TY - JOUR
T1 - Noninvasive detection of any-stage cancer using free glycosaminoglycans
AU - Bratulic, Sinisa
AU - Limeta, Angelo
AU - Dabestani, Saeed
AU - Birgisson, Helgi
AU - Enblad, Gunilla
AU - Stålberg, Karin
AU - Hesselager, Göran
AU - Häggman, Michael
AU - Höglund, Martin
AU - Simonson, Oscar E.
AU - Stålberg, Peter
AU - Lindman, Henrik
AU - Bång-Rudenstam, Anna
AU - Ekstrand, Matias
AU - Kumar, Gunjan
AU - Cavarretta, Ilaria
AU - Alfano, Massimo
AU - Pellegrino, Francesco
AU - Mandel-Clausen, Thomas
AU - Salanti, Ali
AU - Maccari, Francesca
AU - Galeotti, Fabio
AU - Volpi, Nicola
AU - Daugaard, Mads
AU - Belting, Mattias
AU - Lundstam, Sven
AU - Stierner, Ulrika
AU - Nyman, Jan
AU - Bergman, Bengt
AU - Edqvist, Per Henrik
AU - Levin, Max
AU - Salonia, Andrea
AU - Kjölhede, Henrik
AU - Jonasch, Eric
AU - Nielsen, Jens
AU - Gatto, Francesco
N1 - Funding Information:
The authors wish to thank the Biobank Väst and Uppsala Biobank, the U-CAN consortium, site coordinator Lennart Råhlén, and the research nurses Lena Solitander and Elisabeth Kapocs for help in the coordination, supervision, and collection of clinical data and samples used in this study. The Lifelines initiative has been made possible by the subsidy from the Dutch Ministry of Health, Welfare, and Sport, the Dutch Ministry of Economic Affairs, the University Medical Center Groningen (UMCG), Groneningen University, and the Provinces in the North of the Netherlands (Drenthe, Friesland, Groningen). The computations were performed on resources at Chalmers Centre for Computational Science and Engineering (C3SE) provided by the Swedish National Infrastructure for Computing (SNIC), partially funded by the Swedish Research Council (Grant No. 2018-05973). J. Nielsen acknowledges the financial support by the Knut and Alice Wallenberg Foundation (Grant Nos. 2017.0328 and 2018.0266), Cancerfonden (Grant No. 17 0625), and the Ingabritt och Arne Lundbergs Forskningsstiftelse (Grant Nos. LU2016-0011 and LU2020-0023). F. Gatto acknowledges support from the European Union’s Horizon 2020 research and innovation program (Grant No. 849251), the EIT Healthy 2019 Digital Sandbox (Grant No. 2019-DS1001-6543), VINNOVA (2016-00763), and Västra Götaland Region. H.K. acknowledges support from the Märta and Gustaf Ågren Foundation and grants from the Swedish state under the agreement between the Swedish government and the country councils, the ALF-agreement (No. ALFGBG-873181) for the collection of samples from bladder cancer patients.
Funding Information:
ACKNOWLEDGMENTS. The authors wish to thank the Biobank Väst and Uppsala Biobank, the U-CAN consortium, site coordinator Lennart Råhlén, and the research nurses Lena Solitander and Elisabeth Kapocs for help in the coordination, supervision, and collection of clinical data and samples used in this study. The Lifelines initiative has been made possible by the subsidy from the Dutch Ministry of Health, Welfare, and Sport, the Dutch Ministry of Economic Affairs, the University Medical CenterGroningen(UMCG),GroneningenUniversity,andtheProvincesintheNorth of the Netherlands (Drenthe, Friesland, Groningen). The computations were performed on resources at Chalmers Centre for Computational Science and Engineering (C3SE) provided by the Swedish National Infrastructure for Computing (SNIC), partially funded by the Swedish Research Council (Grant No. 2018-05973). J. Nielsen acknowledges the financial support by the Knut and Alice Wallenberg Foundation (Grant Nos. 2017.0328 and 2018.0266), Cancerfonden (Grant No. 17 0625), and theIngabritt ochArneLundbergsForskningsstiftelse(GrantNos.LU2016-0011and LU2020-0023).F.Gatto acknowledges support from the European Union’s Horizon 2020 research and innovation program (Grant No. 849251), the EIT Healthy 2019 Digital Sandbox (Grant No. 2019-DS1001-6543), VINNOVA (2016-00763), and Västra Götaland Region. H.K. acknowledges support from the Märta and Gustaf Ågren Foundation and grants from the Swedish state under the agreement between the Swedish government and the country councils, the ALF-agreement (No. ALFGBG-873181) for the collection of samples from bladder cancer patients.
Publisher Copyright:
© 2022 the Author(s). Published by PNAS.
PY - 2022/12/13
Y1 - 2022/12/13
N2 - 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.
AB - 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.
KW - cancer biomarkers
KW - liquid biopsy
KW - metabolomics
KW - multi-cancer early detection
KW - prognosis
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U2 - 10.1073/pnas.2115328119
DO - 10.1073/pnas.2115328119
M3 - Article
C2 - 36469776
AN - SCOPUS:85143451035
SN - 0027-8424
VL - 119
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 50
M1 - e2115328119
ER -