Tang Cong, Corredeira Patrícia, Casimiro Sandra, Shi Qi, Han Qiwei, Sukdao Wesley, Cavaco Ana, Melo-Alvim Cecília, Matos Carolina Ochôa, Abreu Catarina, Walsh Steven, Nogueira-Costa Gonçalo, Ribeiro Leonor, Sousa Rita, Barradas Ana Lorena, Fonseca João Eurico, Costa Luís, Yates Emma V, Bernardes Gonçalo J L
GIMM - Gulbenkian Institute for Molecular Medicine; Avenida Prof. Egas Moniz, Lisboa, Portugal.
Proteotype Diagnostics Ltd, Babraham Research Campus, Cambridge, UK.
Nat Commun. 2025 Jul 14;16(1):6474. doi: 10.1038/s41467-025-61685-2.
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.
针对肿瘤发展的免疫反应常常是治疗的靶点，但在诊断方面仍 largely unexplored，尽管早期肿瘤的免疫反应更强。我们提出了一种免疫诊断平台来检测这一反应。我们识别出一组氨基酸残基生物标志物，它们提供了与肿瘤发展相关的癌症特异性免疫激活特征，且不同于自身免疫性疾病和感染性疾病，可在纯血浆中进行光学测量，并在N = 170名参与者中得到验证。通过测量半胱氨酸、游离半胱氨酸、赖氨酸、色氨酸和酪氨酸等蛋白质结合生物标志物的总浓度，并使用监督式机器学习分析结果，我们以0%的假阳性率识别出78%的癌症（N = 97），曲线下面积为0.95。癌症、健康和自身免疫/感染性生物标志物模式在统计学上有显著差异（p < 0.0001）。生物标志物浓度的较小规模变化揭示了患者间免疫激活的差异，这些差异可预测治疗反应。这些生物标志物的特定浓度范围可预测晚期乳腺癌患者对细胞周期蛋白依赖性激酶抑制剂的反应（p < 0.05），识别出98%的反应者（N = 33）。在此，我们提供了一种据我们所知此前未曾报道过的免疫诊断技术平台，并在N = 170的队列中证明了其初步临床应用，包括在多癌早期检测和个性化医疗中的概念验证。
