Noninvasive detection of any-stage cancer using free glycosaminoglycans

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

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.

In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 119, No. 50, 2022, p. e2115328119.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Bratulic, S, Limeta, A, Dabestani, S, Birgisson, H, Enblad, G, Stålberg, K, Hesselager, G, Häggman, M, Höglund, M, Simonson, OE, Stålberg, P, Lindman, H, Bång-Rudenstam, A, Ekstrand, M, Kumar, G, Cavarretta, I, Alfano, M, Pellegrino, F, Mandel-Clausen, T, Salanti, A, Maccari, F, Galeotti, F, Volpi, N, Daugaard, M, Belting, M, Lundstam, S, Stierner, U, Nyman, J, Bergman, B, Edqvist, P-H, Levin, M, Salonia, A, Kjölhede, H, Jonasch, E, Nielsen, J & Gatto, F 2022, 'Noninvasive detection of any-stage cancer using free glycosaminoglycans', Proceedings of the National Academy of Sciences of the United States of America, vol. 119, no. 50, pp. e2115328119. https://doi.org/10.1073/pnas.2115328119

APA

Bratulic, S., Limeta, A., Dabestani, S., Birgisson, H., Enblad, G., Stålberg, K., Hesselager, G., Häggman, M., Höglund, M., Simonson, O. E., Stålberg, P., Lindman, H., Bång-Rudenstam, A., Ekstrand, M., Kumar, G., Cavarretta, I., Alfano, M., Pellegrino, F., Mandel-Clausen, T., ... Gatto, F. (2022). Noninvasive detection of any-stage cancer using free glycosaminoglycans. Proceedings of the National Academy of Sciences of the United States of America, 119(50), e2115328119. https://doi.org/10.1073/pnas.2115328119

Vancouver

Bratulic S, Limeta A, Dabestani S, Birgisson H, Enblad G, Stålberg K et al. Noninvasive detection of any-stage cancer using free glycosaminoglycans. Proceedings of the National Academy of Sciences of the United States of America. 2022;119(50):e2115328119. https://doi.org/10.1073/pnas.2115328119

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. / Noninvasive detection of any-stage cancer using free glycosaminoglycans. In: Proceedings of the National Academy of Sciences of the United States of America. 2022 ; Vol. 119, No. 50. pp. e2115328119.

Bibtex

@article{3a6d60cca41c497a8b446f2705883109,
title = "Noninvasive detection of any-stage cancer using free glycosaminoglycans",
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 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.",
keywords = "Humans, Glycosaminoglycans, Biomarkers, Tumor/genetics, Liquid Biopsy, Early Detection of Cancer, Neoplasms/diagnosis",
author = "Sinisa Bratulic and Angelo Limeta and Saeed Dabestani and Helgi Birgisson and Gunilla Enblad and Karin St{\aa}lberg and G{\"o}ran Hesselager and Michael H{\"a}ggman and Martin H{\"o}glund and Simonson, {Oscar E} and Peter St{\aa}lberg and Henrik Lindman and Anna B{\aa}ng-Rudenstam and Matias Ekstrand and Gunjan Kumar and Ilaria Cavarretta and Massimo Alfano and Francesco Pellegrino and Thomas Mandel-Clausen and Ali Salanti and Francesca Maccari and Fabio Galeotti and Nicola Volpi and Mads Daugaard and Mattias Belting and Sven Lundstam and Ulrika Stierner and Jan Nyman and Bengt Bergman and Per-Henrik Edqvist and Max Levin and Andrea Salonia and Henrik Kj{\"o}lhede and Eric Jonasch and Jens Nielsen and Francesco Gatto",
year = "2022",
doi = "10.1073/pnas.2115328119",
language = "English",
volume = "119",
pages = "e2115328119",
journal = "Proceedings of the National Academy of Sciences of the United States of America",
issn = "0027-8424",
publisher = "The National Academy of Sciences of the United States of America",
number = "50",

}

RIS

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

PY - 2022

Y1 - 2022

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 - Humans

KW - Glycosaminoglycans

KW - Biomarkers, Tumor/genetics

KW - Liquid Biopsy

KW - Early Detection of Cancer

KW - Neoplasms/diagnosis

U2 - 10.1073/pnas.2115328119

DO - 10.1073/pnas.2115328119

M3 - Journal article

C2 - 36469776

VL - 119

SP - e2115328119

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

SN - 0027-8424

IS - 50

ER -

ID: 328244323