Background: Cancer screening tests are considered pivotal for early diagnosis and survival. However, the efficacy of these tests for improving survival has recently been questioned. This study aims to test if cancer screening could be improved by biomarkers in peripheral blood based on multi-omics data.
Methods: We utilize multi-omics data from 500,000 participants in the UK Biobank. Machine learning is applied to search for proteins, metabolites, genetic variants, or clinical variables to diagnose cancers collectively and individually.
Results: Here we show that the overall performance of the potential blood biomarkers do not outperform clinical variables for collective diagnosis. However, we observe promising results for individual cancers in close proximity to peripheral blood, with an Area Under the Curve (AUC) greater than 0.8.
Conclusions: Our findings suggest that the identification of blood biomarkers for cancer might be complicated by variable overlap between molecular changes in tumor tissues and peripheral blood. This explanation is supported by local proteomics analyses of different tumors, which all show high AUCs, greater than 0.9. Thus, multi-omics biomarkers for the diagnosis of individual cancers may potentially be effective, but not for groups of cancers.
This study aimed to find out if we could improve cancer screening tests by looking for signs of cancer in blood samples. We used computer and mathematical models to analyze data from 500,000 people. We found that these blood tests were not better than existing methods for diagnosing multiple types of cancer at once. However, they did show promise for diagnosing individual types of cancer that are close to the bloodstream. This suggests that finding blood markers for cancer is complex and depends on how much the cancer affects the blood. These findings could help in the development of more effective tests for individual types of cancer in the future.
© 2024. The Author(s).