Noninvasive detection of any-stage cancer using free glycosaminoglycans

Sinisa Bratulic, Angelo Limeta, Saeed Dabestani, Helgi Birgisson, Gunilla Enblad, Karin Stålberg, Göran Hesselager, Michael Häggman, Martin Höglund, Oscar E. Simonson, Peter Stålberg, Henrik Lindman, Anna Bång-Rudenstam, Matias Ekstrand, Gunjan Kumar, Ilaria Cavarretta, Massimo Alfano, Francesco Pellegrino, Thomas Mandel-Clausen, Ali SalantiFrancesca Maccari, Fabio Galeotti, Nicola Volpi, Mads Daugaard, Mattias Belting, Sven Lundstam, Ulrika Stierner, Jan Nyman, Bengt Bergman, Per Henrik Edqvist, Max Levin, Andrea Salonia, Henrik Kjölhede, Eric Jonasch, Jens Nielsen, Francesco Gatto

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article numbere2115328119
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number50
Publication statusPublished - 2022 Dec

Subject classification (UKÄ)

  • Cancer and Oncology

Free keywords

  • cancer biomarkers
  • liquid biopsy
  • metabolomics
  • multi-cancer early detection
  • prognosis


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