We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
RANDOX LABORATORIES

Download Mobile App




Machine Learning Approach Detects Cancer by Analyzing DNA in Blood Samples

By LabMedica International staff writers
Posted on 10 Jun 2019
Print article
Image: A new liquid biopsy test called DELFI (DNA evaluation of fragments for early interception) uses artificial intelligence to detect patients with cancer by identifying altered DNA fragmentation in the blood (Photo courtesy of Carolyn Hruban, Johns Hopkins University).
Image: A new liquid biopsy test called DELFI (DNA evaluation of fragments for early interception) uses artificial intelligence to detect patients with cancer by identifying altered DNA fragmentation in the blood (Photo courtesy of Carolyn Hruban, Johns Hopkins University).
Researchers have described a proof-of-principle approach for the screening, early detection, and monitoring of human cancer based on a machine learning approach that evaluates fragmentation patterns of cell-free DNA across the genome.

While cell-free DNA in the blood provides a non-invasive diagnostic avenue for patients with cancer, characteristics of the origins and molecular features of cell-free DNA are poorly understood. To correct this lack, investigators at Johns Hopkins University (Baltimore, MD, USA) developed a machine learning-based approach to identify abnormal patterns of DNA fragments in the blood of patients with cancer.

They used this DELFI (DNA evaluation of fragments for early interception) method to analyze the fragmentation profiles of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric, or bile duct cancer and 245 healthy individuals.

The machine-learning model incorporated genome-wide fragmentation features with sensitivities of detection ranging from 57% to more than 99% among the seven cancer types at 98% specificity. Fragmentation profiles could be used to identify the tissue of origin of the cancers to a limited number of sites in 75% of cases. Combining this approach with mutation-based cell-free DNA analyses detected 91% of patients with cancer.

"For various reasons, a cancer genome is disorganized in the way it is packaged, which means that when cancer cells die they release their DNA in a chaotic manner into the bloodstream," said first author Dr. Jillian Phallen, a postdoctoral research fellow at Johns Hopkins University. "By examining this cell-free DNA (cfDNA), DELFI helps identify the presence of cancer by detecting abnormalities in the size and amount of DNA in different regions of the genome based on how it is packaged."

"We are encouraged about the potential of DELFI because it looks at a completely independent set of cell-free DNA characteristics from those that have posed difficulties over the years, and we look forward to working with our collaborators worldwide to make this test available to patients," said senior author Dr. Victor E. Velculescu, professor of oncology at Johns Hopkins University.

The DELFI method was described in the May 29, 2019, online edition of the journal Nature.

Related Links:
Johns Hopkins University

New
Platinum Member
Flu SARS-CoV-2 Combo Test
OSOM® Flu SARS-CoV-2 Combo Test
Magnetic Bead Separation Modules
MAG and HEATMAG
Complement 3 (C3) Test
GPP-100 C3 Kit
Gold Member
ADAMTS-13 Protease Activity Test
ATS-13 Activity Assay

Print article
77 ELEKTRONIKA

Channels

Clinical Chemistry

view channel
Image: PhD student and first author Tarek Eissa has analyzed thousands of molecular fingerprints (Photo courtesy of Thorsten Naeser / MPQ / Attoworld)

Screening Tool Detects Multiple Health Conditions from Single Blood Drop

Infrared spectroscopy, a method using infrared light to study the molecular composition of substances, has been a foundational tool in chemistry for decades, functioning similarly to a molecular fingerprinting... Read more

Molecular Diagnostics

view channel
Image: Protein ‘signatures’ obtained via a blood sample can be used to predict the onset of 67 diseases (Photo courtesy of Queen Mary University of London)

Protein Signatures in Blood Can Predict Risk of Developing More Than 60 Diseases

Measuring specific proteins to diagnose conditions like heart attacks, where troponin is tested, is a well-established clinical practice. Now, new research highlights the broader potential of protein measurements... Read more

Hematology

view channel
Image: The Truvian diagnostic platform combines clinical chemistry, immunoassay and hematology testing in a single run (Photo courtesy of Truvian Health)

Automated Benchtop System to Bring Blood Testing To Anyone, Anywhere

Almost all medical decisions are dependent upon laboratory test results, which are essential for disease prevention and the management of chronic illnesses. However, routine blood testing remains limited worldwide.... Read more

Microbiology

view channel
Image: The Simplexa C. auris direct kit is a real-time polymerase chain reaction assay run on the LIAISON MDX instrument (Photo courtesy of Diasorin)

Novel Molecular Test to Help Prevent and Control Multi Drug-Resistant Fungal Pathogen in Healthcare Settings

Candida auris (C. auris) is a rapidly emerging multi drug-resistant fungal pathogen that is commonly found in healthcare environments, where it presents a challenge due to its ability to asymptomatically... Read more

Pathology

view channel
Image: The tool can improve precision oncology by accurately predicting molecular subtypes and therapy responses (Photo courtesy of Shutterstock)

Computational Tool Integrates Transcriptomic Data for Improved Breast Cancer Diagnosis and Treatment

Breast cancer is the most commonly diagnosed cancer globally, presenting in various subtypes that require precise identification for effective, personalized treatment. Traditionally, cancer subtyping has... Read more

Industry

view channel
Image: Beckman Coulter will utilize the ALZpath pTau217 antibody to detect key biomarker for Alzheimer\'s disease on its DxI 9000 immunoassay analyzer (Photo courtesy of Beckman Coulter)

Beckman Coulter Licenses Alzpath's Proprietary P-tau 217 Antibody to Develop Alzheimer's Blood Test

Cognitive assessments have traditionally been the primary method for diagnosing Alzheimer’s disease, but this approach has its limitations as symptoms become apparent only after significant brain changes... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.