Noninvasive Detection of Colorectal Carcinomas Using Serum Protein Biomarkers

Academic Article


  • Background: A major roadblock to reducing the mortality of colorectal cancer (CRC) is prompt detection and treatment, and a simple blood test is likely to have higher compliance than all of the current methods. The purpose of this report is to examine the utility of a mass spectrometry–based blood serum protein biomarker test for detection of CRC. Materials and methods: Blood was drawn from individuals (n = 213) before colonoscopy or from patients with nonmetastatic CRC (n = 50) before surgery. Proteins were isolated from the serum of patients using targeted liquid chromatography-tandem mass spectrometry. We designed a machine-learning statistical model to assess these proteins. Results: When considered individually, over 70% of the selected biomarkers showed significance by Mann–Whitney testing for distinguishing cancer-bearing cases from cancer-free cases. Using machine-learning methods, peptides derived from epidermal growth factor receptor and leucine-rich alpha-2-glycoprotein 1 were consistently identified as highly predictive for detecting CRC from cancer-free cases. A five-marker panel consisting of leucine-rich alpha-2-glycoprotein 1, epidermal growth factor receptor, inter-alpha-trypsin inhibitor heavy-chain family member 4, hemopexin, and superoxide dismutase 3 performed the best with 70% specificity at over 89% sensitivity (area under the curve = 0.86) in the validation set. For distinguishing regional from localized cancers, cross-validation within the training set showed that a panel of four proteins consisting of CD44 molecule, GC-vitamin D–binding protein, C-reactive protein, and inter-alpha-trypsin inhibitor heavy-chain family member 3 yielded the highest performance (area under the curve = 0.75). Conclusions: The minimally invasive blood biomarker panels identified here could serve as screening/detection alternatives for CRC in a human population and potentially useful for staging of existing cancer.
  • Published In

    Digital Object Identifier (doi)

    Author List

  • Ivancic MM; Megna BW; Sverchkov Y; Craven M; Reichelderfer M; Pickhardt PJ; Sussman MR; Kennedy GD
  • Start Page

  • 160
  • End Page

  • 169
  • Volume

  • 246