Lung cancer is the second most common form of cancer in both men and women and the leading cause of cancer deaths worldwide.
A recent article published by Genetic Engineering & Biotechnology News highlights tremendous hope from new Artificial Intelligence (AI) technology developed by researchers at the Johns Hopkins Kimmel Cancer Center that can detect multiple stages and sub types of lung cancer across more than 90% of patients included in the study.
A novel artificial intelligence (AI) blood testing technology developed by researchers at the Johns Hopkins Kimmel Cancer Center has been shown to detect over 90% of lung cancers in samples from nearly 800 individuals with and without cancer.
The test approach, called DELFI (DNA evaluation of fragments for early interception), spots unique patterns in the fragmentation of DNA that is shed from cancer cells circulating in the bloodstream, or cell free DNA (cfDNA). A study reported in Nature Communications has now demonstrated how testing for fragmentation features, in combination with evaluating clinical risk factors, analyzing a protein biomarker, and CT imaging, enabled detection of 94% of patients with cancer across stages and subtypes.
Reporting on the study, senior author Victor E. Velculescu, MD, PhD, first author Dimitrios Mathios, PhD, and colleagues, concluded, “The observations that scalable and cost-effective noninvasive cfDNA fragmentation analyses can discriminate lung cancer patients from noncancer individuals may ultimately provide an opportunity to evaluate not only high-risk individuals but the general population for lung cancer.” Their paper is titled, “Detection and characterization of lung cancer using cell-free DNA fragmentomes.”
Lung cancer is the most common cause of cancer death, claiming almost two million lives worldwide each year, and incidence of the disease is on the rise, the authors noted. “The 5-year survival rate is <20%,” the authors said, “largely due to the late stage at diagnosis where treatments are less effective than at earlier stages…”
However, fewer than 6% of people in the United States at risk for lung cancers undergo recommended low-dose computed tomography (LDCT) screening, despite projections that tens of thousands of deaths could be avoided, and even fewer are screened worldwide, explained Velculescu, who is professor of oncology and co-director of the cancer genetics and epigenetics program at the Johns Hopkins Kimmel Cancer Center. This is due to reasons that include concerns about the potential harm from investigation of false positive imaging results, radiation exposure, or worries about complications from invasive procedures. “It is clear that there is an urgent, unmet clinical need for development of alternative, noninvasive approaches to improve cancer screening for high-risk individuals and, ultimately, the general population,” said Mathios, a postdoctoral fellow at the Johns Hopkins Kimmel Cancer Center. “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.”
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 cfDNA present in the circulation from different regions across the genome. Healthy cells package DNA like a well-organized suitcase, in which different regions of the genome are placed carefully in various compartments. The nuclei of cancer cells, by contrast, are like more disorganized suitcases, with items from across the genome thrown in haphazardly. When cancer cells die, they release DNA in a chaotic manner into the bloodstream. DELFI helps identify the presence of cancer using machine learning, a type of artificial intelligence, to examine millions of cfDNA fragments for abnormal patterns, including the size and amount of DNA in different genomic regions. This approach provides a view of cfDNA referred to as the fragmentome.
“To increase the sensitivity of detection of early-stage cancers we have developed a genome-wide approach for analysis of cfDNA fragmentation profiles called DELFI,” the authors commented. “This approach provides a view of cfDNA ‘fragmentomes,’ permitting evaluation in any individual of the size distribution and frequency of millions of naturally occurring cfDNA fragments across the genome.” And as the cfDNA fragmentome can comprehensively represent both genomic and chromatin characteristics, the investigators continued, “… it has the potential to identify a large number of tumor-derived changes in the circulation.” The DELFI approach also only requires low-coverage sequencing of the genome, enabling this technology to be cost-effective in a screening setting, the researchers suggest.
For the reported study, investigators from Johns Hopkins, working with researchers in Denmark and the Netherlands, first performed genome sequencing of cfDNA 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 cough or difficulty breathing. The DELFI 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. “The resulting fragmentation profiles were remarkably consistent among noncancer individuals, including those with nonmalignant lung nodules,” the team reported. “In contrast, cancer patients displayed widespread genome-wide variation.” Remarkably, they continued, the fragmentation profile differences were seen in multiple regions throughout the genome for the majority of cancer patients, including across stages and histologies.
DELFI blood test identifies lung cancer using AI to detect unique patterns in the fragmentation of DNA shed from cancer cells compared to normal profiles. [Carolyn Hruban]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 91% of patients with earlier or less invasive stage I/II cancers and 96% of patients with more advanced stage III/IV cancers.
“In this study, we have used this methodology for lung cancer detection and characterization in a prospectively collected real-world cohort comprising patients with malignant and benign pulmonary nodules as well as noncancer individuals, including those with other clinical conditions,” the investigators wrote. “We propose that facile and scalable analyses of cfDNA fragmentomes could be used to prescreen high-risk populations for lung cancer to increase the accessibility of lung cancer detection and decrease unnecessary follow-up imaging procedures and invasive biopsies.”
“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,” added co-author Rob Scharpf, PhD, associate professor of oncology at the Johns Hopkins Kimmel Cancer Center.
The team concluded, “Through this effort, we provide a framework for incorporating noninvasive liquid biopsies in the clinic, combining cfDNA fragmentation profiles with other markers and LDCT for lung cancer detection.”
A first-of-a-kind national clinical trial, 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 United States, including healthy participants, individuals with lung cancers, and individuals with other cancers. The group would like to further study DELFI in other types of cancers.