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Cancers (MDPI)2020ResearchNon-viral Gene Delivery

Natural Killer (NK) Cell Expression of CD2 as a Predictor of Serial Antibody-Dependent Cell-Mediated Cytotoxicity (ADCC)

Julie J.-J. Tang, Alina P. Sung, Mariel J. Guglielmo, Laura N.-G., Doug Redelman, James Smith-Gagen, Diane Hudig

Summary

Monoclonal antibody anti-tumor therapies rely on NK cell-mediated antibody-dependent cell-mediated cytotoxicity (ADCC), but patient responses vary widely. While tumor antigen escape is one cause of treatment failure, the role of inter-individual variation in NK cell serial killing capacity—the ability of a single NK cell to kill multiple target cells sequentially—has received little attention. A predictive biomarker for serial ADCC capacity. ### Killing Frequency Variability (E:T=1:4, n=24 donors) | Parameter | All Donors | Females | Males | |---------------|----------------|-------------|-----------| | KF range | 1.1 - 2.2 | 1.1 - 2.2 | 1.2 - 2.1 | | Mean.

Purpose: Monoclonal antibody anti-tumor therapies rely on NK cell-mediated antibody-dependent cell-mediated cytotoxicity (ADCC), but patient responses vary widely. While tumor antigen escape is one cause of treatment failure, the role of inter-individual variation in NK cell serial killing capacity—the ability of a single NK cell to kill multiple target cells sequentially—has received little attention. A predictive biomarker for serial ADCC capacity could help select patients most likely to respond to antibody-based immunotherapies.
Hypothesis: Serial ADCC capacity varies significantly among healthy donors and correlates with NK cell immunophenotype. The percentage of CD2-positive cells among CD16A-positive NK cells (%CD2pos of CD16Apos NK cells) will predict serial killing frequency (KF), independent of CD16A genotype (F/F vs. V/V/V/F) and perforin levels. CD16A expression and perforin levels will not be reliable predictors.
Aims: 1. Quantify serial ADCC killing frequencies (KFs) in 24 healthy donors using an optimized ⁵¹Cr-release ADCC assay with obinutuzumab-coated Daudi target cells 2. Determine whether CD16A AA158 genotype (F/F vs. V/F/V/V) affects serial ADCC capacity 3. Assess correlations between serial KFs and NK cell phenotypes: CD16A expression, perforin levels, and %CD2pos of CD16Apos NK cells 4. Evaluate the predictive value of %CD2pos of CD16Apos NK cells using ROC analysis 5. Identify a cutoff value for %CD2pos that could serve as a clinical predictive test
5. Biological System:

Component: Effector Cells; Description: Unstimulated freshly isolated human PBMCs; CD16Apos NK cells identified as CD3negCD7posCD16posCD33negCD45pos

Component: Target Cells; Description: Daudi B lymphoma cells (MHC class I-negative; obinutuzumab-coated); 10,000 cells/well

Component: Antibody; Description: Obinutuzumab (GA101 G2) — type 2 anti-CD20 mAb; non-fucosylated, Fc-engineered for high CD16A affinity; cells pretreated with saturating antibody and washed

Component: CD16A Genotypes; Description: F/F (n=12), V/F (n=11), V/V (n=1) — determined by DNA sequencing and flow cytometry (MEM-154 mAb distinguishes V from F)

Component: Phenotyping Markers; Description: CD16A (3G8), CD2 (RPA-2.10), perforin (delta-G9), CD45, CD3, CD7, CD33, CD56

Component: Cell Counting; Description: TruCount® beads for absolute CD16Apos NK cell enumeration

Component: Assay Format; Description: ⁵¹Cr-release ADCC; 4 h incubation; six E:T ratios (quadruplicate); E:T of 1:4 selected for inter-donor comparisons

Component: Donor Population; Description: 24 healthy Caucasian donors (16 female, 8 male); ages 21-85; Utah, USA

Component: Controls; Description: Isotype controls for perforin staining; no-antibody controls (negligible killing); anti-CD58 blocking experiments

Approach:

Parameter: ADCC Assay; Details: Daudi cells labeled with ⁵¹Cr (0.5 mCi); pretreated with obinutuzumab (1 μg/mL, 30 min, RT); washed 5×; PBMCs added at six E:T ratios; 4 h incubation; supernatant counted; % specific release calculated

Parameter: Killing Frequency (KF) Calculation; Details: At E:T=1:4: KF = (% killing observed) / (25% predicted for one round of killing)

Parameter: CD16A Genotyping; Details: PCR amplification of FCGR3A (excluding FCGR3B); Sanger sequencing; confirmed by flow cytometry with MEM-154 mAb

Parameter: Immunophenotyping; Details: Flow cytometry panel: PacBlue anti-CD45, FITC anti-CD3, FITC anti-CD7, PerCP anti-CD16A, APC-Cy7 anti-CD56, PE anti-CD2, FITC anti-perforin; TruCount beads for absolute counts

Parameter: Perforin Measurement; Details: Intracellular staining after fixation/permeabilization (IntraPrep); FITC anti-perforin (clone delta-G9)

Parameter: EC50 Determination; Details: Four-fold dilutions of obinutuzumab (0.04-625 ng/mL); one E:T; 4 h; EC50 calculated

Parameter: CD2 Ligand Analysis; Details: Daudi and K562 cells stained for CD15 and CD58; anti-CD58 blocking experiments

Parameter: Sample Size; Details: n=24 donors; in vitro experiments in quadruplicate

Parameter: Statistical Tests; Details: Student's t-test; linear regression; ROC analysis (SAS 9.4); Mann-Whitney U test for ROC comparison

Key methods:

Analysis Category: ADCC Cytotoxicity; Methods: ⁵¹Cr-release assay; gamma counting; % specific release = [(Experimental - Spontaneous)/(Max - Spontaneous)] × 100

Analysis Category: Killing Frequency; Methods: KF = (% killing at E:T=1:4) / 25%; linear cytotoxicity determined from log10 E:T vs. % killing

Analysis Category: CD16A Genotyping; Methods: PCR (primers: TCCTACTTCTGCAGGGGGCTTGT / CCAACTCAACTTCCCAGTGTGATTG); Sanger sequencing

Analysis Category: Flow Cytometry; Methods: BD LSR II; TruCount beads; panels for NK identification (CD45, CD3, CD7, CD16A, CD33, CD56), CD2, and perforin

Analysis Category: EC50 Analysis; Methods: 4-fold antibody dilutions (0.04-625 ng/mL); 4 h ADCC; EC50 from dose-response curves

Analysis Category: ROC Analysis; Methods: Sensitivity vs. 1-specificity; AUC calculation; cutoff determination for 100% sensitivity

Analysis Category: CD2 Ligand Staining; Methods: PE-anti-CD15; PE-anti-CD58; flow cytometry on Daudi and K562 cells

Analysis Category: Statistical Analysis; Methods: Excel (linear regressions, t-tests); GraphPad Prism 7; SAS 9.4 (ROC)

Key results: ### Killing Frequency Variability (E:T=1:4, n=24 donors)

Parameter: KF range; All Donors: 1.1 - 2.2; Females: 1.1 - 2.2; Males: 1.2 - 2.1

Parameter: Mean KF; All Donors: 1.6 ± 0.3; Females: 1.6 ± 0.4; Males: 1.7 ± 0.3

Parameter: % donors with KF ≥1.5 (high group); All Donors: 58% (14/24); Females: -; Males: -

Parameter: % donors with KF >1.5 at highest E:T (≥1:18); All Donors: 88% (21/24); Females: -; Males: -

Parameter: KF independence; All Donors: No correlation with age or gender; Females: -; Males: -

CD16A Genotype Effects:

Parameter: Mean KF; F/F (n=12): 1.5 ± 0.4; V/F + V/V (n=12): 1.7 ± 0.3; Significance: n.s. (p>0.05)

Parameter: EC50 (ng/mL); F/F (n=12): Higher (nearly significant); V/F + V/V (n=12): Lower; Significance: p=0.055

Parameter: CD16A MFI; F/F (n=12): ~1.7× lower; V/F + V/V (n=12): ~1.7× higher; Significance: Significant

Phenotypic Predictors of High vs. Low KF (Cutoff ≥1.5):

Parameter: %CD2pos of CD16Apos NK; High KF (n=14): 76.6%; Low KF (n=10): 53.3%; Significance: p<0.001

Parameter: CD16A MFI; High KF (n=14): Similar; Low KF (n=10): Similar; Significance: n.s.

Parameter: Perforin levels; High KF (n=14): Similar; Low KF (n=10): Similar; Significance: n.s.

Parameter: CD2 MFI (on CD2pos cells); High KF (n=14): Similar; Low KF (n=10): Similar; Significance: n.s.

ROC Analysis:

Predictor: %CD2pos of CD16Apos NK; AUC: 0.89; p-value: p<0.001; Interpretation: Good predictive value

Predictor: CD16A MFI; AUC: 0.71; p-value: p<0.01; Interpretation: Modest predictive value

Predictor: %CD2pos vs. CD16A MFI; AUC: p=0.08; p-value: -; Interpretation: Trend favoring CD2

Quadrant Analysis (60% CD2pos Cutoff):

Parameter: Sensitivity cutoff; Value: 60% CD2pos (100% sensitivity)

Parameter: Positive predictive value; Value: 0.88

Parameter: Negative predictive value; Value: 1.0

Parameter: False positives; Value: 2/24

Parameter: False negatives; Value: 0/24

CD2 Counter-Ligand Analysis (Daudi Cells):

Ligand: CD15; Daudi Expression: Undetectable; K562 Expression: Not tested

Ligand: CD58; Daudi Expression: Low; K562 Expression: Substantial

Ligand: Anti-CD58 blocking effect (ADCC); Daudi Expression: None; K562 Expression: Reduced NK killing (control)

Interpretation: The authors conclude that "there are substantial differences in serial ADCC among human donors" and "these differences appear to be predictable by tests for %CD2pos of CD16Apos NK cells." They state: "To the best of our knowledge, we are the first to characterize human variability in serial ADCC capacity." The %CD2pos of CD16Apos NK cells had an ROC AUC of 0.89 (p<0.001), indicating good predictive value, with a 60% CD2pos cutoff yielding 100% sensitivity. The authors propose that "prediction of serial ADCC may be of clinical value to understand variations in patient responses to anti-tumor monoclonal antibodies."
10. Limitations (Explicitly Stated or Evident):

1. Homogeneous donor population: All 24 donors were Caucasian and from Utah, USA (predominantly non-Finnish northern European descent). The authors acknowledge that "it will take assessment of serial ADCC from geographically and socio-economically diverse donors to determine if the predictive value ... applies to a more diverse population."

2. In vitro assay with artificial target: Daudi cells are an immortalized B lymphoma cell line; primary tumor cells or patient-derived xenografts were not tested.

3. Optimized antibody conditions: Obinutuzumab is a highly engineered, non-fucosylated, Fc-optimized antibody. Results may not generalize to antibodies with native Fc regions or lower affinity.

4. No direct in vivo validation: The study did not test whether CD2 phenotype predicts clinical response in patients receiving antibody therapy.

5. Small sample size: n=24 donors; the ROC analysis, while promising, should be validated in a larger, independent cohort.

6. Mechanism of CD2 involvement unclear: CD2-ligand engagement appeared unnecessary during ADCC (no CD58 on Daudi), yet CD2 phenotype predicted KF. The authors note that CD2 may be drawn into the synapse via physical association with CD16A, but this remains speculative.

7. KF as population average: KF averages killing across all CD16Apos NK cells, including those that do not kill. The authors acknowledge that "KF numbers represent an underestimation of serial killer activity."

8. No assessment of adaptive NK cells: CD2 is particularly important for "adaptive" NK cells, but the study did not distinguish adaptive from conventional NK subsets.

9. Overnight culture may introduce priming: PBMCs were cultured overnight without stimulation, which may have allowed NK-CD2-monocyte-CD15 interactions to "prime" NK cells. This was not controlled for.

10. No assessment of NK cell exhaustion: Serial killing capacity may be affected by receptor cleavage or exhaustion; these were not tracked over the 4 h assay.

11. Potential confounding by CD16A cleavage: CD16A is cleaved during killing, which was not measured; this could affect interpretation of KFs.

12. Conflict of interest: The study received funding from an anonymous donor to the Bateman Horne Center and from NIH, but no conflicts were declared.

Report prepared based on the published Cancers article. For full experimental details, supplementary figures, and complete references, please refer to the original publication.

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Natural Killer (NK) Cell Expression of CD2 as a Predictor of Serial Antibody-Dependent Cell-Mediated Cytotoxicity (ADCC) | Brilliant Blue Biosciences