Flow Cytometric Assessment of FcγRIIIa-V158F Polymorphisms and NK Cell Mediated ADCC Revealed Reduced NK Cell Functionality in Colorectal Cancer Patients

Abstract

Antibody-dependent cell-mediated cytotoxicity (ADCC) by NK cells is a key mechanism in anti-cancer therapies with monoclonal antibodies, including cetuximab (EGFR-targeting) and avelumab (PDL1-targeting). Fc gamma receptor IIIa (FcγRIIIa) polymorphisms impact ADCC, yet their clinical relevance in NK cell functionality remains debated. We developed two complementary flow cytometry assays: one to predict the FcγRIIIa-V158F polymorphism using a machine learning model, and a 15-color flow cytometry panel to assess antibody-induced NK cell functionality and cancer-immune cell interactions. Samples were collected from healthy donors and metastatic colorectal cancer (mCRC) patients from the FIRE-6-Avelumab phase II study. The machine learning model accurately predicted the FcγRIIIa-V158F polymorphism in 94% of samples. FF homozygous patients showed diminished cetuximab-mediated ADCC compared to VF or VV carriers. In mCRC patients, NK cell dysfunctions were evident as impaired ADCC, decreased CD16 downregulation, and reduced CD137/CD107a induction. Elevated PD1+ NK cell levels, reduced lysis of PDL1-expressing CRC cells and improved NK cell activation in combination with the PDL1-targeting avelumab indicate that the PD1-PDL1 axis contributes to impaired cetuximab-induced NK cell function. Together, these optimized assays effectively identify NK cell dysfunctions in mCRC patients and offer potential for broader application in evaluating NK cell functionality across cancers and therapeutic settings.

Keywords: antibody-dependent cell cytotoxicity; cetuximab; colorectal neoplasms; flow cytometry; natural killer cells; single nucleotide polymorphism.

Figure 1

Detection of FcγRIIIa-158 phenotypes by flow cytometry. (A) Summary of the assay development to detect the FcγRIIIa-V158F polymorphism including sample source, preparation, and bioinformatics. FcγRIIIa-typing was established on 39 healthy donors followed by validation on a cohort of 52 mCRC patients from the FIRE-6 study. At each point, FcγRIIIa polymorphisms were detected by PCR sequencing and flow cytometry. (B) Gating strategy to detect FcγRIIIa-V158F phenotypes using two different CD16 clones. After selecting lymphocytes, NK cells were identified as CD3-CD14-CD56+ cells, and LNK16 and MEM154 binding was analyzed. Representative examples for each FcγRIIIa phenotype are shown. (C) Scatter plot of MEM154 and LNK16 binding (MFI) on NK cells. Dots represent individuals and FcgRIIIa-158 phenotypes are color-coded.

Figure 2

Machine learning model predicts FcγRIIIa polymorphisms. (A) Visualization of the generated LDA model for a specific training set to which Logistic Regression was subsequently applied for prediction. As highlighted, the major contributors for distinct clustering were the ratios between both CD16 clones (M = MEM154, L = LNK16) either in terms of frequency (F) or mean fluorescent intensity (MFI) on CD56dim NK cells. (B,C) Bar graphs show the mean prediction performance as defined by F1 scores of 10-fold cross-validation for the flow cytometry assay regarding all FcγRIIIa phenotypes (B) or the F1 scores of leave-one-out cross-validation per time point during therapy for all FcγRIIIa phenotypes and weighted for the study cohort (C). Highlighted are the correctly assigned FcγRIIIa phenotypes with respect to PCR-based detection. (D) Performance of prediction model built from gradually smaller size of training sets. For each sample size, a new prediction model was repeatedly trained and validated on the same testing set (repetitions n = 100) using two different approaches for FcγRIIIa phenotype proportions: approach 4:4:2 (blue) simulates the approximate prevalence of each phenotype in the Caucasian population, whereas approach 1:1:1 (green) assumes equal proportions of each phenotype in the training set.

Figure 3

ADCC assay development and gating strategy. (A) Schematic representation of the established assay set-up. Briefly, 3 × 10⁴ SNU-C5 cancer cells were co-cultivated with 3 × 10⁵ PBMCs and stimulated with 100 ng/mL cetuximab or avelumab for 24 h. Anti-cancer response was then analyzed by LDH release and flow cytometry. (B,C) Gating strategy to analyze the viability of cancer cells and activation and regulations of immune checkpoints on immune cells. (B) After doublet exclusion (comparable to Figure 1) and physical discrimination using control samples with either cancer cell or PBMCs alone for pre-gating of respective cell types, EpCAM+ cancer cells, CD14+ monocytes, CD3+ T cells and CD3-CD14-CD56+ NK cells were distinguished. (C) For cancer cells, viability was detected by DAPI and Annexin V staining along with checkpoint expression of PDL1 and CD40. CD56+ NK cells were separated in CD56dimCD16+ and CD56hiCD16low/−. All NK cell subsets were analyzed for CD107a, CD137, NKG2A, NKG2D, CD62L and PD1 expression while T cells were measured for NKG2A, NKG2D, CD137, CD62L and PD1 expression. CD14+ monocytes were gated for CD40, PDL1, CD62L and PD1 expression. (D) Scatter plot of ADCC values from experiments with PBMCs from healthy donors or mCRC patients detected by LDH release assay (ADCC Lysis_LDH) or flow cytometry (∆Tumour cells_FACS). Each dot represents matched values from both readouts and the Pearson’s correlation coefficient together with the p-value for correlation fit is depicted.

Figure 4

Reduced cytotoxic potential in mCRC patients. (A,B) Healthy donors (n = 10) and baseline samples from mCRC patients (n = 35) of the FIRE-6 study prior to therapy initiation were grouped according to their FcγRIIIa polymorphism and (A) assayed for cetuximab (Cet) mediated ADCC against SNU-C5 cancer cells. Additionally, samples from these donors were also stimulated with a combination of cetuximab and avelumab (Cet+Ave) to assess (B) ADCC and regulations of CD16, CD137 and CD107a expressions according to different stimulations. (C) After z-score standardization, t-Distributed Stochastic Neighbor Embedding (tSNE) dimensionality reduction of 154 flow cytometry parameters shows the response to ex vivo cetuximab stimulation in healthy donors (blue, HD) or mCRC patients (red, mCRC). (D,E) Examples of differentially regulated parameters representing (D) NK cell functionalities or (E) regulations on cancer cells and monocytes. ∆-values are calculated as the difference between unstimulated and cetuximab-treated samples. Statistics: (A,B) One-way ANOVA followed by Tukey’s multiple comparison test. (D,E) Mann–Whitney U test. (A,E) Each dot represents the mean of technical triplicates from individual donors.