ArticleCancer reports (Hoboken, N.J.)2024
Development of a Serum Metabolome-Based Test for Early-Stage Detection of Multiple Cancers.
Article in Cancer reports (Hoboken, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Circulating biomarkers in HPV-associated oropharyngeal cancer: Clinical applications of liquid biopsy in diagnosis, prognosis, and surveillance.The journal of liquid biopsy · 2025Review
- The Future of Tumor Markers: Advancing Early Malignancy Detection Through Omics Technologies, Continuous Monitoring, and Personalized Reference Intervals.Biomolecules · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDetection of cancer at the early stage currently offers the only viable strategy for reducing disease-related morbidity and mortality. Various approaches for multi-cancer early detection are being explored, which largely rely on capturing signals from circulating analytes shed by tumors into the blood. The fact that biomarker concentrations are limiting in the early stages of cancer, however, compromises the accuracy of these tests. We, therefore, adopted an alternate approach that involved interrogation of the serum metabolome with machine learning-based data analytics. Here, we monitored for modulations in metabolite patterns that correlated with the presence or absence of cancer. Results obtained confirmed the efficacy of this approach by demonstrating that it could detect a total of 15 cancers in women with an average accuracy of about 99%.
aimsTo further increase the scope of our test, we conducted an investigator-initiated clinical trial involving a total of 6445 study participants, which included both cancer patients and non-cancer volunteers. Our goal here was to maximize the number of cancers that could be detected, while also covering cancers in both females and males. METHODS AND
resultsMetabolites extracted from individual serum samples were profiled by ultra-performance liquid chromatography coupled to a high-resolution mass spectrometer using an untargeted protocol. After processing, the data were analyzed by our cancer detection machine-learning algorithm to differentiate cancer from non-cancer samples. Results revealed that our test platform could indeed detect a total of 30 cancers, covering both females and males, with an average accuracy of ~98%. Importantly, the high detection accuracy remained invariant across all four stages of the cancers.
conclusionThus, our approach of integrating untargeted metabolomics with machine learning-powered data analytics offers a powerful strategy for early-stage multi-cancer detection with high accuracy.
trial registrationRegistration No: CTRI/2023/03/050316.
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