Early detection remains key to successfully treating many cancers, and early detection via cell-free DNA (cfDNA) circulating in the bloodstream — the so-called “liquid biopsy” — has become a research focal point. But using this method to detect cancer at its early stages has been challenging due to low tumor concentrations in DNA blood fragments and the genetic diversity of cancer.
Now, researchers at UCLA’s Jonsson Comprehensive Cancer Center and collaborating organizations report successful results from an experimental cancer-detection system that appears to have overcome these challenges in a novel, cost-effective way.
Their work, published in the journal Nature Communications, highlights an approach that offers more than 12-fold cost-savings over conventional methods to sequence cfDNA methylome, along with a computational model to extract information from this DNA sequencing to aid early detection and diagnosis.
Cell-free DNA methylation has been shown to be one of the most promising biomarkers for early cancer detection. However, the signatures of cfDNA aberrations from diverse cancer types, subtypes, stages and etiologies are heterogeneous, leading to challenges in identifying methylation markers suitable for early detection. This is especially of concern that the currently available sample sizes are small compared to the diversity of diseases and the patient population (age, gender, ethnicity, and comorbidity). Profiling cfDNA methylome can address this challenge, as it retains the genome-wide epigenetic profiles of cancer abnormalities, thereby permitting the classification models to learn and exploit newly significant features as training cohorts grow, as well as expanding their scope to more cancer types. However, the conventional way of profiling the cell-free DNA methylome (whole-genome bisulfite sequencing) is cost-prohibitive for clinical use.
“Our method, cfMethyl-seq, makes cfDNA methylome sequencing a viable option for clinical use,” says Xianghong “Jasmine” Zhou, professor of pathology and laboratory medicine at UCLA and a corresponding author for the study. “Despite the inherent challenges, our study shows tremendous potential for accurate early diagnosis of certain cancers from a single blood test.”
Zhou and colleagues in her UCLA lab focus on precision medicine — the use of patients’ genomic information to develop more personalized, targeted treatments — and big biodata analysis to integrate complex data from various platforms and modalities into practical methods that can be used in clinical settings.
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