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  stud-ies 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 pro-gression 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.