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Lung cancer is one of the pervasive diseases that is generally diagnosed at later stages and has a global mortality estimation of 2.21 million cases in 2020 as per WHO. Most solitary Pulmonary Nodules (PNs) are detected incidentally by chest radiography and CT scans that were ordered to investigate other diseases. Approximately 150,000 solitary PNs are detected annually in the United States of America. The blood test’s purpose is to determine whether patients with lung nodules who have a low or moderate risk of lung cancer are likely to have developed harmless or malignant tumors. The advances in computing combined with an increase in the amount of data collected has enabled the application of AI to develop an AI enabled technology to detect these lung cancers.

A research team from Johns Hopkins Kimmel Cancer Center found that the AI blood testing solution was able to detect over 90% of lung cancers in samples from nearly 800 individuals with and without cancer. The research findings have been published in Nature Communications. Known as DELFI (DNA evaluation of fragments for early interception), the test is designed to detect unique patterns in the fragmentation of DNA shed from cancer cells circulating in the bloodstream.

Researchers applied the DELFI technology to blood samples taken from 796 individuals in Denmark, the Netherlands, and the USA. They determined that the DELFI approach accurately distinguished between patients with and without lung cancer. Investigators combined the test with an analysis of clinical risk factors, a protein marker, and computed tomography image. They found that DELFI helped to detect 94% of patients with cancer across stages and subtypes, including 91% of patients with earlier or less invasive stage I/II cancers and 96% of patients with more advanced stage III/IV cancers.

According to the World Health Organization, lung cancer is the biggest killer, causing 1.8 million deaths in 2020. Despite this, however, the researchers explained that many people at risk of lung cancers do not undergo recommended low-dose computed tomography screening. Victor E Velculescu, M.D., Ph.D., Professor of Oncology and Co-director of the Cancer Genetics and Epigenetics Program at the Johns Hopkins Kimmel Cancer Center, said that this could be due to a number of reasons, including concerns of potential harm from investigation, false positive imaging results, radiation exposure, or worries about complications from invasive procedures.

Lead author Dimitrios Mathios, a postdoctoral fellow at the Johns Hopkins Kimmel Cancer Center, said: “It is clear that there is an urgent, unmet clinical need for development of alternative, non-invasive approaches to improve cancer screening for high-risk individuals and, ultimately, the general population. “We believe that a blood test, or ‘liquid biopsy,’ for lung cancer could be a good way to enhance screening efforts because it would be easy to do, broadly accessible, and cost-effective.”

How does it work?

The DELFI technology uses a blood test to indirectly measure the way DNA is packaged inside the nucleus of a cell by studying the size and amount of cell-free DNA present in the circulation from different regions across the genome. Healthy cells package DNA by organising different regions of the genome into various compartments. The nuclei of cancer cells, by contrast, are more disorganised, with items from across the genome thrown in at random. When cancer cells die, they release DNA in a chaotic manner into the bloodstream.

DELFI helps to identify the presence of cancer using machine learning, a type of artificial intelligence, to examine millions of cell-free DNA fragments for abnormal patterns, including the size and amount of DNA in different genomic regions. This approach provides a view of cell-free DNA referred to as the ‘fragmentome’. The DELFI approach only requires low-coverage sequencing of the genome, enabling this technology to be cost-effective in a screening setting, the researchers say.

Working with researchers in Denmark and the Netherlands, the Johns Hopkins investigators first performed genome sequencing of cell-free DNA in blood samples from 365 individuals participating in a seven-year Danish study called LUCAS. The majority of participants were at high risk for lung cancer and had smoking-related symptoms, such as a cough or difficulty breathing. The DELFI testing approach found that patients who were later determined to have cancer had widespread variation in their fragmentome profiles, while patients found not to have cancer had consistent fragmentome profiles. Subsequently, researchers validated the DELFI technology using a different population of 385 individuals without cancer and 46 individuals with cancer. Overall, the approach detected over 90% of patients with lung cancer, including those with early and advanced stages, and with different subtypes.

Potential for a liquid biopsy test

Study author Rob Scharpf, Ph.D., Associate Professor of Oncology at the Johns Hopkins Kimmel Cancer Center, said: “DNA fragmentation patterns provide a remarkable fingerprint for early detection of cancer that we believe could be the basis of a widely available liquid biopsy test for patients with lung cancer.”

A first-of-a-kind national clinical trial called DELFI-L101, sponsored by the Johns Hopkins University spin-out Delfi Diagnostics, is evaluating a test based on the DELFI technology in 1,700 participants in the US, including healthy participants, individuals with lung cancers and individuals with other cancers. The group plans to further study DELFI in other types of cancers.


InnoHEALTH magazine digital team

Author InnoHEALTH magazine digital team

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