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Exploiting a unique organotypic model of bronchial dysplasia to improve the early detection of lung cancer

Research aim

To develop new biomarkers to detect lung cancer earlier.

Research aim

Background information

We have already developed a laboratory “test tube” model of lung “pre” cancer that closely resembles the human disease.

We now want to establish what are the key genetic events that accelerate this process and use the model to develop novel biomarkers that may be used in early detection.

The greatest challenges facing the lung cancer research community are how we can diagnose lung cancer earlier and how we can develop new treatment strategies for the prevention or treatment of lung cancer. We expect to identify key genetic events that drive the progression of lung cancer. Ultimately, this could lead to molecular tests that does not require a biopsy

Dr Frank McCaughan, Kings College London

What is the problem to be addressed?

Lung cancer is the most common cause of cancer-related death. This is because patients with lung cancer present late, at a stage when the disease is incurable, and the currently available treatments for the majority of patients are not effective long-term.

We know that patients diagnosed with early stage lung cancer survive much longer, on average, than those with advanced disease.

The greatest challenges facing the lung cancer research community are how we can diagnose lung cancer earlier and how we can develop new treatment strategies for the prevention or treatment of lung cancer. This project will address the challenge of early detection.

Expected results and future impact

We expect to identify key genetic events that drive the progression of lung cancer. These events could be used as biomarkers.

We then expect that the model systems we create will be a rich source of new molecular biomarkers for early detection.

Ultimately, this could lead to molecular tests that does not require a biopsy and could be use to help diagnose lung cancer early.

Lead researcher: Dr Frank McCaughan | Location: King’s College London | Research type: Early detection