves the way for a national validation clinical trial and future clinical integration."
The study aligns with EMI’s overarching vision to transform cancer care through innovative, patient-focused research. By combining emerging technologies with cross-disciplinary collaboration, EMI is striving to reframe the understanding and prevention of disease.
"What excites me most about this work is its potential to reshape how we think about cancer prevention," said Dr. Naim Matasci, EMI Senior Director of Applied AI Research and corresponding author. "We can now envision a future where care is not reactive, but predictive and personalized to the individual patient, based on their rate of change of DNA. Important insight into DNA repair and fidelity can also be gleaned from the observed outliers."