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Discrimination of head and neck squamous cell carcinoma patients and healthy adults by 10-color flow cytometry: Development of a score based on leukocyte subsets

 
: Wichmann, Gunnar; Gaede, Clara; Melzer, Susanne; Bocsi, József; Henger, Sylvia; Engel, Christoph; Wirkner, Kerstin; Wenning, John R.; Wald, Theresa; Freitag, Josefine; Willner, Maria; Kolb, Marlen; Wiegand, Susanne; Loeffler, Markus; Dietz, Andreas; Tárnok, Attila

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Volltext ()

Cancers 11 (2019), Nr.6, Art. 814, 18 S.
ISSN: 2072-6694
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IZI ()

Abstract
Background: Leukocytes in peripheral blood (PB) are prognostic biomarkers in head and neck squamous cell carcinoma cancer patients (HNSCC-CPs), but differences between HNSCC-CPs and healthy adults (HAs) are insufficiently described. Methods: 10-color flow cytometry (FCM) was used for in-depth immunophenotyping of PB samples of 963 HAs and 101 therapy-naïve HNSCC-CPs. Absolute (AbsCC) and relative cell counts (RelCC) of leukocyte subsets were determined. A training cohort (TC) of 43 HNSCC-CPs and 43 HAs, propensity score (PS)-matched according to age, sex, alcohol, and smoking, was used to develop a score consecutively approved in a validation cohort (VC). Results: Differences in AbsCC were detected in leukocyte subsets (p < 0.001), but had low power in discriminating HNSCC-CPs and HAs. Consequently, RelCC of nine leukocyte subsets in the TC were used to calculate 36 ratios; receiver operating characteristic (ROC) curves defined optimum cut-off values. Binary classified data were combined in a score based on four ratios: monocytes-to-granulocytes (MGR), classical monocytes-to-monocytes (clMMR), monocytes-to-lymphocytes (MLR), and monocytes-to-T-lymphocytes (MTLR); ≥3 points accurately discriminate HNSCC-CPs and HAs in the PS-matched TC (p = 2.97 × 10−17), the VC (p = 4.404 × 10−178), and both combined (p = 7.74 × 10−199). Conclusions: RelCC of leukocyte subsets in PB of HNSCC-CPs differ significantly from those of HAs. A score based on MGR, clMMR, MLR, and MTLR allows for accurate discrimination.

: http://publica.fraunhofer.de/dokumente/N-555259.html