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Noninvasive detection of any-stage cancer using free glycosaminoglycans

Bratulic, Sinisa ; Limeta, Angelo ; Dabestani, Saeed LU ; Birgisson, Helgi ; Enblad, Gunilla ; Stålberg, Karin ; Hesselager, Göran ; Häggman, Michael ; Höglund, Martin and Simonson, Oscar E. , et al. (2022) In Proceedings of the National Academy of Sciences of the United States of America 119(50).
Abstract

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... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
cancer biomarkers, liquid biopsy, metabolomics, multi-cancer early detection, prognosis
in
Proceedings of the National Academy of Sciences of the United States of America
volume
119
issue
50
article number
e2115328119
publisher
National Academy of Sciences
external identifiers
  • scopus:85143451035
  • pmid:36469776
ISSN
1091-6490
DOI
10.1073/pnas.2115328119
language
English
LU publication?
yes
id
2f563b7b-9a77-49da-a2ca-2fa594a238b8
date added to LUP
2022-12-22 12:18:52
date last changed
2024-05-13 02:24:23
@article{2f563b7b-9a77-49da-a2ca-2fa594a238b8,
  abstract     = {{<p>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.</p>}},
  author       = {{Bratulic, Sinisa and Limeta, Angelo and Dabestani, Saeed and Birgisson, Helgi and Enblad, Gunilla and Stålberg, Karin and Hesselager, Göran and Häggman, Michael and Höglund, Martin and Simonson, Oscar E. and Stålberg, Peter and Lindman, Henrik and Bång-Rudenstam, Anna and Ekstrand, Matias and Kumar, Gunjan and Cavarretta, Ilaria and Alfano, Massimo and Pellegrino, Francesco and Mandel-Clausen, Thomas and Salanti, Ali and Maccari, Francesca and Galeotti, Fabio and Volpi, Nicola and Daugaard, Mads and Belting, Mattias and Lundstam, Sven and Stierner, Ulrika and Nyman, Jan and Bergman, Bengt and Edqvist, Per Henrik and Levin, Max and Salonia, Andrea and Kjölhede, Henrik and Jonasch, Eric and Nielsen, Jens and Gatto, Francesco}},
  issn         = {{1091-6490}},
  keywords     = {{cancer biomarkers; liquid biopsy; metabolomics; multi-cancer early detection; prognosis}},
  language     = {{eng}},
  number       = {{50}},
  publisher    = {{National Academy of Sciences}},
  series       = {{Proceedings of the National Academy of Sciences of the United States of America}},
  title        = {{Noninvasive detection of any-stage cancer using free glycosaminoglycans}},
  url          = {{http://dx.doi.org/10.1073/pnas.2115328119}},
  doi          = {{10.1073/pnas.2115328119}},
  volume       = {{119}},
  year         = {{2022}},
}