Association of killer immunoglobulin-like receptor genotypes and haplotypes with acute lymphoblastic leukemia risk

Abstract

Background

Killer immunoglobulin-like receptors (KIRs) are key molecules used by natural killer (NK) cells to interact with target cells. These receptors exhibit extensive genotypic polymorphism which has been associated with varying outcomes in immune responses against diseases. This study aimed to investigate the relationships between KIR genotypes and haplotypes with acute lymphoblastic leukemia (ALL) in Saudi patients.

Methods

A total of 259 Saudi subjects including 145 cases of acute lymphoblastic leukemia (ALL) and 114 healthy controls living in Riyadh were genotyped for 16 KIR genes and the two HLA-C1 and -C2 allotypes using PCR-SSP genotyping method.

Results

A significant high frequency of the two inhibitory KIR genes; 2DL1 (OR = 2.4; p < 0.0001) and 3DL1(OR = 10.87; p = 0.0068) in ALL compared to healthy group was observed. In contrast, the activating 2DS4 gene was significantly higher in healthy controls (OR = 0.15, p < 0.0001) compared to ALL patients. Haplotype analysis shows that BX haplogroup was strongly associated with the occurrence of ALL (OR = 4.39; p < 0.0001). Further combinatory analysis of KIR genes with their HLA-C1 and -C2 ligands demonstrated strong statistically protective effect of the 2DS1-C2 combination from ALL (OR = 0.06; p = 0.0003).

Conclusion

This study presents strong evidence supporting the connection between certain KIR genotypes, haplotypes, and KIR-HLA combinations with acute ALL in the Saudi population. The heightened occurrence of inhibitory KIR genes (2DL1 and 3DL1) and the BX haplotype in ALL patients indicates a possible involvement of these genetic variability with the dysfunctional of NK cells in the context of ALL disease.

Keywords

Natural killer cells KIR genotype HLA-C polymorphism acute lymphoblastic leukemia Saudi Arabia

Introduction

According to the International Agency for Research on Cancer, the top five cancers leading to mortality in 2020 were colorectal, breast, thyroid, non-Hodgkin lymphoma and leukemia.¹ In Saudi Arabia, leukemia was ranked as the 5^th type of cancer cases among both genders with an overall prevalence of 7.6% in males and 4.4% in females.² According to the same source, leukemia appears as the 1^st cancer disease among the most common types of cancers in children. In the Middle East, the Gulf Cooperation Council (GCC) reports that leukemia was classified 4^th among the most common tumors in the GCC countries. Among the fourth common types of leukemia, acute lymphoblastic leukemia (ALL) is the most prevalent cancer in children worldwide.³ ALL is characterized by a rapid proliferation of poorly differentiated lymphoid progenitor cells in the bone marrow. The incidence of ALL follows a bimodal allocation, the ﬁrst peak occurring in childhood (between 2 and 5 years of age) and a second steady increase at about age 50 years.⁴ In Saudi Arabia, the total incidence of childhood had increased from 1.58/100,000 in 2001 to 2.35/100,000 in 2014.⁵

Although the cause in most cases of childhood leukemia is not yet known, a lot of factors including obesity, high birth weight, viral infections, prenatal and postnatal exposure to irradiation, toxin, tobacco and viral infection, sedentary life style and other environmental factors in a context of genetic background has been reported to predispose to this disease.^6–8

Natural killer cells (NK cells) are innate immune cells of the lymphoid lineage that mediate antitumor and antiviral responses.^9–11 NK cells play a pivotal role in inhibiting early tumor formation and promoting anti-tumor immune responses through cytotoxicity and cytokine production.^9,10 The killing activity of NK cells is regulated by cytokines and a number of expressed transmembrane receptors from various families. The killer cell immunoglobulin-like receptor (KIR) family is one of the most polymorphic receptors of the NK cells that mediate cytotoxicity by interacting with opposing signals to trigger or inhibit cytotoxic activity.^12,13 Individuals with impaired NK cell activities exhibit a notable incidence of malignancy`.¹⁴ Their effector effects are regulated by a balance of signals from inhibitory and activating receptors, such as KIRs, which recognize self or modified HLA class I ligands on target cells.¹⁵ The KIR locus is composed of 16 genes and pseudogenes spanning ∼10–16 kb in chromosome 19q13.4 within the leukocyte receptor complex.¹⁶ The nomenclature of KIR genes is based on the number of extracellular Ig-like domains, which can be either 2 (2D) or 3 (3D), and on the length of the cytoplasmic tail, which can be long (L) or short (S). This nomenclature also reveals the function of the receptor that can be inhibitory or activating, respectively.

Thus, the inhibitory genes were named KIR2DL1, KIR2DL2, KIR2DL3, KIR2DL5, KIR3DL1, KIR3DL2, and KIR3DL3. While the activating genes were identified as KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, KIR2DS5, KIR2DS5 and KIR3DS1. An exception for the KIR2DL4 which was reported to act as inhibitory or activating gene. Two pseudogenes were also characterized; KIR2DP2 and KIR3DP1.^13,17 KIR3DL3, KIR3DP1, KIR2DL4, and KIR3DL2 are always present in the genome and are so-called, framework genes.¹⁸

Based on the gene content, two types of KIR haplotypes have been identified, named A and B haplotypes. There is no single criterion that separates all A and B haplotypes, however, one or more of the following genes characterize group B haplotypes: KIR2DL5, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS5, and KIR3DS1. On the other hand, group A haplotypes are defined by the lack of all these genes.^19,20 Consequently, the number of the B haplotypes is quite greater than the A haplotypes.

KIRs allows NK cells to identify target cells mainly through HLA class I, including HLA-C and HLA-B.²¹ HLA-C molecules class I ligand was classified into two allotypes depending on the amino acid at position 80 in the α1 domain.²² Thus, HLA-C1 allotype is carrying asparagine and the -C2 allotype is carrying lysine at this position. The recognition of HLA-C1 is mediated by the inhibitory KIR2DL2 and KIR2DL3 and the activating KIR2DS2 receptor, while the inhibitory KIR2DL1 and the activating KIR2DS1 receptors bind to the HLA-C2 group.^18,23,24 Malignant or infected cells use a variety of strategies to evade the host's immune response, including downregulation of MHC-I molecule expression.²⁵

The number and type of KIR genes are variable between haplotypes, which diversify humans in their KIR gene content. KIR genotyping has been increasingly used for epidemiological studies that showed links between some KIR genes and the risk of developing certain human diseases. Numerous studies have confirmed the relationships between KIR genes polymorphisms and HLA–KIR gene combinations with diseases such as cancer, autoimmune disease, viral infections and so on.^17,26–28 The aim of this study is to assess the relationships between KIR genotype and haplotypes with ALL in a Saudi population. Unveiling KIR and HLA class I gene polymorphisms that regulate the NK cell repertoire in patients with ALL could help in optimizing hematopoietic stem-cell donor selection and eventually NK-cell-based immunotherapies.

Materials and methods

Study subjects and blood collection

This study included 145 pediatric patients (Males = 87 and Females = 58) diagnosed with ALL between April 2017 until March 2020. The majority of patients were recruited from the pediatric hematology-oncology unit of Department of Pediatrics, King Khaled Hospital University (KKHU), Riyadh, Saudi Arabia. The health control (HC) group consisted of 114 ethnically matched, unrelated healthy volunteers (Males = 77 and Females 38) without any history for cancer or immunological diseases. The average age of ALL patients was 19.20 ± 14.8 years, whereas the average age of HC was 22.45 ± 20.18 years. Three ml of peripheral blood samples were collected from both patients and controls in EDTA-coated vials and stored at −80°C until analysis.

Ethics statement

This study was approved by the ethics committee of King Khaled Hospital University (KKHU) in Riyadh, Saudi Arabia (IRB approval number: E-20-5346). Written informed consent was obtained from all participants or their legal guardians prior to inclusion in the study. The study was conducted according to the ethical guidelines of King Saud University and the principles of the Declaration of Helsinki:

Genomic DNA extraction and KIR genotyping

Genomic DNA was extracted from peripheral blood samples using a genomic DNA purification kit (Applied Biosystems, Thermo Fisher Scientific, USA) according to the manufacturer's instructions. DNA concentration and purity were measured using a NanoDrop 8000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA). Genotyping of 16 KIR genes and HLA-C1/C2 allotypes was performed using duplex sequence-specific primer polymerase chain reaction (SSP-PCR) according to previously described protocols.^29,30 Negative control and positive control with known genotypes were included in each PCR reaction. KIR genotypes of randomly selected samples were confirmed using a commercial KIR genotyping kit (Miltenyi Biotec, Inc, Germany). PCR was performed on a Bio-Rad T100 thermocycler. Amplicons were resolved via electrophoresis on 2% agarose gels stained with ethidium bromide and visualized using a BioDocAnalyze (BDA) Gel Analysis system (Biometra GmbH, Germany).

HLA-C ligand genotyping

All patients and controls individuals were genotyped for the for HLA-C1 and HLA-C2 allotypes suing the same primers and protocol as reported before by Tajik et al.³⁰ A negative PCR control and positive control samples for each HLA-C allotype were used in each amplification.

Statistical analysis

KIR genotypes were identified based on the Allele Frequency Net database (AFND; https://www.allelefrequencies.net/kir6002a.asp accessed in February 2024).³¹ Frequencies of KIR genes, KIR AA and BX genotypes, and HLA-C ligands were calculated for the patient and control groups. Statistical analyses were performed using SigmaPlot v11 statistical tools. Associations were analyzed using X² and Fisher's exact tests with p-values adjusted for multiple comparisons via Holm-Bonferroni correction. Odds ratios and 95% confidence intervals were computed using a logistic regression model to evaluate associations between KIR genotypes, ligand combinations and ALL risk.

Results

Distribution of KIR genotypes in ALL and healthy groups

Sixteen KIR genes comprising 5 activating, 8 inhibitory genes and 2 pseudogenes were genotyped in ALL patients and HC (Table 1). Frequency of individual KIR genes were assessed in both groups. The framework KIR genes (3DL2, 3DL3, 2DL4 and 3DP1) were present in all individuals (Table 1).

Table 1.

Distribution of the KIR genes in ALL patients and healthy controls.

KIR genes	HCN = 114 (%)	ALLN = 145 (%)	X²	OR	95% CI	p-value
Inhibitory genes
KIR3DL2	114 (100.0%)	145 (100.0%)	NA			a
KIR3DL3	114 (100.0%)	145 (100.0%)	NA			a
KIR2DL4	114 (100.0%)	145 (100.0%)	NA			a
KIR2DL1	101 (88.6%)	145 (100.0%)	17.41	2.4	(2.10–2.83)	6.1 × 10^{^−4} **
KIR2DL3	94 (82.7%)	132 (91.0%)	4.22	2.16	(1.02–4.56)	0.06
KIR3DL1*	106 (93.0%)	144 (99.3%)	7.62	10.87	1.34–88.22	0.0068**
KIR2DL2	67 (58.8%)	89 (61.4%)	0.18	1.12	(0.68–1.84)	0.7
KIR2DL5	67 (58.8%)	98 (67.6%)	2.15	1.46	(0.88–2.44)	0.15
Activating genes
KIR2DS4	103 (90.35)	85 (58.62)	32.29	0.15	(0.08–0.31)	9.27 × 10^{^−9} **
KIR2DS1	35 (30.7%)	32 (22.1%)	2.48	0.64	(0.37–1.12)	0.12
KIR2DS2	64 (56.1%)	86 (59.3%)	0.26	1.14	(0.69–1.87)	0.62
KIR2DS3	51 (44.7%)	82 (56.5%)	3.57	1.61	(0.98–2.64)	0.06
KIR2DS5	33 (28.9%)	60 (41.4%)	4.28	1.73	(1.03–2.92)	0.05*
KIR3DS1	24 (21.0%)	46 (31.7%)	3.68	1.74	(0.99–3.08)	0.07
Pseudogenes
KIR3DP1	114 (100.0%)	145 (100.0%)	NA			a
KIR2DP1	114 (100.0%)	143 (98.6%)	1.58	1.01	(0.99–1.03)	0.50

KIR: killer immunoglobulin-like receptor; HC: healthy controls; ALL: acute lymphocyte leukemia; N: number samples; X²: Pearson's chi-squared test; OR: odds ratio; CI: confidence interval; NA not applicable.

**Significant associations after Bonferroni correction are shown in bold (*significance was lost after correction).

When comparing ALL patients with controls, significant differences were found in the frequency of two inhibitory KIR genes (2DL1 and 3DL1) and one activating KIR gene (2DS4). In ALL cases, a high increase in the occurrence of the inhibitory KIR genes 2DL1 (OR = 2.4, p < 0.001) and 3DL1 (OR = 10.87, p = 0.0068) was observed. Conversely, the activating KIR-2DS4 gene was highly increased in HC compared to ALL cases (OR = 0.15, p < 0.001), suggesting a protective effect against ALL. The frequencies of KIR 2DS3, 2DS5 and 3DS1 genes were slightly higher in ALL patients compared to HC but the differences were not significant.

Comparative analysis based on the number of activating and inhibitory KIR genes

Comparison of the frequencies of total activating (aKIR) and inhibitory (iKIR) KIR genes between ALL patients and controls showed that HC had a slightly higher frequency of one aKIR compared to patients (OR = 0.52; 95% CI = 0.30–0.92; p = 0.03), but this association was not significant after Bonferroni correction. Conversely, patients with two aKIRs were significantly more frequent than controls (OR = 2.32; 95% CI = 1.25–4.30; p = 0.008). However, there were no statistically significant differences in aKIR frequencies ranging from 3–6 aKIRs (Table 2). Analysis of iKIR frequency showed that individuals with six iKIRs were more frequent in HC compared to ALL patients (OR = 0.48; 95% CI = 0.27–0.84; p = 0.01), but this association was also not significant after Bonferroni correction (Table 2).

Table 2.

Comparative distribution of activating KIR genes (aKIR) and inhibitory KIR genes (iKIR), between ALL patients and healthy control.

KIR genes	HC	ALL	X²	OR	95% CI	p-value
KIR genes	N = 114 (%)	N = 145 (%)	X²	OR	95% CI	p-value
Activating KIR genes
aKIR = 1	37 (32.5%)	29 (20.0%)	5.22	0.52	(0.30–0.92)	0.03*
aKIR = 2	18 (15.8%)	44 (30.3%)	7.43	2.32	(1.25–4.30)	0.008**
aKIR = 3	25 (21.9%)	34 (23.4%)	0.08	1.09	(0.61–1.96)	0.88
aKIR = 4	12 (10.5%)	19 (13.1%)	0.40	1.28	(0.59–2.76)	0.57
aKIR = 5	12 (10.5%)	12 (8.3%)	0.38	0.77	(0.33–1.78)	0.67
aKIR = 6	9 (7.9%)	6 (4.1%)	1.65	0.50	(0.17–1.46)	0.28
Inhibitory KIR genes
iKIR = 4	2 (1.8%)	0 (0.0%)	2.56	–	–	0.19
iKIR = 5	4 (3.5%)	0 (0.0%)	5.17	–	–	0.04
iKIR = 6	39 (34.2%)	29 (20.0%)	6.66	0.48	(0.27–0.84)	0.01*
iKIR = 7	37 (32.5%)	59 (40.7%)	1.85	1.43	(0.85–2.38)	0.20
iKIR = 8	32 (28.1%)	57 (39.3%)	3.57	1.66	(0.98–2.81)	0.07

aKIR: Activating KIR genes; iKIR: Inhibitory KIR genes. KIR: killer immunoglobulin-like receptor; HC: healthy controls; ALL: acute lymphocyte leukemia; N: number samples; OR: odds ratio; CI: confidence interval; a: no statistics are computed. **Significant associations after Bonferroni correction are shown in bold (*significance was lost after correction).

Distribution of HLA-C genotypes in ALL patients compared to HC

Comparative analysis of HLA-C1 and HLA-C2 distributions is shown in Table 3. The five genetic models (codominant, dominant, recessive, overdominant, and log-additive) tested for association between HLA-C1/C2 polymorphism and ALL risk showed no significant association with disease for all tested models (Table 3).

Table 3.

Genotypic association of HLA-C C1/C2 alleles polymorphisms with ALL following, dominant, recessive, overdominant, and log-additive genetic models.

Model	Genotype	HC	ALL	OR (95% CI)	P-value	AIC	BIC
Model	Genotype	N = 114 (%)	N = 145 (%)	OR (95% CI)	P-value	AIC	BIC
Allele	C1	116 (50.9%)	133 (45.9%)	0.82 (0.59–1.16)	0.28
Allele	C2	112 (49.1%)	157 (54.1%)	1.22 (0.86–1.73)
Codominant	C2/C2	31 (27.2%)	54 (37.2%)	1.00	0.16	357.7	368.3
	C1/C2	50 (43.9%)	49 (33.8%)	1.78 (0.98–3.21)
	C1/ C1	33 (28.9%)	42 (29%)	1.37 (0.73–2.58)
Dominant	C2/C2	31 (27.2%)	54 (37.2%)	1.00	0.086	356.4	363.5
Dominant	C1/C2-C1/ C1	83 (72.8%)	91 (62.8%)	1.59 (0.93–2.71)	0.086	356.4	363.5
Recessive	C2/C2- C1/C2	81 (71%)	103 (71%)	1.00	1	359.3	366.4
Recessive	C1/ C1	33 (28.9%)	42 (29%)	1.00 (0.58–1.72)	1	359.3	366.4
Overdominant	C2/C2-C1/ C1	64 (56.1%)	96 (66.2%)	1.00	0.098	356.6	363.7
Overdominant	C1/C2	50 (43.9%)	49 (33.8%)	1.53 (0.92–2.54)	0.098	356.6	363.7
Log-additive	—	—	—	1.18 (0.86–1.61)	0.31	358.3	365.4

HC: healthy control; ALL: acute lymphoblastic leukemia; OR: odds ratio; CI: confidence interval; AIC: Akaike information criterion; BIC: Bayesian information criterion.

Combinatory analysis of the distribution of KIR genes and their HLA-C1 and C2 ligands frequency in ALL patients and HC

The combined correlation of the KIR genes and HLA-C ligands with ALL is reported in Table 4. The 2DL3, 2DS2, 2DS1, and 2DL2 KIR genes were found to be associated with various HLA-C ligands. It is worthy to notice that the 2DL3 and 2DS2 in the absence of their C1 ligand showed a risk for ALL (2DL3: OR = 1.84; 95% CI = 1.05–3.20; p = 0.04) and (2DS2: OR = 2.02; 95% CI = 1.03–3.93; p = 0.04). On the other hand the 2DS1 KIR gene showed a high protective effect from ALL in the absence of its C2 ligands, (OR = 0.06; 95% CI = 0.01–0.46; p = 0.0003).

Table 4.

Combinatory analysis of KIR genes in the presence and absence of their C1/C2 ligands between ALL patients and healthy controls.

KIR receptors /HLA-C ligands	HC	ALL	X²	OR	95% CI	p-value
KIR receptors /HLA-C ligands	N = 114 (%)	N = 145 (%)	X²	OR	95% CI	p-value
2DL2 + C1+	50 (43.9%)	67 (46.2%)	0.14	1.10	(0.67–1.80)	0.80
2DL2 + C1C1+	23 (20.2%)	34 (23.4%)	0.40	1.21	(0.67–2.20)	0.55
2DL2 + C1C2+	27 (23.7%)	33 (22.8%)	0.03	0.95	(0.53–1.70)	0.88
2DL2 + C1−	17 (14.9%)	22 (15.2%)	0.00	1.02	(0.51–2.03)	NS
2DL3 + C1+	68 (59.6%)	81 (55.9%)	0.37	0.86	(0.52–1.41)	0.61
2DL3 + C1C1+	26 (22.8%)	39 (26.9%)	0.57	1.25	(0.70–2.20)	0.47
2DL3 + C1C2+	42 (36.8%)	42 (29.0%)	1.81	0.70	(0.41–1.18)	0.18
2DL3 + C1−*	26 (22.8%)	51 (35.2%)	4.67	1.84	(1.05–3.20)	0.04
2DL2/3 + C1+	38 (33.3%)	57 (39.3%)	0.98	1.30	(0.78–2.16)	0.36
2DL2/3 + C1C1+	18 (15.8%)	31 (21.4%)	1.30	1.45	(0.76–2.75)	0.27
2DL2/3 + C1C2+	20 (17.5%)	26 (17.9%)	0.01	1.03	(0.54–1.95)	NS
2DL2/3 + C1−	13 (11.4%)	19 (13.1%)	0.17	1.17	(0.55–2.49)	0.71
2DL1 + C2+	76 (66.7%)	103 (71.0%)	0.57	1.23	(0.72–2.08)	0.50
2DL1 + C2C2+	30 (26.3%)	54 (37.2%)	3.48	1.66	(0.97–2.84)	0.08
2DL1 + C1C2+	46 (40.4%)	49 (33.8%)	1.18	0.76	(0.45–1.25)	0.30
2DL1 + C2−	25 (21.9%)	42 (29.0%)	1.65	1.45	(0.82–2.57)	0.25
2DS2 + C1+	49 (43.0%)	52 (35.9%)	1.36	0.74	(0.45–1.23)	0.25
2DS2 + C1C1+	20 (17.5%)	13 (9.0%)	4.22	0.46	(0.21–0.98)	0.06
2DS2 + C1C2+	29 (25.4%)	39 (26.9%)	0.07	1.08	(0.62–1.89)	0.89
2DS2 + C1−*	15 (13.2%)	34 (23.4%)	4.41	2.02	(1.04–3.93)	0.04
2DS1 + C2+	23 (20.2%)	31 (21.4%)	0.06	1.08	(0.59–1.97)	0.88
2DS1 + C2C2+	8 (7.0%)	16 (11.0%)	1.23	1.64	(0.67–4.0)	0.30
2DS1 + C1C2+	15 (13.2%)	15 (10.3%)	0.50	0.76	(0.35–1.63)	0.56
2DS1 + C2-***	12 (10.5%)	1 (0.7%)	12.95	0.06	(0.01–0.46)	0.0003
2DL2+/2DS2+/C1+	43 (37.7%)	41 (28.3%)	2.60	0.65	(0.39–1.11)	0.11
2DL2+/2DS2-/C1+**	7 (6.1%)	26 (17.9%)	7.98	3.34	(1.39–8.01)	0.005
2DL2-/2DS2+/C1+	6 (5.3%)	11 (7.6%)	0.56	1.48	(0.53–4.12)	0.61
2DL2+/2DS2+/C1−	13 (11.4%)	19 (13.1%)	0.17	1.17	(0.55–2.49)	0.71
2DL2-/2DS2-/C1+**	27 (23.7%)	13 (9.0%)	10.59	0.32	(0.15–0.64)	0.002

KIR: killer immunoglobulin-like receptor; HLA: human leukocyte antigen; C+: allele C present; C−: allele C absent; HC: healthy controls; ALL: acute lymphoblastic leukemia; N: number samples; X²: Pearson's chi-squared test; OR: odds ratio; CI: confidence interval; NS: not significant. *Significant associations after Bonferroni correction are shown in bold (*p ≤ 0.05, **p ≤ 0.005 and ***p ≤ 0.0005).

In the presence of their C1 ligand, the combination of KIR 2DL2+/DS2-/HLA-C1 + showed a risk effect for ALL (OR = 3.34; 95% CI = 1.39–8.01, p = 0.005). However, KIR 2DL2-/2DS2-/HLA-C1 + combination showed a protective effect (OR = 0.32; CI = 0.15–0.864; p = 0.002).

Comparative analysis of KIR haplotypes in ALL patients and HC

Based on the gene content, a total of 50 different genotypes were observed, including 6 AA and 44 Bx group genotypes (Figure 1). In healthy group there are 31 genotypes versus 32 genotypes in ALL individuals. Only 14 genotypes are common between the two groups. The most frequent genotype was the AA ID1 in control group (21.04%) and was the second most frequent genotype in ALL group (6.2%). Thus, a significant difference in AA ID1 genotype was found between the two groups (p = 0.00056). The Bx ID110 was the most frequent genotype in ALL patients and was missing in HC (p = 0.00009) (Figure 1).

Figure 1.

Main KIR gene content diversity among patients with ALL and healthy controls. Gene contents are shown by presence (shaded boxes) or absence (white boxes) of 16 KIR genes. For each genotype ID, the haplotype associated groups (AA and Bx), and the genotype subset are shown. Genotypes were obtained from Allele Frequency Net Database (https://www.allelefrequencies.net/kir6002a.asp, accessed in February 2024).

The frequencies of KIR haplotypes among ALL and HC are reported in Table 5. Group A haplotype is less represented in ALL (32.1%) than in HC (43.9%). The frequency of Group A haplotype is lower in the ALL group (32.1%) compared to the HC group (43.9%). In contrast, the occurrence of group B haplotype was notably greater in ALL patients (67.9%) compared to HCs (56.1%). The analysis of KIR genotypes revealed a substantial drop in the presence of the AA genotype in patients with ALL (6.9%) compared to HC (24.56%). Nevertheless, there were no notable disparities in the frequencies of AB and BB genotypes between ALL patients and HC.

Table 5.

Comparison of haplotypes genotypes, and linkage groups between ALL and control groups.

Kir genotypes	PatientsN = 145 (%)	ControlN = 114 (%)	OR	CI	P
AA	10 (6.9)	28 (24.56)
BX	135 (93.1)	86 (75.44)	4.39	2.03–9.50	0.000078
CXTX	94 (64.8)	72 (63.15)	1.075	0.64–1.79	0.79
CXT4	5 (3.45)	5 (4.38)	0.778	0.219–2.75	0.753
C4TX	35 (24.13)	24 (21.05)	1.19	0.66–2.15	0.65
C4T4	11 (7.58)	13 (11.04)	0.63	0.27–1.48	0.38
C4 gene cluster	46 (31.72)	37 (32.45)	0.96	0.57–1.63	1
T4 gene cluster	16 (11.03)	18 (15.78)	0.66	0.32–1.36	0.27

Furthermore, the four examined Bx subsets (C4T4, C4Tx, CxT4, CxTx) (x can be either an A or B haplotype) were compared among the different groups. No significant difference was observed between the healthy control group and ALL cases. The C4T4 variable was slightly higher among HC (11.04%) compared to ALL group (7.58%) but the difference did not reach significance.

Discussion

In the present study we have examined the genetic distribution of 16 KIR genes and their HLA-C ligands among Saudi patients with acute lymphoblastic leukemia (ALL) and a control group. Significant higher occurrence of inhibitory 2DL1 and 3DL1 KIR genes in ALL patients was observed, indicating that these genes might contribute to an elevated risk for ALL by suppressing the immune system's ability to detect and eliminate leukemic cells. Conversely, a notably lower occurrence of the activating 2DS4 KIR gene in ALL patients suggests a potential protective effect. In addition, the Bx haplotype was found to be strongly associated with occurrence of ALL. Some studies have been conducted on the correlation between KIR genes and hematological malignancy in some populations.^32–38 Despite this limited number of published studies, conflicting results have been noted. Recently, Halimi et al. reported in Iranian population an increased frequency of 3DS1 KIR gene in patients with ALL and lower frequency of the inhibitory combinations 2DL1/-C2, 2DL2/3/-C1 and 3DL2/-A3/A11.³⁷ In a study of the association of KIR genes with ALL in a population from north Indians Misra et al. observed an increased incidence of activating 2DS2, 2DS3, 3DS1, 2DS5 and 2DS4 KIR genes in ALL cases compared to controls.³⁴ In addition, the same study reported a predominance of the BB genotype in cases compared to controls. in a case/control study of the association of KIR genes with various blood cancers including ALL and acute myeloid leukemia (AML) in Bulgarian population, Varbanova et al. found a weak association of 2DS4 with AML but not with ALL.³⁸ However, the 2DL5A and 3DS1 KIR genes were found to be slightly protective against AML and Myeloid leukemia (ML), but no associations were found with ALL. In a recent study, Albalawi et al. found significant association between 2DL1, 2DL5, and 2DS2 and higher risk for AML in a Saudi population.³⁹ In contrast, the 2DL3, DL2 and 2DS4 exhibited a protective effect against AML in the same group. In a previous study Osman et al. didn’t found significant association between the frequencies of KIR genes in a Saudi population patient with ALL and AML.³⁵ Similarly, no association was found between KIR genes and haplotypes with ALL in Chinese Han origin children.³⁶ These discrepancies may be attributed to differences in study populations, sample sizes, and the complex interplay between KIR genes, their HLA ligands, and the development of hematological malignancies.

Additionally, our findings showed no statistically significant differences in the HLA-C1 and C2 genotypes between patients with ALL and HC. However, combinatory analysis of the distributions of the KIR genes with their C1/C2 ligands showed that 2DL2+/2DS2-/-C1 + and 2DL2/2DS2-HLA-C1 + and 2DL2-/2DS2-/C1 + and C2 are associated with the occurrence of ALL. Inversely, the combination 2DS1 + C2 was found to protect from ALL. The mechanisms underlying HLA-C1 allotype recognition by 2DL2 and 2DL3 are different.⁴⁰ Similarly, HLA-C1 allotypes vary significantly in their ability to suppress primary NK cell activation. These functional variations are attributed in part to 2DS2 KIR molecule, suggesting that the 2DL2 and 2DL3 KIR molecule binding geometries work in tandem with other parameters to differentiate HLA-C1 functional recognition.⁴⁰ Hirayasu, Ohashi⁴¹ found a significant association between 2DL3-HLA-C1 and cerebral malaria, and implications for KIR and HLA co-evolution. Knapp, Warshow⁴² reported consistent beneficial effects of 2DL3 and HLA-C1 following exposure to the hepatitis C virus. Zuo, Yu⁴³ found that the interaction between HLA-C1 and 2DL2/L3 increases the incidence of acute graft-versus-host disease (aGVHD) after hematopoietic stem cell transplantation. This was caused by the promotion of 2DL2/L3 single-positive/NKG2C-positive NK cell reconstitution.⁴³ 2DS1 receptor, through its recognition of HLA-C2 ligand can contribute to both the activation and tolerance of NK cells.⁴⁴ When This activating gene interacts with high levels of HLA-C2 in people with the HLA-C2/C2 genotype, the NK cells become less reactive.⁴⁵ Thus, this could explain the protective effect of this gene when its cognate receptor is missing. Considering the haplotype profiles, our results showed that the frequency of the KIR AA genotype was significantly higher in healthy controls than in patients with ALL. This result is in agreement with that reported by Verheyden et al. on Belgian Caucasians with chronic myeloid leukemia.⁴⁶ However, these results contrasted with that reported by de Smith et al., which found a significant association between ALL cases and AA haplotype in non-Hispanic white subjects.³³

When comparing the A and B haplotypes, the B haplotype was more common in ALL cases. Individuals with Bx genotypes are more prone to developing ALL than those with AA genotypes. These results are agreeing with those conducted by Misra et al. on ALL among North Indians.³⁴

In conclusion, this study provides compelling evidence of the significant role that specific KIR genes play in the outcome of ALL within the Saudi population highlighting a strong association between, the B haplotype and increased risk for ALL. To further elucidate the intricate relationship between KIR genotype diversity and ALL, future studies should focus on larger, more comprehensive cohort analyses that explore the NK receptor expression profiles and their impact on disease progression and treatment responses.

Footnotes

Acknowledgments

The authors extend their appreciation to the Researchers Supporting Project number (RSP 2025R75), King Saudi University, Riyadh, Saudi Arabia

Data availability

All data relevant to the study are included in the article

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Informed consent statement

Informed consent was obtained from all subjects involved in the study.

Institutional review board statement

the study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of the Ethic Committee of King Saud University under the number, 20-0795.

ORCID iD

Lamjed Mansour

References

Sung

Ferlay

Siegel

, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021; 71: 209–249. 2021/02/05.

Mahdi

Mula-Hussain

Ramzi

, et al. Cancer burden among Arab-world females in 2020: working toward improving outcomes. JCO Global Oncology 2022; 8: e2100415.

Valenzuela-Vazquez

Núñez-Enríquez

Sánchez-Herrera

, et al. Functional characterization of NK cells in Mexican pediatric patients with acute lymphoblastic leukemia: report from the Mexican interinstitutional group for the identification of the causes of childhood leukemia. Plos One 2020; 15: e0227314.

Neaga

Jimbu

Mesaros

, et al.

Why do children with acute lymphoblastic leukemia fare better than adults?

Cancers (Basel) 2021; 13: 3886.

Jastaniah

Essa

Ballourah

, et al.

Incidence trends of childhood acute lymphoblastic leukemia in Saudi Arabia: increasing incidence or competing risks?

Cancer Epidemiol 2020; 67: 101764.

Huang

F-L

Liao

E-C

C-L

, et al. Pathogenesis of pediatric B-cell acute lymphoblastic leukemia: molecular pathways and disease treatments. Oncol Lett 2020; 20: 448–454. 2020/05/04.

Onyije

Olsson

Baaken

, et al. Environmental risk factors for childhood acute lymphoblastic leukemia: an Umbrella review. Cancers (Basel) 2022; 14: 382.

Chunxia

Meifang

Jianhua

, et al. Tobacco smoke exposure and the risk of childhood acute lymphoblastic leukemia and acute myeloid leukemia: a meta-analysis. Medicine (Baltimore) 2019; 98: e16454. 2019/07/16.

Cerwenka

Lanier

. Natural killer cells, viruses and cancer. Nat Rev Immunol 2001; 1: 41–49.

10.

Lanier

. Up on the tightrope: natural killer cell activation and inhibition. Nat Immunol 2008; 9: 495–502.

11.

Cantoni

Falco

Vitale

, et al. Human NK cells and cancer. Oncoimmunology 2024; 13: 2378520.

12.

Rajalingam

. Immunogenetics: methods and applications in clinical practice. Methods Mol Bio 2012; 882: 391–414.

13.

Purdy

Campbell

. Natural killer cells and cancer: regulation by the killer cell ig-like receptors (KIR). Cancer Biol Ther 2009; 8: 2209–2218.

14.

Talmadge

Meyers

Prieur

, et al. Role of natural killer cells in tumor growth and metastasis: c57BL/6 normal and beige mice. J Natl Cancer Inst 1980; 65: 929–935. 1980/11/01.

15.

Rascle

Woolley

Jost

, et al. NK Cell education: physiological and pathological influences. Front Immunol 2023; 14: 1087155. 2023/02/07.

16.

Wende

Colonna

Ziegler

, et al. Organization of the leukocyte receptor cluster (LRC) on human chromosome 19q13. 4. Mamm Genome 1999; 10: 154–160.

17.

Middleton

Williams

Halfpenny

. KIR Genes. Transpl Immunol 2005; 14: 135–142.

18.

Rajalingam

. Human diversity of killer cell immunoglobulin-like receptors and disease. Korean J Hematol 2011; 46: 216–228. 2012/01/20.

19.

Uhrberg

. The KIR gene family: life in the fast lane of evolution. Eur J Immunol 2005; 35: 10–15. 2004/12/08.

20.

Manser

Weinhold

Uhrberg

. Human KIR repertoires: shaped by genetic diversity and evolution. Immunol Rev 2015; 267: 178–196. 2015/08/19.

21.

Downing

D’Orsogna

. High-resolution human KIR genotyping. Immunogenetics 2022; 74: 369–379.

22.

McCluskey

Kanaan

Diviney

. Nomenclature and serology of HLA class I and class II alleles. Current Protocols in Immunology 2017; 118: A. 1S. 1–A. 1S. 6.

23.

Parham

. MHC Class I molecules and KIRs in human history, health and survival. Nat Rev Immunol 2005; 5: 201–214.

24.

Chou

Y-C

Chen

C-H

Chen

M-J

, et al. Killer cell immunoglobulin-like receptors (KIR) and human leukocyte antigen-C (HLA-C) allorecognition patterns in women with endometriosis. Sci Rep-Uk 2020; 10: 1–9.

25.

Dhatchinamoorthy

Colbert

Rock

. Cancer immune evasion through loss of MHC class I antigen presentation. Front Immunol 2021; 12: 636568.

26.

Kulkarni

Martin

Carrington

. The Yin and Yang of HLA and KIR in human disease. In: Seminars in immunology. Elsevier, 2008, pp.343–352.

27.

Falco

Moretta

, et al.

KIR And KIR ligand polymorphism: a new area for clinical applications?

Tissue Antigens 2013; 82: 363–373.

28.

Al Omar

Mansour

Dar

, et al. The relationship between killer cell immunoglobulin-like receptors and HLA-C polymorphisms in colorectal cancer in a Saudi population. Genet Test Mol Biomarkers 2015; 19: 617–622.

29.

Mansour

Alkhuriji

Babay

, et al. Association of killer immunoglobulin-like receptor and human leukocyte antigen class I ligand with recurrent abortion in Saudi women. Genet Test Mol Biomarkers 2020; 24: 78–84. 2020/01/31.

30.

Tajik

Shahsavar

Nasiri

, et al. Compound KIR-HLA genotype analyses in the Iranian population by a novel PCR–SSP assay. Int J Immunogenet 2010; 37: 159–168.

31.

Gonzalez-Galarza Faviel

McCabe

Santos Eduardo

, et al. Allele frequency net database (AFND) 2020 update: gold-standard data classification, open access genotype data and new query tools. Nucleic Acids Res 2019; 48: D783–D788.

32.

Babor

Fischer

Uhrberg

. The role of KIR genes and ligands in leukemia surveillance. Front Immunol 2013; 4: 27.

33.

de Smith

Walsh

Ladner

, et al. The role of KIR genes and their cognate HLA class I ligands in childhood acute lymphoblastic leukemia. Blood J Am Soc Hematol 2014; 123: 2497–2503.

34.

Misra

Prakash

Moulik

, et al. Genetic associations of killer immunoglobulin like receptors and class I human leukocyte antigens on childhood acute lymphoblastic leukemia among north Indians. Hum Immunol 2016; 77: 41–46.

35.

Osman

AlJuryyan

Alharthi

, et al. Association between the killer cell immunoglobulin-like receptor a haplotype and childhood acute lymphoblastic leukemia. Hum Immunol 2017; 78: 510–514.

36.

Jiang

Guo

Yuan

, et al. Killer cell immunoglobulin-like receptor gene cluster predisposes to susceptibility to B-cell acute lymphoblastic leukemia in Chinese children. Int J Clin Exp Pathol 2020; 13: 536–542. 2020/04/10.

37.

Halimi

Mirzazadeh

Kalantar

, et al. Activating KIR/HLA-I combinations as a risk factor of adult B-ALL. Hum Immunol 2024; 85: 110750.

38.

Varbanova

Mihailova

Naumova

, et al. Certain killer immunoglobulin-like receptor (KIR)/KIR HLA class I ligand genotypes influence natural killer antitumor activity in myelogenous leukemia but not in acute lymphoblastic leukemia: a case control leukemia association study. Turk J Haematol 2019; 36: 238–246. 2019/07/25.

39.

Albalawi

AlKhulaifi

Al-Tamimi

, et al. Killer immunoglobulin-like receptors and HLA C1/C2 genes diversities and susceptibility to acute myeloid leukemia in Saudi Arabian patients. Journal of King Saud University - Science 2023; 35: 102723.

40.

Moradi

Stankovic

O’Connor

, et al. Structural plasticity of KIR2DL2 and KIR2DL3 enables altered docking geometries atop HLA-C. Nat Commun 2021; 12: 1–11.

41.

Hirayasu

Ohashi

Kashiwase

, et al. Significant association of KIR2DL3-HLA-C1 combination with cerebral malaria and implications for co-evolution of KIR and HLA. Plos Pathog 2012; 8: e1002565.

42.

Knapp

Warshow

Hegazy

, et al. Consistent beneficial effects of killer cell immunoglobulin-like receptor 2DL3 and group 1 human leukocyte antigen-C following exposure to hepatitis C virus. Hepatology 2010; 51: 1168–1175.

43.

Zuo

X-X

Liu

X-F

, et al. The interaction of HLA-C1/KIR2DL2/L3 promoted KIR2DL2/L3 single-positive/NKG2C-positive natural killer cell reconstitution, raising the incidence of aGVHD after hematopoietic stem cell transplantation. Front Immunol 2022; 13: 814334.

44.

Luduec

Boudreau

J-B

Freiberg

, , et al. Novel approach to cell surface discrimination between KIR2DL1 subtypes and KIR2DS1 identifies hierarchies in NK repertoire, education, and tolerance. Front Immunol 2019; 10: 734.

45.

Venstrom

Pittari

Gooley

, et al. HLA-C–dependent prevention of leukemia relapse by donor activating KIR2DS1. New Engl J Med 2012; 367: 805–816.

46.

Verheyden

Bernier

Demanet

. Identification of natural killer cell receptor phenotypes associated with leukemia. Leukemia 2004; 18: 2002–2007.