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Development of a multi-modal method for ultra-sensitive detection of lung cancer in blood

Internationalisation Fellowships


This project aims to develop a new ultra-sensitive test to find early-stage lung cancers in simple blood samples. Lung cancer is the most lethal cancer worldwide with less than 20% of patients surviving longer than five years after diagnosis. The main reason why people die from lung cancer, and cancer in general, is because it is diagnosed too late when treatments are less effective. Detection of cancer at an early stage, while it is still curable by surgical resection is one of the most effective ways to increase survival from this disease.


An ultra-sensitive test for blood-based detection of cancer, before symptoms arise, holds potential to increase patient survival. A multi-modal strategy, as the one proposed in this study, could significantly increase the chance of detecting circulating tumor DNA, which is present at very low levels in early stage cancer patients. This would pave the way for a broadly applicable test for improved blood-based detection of cancer. Such a test has numerous potential clinical applications for many types of cancer including cancers where invasive tissue biopsies are difficult to obtain and associated with significant risks, and where monitoring of the disease is often very limited or not possible. Thus, if successful, this project will have a major impact on diagnosis and treatment of future cancer patients, and may significantly contribute to improve the survival.


I will identify lung-cancer-specific DNA methylation markers and develop an assay for detection of these markers in blood. This data will be combine with data from analysis other cancer-specific markers into a multi-modal test. A machine-learning algorithm will be applied to develop a classification model that can differentiate blood samples from lung cancer patients and non-cancer controls. Initially, performance will be tested in blood samples from advanced lung cancer patients and in the second phase, in blood samples from patients with early-stage and asymptomatic lung cancers.


This project aims to develop a new ultra-sensitive test to find early-stage lung cancers in simple blood samples. A blood-based test for early detection of lung cancer holds potential to improve current screening methods which suffers from high costs and risks such as radiation exposure and the potential harm associated with false-positive imaging results. In this project, we combine different types of cancer-specific markers to develop an ultra-sensitive test for early detection of lung cancer in the blood. If successful, this project will pave the way for improved blood-based detection of lung cancer to find cancer earlier when the chances to cure the patients are high and treatment side effects can be minimized. A sensitive test to find cancer in blood may enhance the clinical approach for establishing the diagnosis, monitoring treatment response and detecting resistance to therapies. Furthermore, the multi-modal test developed in this project could be adjusted to target other cancers by selecting markers specific for these cancer types. Hence, this project may significantly improve the life and survival of future cancer patients.