HLA alleles, haplotypes frequencies, and their association with hematological disorders: a report from 1550 families whose patients underwent allogeneic bone marrow transplantation in Egypt.

HLA alleles are representative of ethnicities and may play important roles in predisposition to hematological disorders. We analyzed DNA samples for HLA-A, -B, -C, -DRB1, and -DQB1 loci, from 1550 patients and 4450 potential related donors by PCR-SSO (Polymerase chain reaction sequence-specific oligonucleotides) and estimated allele frequencies in donors and patients from 1550 families who underwent bone marrow transplantation (BMT) in Egypt. We also studied the association between HLA allele frequencies and incidence of acute myeloid leukemia, acute lymphoblastic leukemia, and severe aplastic anemia. The most frequently observed HLA class I alleles were HLA- A*01:01 (16.9%), A*02:01 (16.1%), B*41:01 (8.7%), B*49:01 (7.3%), C*06:02 (25.1%), and C*07:01 (25.1%), and the most frequently observed class II alleles were HLA-DRB1*11:01 (11.8%), DRB1*03:01 (11.6%), DQB1*03:01 (27.5%), and DQB1*05:01 (18.9%). The most frequently observed haplotypes were A*33:01~B*14:02 ~ DRB1*01:02 (2.35%) and A*01:01~B*52:01~DRB1*15:01 (2.11%). HLA-DRB1*07:01 was associated with higher AML odds (OR, 1.26; 95% CI, 1.02-1.55; p = 0.030). Only HLA-B38 antigen showed a trend towards increased odds of ALL (OR, 1.52; 95% CI, 1.00-2.30; p = 0.049) HLA-A*02:01, -B*14:02, and -DRB1*15:01 were associated with higher odds of SAA (A*02:01: OR, 1.35; 95% CI, 1.07-1.70; p = 0.010; B*14:02: OR, 1.43; 95% CI, 1.06-1.93; p = 0.020; DRB1*15:01: OR, 1.32; 95% CI, 1.07-1.64; p = 0.011). This study provides estimates of HLA allele and haplotype frequencies and their association with hematological disorders in an Egyptian population.

Novel gene-specific Bayesian Gaussian mixture model to predict the missense variants pathogenicity of Sanfilippo syndrome.

MPS III is an autosomal recessive lysosomal storage disease caused mainly by missense variants in the NAGLU, GNS, HGSNAT, and SGSH genes. The pathogenicity interpretation of missense variants is still challenging. We aimed to develop unsupervised clustering-based pathogenicity predictor scores using extracted features from eight in silico predictors to predict the impact of novel missense variants of Sanfilippo syndrome. The model was trained on a dataset consisting of 415 uncertain significant (VUS) missense NAGLU variants. Performance The SanfilippoPred tool was evaluated by validation and test datasets consisting of 197-labelled NAGLU missense variants, and its performance was compared versus individual pathogenicity predictors using receiver operating characteristic (ROC) analysis. Moreover, we tested the SanfilippoPred tool using extra-labelled 427 missense variants to assess its specificity and sensitivity threshold. Application of the trained machine learning (ML) model on the test dataset of labelled NAGLU missense variants showed that SanfilippoPred has an accuracy of 0.93 (0.86-0.97 at CI 95%), sensitivity of 0.93, and specificity of 0.92. The comparative performance of the SanfilippoPred showed better performance (AUC = 0.908) than the individual predictors SIFT (AUC = 0.756), Polyphen-2 (AUC = 0.788), CADD (AUC = 0.568), REVEL (AUC = 0.548), MetaLR (AUC = 0.751), and AlphMissense (AUC = 0.885). Using high-confidence labelled NAGLU variants, showed that SanfilippoPred has an 85.7% sensitivity threshold. The poor correlation between the Sanfilippo syndrome phenotype and genotype represents a demand for a new tool to classify its missense variants. This study provides a significant tool for preventing the misinterpretation of missense variants of the Sanfilippo syndrome-relevant genes. Finally, it seems that ML-based pathogenicity predictors and Sanfilippo syndrome-specific prediction tools could be feasible and efficient pathogenicity predictors in the future.

ABCB1 Single Nucleotide Polymorphism Genotypes as Predictors of Paclitaxel-Induced Peripheral Neuropathy in Breast Cancer.

Background: Paclitaxel is a key antineoplastic agent in the treatment of breast cancer and many other malignancies. However, paclitaxel-induced peripheral neuropathy (PIPN) is a common adverse event that occurs with paclitaxel therapy and frequently causes considerable pain and a decline in patients' quality of life. Single nucleotide polymorphisms (SNPs) in the ABCB1 gene have been frequently associated with increased severity of PIPN. However, the validity of ABCB1 SNP markers to predict the incidence of PIPN has not been confirmed. Methods: We extracted genomic DNA from samples collected from 92 Egyptian female breast cancer patients receiving weekly paclitaxel and used them to genotype ABCB1 G1236A (rs1128503) and ABCB1 G3435A (rs1045642). Markers that correlated with PIPN, together with baseline clinical factors, were used to fit additive, dominant, overdominant, and recessive genetic models. We applied a repeated k-fold cross-validation algorithm to select the model with the highest predictive accuracy. We finally performed model diagnostics and receiver operating characteristics (ROCs) analysis for the model with the highest classification accuracy. Results: The additive model achieved the highest classification accuracy. The G1236A homozygous AA variant correlated with grade ≥2 PIPN (p = 0.018). PIPN also correlated with body surface area (BSA) (p = 0.003) and history of diabetes before treatment (p = 0.015). ROCs analysis showed a sensitivity of 76.9%, a specificity of 86.8%, a positive predictive value of 83.64%, and a negative predictive value of 81.08% for the additive model. Conclusion: The ABCB1 G1236A, BSA, and history of diabetes are valid predictors of PIPN, which can enable the personalization of paclitaxel dosing to prevent PIPN.