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Blood test detects over 50 types of cancer, some before symptoms appear


Illustration of how cfDNA fragments from the blood are processed: cfDNA was extracted from plasma, subjected to bisulfite treatment, and regions of interest were pulled down, followed by sequencing and alignment. In this way the methylation state of fragments was obtained. @ Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Annals of Oncology, 2020; DOI: 10.1016/j.annonc.2020.02.011

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 in the leading cancer journal Annals of Oncology [1] today (Tuesday) the researchers show that the test, which could eventually be used in national cancer screening programmes, has 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 programmes, 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%. Tumours 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 tumours. The blood test reported in this study analyses chemical changes to the DNA called "methylation" that usually control gene expression. Abnormal methylation patterns and the resulting changes in gene expression can contribute to tumour growth, so these signals in cfDNA have the potential to detect and localise 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 tumours. 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 company involved in this research, GRAIL, Inc. (California, USA). Senior author of the paper, Dr Michael Seiden (MD, PhD), President of US Oncology (Texas, USA), 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." In the part of the Circulating Cell-free Genome Atlas (CCGA) study reported today, blood samples from 6,689 participants with previously untreated cancer (2482 patients) and without cancer (4207 patients) from North America were divided into a training set and a validation set. Of these, results from 4316 participants were available for analysis: 3052 in the training set (1531 with cancer, 1521 without cancer) and 1264 in the validation set (654 with cancer and 610 without cancer). Over 50 types of cancer were included. The machine learning classifier analysed 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 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 ability to correctly identify when cancer was present (the true positive rate) was also consistent between the two sets. In 12 types of cancer that are often the most deadly (anal, bladder, bowel, oesophageal, stomach, head and neck, liver and bile duct, lung, ovarian and pancreatic cancers, lymphoma, and cancers of white blood cells such as multiple myeloma), the true positive rate was 67.3% across clinical stages I, II and III. These 12 cancers account for about 63% of cancer deaths each year in the USA and, at present, there is no way of screening for the majority of them before symptoms show. The true positive rate was 43.9% for all cancer types in the study across the three clinical stages. Detection improved with each cancer stage. In the 12 pre-specified 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. In a study involving thousands of participants, a new blood test detected more than 50 types of cancer as well as their location within the body with a high degree of accuracy, according to an international team of researchers led by Dana-Farber Cancer Institute and the Mayo Clinic. The results, published online today by the Annals of Oncology, indicate that the test -- which identified some particularly dangerous cancers that lack standard approaches to screening -- can play a key role in early detection of cancer. Early detection can often be critical to successful treatment. Developed by GRAIL, Inc., of Menlo Park, Calif., the test uses next-generation sequencing to analyze the arrangement of chemical units called methyl groups on the DNA of cancer cells. Adhering to specific sections of DNA, methyl groups help control whether genes are active or inactive. 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. "Our previous work indicated that methylation-based tests outperform traditional DNA-sequencing approaches to detecting multiple forms of cancer in blood samples," said Dana-Farber's Geoffrey Oxnard, MD, co-lead author of the study with Minetta Liu, MD, of the Mayo Clinic. "The results of this study suggest that such assays could be a feasible way of screening people for a wide variety of cancers." In the study, investigators used the test to analyze cell-free DNA (DNA from normal and cancerous cells that had entered the bloodstream upon the cells' death) 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 overall specificity of the test was 99.3%, meaning that only 0.7% of the results incorrectly indicated that cancer was present. The sensitivity of the assay for 12 cancers that account for nearly two-thirds of U.S. cancer deaths was 67.3%, meaning the test could find the cancer two-thirds of the time but a third of the time the test returned a negative result. Within this group, the sensitivity was 39% for patients with stage I cancer, 69% for those with stage II, 83% for those with stage III, and 92% for those with stage IV. The stage I-III sensitivity across all 50 cancer types was 43.9%. When cancer was detected, the test correctly identified the organ or tissue where the cancer originated in more than 90% of cases -- critical information for determining how the disease is diagnosed and managed. "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," Oxnard observed. "The test can be an important part of clinical trials for early cancer detection." A strength of the CCGA study is that it includes more than 15,000 participants from 142 clinics in North America, ensuring results are generalisable to a diverse population. The ongoing studies are assessing the test's performance in even broader populations. Limitations include: all the participants with cancer had already been diagnosed with cancer (e.g. via screening or patients presenting with symptoms); the study was not designed to establish the test's impact on death from cancer or other causes; at the time of this analysis, not all patients had been followed for a year, which is needed to ensure their non-cancer status was accurate; and some inaccuracy occurred in the detection of the tissue of origin for cancers that are driven by the human papilloma virus (HPV), such as cancers of the cervix, anus, and head and neck - this information is being used to improve the test's performance. Editor-in-chief of Annals of Oncology, Professor Fabrice André, Director of Research at the Institut Gustave Roussy, Villejuif, France, said: "This is a landmark study and a first step toward the development of easy-to-perform screening tools. Earlier detection of more than 50% of cancers could save millions of lives every year worldwide and could dramatically reduce morbidity induced by aggressive treatments. "While numbers are still small, the performance of this new technology is particularly intriguing in pancreatic cancer, for which mortality rates are very high because it is usually diagnosed when it's at an advanced stage." Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA M.C. Liu, G.R. Oxnard, E.A. Klein, C. Swanton, M.V. Seiden, Steven R. Cummings, Farnaz Absalan, Gregory Alexander, Brian Allen, Hamed Amini, Alexander M. Aravanis, Siddhartha Bagaria, Leila Bazargan, John F. Beausang, Jennifer Berman, Craig Betts, Alexander Blocker, Joerg Bredno, Robert Calef, Gordon Cann, Jeremy Carter, Christopher Chang, Hemanshi Chawla, Xiaoji Chen, Tom C. Chien, Daniel Civello, Konstantin Davydov, Vasiliki Demas, Mohini Desai, Zhao Dong, Saniya Fayzullina, Alexander P. Fields, Darya Filippova, Peter Freese, Eric T. Fung, Sante Gnerre, Samuel Gross, Meredith Halks-Miller, Megan P. Hall, Anne-Renee Hartman, Chenlu Hou, Earl Hubbell, Nathan Hunkapiller, Karthik Jagadeesh, Arash Jamshidi, Roger Jiang, Byoungsok Jung, TaeHyung Kim, Richard D. Klausner, Kathryn N. Kurtzman, Mark Lee, Wendy Lin, Jafi Lipson, Hai Liu, Qinwen Liu, Margarita Lopatin, Tara Maddala, M. Cyrus Maher, Collin Melton, Andrea Mich, Shivani Nautiyal, Jonathan Newman, Joshua Newman, Virgil Nicula, Cosmos Nicolaou, Ongjen Nikolic, Wenying Pan, Shilpen Patel, Sarah A. Prins, Richard Rava, Neda Ronaghi, Onur Sakarya, Ravi Vijaya Satya, Jan Schellenberger, Eric Scott, Amy J. Sehnert, Rita Shaknovich, Avinash Shanmugam, K.C. Shashidhar, Ling Shen, Archana Shenoy, Seyedmehdi Shojaee, Pranav Singh, Kristan K. Steffen, Susan Tang, Jonathan M. Toung, Anton Valouev, Oliver Venn, Richard T. Williams, Tony Wu, Hui H. Xu, Christopher Yakym, Xiao Yang, Jessica Yecies, Alexander S. Yip, Jack Youngren, Jeanne Yue, Jingyang Zhang, Lily Zhang, Lori (Quan) Zhang, Nan Zhang, Christina Curtis, Donald A. Berry Annals of Oncology (2020) DOI: 10.1016/j.annonc.2020.02.011 Dana-Farber Cancer Institute

 

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