Cancer mortality is exacerbated by late-stage diagnosis. Liquid biopsies based on genomic biomarkers can noninvasively diagnose cancers. However, validation studies have reported similar to 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.

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. - ISSN 0027-8424. - 119:50(2022), pp. 1-11. [10.1073/pnas.2115328119]

Noninvasive detection of any-stage cancer using free glycosaminoglycans

Maccari, Francesca;Galeotti, Fabio;Volpi, Nicola;
2022

Abstract

Cancer mortality is exacerbated by late-stage diagnosis. Liquid biopsies based on genomic biomarkers can noninvasively diagnose cancers. However, validation studies have reported similar to 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.
2022
119
50
1
11
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. - ISSN 0027-8424. - 119:50(2022), pp. 1-11. [10.1073/pnas.2115328119]
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
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