{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T09:44:32Z","timestamp":1753868672757,"version":"3.41.2"},"reference-count":52,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T00:00:00Z","timestamp":1731974400000},"content-version":"vor","delay-in-days":18,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Reports"],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Detection of cancer at the early stage currently offers the only viable strategy for reducing disease\u2010related morbidity and mortality. Various approaches for multi\u2010cancer 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\u2010based 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%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Aims<\/jats:title><jats:p>To further increase the scope of our test, we conducted an investigator\u2010initiated clinical trial involving a total of 6445 study participants, which included both cancer patients and non\u2010cancer volunteers. Our goal here was to maximize the number of cancers that could be detected, while also covering cancers in both females and males.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods and Results<\/jats:title><jats:p>Metabolites extracted from individual serum samples were profiled by ultra\u2010performance liquid chromatography coupled to a high\u2010resolution mass spectrometer using an untargeted protocol. After processing, the data were analyzed by our cancer detection machine\u2010learning algorithm to differentiate cancer from non\u2010cancer 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Thus, our approach of integrating untargeted metabolomics with machine learning\u2010powered data analytics offers a powerful strategy for early\u2010stage multi\u2010cancer detection with high accuracy.<\/jats:p><jats:p><jats:bold>Trial Registration:<\/jats:bold> Registration No: CTRI\/2023\/03\/050316<\/jats:p><\/jats:sec>","DOI":"10.1002\/cnr2.70042","type":"journal-article","created":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T10:52:24Z","timestamp":1732013544000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Development of a Serum Metabolome\u2010Based Test for Early\u2010Stage Detection of Multiple Cancers"],"prefix":"10.1002","volume":"7","author":[{"given":"Rajnish","family":"Nagarkar","sequence":"first","affiliation":[{"name":"HCG Manavta Cancer Centre  Nashik Maharashtra India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mamillapalli","family":"Gopichand","sequence":"additional","affiliation":[{"name":"HealthCare Global Enterprises Limited, City Cancer Centre  Vijayawada India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6737-083X","authenticated-orcid":false,"given":"Suparna Kanti","family":"Pal","sequence":"additional","affiliation":[{"name":"Indoriv Clinical  Kolkata India"},{"name":"Department of Radiotherapy IPGME&amp;R  Kolkata India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ankur","family":"Gupta","sequence":"additional","affiliation":[{"name":"PredOmix Technologies Private Limited  Gurugram India"},{"name":"PredOmix Health Sciences Private Limited  Singapore Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Najmuddin Md","family":"Saquib","sequence":"additional","affiliation":[{"name":"PredOmix Technologies Private Limited  Gurugram India"},{"name":"PredOmix Health Sciences Private Limited  Singapore Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmad","family":"Ahmad","sequence":"additional","affiliation":[{"name":"PredOmix Technologies Private Limited  Gurugram India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ganga","family":"Sagar","sequence":"additional","affiliation":[{"name":"PredOmix Technologies Private Limited  Gurugram India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kanury V. 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