Melanie M Ivancic1, Bryant W Megna2, Yuriy Sverchkov3, Mark Craven3, Mark Reichelderfer4, Perry J Pickhardt5, Michael R Sussman6, Gregory D Kennedy7. 1. University of Wisconsin-Madison Biotechnology Center, Madison, Wisconsin; Department of Oncology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin; Department of Biochemistry, University of Wisconsin, Madison, Wisconsin. 2. Department of Oncology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin; Division of Gastroenterology and Hepatology, Department of Medicine, University of Wisconsin, Madison, Wisconsin; Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota. 3. Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin. 4. Division of Gastroenterology and Hepatology, Department of Medicine, University of Wisconsin, Madison, Wisconsin. 5. Department of Radiology, University of Wisconsin, UW Medical Foundation Madison, Wisconsin. 6. University of Wisconsin-Madison Biotechnology Center, Madison, Wisconsin; Department of Biochemistry, University of Wisconsin, Madison, Wisconsin. 7. Department of Surgery, University of Alabama-Birmingham, Birmingham, Alabama. Electronic address: gkennedy@uabmc.edu.
Abstract
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.
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.
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