In Proceedings of the National Academy of Sciences of the United States of America
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.
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
2022-Dec-13
cancer biomarkers, liquid biopsy, metabolomics, multi-cancer early detection, prognosis
