Advertisement

Methylation Signatures From Sequencing Circulating Cell-Free DNA Detected Different Types of Cancer Across Multiple Stages


Advertisement
Get Permission

Researchers have developed the first blood test that can accurately detect more than 50 types of cancer and identify in which tissue the cancer originated—often before there are any clinical signs or symptoms of the disease. These findings were published by Liu et al in Annals of Oncology.

In their report, researchers showed that the test has a 0.7% false-positive rate for cancer detection. The test was able to predict the tissue in which the cancer originated in 96% of samples, and it was accurate in 93%.

Photo credit: Getty

Role of cfDNA

Tumors shed DNA into the blood, and this contributes to what is known as cell-free DNA (cfDNA). However, as the cfDNA can come from other types of cells as well, it can be difficult to pinpoint cfDNA that comes from tumors specifically. The blood test reported in this study analyzed methylation, which usually controls gene expression. Abnormal methylation patterns and the resulting changes in gene expression can contribute to tumor growth, so these signals in cfDNA have the potential to detect and localize cancer.

The blood test targets approximately 1 million of the 30 million methylation sites in the human genome. An algorithm was used to predict the presence of cancer and the type of cancer based on the patterns of methylation in the cfDNA shed by tumors. The classifier was trained using a methylation database of cancer and noncancer signals in cfDNA.

Senior author of the paper Michael Seiden, MD, PhD, President of U.S. Oncology, said, “Our earlier research showed that the methylation approach outperformed both whole-genome and targeted sequencing in the detection of multiple deadly cancer types across all clinical stages, and in identifying the tissue of origin. It also allowed us to identify the most informative regions of the genome, which are now targeted by the refined methylation test that is reported in this paper.”

Study Methods

In the study, blood samples from 6,689 participants in North America with previously untreated cancer (n = 2,482) and without cancer (n = 4,207 patients) were divided into a training set and a validation set. Of these, results from 4,316 participants were available for analysis: 3,052 in the training set (1,531 with cancer, 1,521 without cancer) and 1,264 in the validation set (654 with cancer and 610 without cancer). Over 50 types of cancer were included in the analysis.

The machine-learning classifier analyzed blood samples from the participants to identify methylation changes and to classify the samples as cancer or noncancer, as well as to identify the tissue of origin.

Results

The researchers found that the classifier’s performance was consistent in both the training and validation sets, with a false-positive rate of 0.7% in the validation set.

The classifier’s true positive rate was also consistent between the two sets. In 12 types of cancer—anal, bladder, bowel, esophageal, stomach, head and neck, liver and bile duct, lung, ovarian, and pancreatic cancers, as well as lymphoma and multiple myeloma—the true positive rate was 67.3% across clinical stages I, II, and III. The true positive rate was 43.9% for all cancer types in the study across the three clinical stages.

KEY POINTS

  • The researchers found that the classifier’s performance was consistent in both the training and validation sets, with a false positive rate of 0.7% in the validation set.
  • The true positive rate was 43.9% for all cancer types in the study across three clinical stages.
  • In 12 prespecified cancers, the true positive rate was 39% in stage I, 69% in stage II, 83% in stage III, and 92% in stage IV. In all of more than 50 cancer types, the corresponding rates were 18%, 43%, 81%, and 93%, respectively.

Detection improved with each cancer stage. In the 12 prespecified cancers, the true positive rate was 39% in stage I, 69% in stage II, 83% in stage III, and 92% in stage IV. In all of more than 50 cancer types, the corresponding rates were 18%, 43%, 81%, and 93%, respectively.

The test was also consistent between the training and validation sets in its ability to identify the tissue where cancer had originated, with an accuracy of 93% in the validation set.

Dr. Seiden said, “These data support the ability of this targeted methylation test to meet what we believe are the fundamental requirements for a multicancer early detection blood test that could be used for population-level screening: the ability to detect multiple deadly cancer types with a single test that has a very low false-positive rate, and the ability to identify where in the body the cancer is located with high accuracy to help health-care providers to direct next steps for diagnosis and care.”

Disclosure: The study was funded by GRAIL. For full disclosures of the study authors, visit annalsofoncology.org.

The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.
Advertisement

Advertisement



Advertisement