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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| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2041-1723 |