Researchers in Israel have developed a blood test that identifies DNA methylation patterns associated with lung cancer without requiring DNA sequencing. In a proof-of-concept study, the approach achieved sensitivity and specificity above 90 percent in patients with stage 2–4 lung cancer. The researchers believe the relatively simple and inexpensive technique could eventually complement CT imaging and potentially help monitor treatment response.
Lung cancer remains the leading cause of cancer-related mortality worldwide. Low-dose CT (LDCT) can help detect the disease earlier, but screening frequently identifies lung nodules that ultimately prove benign. This can result in additional diagnostic procedures, including unnecessary biopsies or surgery.
Liquid biopsies offer another route by searching blood for biological traces of cancer. Many emerging tests analyse methylation patterns in cell-free DNA (cfDNA), but currently rely on DNA sequencing. According to the researchers, sample preparation, the required sequencing depth and subsequent computational analysis make these approaches relatively complex and expensive.
Researchers from Tel Aviv University and several Israeli medical centres have now investigated a sequencing-free alternative. The study, published in npj Precision Oncology, included 103 participants: 51 patients with stage 2–4 lung cancer and 52 age-matched healthy controls.
Fluorescent fingerprint
The technique searches for differences in DNA methylation, chemical modifications of DNA that can vary between healthy and cancerous cells. Cell-free DNA is extracted from blood plasma and processed so that originally methylated CpG sites can be labelled with a fluorescent marker.
The labelled DNA is subsequently applied to a conventional microarray. The fluorescence intensity at different locations on the array reflects methylation levels in particular genomic regions, effectively creating a pattern that can be compared between samples.
The researchers initially analysed samples from 22 lung cancer patients and 21 healthy controls. They identified a signature consisting of 170 genomic regions with strongly differing methylation patterns. A separate validation set containing 29 cancer samples and 31 healthy controls was then classified in a blinded analysis.
The test reached a sensitivity of 93.1 percent at a specificity of 90.3 percent, with an area under the ROC curve of 0.947. The researchers stress, however, that the control group consisted of healthy people. In clinical practice, the test would also need to distinguish cancer from benign lung conditions, meaning its real-world specificity may be lower.
Cancer subtypes
The methylation patterns also contained information about tumour type. A separate set of 168 markers distinguished lung adenocarcinoma from squamous cell carcinoma with an AUC of 0.881. The approach correctly classified 86.4 percent of squamous cell carcinoma samples and 88.2 percent of adenocarcinomas.
There are also early indications that the method could help monitor treatment. The researchers followed two patients receiving chemo-immunotherapy. In the responding patient, the methylation profile shifted towards that of healthy controls, in line with PET-CT findings. No comparable change occurred in the non-responding patient. With only two patients, however, the researchers emphasise that larger studies are needed.
Toward clinical validation
The method currently takes two to three days and costs approximately $60 per sample using commercially available arrays. The researchers are developing a dedicated lung cancer array, while a larger prospective clinical trial is being prepared to investigate whether the test can predict response to systemic treatment. Validation in early-stage disease will also be necessary before the technology could fulfil its potential for early detection.
The study fits into a broader shift toward new technologies for understanding and detecting cancer. Researchers are increasingly combining molecular analysis with engineered models such as tumors-on-a-chip, organoids and 3D-bioprinted tissue. Such approaches can reveal biomarkers and recreate tumour development and treatment response in human-relevant models.
For example, University of Pennsylvania researchers developed a tumor-on-a-chip model to study how cancers evade immune attack. Other researchers are exploring organ-on-a-chip systems, organoids and bioprinting to study the earliest stages of cancer development. Together with increasingly accessible molecular diagnostics, these technologies could provide clinicians with more detailed information about cancer while supporting more personalised approaches to diagnosis and treatment.
