Identifying Cancer Type from a Single Blood Test
Credit: CDC/ Amanda Mills acquired from Public Health Image Library
Researchers in the United States have developed a computer program that can simultaneously detect cancer and identify where in the body the cancer is located, from a patient’s blood sample. The program is described in research published this week in the open access journal Genome Biology.
Professor Jasmine Zhou, co-lead author from the University of California at Los Angeles, said: “Non-invasive diagnosis of cancer is important, as it allows the early diagnosis of cancer, and the earlier the cancer is caught, the higher chance a patient has of beating the disease. We have developed a computer-driven test that can detect cancer, and also identify the type of cancer, from a single blood sample. The technology is in its infancy and requires further validation, but the potential benefits to patients are huge.”
The program works by looking for specific molecular patterns in cancer DNA that is free flowing in the patients’ blood and comparing the patterns against a database of tumour epigenetics, from different cancer types, collated by the authors. DNA from tumour cells is known to end up in the bloodstream in the earliest stages of cancer so offers a unique target for early detection of the disease.
Professor Zhou explained: “We built a database of epigenetic markers, specifically methylation patterns, which are common across many types of cancer and also specific to cancers originating from specific tissue, such as the lung or liver. We also compiled the same ‘molecular footprint’ for non-cancerous samples so we had a baseline footprint to compare the cancer samples against. These markers can be used to deconvolute the DNA found freely in the blood into tumor DNA and non-tumor DNA.”
In this study, the new computer program and two other methods (called Random Forest and Support Vector Machine) were tested with blood samples from 29 liver cancer patients, 12 lung cancer patients and 5 breast cancer patients. Tests were run 10 times on each sample to validate the results. The Random Forest and Support Vector Machine methods had an overall error rate (the chance that the test produces a false positive) of 0.646 and 0.604 respectively, while the new program obtained a lower error rate of 0.265.
Twenty-five out of the 29 liver cancer patients and 5 out of 12 lung cancer patients tested in this study had early stage cancers, which the program was able to detect in 80% of cases. Although the level of tumour DNA present in the blood is much lower during the early stages of these cancers, the program was still able to make a diagnosis demonstrating the potential of this method for the early detection of cancer, according to the researchers.
Professor Zhou added: “Owing to the limited number of blood samples, the results of this study are evaluated only on three cancer types (breast, liver and lung). In general, the higher the fraction of tumor DNAs in blood, the more accurate the program was at producing a diagnostic result. Therefore, tumors in well-circulated organs, such as the liver or lungs are easier to diagnose early using this approach, than in less-circulated organs such as the breast.”
Changing Lanes: Algorithm Helps AI Drive More Like HumansNews
For self-driving cars, algorithms for changing lanes are beset by one of two problems. Either, they rely on detailed statistical models of the driving environment, which are too complex to analyze on the fly; or they’re so simple that they can lead to impractically conservative decisions, such as never changing lanes at all. Now a new algorithm hopes to split the difference, allowing aggressive lane changes than the simple models do but relies only on immediate information about other vehicles’ directions and velocities to make decisions.
New Portable Malaria Screening Instrument DevelopedNews
A new prototype for a portable instrument capable of early-stage malaria detection has been developed by a team of researchers at the USC Viterbi School of Engineering.READ MORE
Comments | 0 ADD COMMENT
2nd Annual Artificial Intelligence in Drug Development Congress
Sep 20 - Sep 21, 2018