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.
In a paper published today on March 31st in the leading cancer journal Annals of Oncology, the researchers show that the testâwhich could eventually be used in national cancer screening programsâhas a 0.7% false positive rate for cancer detection, meaning that less than 1% of people would be wrongly identified as having cancer.
As a comparison, about 10% of women are wrongly identified as having cancer in national breast cancer screening programs, although this rate can be higher or lower depending on the number and frequency of screenings and the type of mammogram performed.
The test was able to predict the tissue in which the cancer originated in 96% of samples, and it was accurate in 93%.
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. The blood test reported in this study analyses chemical changes to the DNA called âmethylationâ that usually control 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 one million of the 30 million methylation sites in the human genome. A machine learning classifier (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 non-cancer signals in cfDNA. The database is believed to be the largest in the world and is owned by the California-based company involved in this research, GRAIL, Inc..
Senior author of the paper and President of US Oncology Dr. Michael Seiden said: âOur 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.â
In the part of the Circulating Cell-free Genome Atlas (CCGA) study reported today, blood samples from 6,689 participants with previously untreated cancer (2,482 patients) and without cancer (4,207 patients) from North America 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.
The machine learning classifier analyzed blood samples from the participants to identify methylation changes and to classify the samples as cancer or non-cancer, and to identify the tissue of origin.
The researchers found that the classifierâs performance was consistent in both the training and validation sets, with a false positive rate of 0.7% in the validation set.
Source : GNN
