Active Lungs & Breathing Cancer

Fluorescence lifetime imaging signatures for early diagnosis of lung cancer, and its regulation through the adenosine pathway.

In plain English

AI plain-English summary

A bronchoscope fitted with a specialised fibre can now measure the light emitted by living lung tissue to distinguish cancer from healthy cells in real time. Current CT scans often miss early lung cancers or take weeks to show whether a treatment is working, delaying critical decisions. This project tests whether fluorescence lifetime imaging (FLIM) can fill that gap by providing immediate metabolic profiles of suspicious lesions during a routine bronchoscopy. The researcher will also use chemical probes that reveal the activity of fibroblasts and T cells—two cell types that determine how well chemotherapy or immunotherapy works—potentially flagging treatment effectiveness days or weeks before any change appears on a scan. If successful, FLIM could become a bedside tool that speeds up both diagnosis and treatment evaluation for lung cancer, the world’s leading cause of cancer death. The work is applied, not fundamental: it directly tests a clinical technology already designed for use in patients, with the goal of shortening the diagnostic and therapeutic timeline.

View original technical description
Lung cancer is the leading cause of cancer related deaths worldwide. Unfortunately, current methods like CT scans have limitations in detecting lung cancer early and evaluating treatment response promptly, causing delays in diagnosis/therapy. To address this, I will investigate the potential of a technology called fluorescent lifetime imaging (FLIM). FLIM can detect emitted light from cells in the body, and I have shown that the light emitted from cells within a cancer differs from that of non-cancerous cells. Although the reason for this discrepancy is unknown, my findings suggest a correlation with how cells regulate their metabolism, through a chemical called adenosine. The FLIM technology is state-of-the-art and designed for use during bronchoscopy (where a camera is inserted into the lungs to examine suspected cancers). Here, using specialised fibres that can reach lesions/tumours we can obtain real-time metabolic profiles of the cancerous tissue, enabling immediate cancer diagnosis. I will also use small chemical compounds that detect the overactivity of two cell types crucial in the response to treatments: fibroblasts and T cells. This approach may provide early indications of treatment effectiveness, such as chemotherapy, before changes are visible on CT scans. Aims: 1) Define FLIM signals in lung cancer at both the cellular and whole cancer levels. 2) To investigate the relationship between FLIM signals and the activity of the adenosine pathway in lung cancer, linked to drug treatments. Objectives: Objective 1. Determine the cell specific FLIM signal in lung cancer and relate this to both the cells level of activation and function. Using surgically resected cancers, I will breakdown these into their individual cell component and measure the cell specific FLIM signal in each cell type. Parallel confirmatory experiments will tell us the cell activation and function levels. Then, using advanced culture methods that mimic cancer conditions I will understand what changes occur with drug treatments as we would use in the clinic, and relate this to how efficiently the cancer cells are killed. Finally, using an established large pathology set of lung cancers, I will make a unique atlas of all the cell types in cancer and their FLIM signal. This will be made into a shared community resource for other researchers. Objective 2. I will use a small device to allow us to understand how multiple combinations of drugs work in lung cancer, linked to FLIM. Using a device capable of delivering 20 drug combinations to small, confined areas of the cancer (less than 1 mm distance), I will study multiple combinations of drugs that target the adenosine pathway in lung cancer patients who have had their cancer removed. Using multiple laboratory techniques, including FLIM, I will measure how well the drug combinations have performed. The optimal combinations will be delivered (without the device) to patients a few days before their surgery for confirmation. Ultimately, we will aim to do the whole device experiments in patients' cancers before surgery, but this will be done with further research funding and on completion of the above stages of research. Objective 3. Assess FLIM signals in patients and combine this with small chemical compounds that report activity of fibroblasts and T cells. I will use an existing trial infrastructure in Edinburgh and will recruit patients with i) suspected cancer, ii) undergoing surgery, iii) planned for drug-based cancer treatment. Each group will have FLIM imaging combined with the chemical probes that can identify activated fibroblasts and signatures of cell death caused by T cells. This will gain valuable insights into the behaviour of the cancer and its response to therapy by FLIM. Together, this will determine if FLIM can be used as a tool for immediate diagnosis and early assessment of treatment response in lung cancer, ultimately enhancing outcomes for patients.

View the original record at the funder ↗

Researchers

Ahsan Akram (Principal Investigator)James Hopgood (Co-Investigator)Marc Vendrell (Co-Investigator)Qiang Wang (Co-Investigator)

Related Research

Grants with similar aims, by meaning.

Towards in-vivo in-situ early lung cancer detection using fluorescence lifetime imaging endomicroscopy: a preliminary study on data-driven characterisation of lung cancer using label-free autofluorescence lifetime images on unstained lung tissue
Exploring c-Met and HER3 crosstalk by nano-proximity imaging for understanding its clinical significance in lung adenocarcinoma.
Identification and targeting of early T cell dysfunction in pulmonary preinvasive neoplasia
Fluorescence Lifetime Imaging of New Functional Biomaterials for Non Invasive Early Tumour Diagnosis
Characterising cancer-associated fibroblast heterogeneity in lung cancer: relating molecular phenotype to function

Original classification

Fellowship

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.