New approach accurately predicts lung cancer mutations

Scientists have developed a new method for accurately predicting gene changes that cause lung cancer, without the need for slower, more expensive lab techniques.

The technology was able to identify specific genetic changes with high accuracy, offering a potentially faster, more efficient and cheaper testing option than traditional methods.

The findings have the potential to accelerate testing for lung cancer patients, helping doctors to identify the right treatment for patients more quickly, experts say.

Lung cancer remains the leading cause of cancer-related death worldwide. Some lung cancers carry specific DNA genetic changes, such as mutations in the EGFR gene, which can determine whether patients would benefit from targeted treatments.

Detecting these mutations currently requires laboratory tests like gene sequencing, which can be expensive, time-consuming, and use up valuable tissue from small biopsy samples. Availability of tissue is often limited, so there is a need for non-invasive approaches to identify EGFR mutations.

Researchers from the University of Edinburgh and NHS Lothian have developed a new approach using a technique called fluorescence lifetime imaging microscopy (FLIM) to predict EGFR mutations without the need for genetic testing or tissue staining.

The technology captures natural light signals from tissue samples, which are then analyzed by artificial intelligence for patterns.

In the study, the method was able to predict the presence of EGFR mutations with very high accuracy. It could also distinguish between the two most common types of EGFR mutations that are important for treatment decisions.

Expanded lung cancer screening programmes are increasingly detecting suspected cancers at an earlier stage, placing pressure on diagnostic pathways to deliver fast, accurate results from limited tissue samples.

Experts say the new approach has the ability to speed up diagnosis, as well as preserving limited biopsy material – the method uses untreated tissue, leaving it intact and available for further analysis.

The findings build on an earlier study from the team, which demonstrated that the FLIM technique could be used to accurately distinguish between major types of non-small cell lung cancer, as well as non-cancerous tissue.

The research team is now working towards clinical validation of these approaches, with further work aiming to extend the platform to other cancer types, additional targetable mutations, and integration into clinical workflows.

The study is published in Cancer Research, a journal of the American Association for Cancer Research: https://doi.org/10.1158/0008-5472.CAN-25-5589 [URL will become active after embargo lifts]. It was supported by NVIDIA Academic Hardware, Pathological Society and UKRI.

Professor Ahsan Akram, co-lead of the study from the Institute for Regeneration and Repair, said: "This is a significant step towards a future where a single, non-destructive fluorescence scan of a biopsy could quickly inform clinicians whether a patient has cancer, what type of cancer they have and now, with this work, if it is likely to respond to targeted treatment, helping to ensure the right treatment reaches the right patient more quickly."

Dr Qiang Wang, co-lead of the study from the Institute for Regeneration and Repair, said: "This approach has the potential to take processes that currently cost thousands of pounds and require weeks of lab work and reduce them to something that takes minutes and costs hundreds. That is a step change in what is clinically achievable, particularly for centres and health systems where access to complex molecular testing is limited."

Clinicians are increasingly seeing more patients with earlier-stage disease and dealing a growing number of biopsy samples, placing significant pressure on diagnostic services. Technologies like this, which can deliver more information from smaller tissue samples at speed, will be essential for developing clinically effective diagnostic pathways."

Dr. David Dorward, Consultant Thoracic Pathologist, NHS Lothian