Noninvasive detection of any-stage cancer using free glycosaminoglycans

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 119; no. 50; pp. 1 - 11
• Main Authors: Bratulic, Sinisa, Limeta, Angelo, Dabestani, Saeed, Birgisson, Helgi, Enblad, Gunilla, Stålberg, Karin, Hesselager, Göran, Häggman, Michael, Höglund, Martin, Simonson, Oscar E., Stålberg, Peter, Lindman, Henrik, Bång-Rudenstam, Anna, Ekstrand, Matias, Kumar, Gunjan, Cavarretta, Ilaria, Alfano, Massimo, Pellegrino, Francesco, Mandel-Clausen, Thomas, Salanti, Ali, Maccari, Francesca, Galeotti, Fabio, Volpi, Nicola, Daugaard, Mads, Belting, Mattias, Lundstam, Sven, Stierner, Ulrika, Nyman, Jan, Bergman, Bengt, Edqvist, Per-Henrik, Levin, Max, Salonia, Andrea, Kjölhede, Henrik, Jonasch, Eric, Nielsen, Jens, Gatto, Francesco
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 13.12.2022
• Subjects: Biological Sciences; Biomarkers; Biomarkers, Tumor - genetics; Biopsy; Brain tumors; Cancer; Cancer and Oncology; cancer biomarkers; Cancer och onkologi; Clinical Medicine; diagnosis; Early Detection of Cancer; genetics; Genomics; Glycosaminoglycans; Humans; Klinisk medicin; Liquid Biopsy; Machine learning; Medical and Health Sciences; Medical Biotechnology; Medicin och hälsovetenskap; Medicinsk bioteknologi; metabolomics; multi-cancer early detection; Neoplasms; Neoplasms - diagnosis; Population studies; prognosis; Sensitivity; Tumor; Tumors; multi-cancer early detection; cancer biomarkers; metabolomics; prognosis; liquid biopsy
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.2115328119

Cancer mortality is exacerbated by late-stage diagnosis. Liquid biopsies based on genomic biomarkers can noninvasively diagnose cancers. However, validation studies have reported ~10% sensitivity to detect stage I cancer in a screening population and specific types, such as brain or genitourinary tumors, remain undetectable. We investigated urine and plasma free glycosaminoglycan profiles (GAGomes) as tumor metabolism biomarkers for multi-cancer early detection (MCED) of 14 cancer types using 2,064 samples from 1,260 cancer or healthy subjects. We observed widespread cancer-specific changes in biofluidic GAGomes recapitulated in an in vivo cancer progression model. We developed three machine learning models based on urine (Nurine = 220 cancer vs. 360 healthy) and plasma (Nplasma = 517 vs. 425) GAGomes that can detect any cancer with an area under the receiver operating characteristic curve of 0.83–0.93 with up to 62% sensitivity to stage I disease at 95% specificity. Undetected patients had a 39 to 50% lower risk of death. GAGomes predicted the putative cancer location with 89% accuracy. In a validation study on a screening-like population requiring ≥ 99% specificity, combined GAGomes predicted any cancer type with poor prognosis within 18 months with 43% sensitivity (21% in stage I; N = 121 and 49 cases). Overall, GAGomes appeared to be powerful MCED metabolic biomarkers, potentially doubling the number of stage I cancers detectable using genomic biomarkers.