Development of a Serum Metabolome-Based Test for Early-Stage Detection of Multiple Cancers

Sep 21, 2026

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Early-stage cancer detection is critical for reducing morbidity and mortality, yet multi-cancer screening approaches that depend on circulating biomarkers are limited by low biomarker concentrations in early disease and reduced test accuracy. This study uses serum metabolome interrogation combined with machine-learning analytics to identify metabolite pattern modulations associated with cancer presence. An initial analysis demonstrated detection of 15 cancers in women at about 99% average accuracy, supported by an investigator-initiated trial.

LanguageEnglish

CategoryResearch & Report

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Pages12

Reading Time30 min

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Abstract

Background

Aims

Methods and Results

Conclusion

Introduction

Cancer Reports ORIGINAL ARTICLE OPEN ACCESS Development of a Serum Metabolome-Based Test for Early-Stage Detection of Multiple Cancers Rajnish Nagarkar1 | Mamillapalli Gopichand2 | Suparna Kanti Pal3,4 | Ankur Gupta5,6 | Najmuddin Md Saquib5,6 | Ahmad Ahmad5 | Ganga Sagar5 | Kanury V. S. Rao5,6 | Zaved Siddiqui5,6 | Imliwati Longkumer7 1HCG Manavta Cancer Centre, Nashik, Maharashtra, India | 2HealthCare Global Enterprises Limited, City Cancer Centre, Vijayawada, India | 3Indoriv Clinical, Kolkata, India | 4Department of Radiotherapy, IPGME&R, Kolkata, India | 5PredOmix Technologies Private Limited, Gurugram, India | 6PredOmix Health Sciences Private Limited, Singapore, Singapore | 7North East Cancer Hospital and Research Institute, Jorabat, Guwahati, Assam, India Correspondence: Zaved Siddiqui ([zaved@predomix.com](zaved@predomix.com)) | Imliwati Longkumer ([ilongkumer@yahoo.co.in](ilongkumer@yahoo.co.in)) Received: 19 July 2024 | Revised: 30 September 2024 | Accepted: 14 October 2024 Keywords: artificial intelligence | cancer | metabolomics ABSTRACT Background: Detection 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% . Aims: To 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 Results: Metabolites 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. Conclusion: Thus, 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 Registration: Registration No: CTRI/2023/03/050316 1 | Introduction Cancer threatens to emerge as the leading cause of premature mortality across the globe [1, 2] . Current therapies at best provide only a marginal prolongation of survival and more effective therapies are clearly needed [3] . On the other hand, population-scale detection of cancers in their early stage of development and/or progression offers a feasible strategy for reducing cancer-associated mortality [4–9] . Besides impacting on mortality, early detection also helps to reduce treatment cost and improve quality of life by minimizing disease- and treatment-related morbidity [10, 11] . Unfortunately, though, This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is proper

FAQs

Why are early-stage multi-cancer tests based on circulating biomarkers limited in accuracy?

Early-stage cancers yield low circulating biomarker concentrations, which compromises detection sensitivity and accuracy.

What alternative approach did the study adopt for early-stage multi-cancer detection?

The study interrogated the serum metabolome and applied machine-learning-based data analytics to detect metabolite pattern changes correlated with cancer.

How many cancers and with what approximate accuracy did the trial results report?

The platform was reported to detect 30 cancers across females and males with an average accuracy of about 98%, and accuracy remained consistent across all four cancer stages.

Sep 21, 2026

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