Written by Lauren Robertson, Science Writer.
Researchers at UCLA’s Jonsson Comprehensive Cancer Center have reported successful results from a cell-free DNA (cfDNA) liquid biopsy that overcomes some of the current challenges with conventional tests. The study, published in Nature Communications, outlines an approach that could reduce the cost of conventional methods for sequencing cfDNA 12-fold.
The cell-free way
Detecting cancer in the early stages – before it has chance to metastasise – is crucial to successfully treating the disease. Liquid biopsy is one non-invasive option for early cancer diagnosis that uses a simple blood test to detect biomarkers such as cfDNA methylation. However, the cfDNA aberration signatures from different cancers at different stages are very heterogenous, which can cause problems when it comes to identifying methylation markers. There are also challenges relating to the low concentration of cfDNA in blood fragments, the molecular heterogeneity of cancer, and small sample sizes that are not representative of patient populations.
Profiling the entire cfDNA methylome can overcome these issues, as this approach retains the genome-wide epigenetic profiles of cancer abnormalities. However, the traditional method of profiling – whole-genome bisulfite sequencing (WGBS) – is costly and not viable for clinical use.
Instead, a team of researchers at UCLA have come up with an alternative approach consisting of a cost-effective assay (cfMethyl-Seq) combined with a computational method to extract methylation information (cancer-specific and tissue-specific hypermethylation and hypomethylation markers). The cfMethyl-Seq assay works by only profiling CpG-rich regions instead of the whole genome, making it much cheaper than conventional WGBS.
“Our method, cfMethyl-seq, makes cfDNA methylome sequencing a viable option for clinical use,” said 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.”
Accuracy assured
To evaluate how their approach might work in a clinical setting, the team tested if it could detect four common cancers – colon, liver, lung and stomach cancer – in the early stages. They collected blood samples from 408 study participants (217 cancer patients and 191 cancer-free controls) and profiled their methylomes. To prevent bias, the samples were collected both from UCLA’s hospitals as well as from commercial labs. They also performed cross-batch validations, age-matched validations, and independent validations.
The resulting data was then entered into a sophisticated computer model to measure the accuracy of the new approach in detecting cancer. They also wanted to see how well it was able to locate the tissue of origin of the tumour.
Their results showed the model was 80.7% accurate in detecting cancer across all stages and 74.5% accurate in detecting early-stage cancers, with just under 98% specificity. It was 89% accurate at detecting the tissue-of-origin in all stages of cancer, and 85% for early-stage cancer. They found only one false positive sample.
The good news is that their approach also shows the potential to improve with larger sample sizes. “The key to early cancer detection is to identify the true cancer biomarkers, which requires a large cohort of training samples to cover the heterogeneity of cancer and population, especially for pan-cancer detection,” said Zhou. “Our cfDNA methylome approach allows the inclusion of new markers and the better weighting of existing markers as training cohorts grow. Indeed, our data show that as training sample sizes increase, the detection power of our method continues to increase.”
As for the future? “With its cost-effective methylome sequencing, cfMethyl-seq can truly facilitate a big data approach for cancer detection,” adds Zhou. The next step is to secure funding for large clinical trials to further test and validate the technology.
