New cfDNA Test Accurately Detects 50 Cancer Types, Tissue of Origin

New cfDNA Test Accurately Detects 50 Cancer Types, Tissue of Origin
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A study of a new blood test detected more than 50 types of cancer, along with their location in the body, with a high degree of accuracy. The test analyzes the arrangement of methyl groups on circulating cell-free DNA (cfDNA) released from cancer cells.

In cancer cells, the placement of methyl groups, or methylation pattern, is often markedly different from that of normal cells—to the extent that abnormal methylation patterns are even more characteristic of cancer cells than genetic mutations are. When tumor cells die, their DNA, with methyl groups firmly attached, empties into the blood, where it can be analyzed by the new test.

The Circulating Cell-free Genome Atlas (CCGA) study, launched by GRAIL, Inc, the test’s developer, was designed to determine whether genome-wide cfDNA sequencing in combination with machine learning could detect and localize a large number of cancer types at sufficiently high specificity to be considered for a general population-based cancer screening program.

The present study is the second CCGA substudy, which was intended to develop, train, and validate a methylation-based assay for simultaneous multi-cancer detection across stages as well as tissue of origin (TOO) localization in preparation for clinical validation and utility studies. In earlier work, the team found that methylation-based tests outperform traditional DNA-sequencing approaches to detecting multiple forms of cancer in blood samples.

For this investigation, researchers analyzed cfDNA in 6,689 blood samples, including 2,482 from people diagnosed with cancer and 4,207 from people without cancer.

The samples from patients with cancer represented more than 50 cancer types, including breast, colorectal, esophageal, gallbladder, bladder, gastric, ovarian, head and neck, lung, lymphoid leukemia, multiple myeloma, and pancreatic cancer. The analysis focused on a targeted panel of >100 000 informative methylation regions.

The test uses a machine learning algorithm designed to identify patterns within data and as a result learn to classify it. In this study, the overall specificity of the methylation-based cfDNA test was 99.3%; meaning that less than 1% of those without cancer were wrongly identified by the system as having the disease.

The sensitivity of the assay for 12 cancers that account for nearly two-thirds of U.S. cancer deaths was 67.3%. Within this group, sensitivity increased with increasing stage of disease. Sensitivity for patients in stages 1-IV was 39%,  69%, 83%, and 92%, respectively. Overall  stage I-III sensitivity across all 50 cancer types was 43.9%.

When the samples were detected as cancerous, confirmatory analysis showed that 93 percent of those results were accurate.

“Our results show that this approach to testing cell-free DNA in blood can detect a broad range of cancer types at virtually any stage of the disease, with specificity and sensitivity approaching the level needed for population-level screening,” the authors observe.

The results, published in the Annals of Oncology, indicate that the test – which identified some particular cancers that lack standard screening approaches – could be useful for early cancer detection.  For example, the system correctly identified 63% of those with stage I pancreatic cancer, rising to 100% in stage IV.

“Methylation outperformed whole-genome sequencing and targeted mutation panels in cancer detection and TOO localization for a number of reasons,” the authors say. “This targeted methylation approach interrogated approximately 1 million informative CpG sites out of the roughly 30 million CpGs across the genome that can be methylated or unmethylated.”

Dana-Farber’s Geoffrey Oxnard, M.D., co-lead author of the study said the test is now being explored in clinical trials. One such trial is the PATHFINDER multi-center clinical trial which will evaluate the test in 6,200 patients in the U.S. “You need to use a test like this in an independent group at risk of cancer to actually show that you can find the cancers, and figure out what to do about it when you find them,” he said.