Dataset Information

A non-invasive method for concurrent detection of multiple early-stage cancers in women.

ABSTRACT: Untargeted serum metabolomics was combined with machine learning-powered data analytics to develop a test for the concurrent detection of multiple cancers in women. A total of fifteen cancers were tested where the resulting metabolome data was sequentially analysed using two separate algorithms. The first algorithm successfully identified all the cancer-positive samples with an overall accuracy of > 99%. This result was particularly significant given that the samples tested were predominantly from early-stage cancers. Samples identified as cancer-positive were next analysed using a multi-class algorithm, which then enabled accurate discernment of the tissue of origin for the individual samples. Integration of serum metabolomics with appropriate data analytical tools, therefore, provides a powerful screening platform for early-stage cancers.

SUBMITTER: Gupta A

PROVIDER: S-EPMC10625604 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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A non-invasive method for concurrent detection of multiple early-stage cancers in women.

Gupta Ankur A Siddiqui Zaved Z Sagar Ganga G Rao Kanury V S KVS Saquib Najmuddin N

Scientific reports 20231104 1

Untargeted serum metabolomics was combined with machine learning-powered data analytics to develop a test for the concurrent detection of multiple cancers in women. A total of fifteen cancers were tested where the resulting metabolome data was sequentially analysed using two separate algorithms. The first algorithm successfully identified all the cancer-positive samples with an overall accuracy of > 99%. This result was particularly significant given that the samples tested were predominantly fr ...[more]

PMID: 37925521

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Project description: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.

Project description:BackgroundNon-invasive multi-cancer early detection (MCED) tests have shown promise in enhancing early cancer detection. However, their clinical utility across diverse populations remains underexplored, limiting their routine implementation. This study aims to validate the clinical utility of a multimodal non-invasive circulating tumor DNA (ctDNA)-based MCED test, SPOT-MAS (Screening for the Presence Of Tumor by DNA Methylation And Size).MethodsWe conducted a multicenter prospective study, K-DETEK (ClinicalTrials.gov identifier: NCT05227261), involving 9057 asymptomatic individuals aged 40 years or older across 75 major hospitals and one research institute in Vietnam. Participants were followed for 12 months.ResultsOf the 9024 eligible participants, 43 (0.48%) tested positive for ctDNA. Among these, 17 were confirmed with malignant lesions in various primary organs through standard-of-care (SOC) imaging and biopsy, with 9 cases matching our tissue of origin (TOO) predictions. This resulted in a positive predictive value of 39.53% (95%CI 26.37-54.42) and a TOO accuracy of 52.94% (95%CI 30.96-73.83). Among the 8981 participants (99.52%) who tested negative, 8974 were confirmed cancer-free during a 12-month period after testing, yielding a negative predictive value of 99.92% (95% CI 99.84-99.96). The test demonstrated an overall sensitivity of 70.83% (95%CI 50.83-85.09) and a specificity of 99.71% (95% CI 99.58-99.80) for detecting various cancer types, including those without SOC screening options.ConclusionsThis study presents a prospective validation of a multi-cancer early detection (MCED) test conducted in a lower middle-income country, demonstrating the potential of SPOT-MAS for early cancer detection. Our findings indicate that MCED tests could be valuable additions to national cancer screening programs, particularly in regions where such initiatives are currently limited.Trial registrationClinicalTrials.gov ID: NCT05227261. Date of registration: 07/02/2022.

Dataset Information

A non-invasive method for concurrent detection of multiple early-stage cancers in women.

Publications

A non-invasive method for concurrent detection of multiple early-stage cancers in women.

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