Abstract

Large granular lymphocyte leukemia (LGL) is a disease of semiautonomous proliferation of cytotoxic T-cells (CTL) often accompanied by immune cytopenias, particularly neutropenia. LGL related cytopenias have been attributed to LGL cellular cytotoxicity or proapoptotic cytokines rather than intrinsic properties of the neutrophils. The association of LGL with autoimmunity suggests that genetic predisposition may contribute to disease pathogenesis. We studied 69 patients with LGL leukemia using a case-control approach; control populations included ethnically matched healthy individuals (N=82) and disease controls of aplastic anemia (N=48) and kidney transplant recipients (N=48). Initially, we applied the Illumina 12K non-synonymous SNP array to a subcohort of 36 LGL patients and 54 healthy controls (training set). Results were subjected to independent hypothesis-generating biostatistical algorithms. First, Exemplar automated analysis determined disease prediction based on independent χ2 analysis for each SNP. As expected, no SNP in this underpowered study reached Bonferroni corrected statistical significance, but our analysis allowed for ranking based on p-value. Second, Random Forests, a nonparametric tree method was applied, whereby all SNP information was calculated multivariately to predict disease. In a non-Mendelian inherited disease, this method more closely reflects the biology of complex polygenic traits; remarkably, those SNP identified by Random Forest were among the highest ranking SNP by Exemplar. Our initial hypothesis-generating set identified 1 SNP in unknown gene C8orf31 and 4 SNP within the MHC class I related-chain A (MICA) gene. We focused on MICA, a non-peptide presenting, tightly regulated stress response HLA molecule that could play a role in pathogenesis of neutropenia in LGL. To further substantiate our finding, the initial training set results were subjected to technical validation; fidelity was rechecked by PCR genotyping with 93% concordance. Biological validation was determined by confirmation in an independent test set consisting of 33 LGL patients and additional 28 controls. As only limited numbers of SNP were tested, there was no need for α-error adjustment. MICA SNP rs1063635 was found to have the most predictive value in both the training set (PPV=56%, NPV=89%) and test set (PPV=64%, NPV=86%). Overall, the control frequency of this SNP in homozygous form was 12% vs 60% in LGL (p<.01, OR=9.1). MICA alleles have been implicated in autoimmune diseases and malignancies. Although this SNP may not define a particular MICA genotype, it is possible that it is in linkage disequilibrium with genotype-defining polymorphisms. To study the functional consequences of our findings, flow cytometric analysis using anti-MICA antibodies was performed, which identified higher expression of MICA in neutrophils from patients as compared to controls (p=.04). MICA overexpression decreased after immunosuppressive therapy (p<.01). While the mechanism of MICA induction is unknown, we stipulate that the presence of MICA alleles leads to a persistent stimulatory signal in LGL predisposing to clonal outgrowth. In sum, our findings suggest that MICA polymorphisms may represent a predisposition factor in LGL and/or LGL-associated neutropenia.

Author notes

Disclosure:Research Funding: Aaron Viny is a Howard Hughes Medical Institute medical research training fellow.