A simple blood test could flag cancer earlier than current scans allow, by detecting tell-tale patterns of small molecules in the bloodstream. This matters because most cancers are diagnosed late, when treatment options are limited. Doctors currently rely on CT and MRI scans, but they often lack enough information to decide when to image a patient or how to interpret ambiguous results. The result is missed diagnoses, wasted healthcare resources, and unnecessary stress for patients and families. Oxomics’ technology uses machine learning to spot cancer-specific metabolite patterns in a blood sample. The company has already shown it can find cancers in patients with vague symptoms, sometimes before tumours are visible on scans. This grant will turn that prototype into a regulated clinical test by refining the classification software, building evidence for specific cancers, and working with regulators on quality control. If successful, the test could slot into existing care pathways, sending the right patients for the right imaging at the right time. That would mean earlier treatment, fewer unnecessary scans, and a less wasteful diagnostic process—without requiring new hospital equipment or specialist training.
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Early diagnosis of cancer improves patient outcomes and saves lives. Unfortunately, a lot of cancers are currently diagnosed late, at stages where treatment options are limited. One underlying cause of this problem is imperfect prescription of routine imaging technologies like CT scanning and MRI, both of which are commonly used for cancer diagnosis. Currently, almost all cancers are diagnosed by observing a symptomatic patient with imaging, but doctors are working with incomplete supporting information and a requirement to make imperfect calls about when to image patients and what imaging results indicate, if anything at all. Apart from poorer patient outcomes, the current approach is both costly and wasteful for the healthcare system while also being stressful for patients and their families. Oxomics technology diagnoses cancer using a simple, universal blood test, coupled with machine learning which identifies cancer-specific patterns of small molecules in the blood called metabolites. Our test fits into existing patient care pathways and offers rapid earlier diagnoses, allowing referral of the right patients for the right imaging at the right time, improving patient outcomes. We have already shown that our technology can be used to accurately find cancer in an under-served population of patients with nonspecific symptoms, including cases where we found cancers before they were visible by imaging. The goal of this application is to develop our technology further so that it is available as a regulated clinical test that can be used every day by doctors. We will do this by developing the underlying classification software, building clinical evidence in specific key cancers and working with regulators to ensure quality and reproducibility from our tests.
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