Ann Surg Treat Res.  2018 Jun;94(6):312-321. 10.4174/astr.2018.94.6.312.

Diagnostic value of peripheral blood immune profiling in colorectal cancer

• Choi J
• Maeng HG
• Lee SJ
• Kim YJ
• Kim DW
• Lee HN
• Namgung JH
• Oh HM
• Kim TJ
• Jeong JE
• Park SJ
• Choi YM
• Kang YW
• Yoon SG
• Lee JK

• KMID: 2412401
• DOI: http://doi.org/10.4174/astr.2018.94.6.312

Abstract

PURPOSE
Little is known about the clinical value of peripheral blood immune profiling. Here, we aimed to identify colorectal cancer (CRC)-related peripheral blood immune cells and develop liquid biopsy-based immune profiling models for CRC diagnosis.
METHODS
Peripheral blood from 131 preoperative patients with CRC and 174 healthy controls was analyzed by flow cytometry and automated hematology. CRC-related immune factors were identified by comparing the mean values of immune cell percentages and counts. Subsequently, CRC diagnostic algorithms were constructed using binary logistic regression.
RESULTS
Significant differences were observed in percentages and counts of white blood cells, lymphocytes, neutrophils, regulatory T cells, and myeloid-derived suppressor cells (MDSCs) of patients and controls. The neutrophil/lymphocyte and Th1/Th2 ratios were also significantly different. Likewise, the percentages and counts of peripheral blood programed death 1, cytotoxic T lymphocyte antigen 4, B-and T-lymphocyte attenuator, and lymphocyte activation gene-3 were higher in patients with CRC. The binary logistic regression model included 12 variables, age, CD3+%, NK%, CD4+CD279+%, CD4+CD25+%, CD4+CD152+%, CD3+CD366+%, CD3+CD272+%, CD3+CD223+%, CD158b−CD314+CD3−CD56+%, Th2%, and MDSCs cells/µL, for the prediction of cancer. Results of retrospective and prospective evaluation of the area under the curve, sensitivity, and specificity were 0.980 and 0.940, 91.53% and 85.80%, and 93.50% and 86.20%, respectively.
CONCLUSION
Peripheral blood immune profiling may be valuable in evaluating the immunity of CRC patients. Our liquid biopsy-based immune diagnostic method and its algorithms may serve as a novel tool for CRC diagnosis. Future largescale studies are needed for better characterization of its diagnostic value and potential for clinical application.

Keyword

Colorectal neoplasms; Early diagnosis of cancer; Blood cells; Flow cytometry

MeSH Terms

Blood Cells
Colorectal Neoplasms*
CTLA-4 Antigen
Diagnosis
Early Detection of Cancer
Flow Cytometry
Hematology
Humans
Immunologic Factors
Leukocytes
Logistic Models
Lymphocyte Activation
Lymphocytes
Methods
Neutrophils
Prospective Studies
Retrospective Studies
Sensitivity and Specificity
T-Lymphocytes
T-Lymphocytes, Regulatory
CTLA-4 Antigen
Immunologic Factors

Figure

• 

  Fig. 1 Receiver operating curve analysis of cancer prediction using a binary logistic regression model. Retrospective (62 patients and 100 healthy controls) and prospective (69 patients and 74 healthy controls) diagnostic values are presented as the area under the curve (AUC), sensitivity, and specificity. (A) Logit model including 11 variables: CD3+%, NK %, CD4+CD279+%, CD4+CD25+%, CD4+CD152+%, CD3+CD366+%, CD3+CD272+%, CD3+CD223+%, CD158b−CD314+CD3−CD56+%, Th2%, and MDSCs cells/µL. (B) Modified logit model including 12 variables (age plus the original 11 variables).

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