Immune Surveillance in Chronic Myeloid Leukemia: Tumor Antigen Expression and CD8+ T Cell Function in the Context of Treatment- Free Remission

Kwaśnik, Paulina; Stępień, Martyna; Skórka, Katarzyna; Paziewska, Magdalena; Karczmarczyk, Agnieszka; Pinkosz, Michalina; Cech, Paweł; Zaleska, Joanna; Kiełbus, Michał; Link-Lenczowska, Dorota; Zawada, Magdalena; Sacha, Tomasz; Giannopoulos, Krzysztof

doi:10.2478/aite-2026-0004

Figures & Tables

Screening of HMMR, WT1, PRTN3, AURKA, DNAJC2, USP32, SPAG9, and PRAME gene expression in CML patients. The data is presented in a box and a whisker plot, where the whiskers indicate the min-max value, and the box marks the median. We observed statistically significant higher gene expression of HMMR vs. WT1, AURKA vs. WT1, DNAJC2 vs. WT1, USP32 vs. WT1, SPAG9 vs. WT1, PRTN3 vs. HMMR, WT1, AURKA, DNAJC2, USP32, SPAG9, and PRAME. In the remaining pairs, no statistical significance was obtained. CML, chronic myeloid leukemia.

Evaluation of the correlation of HMMR, WT1, PRTN3, AURKA, DNAJC2, USP32, SPAG9 and PRAME gene expression in CML patients. The results are presented as the log10 value of 2−ΔΔCt with the regression line marked. The graph shows only statistically significant correlations between HMMR and USP32 (A), USP32 and SPAG9 (B), HMMR and PRTN3 (C), HMMR and AURKA (D), PRTN3 and AURKA (E), AURKA and DNAJC2 (F), HMMR and WT1 (G), AURKA and USP32 (H), AURKA and SPAG9 (I), DNAJC2 and USP32 (J), DNAJC2 and SPAG9 (K), WT1 and PRTN3 (L). CML, chronic myeloid leukemia.

FI factor for peptides derived from SPAG9 and NY-REN-60 antigens in the T2 peptide-binding assay. The affinity of 12 newly synthesized peptides (from SPAG9: S1–S12; from NY-REN-60: N1-N11) to the HLA-A2 receptor was assessed by adding the peptides at specific concentrations: 0.5 μg/mL, 1 μg/mL, 2 μg/mL, 5 μg/mL, 10 μg/mL, 20 μg/mL, and 50 μg/mL to the T2 cell line using two replicates. The FI factor was calculated as the ratio of the MFI of HLA-A*0201 on T2 cells with peptide to the MFI of HLA-A*0201 on T2 cells without peptide, using the formula FI = MFI (T2 with peptide)/MFI (T2 without peptide). FI, Fluorescence intensity; MFI, mean fluorescence intensity.

Results of specific IFN-γ (red, brownish dots) and granzyme B (blue dots) release in response to peptide derived from tumor-associated antigens stimuli. The ELISpot assay was performed after mixed lymphocyte-peptide culture. CD8+ cells were pulsed with CD8− cells stimulated by N11 peptide derived from NY-REN-60 antigen and S4 peptide derived from SPAG9 antigen, as well as the mixture of peptides: RHAMM-R3165–173, WT1126–134, PRAME300–309, MPP11437–445, Aur-A207–215, BCR-ABL922–930, and PR3-PR1169–177. NC was CD8+ CML cells cultured without any peptide; PWM as a non-specific control was added to NC cells on ELISpot plates. IFN-γ and granzyme B spots were obtained from 104 cytotoxic T cells per well and spot averages were calculated from triplets. CML, chronic myeloid leukemia; ELISpot, Enzyme-Linked ImmunoSpot; IFN-γ, interferon-γ; NC, negative control; PWM, pokeweed mitogen.

Kaplan–Meier survival curves for high-risk and low-risk patient groups. The Kaplan–Meier plot illustrates RFS in patients stratified into high-risk and low-risk groups based on the median risk score derived from the Cox proportional hazards model and appropriately colored. The survival curves show a clear separation between the two groups, with the high-risk group exhibiting significantly worse RFS, while the low-risk group demonstrates longer recurrence-free intervals. The p-value (p < 0.05) indicates that the difference between the groups is statistically significant, confirming the model’s ability to distinguish between different risk categories. RFS, recurrence-free survival.

Evaluation of the correlation between analyzed genes and the gender of the CML patients. Results, shown as mean with SD, were obtained by performing Mann-Whitney non-parametric test. There was no statistically significant correlation between the expression of each of the analyzed genes and the gender of the CML patients.

Forest plot of the Cox proportional hazards model for immune cell markers. The forest plot illustrates the hazard ratios (HR) and 95% confidence intervals (CI) for immune cell markers included in the Cox proportional hazards model. Each marker is represented by a point estimate for the HR and a horizontal line indicating its 95% confidence interval. The vertical dashed line at HR = 1 represents the threshold for no effect. Markers positioned entirely to the left of this line suggest a protective effect, while those to the right indicate an increased risk. The number of observations (N) and the number of events are also indicated. A global p-value from the log-rank test confirms the overall model significance. Additionally, the Akaike Information Criterion (AIC) and Concordance Index (C-index) reflect the model’s goodness-of-fit and predictive accuracy.

Univariate Summary

Univariate Model	Hazard Ratio (95% CI)	P-value
CD4_plus_CD25_plus_FoxP3_plus.txt	0.89 (0.77 – 1.01)	0.077
CD56dim16_plus_PD1_plus.txt	1.06 (1.01 – 1.11)	0.024
CD8_plus_PD1_plus.txt	1.03 (1.02 – 1.05)	< 0.001
iNKT_plus_CD161_plus.txt	0.32 (0.11 – 0.94)	0.039
Model Comparison_Metrics
Model	AIC	Concordance
CD8_plus_PD1_plus.txt	467.8898	0.6512
iNKT_plus_CD161_plus.txt	477.1068	0.5927
CD56dim16_plus_PD1_plus.txt	478.4037	0.5312
CD4_plus_CD25_plus_FoxP3_plus.txt	479.6236	0.5984

Sequences of peptides used in the peptide mix in the MLPC

Peptide	Peptide sequence
RHAMM-R3_165–173	ILSLELMKL
WT1_126–134	RMFPNAPYL
PRAME_300–309	ALYVDSLFFL
MPP11_437–445	STLCQVEPV
Aur-A_207–215	YLILEYAPL
BCR-ABL_922–930	GFKQSSKAL
PR3-PR1_169–177	VLQELNVTV

SPAG9 and NY-REN-60 derived T cell epitope peptides predicted by computer algorithms SYFPEITHI, IEDB, and NetCTL

Peptide	Peptide sequence	Peptide position	SYFPEITHI ranking	IEDB ranking	NetCTL ranking
S1	SLLGGITVV	837	32	0.4	1.27
S2	ALADGTLAI	1016	29	0.7	1.42
S3	AIIESTPEL	343	29	1.6	1.30
S4	ELMPLVVAV	49	28	0.4	1.03
S5	VMSERVSGL	19	26	1.6	1.21
S6	RLMELQEAV	521	25	0.3	1.20
S7	SLFEELSSA	381	25	1.0	1.19
S8	KLKDSILSI	998	26	2.3	1.34
S9	VLQGELEAV	447	28	1.7	1.02
S10	AVLENLDSV	56	26	1.8	1.07
S11	LILENTQLL	413	26	3.0	1.08
S12	DLIAKVDEL	433	28	5.6	-
N1	WLLSGGVYV	162	26	0.4	1.26
N2	SLFGMPLIV	1099	26	0.5	1.23
N3	FMNSSIQCV	744	24	0.4	1.49
N4	SLSEGLFNA	231	24	0.5	1.26
N5	FLVPRDPAL	1311	25	0.9	1.33
N6	LLFQVCHIV	355	24	0.43	1.23
N7	LLAFLLDGL	829	29	1.3	1.08
N8	GLHEDLNRV	836	28	1.3	1.10
N9	LLDDEDHKL	679	26	2.0	1.60
N10	NLIVGLVLL	79	30	1.6	0.93
N11	FLCAFEIPV	991	22	0.1	1.37
N12	MMRTELYFL	1083	23	0.9	1.24

Model_Comparison

Model	AIC	Concordance
cox_model_summary_stepwise_model	430.0106	0.7923
cox_model_summary_model_01	433.9947	0.7621
cox_model_summary_model_02	436.375	0.7352
cox_model_summary_final_model	440.9907	0.7478
cox_model_summary_full_model	446.2834	0.817
cox_model_summary_model_03	448.5368	0.6547
cox_model_summary_final_model
Characteristic	Hazard Ratio (95% CI)	P-value
iNKT_plus_CD161_plus	0.24 (0.08 – 0.73)	0.013
CD4_plus_CD25_plus_FoxP3_plus	0.87 (0.75 – 1.00)	0.049
CD8_plus_PD1_plus	1.04 (1.02 – 1.05)	< 0.001
cox_model_summary_full_model
Characteristic	Hazard Ratio (95% CI)	P-value
DC	0.83 (5.50 – 1.27)	0.394
cDC	4.85 (3.38 – 69.52)	0.245
pDC	0.25 (4.71 – 13.67)	0.5
cDC_PD1_plus	0.96 (9.18 – 1.00)	0.076
pDC_PD1_plus	1.04 (9.95 – 1.08)	0.086
CD56dimCD16_plus	1.05 (9.69 – 1.13)	0.254
CD56brightCD16_minus	0.58 (1.69 – 2.00)	0.391
CD56brightCD16_plus	0.96 (4.83 – 1.89)	0.895
iNKT	2.71 (6.05 – 121.03)	0.608
iNKT_plus_CD161_plus	0.01 (4.82 – 2.14)	0.093
NKT	1.04 (9.69 – 1.12)	0.265
CD56dim16_plus_PD1_plus	1.15 (1.05 – 1.26)	0.004
CD56bright16_minus_PD1_plus	0.96 (8.51 – 1.09)	0.526
iNKT_plus_PD1_plus	0.98 (9.65 – 1.00)	0.138
NKT_PD1_plus	0.97 (9.31 – 1.01)	0.102
CD4_plus	1.04 (1.00 – 1.09)	0.045
CD4_plus_PD1_plus	1.03 (9.84 – 1.08)	0.208
CD8_plus	1.08 (1.01 – 1.14)	0.013
CD8_plus_PD1_plus	1.05 (1.02 – 1.07)	< 0.001
CD19_plus	1.10 (1.03 – 1.18)	0.006
CD19_plus_PD1_plus	1.01 (9.85 – 1.03)	0.446
CD4_plus_CD25_plus_FoxP3_plus	0.75 (6.27 – 0.90)	0.002
cox_model_summary_model_01
Characteristic	Hazard Ratio (95% CI)	P-value
iNKT_plus_CD161_plus	0.10 (0.03 – 0.40)	0.001
CD4_plus_CD25_plus_FoxP3_plus	0.86 (0.75 – 1.00)	0.05
CD8_plus_PD1_plus	1.03 (1.02 – 1.05)	< 0.001
CD56dim16_plus_PD1_plus	1.11 (1.05 – 1.18)	< 0.001
cox_model_summary_model_02
Characteristic	Hazard Ratio (95% CI)	P-value
iNKT_plus_CD161_plus	0.09 (0.02 – 0.40)	0.001
CD8_plus_PD1_plus	1.03 (1.02 – 1.05)	< 0.001
CD56dim16_plus_PD1_plus	1.12 (1.05 – 1.19)	< 0.001
cox_model_summary_model_03
Characteristic	Hazard Ratio (95% CI)	P-value
CD8_plus_PD1_plus	1.03 (1.02 – 1.05)	< 0.001
CD56dim16_plus_PD1_plus	1.05 (0.99 – 1.10)	0.089
cox_model_summary_stepwise_model
Characteristic	Hazard Ratio (95% CI)	P-value
cDC_PD1_plus	0.96 (0.92 – 1.00)	0.044
pDC_PD1_plus	1.04 (1.00 – 1.08)	0.061
iNKT_plus_CD161_plus	0.08 (0.02 – 0.34)	< 0.001
CD56dim16_plus_PD1_plus	1.08 (1.02 – 1.15)	0.013
CD4_plus	1.03 (1.00 – 1.06)	0.035
CD8_plus	1.06 (1.02 – 1.11)	0.003
CD8_plus_PD1_plus	1.04 (1.02 – 1.06)	< 0.001
CD19_plus	1.07 (1.01 – 1.12)	0.012
CD4_plus_CD25_plus_FoxP3_plus	0.79 (0.67 – 0.93)	0.004

Univariate and multivariate Cox proportional hazards analysis of immune cell populations for recurrence risk

Variable	Univariate analysis		Multivariate analysis
Variable	HR (95% CI)	p-value	HR (95% CI)	p-value
iNKT⁺CD161⁺	0.32 (0.11–0.94)	0.039	0.09 (0.02–0.40)	0.001
CD8⁺PD1⁺	1.03 (1.02–1.05)	<0.001	1.03 (1.02–1.05)	<0.001
CD56^dimCD16⁺PD1⁺	1.06 (1.01–1.11)	0.024	1.12 (1.05–1.19)	<0.001

Correlation between patients age and expression of the analyzed genes_

Analyzed gene	HMMR	WT1	PRTN3	AURKA	DNAJC2	USP32	SPAG9	PRAME
r	0.1019	−0.0712	−0.0888	−0.0792	0.1098	0.2229	0.0955	−0.0251
p	0.5206	0.6625	0.5760	0.6179	0.4888	0.1668	0.5527	0.8812

Immune Surveillance in Chronic Myeloid Leukemia: Tumor Antigen Expression and CD8+ T Cell Function in the Context of Treatment- Free Remission

Figures & Tables

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Univariate Summary

Sequences of peptides used in the peptide mix in the MLPC

SPAG9 and NY-REN-60 derived T cell epitope peptides predicted by computer algorithms SYFPEITHI, IEDB, and NetCTL

Model_Comparison

Univariate and multivariate Cox proportional hazards analysis of immune cell populations for recurrence risk

Correlation between patients age and expression of the analyzed genes_

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