Characterisation of the Novel HLA-C*08:01:37 Allele by Next-Generation Sequencing.
HLA-C*08:01:37 differs from HLA-C*08:01:01:01 by one single nucleotide substitution at position 927 G>A in exon 5.
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HLA-C*08:01:37 differs from HLA-C*08:01:01:01 by one single nucleotide substitution at position 927 G>A in exon 5.
HLA-C*07:1193 differs from HLA-C*07:02:01:03 in exon 5 codon 292 (GCT>GTT).
HLA-A*02:540:02N differs from A*02:01:01:01 by one nucleotide substitution in codon 99 in exon 3.
HLA-DRB3*01:127 differs from the HLA-DRB3*01:01:02:01 allele by one nucleotide substitution in the exon 3.
A novel HLA-B allele was identified in a Brazilian volunteer bone marrow donor.
The HLA-DPB1*1000:01 allele is characterised by a single nucleotide substitution in exon 3.
HLA-B*57:212 differs from HLA-B*57:01:01:01 by a single nucleotide substitution in exon 5.
HLA-A*23:163 differs from HLA-A*23:01:01:03 by a single nucleotide substitution at position 925 of the cDNA.
HLA-DQB1*06:03:60 differs from HLA-DQB1*06:03:01:01 by a single synonymous nucleotide substitution at position 174 in Exon 2.
HLA-DQB1*05:386 differs from HLA-DQB1*05:01:01:01 by a non-synonymous substitution in exon 4.
HLA-DQB1*02:02:41 differs from HLA-DQB1*02:02:01:01 by one synonymous nucleotide substitution at Codon 39 in Exon 2.
The novel allele HLA-A*02:04:03 was detected with long-read sequencing but failed to be detected by short-read sequencing.
Compared with HLA-A*02:07:01:01, the alleles HLA-A*02:1205 and HLA-A*02:07:27 each show one nucleotide substitution, respectively.
HLA-A*02:1229 differs from HLA-A*02:07:01:01 by a single nucleotide substitution at position 1014 T>A.
HLA-A*03:541 differs from HLA-A*03:01:01:01 by a single nucleotide substitution at position 728 of the cDNA in exon 4.
BACKGROUND: Mutations in four major driver genes -KRAS, CDKN2A, TP53, and SMAD4- are central to the pathogenesis of pancreatic ductal adenocarcinoma (PDAC) and critically inform diagnosis, therapeutic decision-making, and prognostic assessment. Although next-generation sequencing (NGS) is widely regarded as the gold standard for detecting these mutations, its clinical application is often limited by suboptimal analytical efficiency and substantial economic cost. Among these genes, immunohistochemical (IHC) staining for the proteins encoded by TP53 and SMAD4 has been extensively adopted in routine pathology practice. However, standardized IHC pattern classification schemes and rigorous validation of their predictive accuracy for underlying genomic alterations remain lacking in PDAC. METHODS: We retrospectively enrolled 63 PDAC patients and systematically characterized the typical IHC expression patterns of p53 and Smad4. Targeted NGS was subsequently performed on all available tumor specimens, and the resulting mutational profiles were correlated with corresponding IHC findings. Diagnostic performance including sensitivity, specificity and accuracy of p53 IHC for predicting TP53 mutations and of Smad4 IHC for predicting SMAD4 mutations was rigorously evaluated. RESULTS: Among the four canonical driver genes, co-occurring double- or triple-gene mutations were prevalent; within TP53 and SMAD4, missense mutations constituted the most frequent variant type. Using NGS as the reference standard, we validated the diagnostic utility of a three-tiered p53 IHC classification system, particularly in fine-needle biopsy (FNB) specimens. Furthermore, we proposed a novel, refined Smad4 IHC pattern classification that incorporates an "intermediate" category, thereby expanding upon conventional binary interpretation. This new scheme achieved markedly improved mutation prediction accuracy (0.76) compared with traditional approaches (0.57). CONCLUSION: Our study highlights the complementary diagnostic value of p53 and Smad4 IHC relative to molecular testing in PDAC, especially when tissue is limited, as commonly encountered in FNB specimens. The newly established Smad4 IHC classification system, which integrates an intermediate expression category into the conventional two-tier framework, demonstrates superior clinical utility and enhances predictive accuracy for SMAD4 genomic alterations.
BACKGROUND/AIM: In breast cancer, knowledge of the associations between clinicopathologic characteristics, genetic changes, and subtype-specific patterns is expanding. This study investigated how pathological and clinical variables affect the actionability of Next Generation Sequencing (NGS)-based tumor molecular data. MATERIALS AND METHODS: 227 breast cancer patients referred to Genekor's laboratory for tumor molecular profile analysis were included in the study. Pathology records were used to assess critical clinicopathological features, including HER2, ER, PR, Ki67, grade, metastatic site, and age. A 1021-gene NGS-based multigene panel was utilized to assess tumor biology alongside tumor mutational burden (TMB) and microsatellite instability (MSI). RESULTS: Comprehensive genomic profiling revealed that 95.6% of the patients harbored at least one oncogenic or likely oncogenic alteration, highlighting the high diagnostic yield of NGS-based testing. Distinct subtype-specific patterns were observed: HR+/HER2- tumors were enriched for PIK3CA and ESR1 gene alterations, whereas triple-negative breast cancer (TNBC) was dominated by TP53 alterations. Clinically actionable alterations were most common in HR+/HER2- tumors (~60% on-label), whereas TNBC more often harbored off-label or trial-associated targets. The inclusion of tumor-agnostic biomarkers (TMB/MSI) increased on-label actionability up to 64.5% in HR+/HER2- tumors, primarily driven by TMB-high cases. Median TMB values were low, and age was the only independent predictor. Furthermore, the presence of actionable alterations was significantly higher in metastatic tumors, and TP53 alterations were associated with aggressive tumor characteristics. CONCLUSION: Comprehensive NGS-based genomic profiling identifies clinically actionable alterations in over half of breast cancer patients, with substantial variability across molecular subtypes. The HR+/HER2- subtype demonstrates the highest prevalence of on-label actionable biomarkers. These findings support the routine implementation of comprehensive genomic profiling, especially in metastatic HER2-negative breast cancer, to guide precision oncology strategies and enable enrollment in biomarker-driven clinical trials.
INTRODUCTION: Faba bean breeding and genomics have seen steady progress in recent years, supported by genome sequences and high-density genotyping platforms. These tools have been valuable for trait mapping, diversity assessment, and genomic research, but they have limited routine use in breeding programs due to their relatively high cost. Recent progress in establishing an optimized, cost-efficient genotyping-by-sequencing protocol tailored to the large and complex faba bean genome has created the foundation for a more accessible genotyping solution. METHODS: Using this approach, we explored the genetic diversity of faba bean germplasm from various panels, providing a comprehensive representation of the crop's genetic landscape. From this dataset, we identified and selected a high-quality set of informative SNP markers that are evenly distributed across the genome. Building on these resources, we designed a breeder-friendly 10K SNP chip. RESULTS: The 10K SNP chip delivers high accuracy, broad genomic coverage, and affordability. The chip was validated across diverse germplasm panels, demonstrating strong clustering performance, high reproducibility, and applicability to breeding-relevant germplasm. DISCUSSION: This platform offers a cost-effective alternative to higher-density arrays, enabling its integration into genomic selection, marker-assisted breeding, and diversity monitoring, ultimately supporting accelerated genetic gain and the delivery of improved varieties to farmers.