Colorectal Cancer Detection Using Targeted Serum Metabolic Profiling

Abstract

Colorectal cancer (CRC) is one of the most prevalent and deadly cancers in the world. Despite an expanding knowledge of its molecular pathogenesis during the past two decades, robust biomarkers to enable screening, surveillance, and therapy monitoring of CRC are still lacking. In this study, we present a targeted liquid chromatography-tandem mass spectrometry-based metabolic profiling approach for identifying biomarker candidates that could enable highly sensitive and specific CRC detection using human serum samples. In this targeted approach, 158 metabolites from 25 metabolic pathways of potential significance were monitored in 234 serum samples from three groups of patients (66 CRC patients, 76 polyp patients, and 92 healthy controls). Partial least-squares-discriminant analysis (PLS-DA) models were established, which proved to be powerful for distinguishing CRC patients from both healthy controls and polyp patients. Receiver operating characteristic curves generated based on these PLS-DA models showed high sensitivities (0.96 and 0.89, respectively, for differentiating CRC patients from healthy controls or polyp patients), good specificities (0.80 and 0.88), and excellent areas under the curve (0.93 and 0.95). Monte Carlo cross validation was also applied, demonstrating the robust diagnostic power of this metabolic profiling approach.

Diagnostic Metabolomic Blood Tests for Endoluminal Gastrointestinal Cancer—A Systematic Review and Assessment of Quality

“…Nine studies investigated esophageal cancer (39-47), 3 gastric cancer (48)(49)(50), and 15 colorectal cancer (29,30,(51)(52)(53)(54)(55)(56)(57)(58)(59)(60)(61)(62)(63). Two studies investigated more than one GI malignancy (64,65).…”

“…These compounds are easily identified on both NMR and MS platforms, and several studies employed a targeted amino acid methodology (47,55,63,64). However, amino acids were not specific as clinical cancer biomarkers.…”

“…Such nonendoscopic triage tools should therefore be deployed to minimize diagnostic uncertainty and improve service efficiency, and will be maximally influential in primary care (7,77). By extension, augmenting metabolomic biomarker performance with clinical features and standard serologic markers was an uncommon but seemingly successful strategy (55,63). Third, given that all studies reported positive findings also suggests a potential for publication bias.…”

“…After correction for multiplicity, 62 metabolites were significantly different between cancer and controls. Most recently, Zhu and colleagues improved a MVM of metabolite differences by adjusting with four clinical factors (63). The improved model was 96% sensitive and 80% specific for cancer discrimination.…”

Comparing metabolite profiles of habitual diet in serum and urine

“…Associations between dietary variables and metabolites were estimated with the use of a partial Pearson correlation while adjusting for factors previously associated with metabolites and dietary intake (27)(28)(29)(30)(31)(32)(33)(34)(35), including age (years), sex, smoking (never, past, or current), fasting status at time of blood draw (fasting or nonfasting), case-control status, BMI (kg/m 2 ), physical activity index (weekly frequency of moderate-intensity leisure activity plus 2 3 weekly frequency of vigorous-intensity leisure activity), education (#12 y, vocational or 1-3 y college, $4 y college, or graduate), and daily caloric intake (kcal/d). Analyses of single vitamin and nutrient supplement use were adjusted for multivitamin use.…”