Immunodiagnostic plasma amino acid residue biomarkers detect cancer early and predict treatment response
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 prov...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-61685-2 |
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| author | 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 Leonor Ribeiro Rita Sousa Ana Lorena Barradas João Eurico Fonseca Luís Costa Emma V. Yates Gonçalo J. L. Bernardes |
| author_facet | 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 Leonor Ribeiro Rita Sousa Ana Lorena Barradas João Eurico Fonseca Luís Costa Emma V. Yates Gonçalo J. L. Bernardes |
| author_sort | Cong Tang |
| collection | DOAJ |
| description | 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 < 0.0001). Smaller-scale changes in biomarker concentrations reveal inter-patient differences in immune activation that predict treatment response. Specific concentration ranges of these biomarkers predict response to Cyclin-dependent kinase inhibitors in advanced breast cancer patients (p < 0.05), identifying 98% of responders (N = 33). Here we provide an immunodiagnostic technology platform that, to our knowledge, has not been previously reported, and prove initial clinical application in a cohort of N = 170, including proof of concept in Multi Cancer Early Detection and personalized medicine. |
| format | Article |
| id | doaj-art-61dea61a860c4e688fbc6f830f059e39 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-61dea61a860c4e688fbc6f830f059e392025-08-20T03:42:52ZengNature PortfolioNature Communications2041-17232025-07-0116111410.1038/s41467-025-61685-2Immunodiagnostic plasma amino acid residue biomarkers detect cancer early and predict treatment responseCong Tang0Patrícia Corredeira1Sandra Casimiro2Qi Shi3Qiwei Han4Wesley Sukdao5Ana Cavaco6Cecília Melo-Alvim7Carolina Ochôa Matos8Catarina Abreu9Steven Walsh10Gonçalo Nogueira-Costa11Leonor Ribeiro12Rita Sousa13Ana Lorena Barradas14João Eurico Fonseca15Luís Costa16Emma V. Yates17Gonçalo J. L. Bernardes18GIMM - Gulbenkian Institute for Molecular Medicine; Avenida Prof. Egas MonizGIMM - Gulbenkian Institute for Molecular Medicine; Avenida Prof. Egas MonizGIMM - Gulbenkian Institute for Molecular Medicine; Avenida Prof. Egas MonizNova School of Business and Economics, R. da Holanda 1Nova School of Business and Economics, R. da Holanda 1Proteotype Diagnostics Ltd, Babraham Research CampusGIMM - Gulbenkian Institute for Molecular Medicine; Avenida Prof. Egas MonizServiço de Oncologia Médica, ULSSM, Unidade Local de Saúde de Santa MariaServiço de Reumatologia, ULS de Santa Maria, Centro Académico de Medicina de LisboaServiço de Oncologia Médica, ULSSM, Unidade Local de Saúde de Santa MariaProteotype Diagnostics Ltd, Babraham Research CampusServiço de Oncologia Médica, ULSSM, Unidade Local de Saúde de Santa MariaServiço de Oncologia Médica, ULSSM, Unidade Local de Saúde de Santa MariaServiço de Oncologia Médica, ULSSM, Unidade Local de Saúde de Santa MariaGIMM - Gulbenkian Institute for Molecular Medicine; Avenida Prof. Egas MonizServiço de Reumatologia, ULS de Santa Maria, Centro Académico de Medicina de LisboaGIMM - Gulbenkian Institute for Molecular Medicine; Avenida Prof. Egas MonizProteotype Diagnostics Ltd, Babraham Research CampusGIMM - Gulbenkian Institute for Molecular Medicine; Avenida Prof. Egas MonizAbstract 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 < 0.0001). Smaller-scale changes in biomarker concentrations reveal inter-patient differences in immune activation that predict treatment response. Specific concentration ranges of these biomarkers predict response to Cyclin-dependent kinase inhibitors in advanced breast cancer patients (p < 0.05), identifying 98% of responders (N = 33). Here we provide an immunodiagnostic technology platform that, to our knowledge, has not been previously reported, and prove initial clinical application in a cohort of N = 170, including proof of concept in Multi Cancer Early Detection and personalized medicine.https://doi.org/10.1038/s41467-025-61685-2 |
| spellingShingle | 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 Leonor Ribeiro Rita Sousa Ana Lorena Barradas João Eurico Fonseca Luís Costa Emma V. Yates Gonçalo J. L. Bernardes Immunodiagnostic plasma amino acid residue biomarkers detect cancer early and predict treatment response Nature Communications |
| title | Immunodiagnostic plasma amino acid residue biomarkers detect cancer early and predict treatment response |
| title_full | Immunodiagnostic plasma amino acid residue biomarkers detect cancer early and predict treatment response |
| title_fullStr | Immunodiagnostic plasma amino acid residue biomarkers detect cancer early and predict treatment response |
| title_full_unstemmed | Immunodiagnostic plasma amino acid residue biomarkers detect cancer early and predict treatment response |
| title_short | Immunodiagnostic plasma amino acid residue biomarkers detect cancer early and predict treatment response |
| title_sort | immunodiagnostic plasma amino acid residue biomarkers detect cancer early and predict treatment response |
| url | https://doi.org/10.1038/s41467-025-61685-2 |
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