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Immunodiagnostic plasma amino acid residue biomarkers detect cancer early and predict treatment response

Author

Listed:
  • Cong Tang

    (GIMM - Gulbenkian Institute for Molecular Medicine; Avenida Prof. Egas Moniz
    Babraham Research Campus
    No.9 Fengcheng Third Road)

  • Patrícia Corredeira

    (GIMM - Gulbenkian Institute for Molecular Medicine; Avenida Prof. Egas Moniz)

  • Sandra Casimiro

    (GIMM - Gulbenkian Institute for Molecular Medicine; Avenida Prof. Egas Moniz)

  • Qi Shi

    (R. da Holanda 1)

  • Qiwei Han

    (R. da Holanda 1)

  • Wesley Sukdao

    (Babraham Research Campus)

  • Ana Cavaco

    (GIMM - Gulbenkian Institute for Molecular Medicine; Avenida Prof. Egas Moniz)

  • Cecília Melo-Alvim

    (Unidade Local de Saúde de Santa Maria)

  • Carolina Ochôa Matos

    (ULS de Santa Maria, Centro Académico de Medicina de Lisboa
    Universidade de Lisboa, Centro Académico de Medicina de Lisboa)

  • Catarina Abreu

    (Unidade Local de Saúde de Santa Maria)

  • Steven Walsh

    (Babraham Research Campus)

  • Gonçalo Nogueira-Costa

    (Unidade Local de Saúde de Santa Maria)

  • Leonor Ribeiro

    (Unidade Local de Saúde de Santa Maria)

  • Rita Sousa

    (Unidade Local de Saúde de Santa Maria)

  • Ana Lorena Barradas

    (GIMM - Gulbenkian Institute for Molecular Medicine; Avenida Prof. Egas Moniz)

  • João Eurico Fonseca

    (ULS de Santa Maria, Centro Académico de Medicina de Lisboa
    Universidade de Lisboa, Centro Académico de Medicina de Lisboa)

  • Luís Costa

    (GIMM - Gulbenkian Institute for Molecular Medicine; Avenida Prof. Egas Moniz
    Unidade Local de Saúde de Santa Maria)

  • Emma V. Yates

    (Babraham Research Campus)

  • Gonçalo J. L. Bernardes

    (GIMM - Gulbenkian Institute for Molecular Medicine; Avenida Prof. Egas Moniz
    University of Cambridge; Lensfield Road
    Spanish National Cancer Research Centre (CNIO))

Abstract

The immune response to tumour development is frequently targeted with therapeutics but remains largely unexplored in diagnostics, despite being stronger for early-stage tumours. We present an immunodiagnostic platform to detect this. We identify a panel of amino acid residue biomarkers providing a signature of cancer-specific immune activation associated with tumour development and distinct from autoimmune and infectious diseases, measurable optically in neat blood plasma, and validate within N = 170 participants. By measuring the total concentrations of cysteine, free cysteine, lysine, tryptophan, and tyrosine protein-incorporated biomarkers and analyzing the results with supervised machine learning, we identify 78% of cancers with 0% false positive rate (N = 97) with an AUROC of 0.95. The cancer, healthy, and autoimmune/infectious biomarker pattern are statistically significantly different (p

Suggested Citation

  • Cong Tang & Patrícia Corredeira & Sandra Casimiro & Qi Shi & Qiwei Han & Wesley Sukdao & Ana Cavaco & Cecília Melo-Alvim & Carolina Ochôa Matos & Catarina Abreu & Steven Walsh & Gonçalo Nogueira-Costa, 2025. "Immunodiagnostic plasma amino acid residue biomarkers detect cancer early and predict treatment response," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-61685-2
    DOI: 10.1038/s41467-025-61685-2
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    References listed on IDEAS

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    1. Grimur Hjorleifsson Eldjarn & Egil Ferkingstad & Sigrun H. Lund & Hannes Helgason & Olafur Th. Magnusson & Kristbjorg Gunnarsdottir & Thorunn A. Olafsdottir & Bjarni V. Halldorsson & Pall I. Olason & , 2023. "Large-scale plasma proteomics comparisons through genetics and disease associations," Nature, Nature, vol. 622(7982), pages 348-358, October.
    2. Samir M. Hanash & Sharon J. Pitteri & Vitor M. Faca, 2008. "Mining the plasma proteome for cancer biomarkers," Nature, Nature, vol. 452(7187), pages 571-579, April.
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