Association between MICA polymorphisms, s-MICA levels, and pancreatic cancer risk in a population-based case-control study

Role of NKG2D ligands and receptor in haploidentical related donor hematopoietic cell transplantation

The recurrence of malignancy after hematopoietic cell transplantation (HCT) is the primary cause of transplantation failure. The NKG2D axis is a powerful pathway for antitumor responses, but its role in the control of malignancy after HCT is not well-defined. We tested the hypothesis that gene variation of the NKG2D receptor and its ligands MICA and MICB affect relapse and survival in 1629 patients who received a haploidentical HCT for the treatment of a malignant blood disorder. Patients and donors were characterized for MICA residue 129, the exon 5 short tandem repeat (STR), and MICB residues 52, 57, 98, and 189. Donors were additionally defined for the presence of NKG2D residue 72. Mortality was higher in patients with MICB-52Asn relative to those with 52Asp (hazard ratio [HR], 1.83; 95% confidence interval [CI], 1.24-2.71; P = .002) and lower in those with MICA-STR mismatch than in those with STR match (HR, 0.66; 95% CI, 0.54-0.79; P = .00002). Relapse was lower with NKG2D-72Thr donors than with 72Ala donors (relapse HR, 0.57; 95% CI, 0.35-0.91; P = .02). The protective effects of patient MICB-52Asp with donor MICA-STR mismatch and NKG2D-72Thr were enhanced when all 3 features were present. The NKG2D ligand/receptor pathway is a transplantation determinant. The immunobiology of relapse is defined by the concerted effects of MICA, MICB, and NKG2D germ line variation. Consideration of NKG2D ligand/receptor pairings may improve survival for future patients.

Associations between MICA and MICB Genetic Variants, Protein Levels, and Colorectal Cancer: Atherosclerosis Risk in Communities (ARIC)

BACKGROUND

The MHC class I chain-related protein A (MICA) and protein B (MICB) participate in tumor immunosurveillance and may be important in colorectal cancer, but have not been examined in colorectal cancer development.

METHODS

sMICA and sMICB blood levels were measured by SomaScan in Visit 2 (1990-92, baseline) and Visit 3 (1993-95) samples in cancer-free participants in the Atherosclerosis Risk in Communities Study. We selected rs1051792, rs1063635, rs2516448, rs3763288, rs1131896, rs2596542, and rs2395029 that were located in or in the vicinity of MICA or MICB and were associated with cancer or autoimmune diseases in published studies. SNPs were genotyped by the Affymetrix Genome-Wide Human SNP Array. We applied linear and Cox proportional hazards regressions to examine the associations of preselected SNPs with sMICA and sMICB levels and colorectal cancer risk (236 colorectal cancers, 8,609 participants) and of sMICA and sMICB levels with colorectal cancer risk (312 colorectal cancers, 10,834 participants). In genetic analyses, estimates adjusted for ancestry markers were meta-analyzed.

RESULTS

Rs1051792-A, rs1063635-A, rs2516448-C, rs3763288-A, rs2596542-T, and rs2395029-G were significantly associated with decreased sMICA levels. Rs2395029-G, in the vicinity of MICA and MICB, was also associated with increased sMICB levels. Rs2596542-T was significantly associated with decreased colorectal cancer risk. Lower sMICA levels were associated with lower colorectal cancer risk in males (HR = 0.68; 95% confidence interval, 0.49-0.96) but not in females (Pinteraction = 0.08).

CONCLUSIONS

Rs2596542-T associated with lower sMICA levels was associated with decreased colorectal cancer risk. Lower sMICA levels were associated with lower colorectal cancer risk in males.

IMPACT

These findings support an importance of immunosurveillance in colorectal cancer.

Study of the association between a MICA gene polymorphism and cholangiocarcinoma in Egyptian patients

Introduction

An inflammatory environment is the common pathway for the development of cholangiocarcinoma (CCA). The natural killer group 2D receptor (NKG2D), an activating receptor for NK cells, is a potent immune axis in the antitumor and antimicrobial immune response through its binding to NKG2D ligands (NKG2DLs). NKG2DLs are normally absent or poorly expressed in most cells; conversely, they are upregulated in stressed cells. We studied the rs2596542 polymorphism located upstream of the MICA gene, which encodes an NKG2DL, in patients with CCA as a marker for early disease detection and a possible therapeutic target.

Material and methods

A case-control study was conducted on 40 patients with CCA and 45 healthy individuals (as controls). After routine examination, the rs2596542 polymorphism of the MICA gene was investigated using real-time PCR.

Results

We found that a TT homozygous genotype was significantly predominant in patients with CCA (p = 0.039), with the T allele being dominantly distributed in CCA (p = 0.007). High levels of CA19-9 were significantly associated with the TT genotype in the patients. However, we did not detect significant differences in rs2596542C/T genotype and allele distribution between patients with CCA with cirrhosis and those without cirrhosis (p > 0.05).

Conclusions

The MICA rs2596542 polymorphism may affect the susceptibility to CCA, but not its progression. The TT genotype could be used as a potential diagnostic marker for CCA and triggering the MICA pathway could be a promising therapeutic target.

Single-Nucleotide Polymorphisms in MICA and MICB Genes Could Play a Role in the Outcome in AML Patients after HSCT

NKG2D and its ligands, MICA and MICB, are known as the key regulators of NK cells. NK cells are the first reconstituted cells after the allogeneic hematopoietic stem cell transplantation (HSCT); therefore, it is crucial to understand their role in HSCT outcome. In the presented study, we investigated the single amino acid changes across the exons 2–4 of MICA and MICB genes, and point mutations within the NKG2D gene, which defines the type of NKG2D haploblock (HNK/LNK) in the donors (n = 124), as well as in patients with acute myeloid leukemia (n = 78). In our cohort, we found that graft from a donor with at least one MICA allele containing glycine at position 14 (MICA-14Gly) is significantly associated with deterioration of a patient’s overall survival (OS) (p < 0.05). We also observed a negative effect of MICB-58 (Lys → Glu) polymorphism on relapse-free survival (RFS), although it was not statistically significant in multivariate analysis (p = 0.069). To our knowledge, this is the first work describing the role of MICA-14 and MICB-58 polymorphisms on HSCT outcome.

jSRC: a flexible and accurate joint learning algorithm for clustering of single-cell RNA-sequencing data

Single-cell RNA-sequencing (scRNA-seq) explores the transcriptome of genes at cell level, which sheds light on revealing the heterogeneity and dynamics of cell populations. Advances in biotechnologies make it possible to generate scRNA-seq profiles for large-scale cells, requiring effective and efficient clustering algorithms to identify cell types and informative genes. Although great efforts have been devoted to clustering of scRNA-seq, the accuracy, scalability and interpretability of available algorithms are not desirable. In this study, we solve these problems by developing a joint learning algorithm [a.k.a. joints sparse representation and clustering (jSRC)], where the dimension reduction (DR) and clustering are integrated. Specifically, DR is employed for the scalability and joint learning improves accuracy. To increase the interpretability of patterns, we assume that cells within the same type have similar expression patterns, where the sparse representation is imposed on features. We transform clustering of scRNA-seq into an optimization problem and then derive the update rules to optimize the objective of jSRC. Fifteen scRNA-seq datasets from various tissues and organisms are adopted to validate the performance of jSRC, where the number of single cells varies from 49 to 110 824. The experimental results demonstrate that jSRC significantly outperforms 12 state-of-the-art methods in terms of various measurements (on average 20.29% by improvement) with fewer running time. Furthermore, jSRC is efficient and robust across different scRNA-seq datasets from various tissues. Finally, jSRC also accurately identifies dynamic cell types associated with progression of COVID-19. The proposed model and methods provide an effective strategy to analyze scRNA-seq data (the software is coded using MATLAB and is free for academic purposes; https://github.com/xkmaxidian/jSRC).

Major Histocompatibility Complex Class I-Related Chain A (MICA) Allelic Variants Associate With Susceptibility and Prognosis of Gastric Cancer

Gastric cancer (GC) is the fifth most prevalent type of cancer worldwide. Gastric tumor cells express MICA protein, a ligand to NKG2D receptor that triggers natural killer (NK) cells effector functions for early tumor elimination. MICA gene is highly polymorphic, thus originating alleles that encode protein variants with a controversial role in cancer. The main goal of this work was to study MICA gene polymorphisms and their relationship with the susceptibility and prognosis of GC. Fifty patients with GC and 50 healthy volunteers were included in this study. MICA alleles were identified using Sanger sequencing methods. The analysis of MICA gene sequence revealed 13 MICA sequences and 5 MICA-short tandem repeats (STR) alleles in the studied cohorts We identified MICA^*002 (^*A9) as the most frequent allele in both, patients and controls, followed by MICA^*008 allele (^*A5.1). MICA^*009/049 allele was significantly associated with increased risk of GC (OR: 5.11 [95% CI: 1.39–18.74], p = 0.014). The analysis of MICA-STR alleles revealed a higher frequency of MICA^*A5 in healthy individuals than GC patients (OR = 0.34 [95% CI: 0.12–0.98], p = 0.046). Survival analysis after gastrectomy showed that patients with MICA^*002/002 or MICA^*002/004 alleles had significantly higher survival rates than those patients bearing MICA^*002/008 (p = 0.014) or MICA^*002/009 (MICA^*002/049) alleles (p = 0.040). The presence of threonine in the position MICA-181 (MICA^*009/049 allele) was more frequent in GC patients than controls (p = 0.023). Molecular analysis of MICA-181 showed that the presence of threonine provides greater mobility to the protein than arginine in the same position (MICA^*004), which could explain, at least in part, some immune evasion mechanisms developed by the tumor. In conclusion, our findings suggest that the study of MICA alleles is crucial to search for new therapeutic approaches and may be useful for the evaluation of risk and prognosis of GC and personalized therapy.

MICA polymorphisms associated with antithyroid drug-induced agranulocytosis in the Chinese Han population

Background

Graves' disease (GD) is a clinical autoimmune thyroid disease. During the treatment of GD, antithyroid drug‐induced agranulocytosis (TIA) is a common and even life‐threatening adverse drug reaction. Previous studies suggested that susceptibility to TIA is strongly associated with HLA‐B*27:05, HLA‐B*38:02, and HLA‐DRB1*08:03 genetic variation and six single nucleotide polymorphisms (SNPs) in MICA genes.

Aims

The purpose of this study is to further study the associations between TIA, HLA‐B and MICA.

Materials & Methods

We genotyped MICA‐STR and MICA‐129 variants in 41 TIA and 308 control patients with GD and investigated the linkage effect among SNPs and short tandem repeat (STR) of MICA and HLA‐B alleles.

Results

The results showed that MICA*A5.1 was significantly associated with TIA (p = .007, odd ratio = 1.958, 95% confidence interval, 1.192–3.214). In addition, high linkage among MICA‐129 and six SNPs MICA and HLA‐B was detected, and two haplotypes (AAAACAAAAACGGCCTA and AACAAAAAAAACATTAA (p = 5.14E−07 and p = 3.42E−08, respectively)) were significantly associated with TIA. Furthermore, when we analyzed only MICA‐129 and HLA‐B separately, the haplotypes (AAAACAAAAAA with p = 2.49E−07 and AACAAAAAAAA with p = 2.14E−09) were identified with more significant effects. MICA‐129 was completely linked to six SNPs with haplotypes ACATTACA (p = 2.05E−05) significantly associated with TIA.

Conclusion

These data indicated that there was a significant linkage effect between MICA‐129 and other alleles, suggesting that they exert interactive effects as risk factors for the development of TIA.

High-Throughput MICA/B Genotyping of Over Two Million Samples: Workflow and Allele Frequencies

MICA and MICB are ligands of the NKG2D receptor and thereby influence NK and T cell activity. MICA/B gene polymorphisms, expression levels and the amount of soluble MICA/B in the serum have been linked to autoimmune diseases, infections, and cancer. In hematopoietic stem cell transplantation, MICA matching between donor and patient has been correlated with reduced acute and chronic graft-vs.-host disease and improved survival. Hence, we developed an extremely cost-efficient high-throughput workflow for genotyping MICA/B for newly registered potential stem cell donors. Since mid-2017, we have genotyped over two million samples using NGS amplicon sequencing for MICA/B exons 2–5. In donors of German origin, MICA*008 is the most common MICA allele with a frequency of 42.3%. It is followed by MICA*002 (11.7%) and MICA*009 (8.8%). The three most common MICB alleles are MICB*005 (43.9%), MICB*004 (21.7%), and MICB*002 (18.9%). In general, MICB is less diverse than MICA and only 6 alleles, instead of 15, account for a cumulative allele frequency of 99.5%. In 0.5% of the samples we observed at least one allele of MICA or MICB which has so far not been reported to the IPD/IMGT-HLA database. By providing MICA/B typed voluntary donors, clinicians become empowered to include MICA/B into their donor selection process to further improve unrelated hematopoietic stem cell transplantation.