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Noninvasive detection of any-stage cancer using free glycosaminoglycans
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 tu...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9897435/ https://www.ncbi.nlm.nih.gov/pubmed/36469776 http://dx.doi.org/10.1073/pnas.2115328119 |
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author | 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 |
author_facet | 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 |
author_sort | Bratulic, Sinisa |
collection | PubMed |
description | 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 (N(urine) = 220 cancer vs. 360 healthy) and plasma (N(plasma) = 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. |
format | Online Article Text |
id | pubmed-9897435 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-98974352023-02-04 Noninvasive detection of any-stage cancer using free glycosaminoglycans 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 Proc Natl Acad Sci U S A Biological Sciences 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 (N(urine) = 220 cancer vs. 360 healthy) and plasma (N(plasma) = 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. National Academy of Sciences 2022-12-05 2022-12-13 /pmc/articles/PMC9897435/ /pubmed/36469776 http://dx.doi.org/10.1073/pnas.2115328119 Text en Copyright © 2022 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Biological Sciences 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 Noninvasive detection of any-stage cancer using free glycosaminoglycans |
title | Noninvasive detection of any-stage cancer using free glycosaminoglycans |
title_full | Noninvasive detection of any-stage cancer using free glycosaminoglycans |
title_fullStr | Noninvasive detection of any-stage cancer using free glycosaminoglycans |
title_full_unstemmed | Noninvasive detection of any-stage cancer using free glycosaminoglycans |
title_short | Noninvasive detection of any-stage cancer using free glycosaminoglycans |
title_sort | noninvasive detection of any-stage cancer using free glycosaminoglycans |
topic | Biological Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9897435/ https://www.ncbi.nlm.nih.gov/pubmed/36469776 http://dx.doi.org/10.1073/pnas.2115328119 |
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