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

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.

Authors

Institutions

Publication Details

Journal
Nature Communications
Published
2025-07-14
DOI
https://doi.org/10.1038/s41467-025-61685-2
Citations
2
Primary Topic
Cancer Immunotherapy and Biomarkers
Type
article
Field-Weighted Citation Impact
2.09

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

A Barradas, Steven Walsh, Emma V. Yates, João Eurico Fonseca et al.
2 citations
Nature Communications
Cancer Immunotherapy and Biomarkers
2.09
article

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

A Barradas, Steven Walsh, Emma V. Yates, João Eurico Fonseca, Gonçalo J. L. Bernardes, Sandra Casimiro, Patrícia Corredeira, Catarina M. Abreu, Luís Costa, Qiwei Han, Cecília Melo-Alvim, Cong Tang, Gonçalo Nogueira-Costa, Wesley Sukdao, Ana Cavaco, Qi Shi, Rita Sousa, Leonor Ribeiro, Carolina Ochôa Matos
article en
2 citations

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.

Nature CommunicationsVol. 16(1)
Babraham Institute (GB), University of Lisbon (PT), University of Cambridge (GB), Spanish National Cancer Research Centre (ES), Hospital de Egas Moniz (PT), Administração Regional de Saúde de Lisboa e Vale do Tejo (PT), Babraham Bioscience Technologies (United Kingdom) (GB), Proteome Sciences (United Kingdom) (GB), Hospital de Santa Maria (PT), Tang Du Hospital (CN), Instituto Gulbenkian de Ciência (PT), Universidade Nova de Lisboa (PT)
Fundação para a Ciência e a Tecnologia
Good health and well-being
Openalex Percentile: Top 11%
Cancer Immunotherapy and Biomarkers
2.09
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.