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 -