The LCAOS project will develop and test a new diagnostic tool, able to detect:
(i) the presence of lung cancer (LC), and
(ii) an increased risk of a patient developing LC in the future.
Diagnostic tests currently available are unsuitable for widespread screening because they are costly, occasionally miss tumours, are not time-efficient, nor free of complications.
LCAOS will overcome these problems by using an approach based on volatile biomarkers emitted from cell membranes. A multidisciplinary effort, incorporating nanotechnology, biomedical engineering, medical oncology, and computation strategies, will develop a highly-sensitive, inexpensive, and fast-response, non-invasive, artificial nose (known as, NaNose), building on the coordinator’s earlier success in this area. The NaNose will be able to detect pre-neoplastic volatile biomarkers that indicate an increased genetic risk of LC, and the presence of LC. It has already been established that these biomarkers can be detected either directly from the headspace of the cancer cells or via exhaled breath.
(i) develop arrays of chemically-sensitive field effect transistors (FETs) of non-oxidized, molecule-terminated silicon nanowires (Si NWs);
(ii) test the ability of these devices to sense volatile LC biomarkers from in-vitro tissue, and exhaled human breath;
(iii) study the signal transduction mechanism of the volatile biomarkers, using pattern recognition;
(iv) improve systems to enable the NaNose to distinguish the targeted biomarkers from environmental clutter, using methylation, expression profiling, and genome-wide sequencing; and
(v) perform clinical-related studies to assess LC conditions in actual patients & tissues, and in the presence of real-world confounding signals.
Validation will be carried out by clinician partners and professional mathematicians and computer scientists. Resources will also be allocated to ensure the commercial potential of the sensor device layout.
Field of science
- /medical and health sciences/medical biotechnology
- /natural sciences/computer and information sciences/artificial intelligence/pattern recognition
- /medical and health sciences/clinical medicine/cancer
Call for proposal
See other projects for this call
Funding SchemeCP-FP - Small or medium-scale focused research project