Next-generation sequencing (NGS) has become fundamental to high-resolution HLA typing, but methodological differences between long-range PCR (LR PCR) and hybrid capture (HC) approaches can lead to discrepant results. This study examined cases where an allele identified as DPB1*13:01 by LR PCR was assigned DPB1*107:01 by HC. This discordance was attributed to both primer-site mutations and incomplete coverage of non-coding regions in LR PCR, resulting in allele dropout and preferential amplification. Conversely, typing by HC captures full genomic regions and minimizes PCR-generated errors, enabling accurate allele assignment. Findings indicate that DPB1*107:01 may be more prevalent than current CIWD classifications suggest, while broader implications include the need for proficiency testing programs to incorporate method-specific grading criteria that reflect the diversity of available typing technologies. This study underscores the importance of comprehensive genomic coverage for accurate HLA typing and supports the expanded adoption of HC-based sequencing in clinical laboratories.

Introduction: The translation of gene expression profiles of SCLC to clinical testing remains relatively unexplored. In this study, gene expression variations in SCLC were evaluated to identify potential biomarkers. Methods: RNA expression profiling was performed on 44 tumor samples from 35 patients diagnosed with SCLC using the clinically validated RNA Salah Targeted Expression Panel (RNA STEP). RNA sequencing (RNA-Seq) and immunohistochemistry were performed on two different SCLC cohorts, and correlation analyses were performed for the ASCL1, , NEUROD1, , POU2F3, , and YAP1 genes and their corresponding proteins. RNA STEP and RNA-Seq results were evaluated for gene expression profiles and heterogeneity between SCLC primary and metastatic sites. RNA STEP gene expression profiles of independent SCLC samples (n = 35) were compared with lung adenocarcinoma (n = 160) and squamous cell carcinoma results (n = 25). Results: The RNA STEP results were highly correlated with RNA-Seq and immunohistochemistry results. The dominant transcription regulator by RNA STEP was ASCL1 in 74.2% of the samples, NEUROD1 in 20%, and POU2F3 in 2.9%. The ASCL1, , NEUROD1, , and POU2F3 gene expression profiles were heterogeneous between primary and metastatic sites. SCLCs displayed markedly high expression for targetable genes DLL3, , EZH2, , TERT, , and RET. . SCLCs were found to have relatively colder immune profiles than lung adenocarcinomas and squamous cell carcinomas, characterized by lower expression of HLA genes, immune cell, and immune checkpoint genes, except the LAG3 gene. Conclusions: Clinical-grade SCLC RNA expression profiling has value for SCLC subtyping, design of clinical trials, and identification of patients for trials and potential targeted therapy. (c) 2024 The Authors. Published by Elsevier Inc. on behalf of the International Association for the Study of Lung Cancer. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Introduction: Medullary thyroid carcinoma (MTC) is an aggressive cancer that is often caused by driver mutations in RET. Splice site variants (SSV) reflect changes in mRNA processing, which may alter protein function. RET SSVs have been described in thyroid tumors in general but have not been extensively studied in MTC.Methods: The prevalence of RET SSVs was evaluated in 3,624 cases with next generation sequence reports, including 25 MTCs. Fisher exact analysis was performed to compare RET SSV frequency in cancers with/without a diagnosis of MTC.Results: All 25 MTCs had at least one of the two most common RET SSVs versus 0.3% of 3,599 cancers with other diagnoses (p < 0.00001). The 11 cancers with non-MTC diagnoses that had the common RET SSVs were 4 neuroendocrine cancers, 4 non-small cell lung carcinomas, 2 non-MTC thyroid cancers, and 1 melanoma. All 25 MTCs analyzed had at least one of the two most common RET SSVs, including 4 with no identified mutational driver.Discussion: The identification of RET SSVs in all MTCs, but rarely in other cancer types, demonstrates that these RET SSVs distinguish MTCs from other cancer types. Future studies are needed to investigate whether these RET SSVs play a pathogenic role in MTC.

This study describes the validation of a clinical RNA expression panel with evaluation of concordance between gene copy gain by a next-generation sequencing (NGS) assay and high gene expression by an RNA expression panel. The RNA Salah Targeted Expression Panel (RNA STEP) was designed with input from oncologists to include 204 genes with utility for clinical trial prescreening and therapy selection. RNA STEP was validated with the nanoString platform using remnant formalin-fixed, paraffin-embedded-derived RNA from 102 patients previously tested with a validated clinical NGS panel. The repeatability, reproducibility, and concordance of RNA STEP results with NGS results were evaluated. RNA STEP demonstrated high repeatability and reproducibility, with excellent correlation (r > 0.97, P < 0.0001) for all comparisons. Comparison of RNA STEP high gene expression (log2 ratio ≥ 2) versus NGS DNA-based gene copy number gain (copies ≥ 5) for 38 mutually covered genes revealed an accuracy of 93.0% with a positive percentage agreement of 69.4% and negative percentage agreement of 93.8%. Moderate correlation was observed between platforms (r = 0.53, P < 0.0001). Concordance between high gene expression and gene copy number gain varied by specific gene, and some genes had higher accuracy between assays. Clinical implementation of RNA STEP provides gene expression data complementary to NGS and offers a tool for prescreening patients for clinical trials.

Background: Despite an excellent long-term prognosis, up to 20% of patients with papillary thyroid carcinoma (PTC) experience regional or distant recurrence.About one third of these recurrences become radioiodine-refractory (RAIR), which significantly worsens prognosis.BRAF V600E and TERT promoter mutations were recently identified as predictors of RAIR in distant metastases of PTC.Locoregionally recurrent PTCs are not infrequent, however there is a lack of knowledge about molecular events in these lesions due to rarity of sampling.We aimed to study histopathological characteristics, PD-L1 expression, BRAF-TERT signature, and clinical correlates in RAIR cervical recurrences of PTC.Design: A total of 66 cases (mean age -56.1, sex ratio -0.37) were qualified as eligible from a cohort of 1857 patients with PTC who received post-thyroidectomy RAI ablation.Locoregional recurrence was defined as a histopathologically confirmed tumor recurrence within the neck.A case was considered RAIR if the patient had an elevated serum thyroglobulin (>10 ng/ml) under a high thyrotropin level with structural disease in the setting of a negative RAI scan, or disease progressed after receiving more than 600 mCi of RAI.We performed pyrosequencing for BRAF V600E and TERT promoter C228T and C250T mutations.PD-L1 expression was evaluated with Ventana SP263 clone.Results: Histologically, most of the cervical recurrences were of lymph node origin, with frequent extranodal extension.Predominant patterns were papillary, tall cell, and solid.TERT promoter mutations were found in 28/65 (43%) cases, including 23 cases with TERT C228T and 5 cases with TERT C250T.BRAF V600E mutation was found in 52/66 (78.8%) cases.Coexistence of TERT promoter and BRAF mutations was found in 25/66 (37.9%) recurrent PTCs, while 11/66 (16.7%) cases were BRAF-/TERT-.PD-L1 expression (> 1%) was detected in 21/36 cases available for evaluation, including 10/36 cases with immunoexpression in over 25% cancer cells.On a mean follow-up of 63.5 months, 18/66 (27.3%) patients died of disease.TERT promoter mutation and BRAF/TERT combination were found less frequently in surviving patients; however only BRAF/TERT co-mutation was statistically associated with a death by disease (p = 0.04).Conclusions: This is the first study describing the high prevalence of BRAF and TERT promoter mutations in a carefully selected cohort of RAIR locoregional recurrences of PTC.RAIR tumors showed frequent PD-L1 expression, which may have clinical implications.

We investigated the impact of donor-recipient HLA-DPB1 matching on outcomes of allogeneic hematopoietic stem cell transplantation with in vivo T-cell depletion using antithymocyte globulin (ATG) for patients with hematological malignancies. All donor-recipient pairs had high-resolution typing for HLA-A, HLA-B, HLA-C, HLA-DRB1, HLA-DQB1, HLA-DPB1, and HLA-DRB3/4/5 and were matched at HLA-A, HLA-B, HLA-C, and HLA-DRB1. HLA-DPB1 mismatches were categorized by immunogenicity of the DPB1 matching using the DPB T-cell epitope tool. Of 1004 donor-recipient pairs, 210 (21%) were DPB1 matched, 443 (44%) had permissive mismatches, 184 (18%) had nonpermissive mismatches, in graft-versus-host (GVH) direction, and 167 (17%) had nonpermissive mismatches in host-versus-graft (HVG) direction. Compared with HLA-DPB1 permissive mismatched pairs, nonpermissive GVH mismatched pairs had the highest risk for grade II to IV acute graft-versus-host disease (aGVHD) (hazard ratio [HR], 1.4; P = .01) whereas matched pairs had the lowest risk (HR, 0.5; P < .001). Grade III to IV aGVHD was only increased with HLA-DPB1 nonpermissive GVH mismatched pairs (HR, 2.3; P = .005). The risk for disease progression was lower with any HLA-DPB1 mismatches, permissive or nonpermissive. However, the favorable prognosis of HLA-DPB1 mismatches on disease progression was observed only in peripheral blood stem cell recipients who were in the intermediate-risk group by the Disease Risk Index (HR, 0.4; P = .001) but no other risk groups. Our results suggest avoidance of nonpermissive GVH HLA-DPB1 mismatches for lowering the risk for grade II to IV and III to IV aGVHD. Permissive or nonpermissive HVG HLA-DPB1 mismatches may be preferred over HLA-DPB1 matches in the intermediate-risk patients to decrease the risk for disease progression.

In order to study the gene structure of HLA haplotypes, these haplotypes must be determined in a large sample from a given population. Diploid combinations of alleles from different loci must be placed in phase with each other. In this study, this accomplished by family segregation analysis. Samples come from a Chinese population in Taiwan collected for disease association. 490 mother-father-child trios were used in this study. HLA typing is based on Mia Fora next-generation sequencing by Immucor. The following loci are included: A, B, C, DRB1, DRB345, DQA1, DQB1, DPA1 and DPB1. A simple propositional-logic algorithm based on basic Mendelian genetics principles was used to do family segregation analysis and determine the haplotypes in each family. Full 2 × 2 contingency tables, for each pair of variables, with their corresponding chi-square, p value and other measures of correlation are presented for the following pairs of HLA loci or groups of loci: 1) B vs C, 2) DRB1 vs DRB345, 3) DQA1 vs DQB1, 4) DPA1 vs DPB1, 5) A vs B-C, 6) DRB1-DRB345 vs DQA1-DQB1, 7) DPA1-DPB1 vs DRB1-DRB345-DQA1-DQB1, and 8) A vs DRB1-DRB345-DQA1-DQB1. Results may be biased by the disease association in this project. (The disease in question cannot be disclosed at this time.) Nevertheless, the bias effect will hardly diminish the value of the linkage disequilibrium data presented here. It is possible that haplotype frequencies are over-represented or under-represented, but the identity of the haplotype itself will not be affected by this bias. One of the main finding in this study is that, contrary to what is generally believed, NGS does not disclose a hidden abundance of HLA alleles, it rather helps us characterise in full detail alleles we were already familiar with. This study shows the distribution of HLA alleles in a given population, but their inter-loci structure, their association with alleles in other loci. The second main finding in this study is that in those cases where more than one genetic variant is represented by a single previously known protein allele, the linkage disequilibrium measures increase significantly when this genetic variants are taken into account individually.

This is a study of the haplotype frequency and distribution in an European population. In order to study the gene structure of HLA haplotypes, these haplotypes must be determined in a large sample from a given population. Diploid combinations of alleles from different loci must be placed in phase with each other. [Two other parallel studies are performed in two other ethnic populations.]. Approximately 6000 samples come from an European collected for disease association. HLA typing is based on Mia Fora next-generation sequencing by Immucor. The following loci are included: A, B, C, DRB1, DRB345, DQA1, DQB1, DPA1 and DPB1. A modification of the EM algorithm was used to determine the haplotypes in the population. Linkage disequilibrium is performed by means of 2 × 2 contingency tables. Full 2 × 2 contingency tables, for each pair of variables, with their corresponding chi-square, p value and other measures of correlation are presented for the following pairs of HLA loci or groups of loci: 1) B vs. C, 2) DRB1 vs. DRB345, 3) DQA1 vs. DQB1, 4) DPA1 vs. DPB1, 5) A vs. B-C, 6) DRB1-DRB345 vs. DQA1-DQB1, 7) DPA1-DPB1 vs. DRB1-DRB345-DQA1-DQB1, and 8) A vs. DRB1-DRB345-DQA1-DQB1. Results may be biased by the disease association in this project. (The disease in question cannot be disclosed at this time.) Nevertheless, the identity of the haplotype itself will not be affected by this bias. One of the main finding in this study is that, contrary to what is generally believed, NGS does not disclose a hidden abundance of HLA alleles, it rather helps us characterise in full detail alleles we were already familiar with. This study shows the association of HLA alleles with alleles in other loci. The second main finding in this study is that in those cases where more than one genetic variant is represented by a single previously known protein allele, the linkage disequilibrium measures increase significantly when this genetic variants are taken into account individually.

This is a study of the genetic polymorphism of the different DNA segments of the HLA genes and how this genetic polymorphism affects genetic diversity. Information Theory was used in the study of the intra-locus gene structure of HLA-A just as it had previously been used in the study of inter-loci genetic structures (see Tissue Antigens 2012 80:341). Information measures were used to evaluate the degree of genetic polymorphism of different gene segments as well as the relationships among them. The following genes are included in this study: A, B, C, DRB1, DRB345, DQB1, DQA1, DPB1 and DPA1. Entropy tables are given for all the gene segments from the 5′UTR to the 3′UTR for genes HLA-A, B, C, DQB1 and DPA1. For DRB1 and DRB345 only exon 1, not intron, and exon 2 through the 3′UTR are included. For DQA1 segments from 5′UTR through exon 4 are included. And for DPB1 exon 2 through exon 4 are included. 2 × 2 contingency tables showing the association between different gene segments with statistics to measure such association such as the chi-square a p-value, are also provided. In addition ‘well-conserved regions’ with high internal entropy are also identified and presented. The proper characterisation of gene structure is the foundation to study the function of proteins encoded by the gene or genes in question. The use of Information Theory has proven to be a sound and robust method to study and measure genetic polymorphism and genetic diversity. Here we show how these tools can be used to evaluate HLA function, particularly in regard to epitope definition and peptide binding.

This is a study of the haplotype frequency and distribution in a population of African origin. In order to study the gene structure of HLA haplotypes, these haplotypes must be determined in a large sample from a given population. Diploid combinations of alleles from different loci must be placed in phase with each other. [Two other parallel studies are performed in two other ethnic populations.] Approximately 3000 samples come from a population of African origin collected for disease association. HLA typing is based on Mia Fora next-generation sequencing by Immucor. The following loci are included: A, B, C, DRB1, DRB345, DQA1, DQB1, DPA1 and DPB1. A modification of the EM algorithm was used to determine the haplotypes in the population.Linkage disequilibrium is performed by means of 2x2 contingency tables for each pair of alleles or allele groups. Full 2x2 contingency tables, for each pair of variables, with their corresponding chi-square, p value and other measures of correlation are presented for the following pairs of HLA loci or groups of loci: 1) B vs C, 2) DRB1 vs DRB345, 3) DQA1 vs DQB1, 4) DPA1 vs DPB1, 5) A vs B-C, 6) DRB1-DRB345 vs DQA1-DQB1, 7) DPA1-DPB1 vs DRB1-DRB345-DQA1-DQB1, and 8) A vs DRB1-DRB345-DQA1-DQB1. Results may be biased by the disease association in this project. (The disease in question cannot be disclosed at this time.) Nevertheless, the identity of the haplotype itself will not be affected by this bias. One of the main finding in this study is that, contrary to what is generally believed, NGS does not disclose a hidden abundance of HLA alleles, it rather helps us characterise in full detail alleles we were already familiar with. This study shows the association of HLA alleles with alleles in other loci. The second main finding in this study is that in those cases where more than one genetic variant is represented by a single previously known protein allele, the linkage disequilibrium measures increase significantly when this genetic variants are taken into account individually.

Recombinant meiotic event(s) can occur within human families. Chromosomal crossover is the exchange of genetic material between homologous chromosomes that results in recombinant chromosomes. Even though crossover at HLA region is a rare event, the resulting haplotype would introduce linkage disequilibrium at HLA loci which could decrease the chance for hematopoietic stem cell transplant (HSCT) patient to find a suitable matched donor. The aim of the study was to investigate the frequency of recombination and the crossover location within the HLA class I and II regions. HSCT patients and related family members as potential donors with clearly defined haplotypes within family were included in the study. All the patients and majority of family members were HLA typed at class I-A, B, C and class II-DR, DQ, DP using molecular methods (SSO and/or SBT) with some related donors serologically typed. Crossover haplotypes in patients (N = 5219) or related donors (N = 15,224) were analyzed and the crossover location between HLA loci was identified for each case. Crossover between HLA loci was identified in 202 individuals. The frequency of recombination was found to be 0.73% (38/5219) in HSCT patients and 1.08% (164/15224) in related potential donors. The overall crossover rate was 0.99% in the study population. There were two potential donors with recombination on both haplotypes. Five individuals in four families inherited the recombinant haplotype from a parent. Seven families had two members with recombination haplotype. The rate of recombination was 50.5% between HLA-B x DR, 36.63% between HLA-A x C, 9.41% between HLA-DQ x DP, and 3.47% between HLA-C x B loci. Crossover events were observed between nearly all the neighboring HLA loci (A-C, C-B, B-DR, DQ-DP) except for between HLA-DR and DQ. We didn't observe any recombination event between these loci in the study. The recombination rate is overall about 1% in the study population. The crossover hot spots appear to be between HLA-B and DR, HLA-A and HLA-C. It's interesting to note that crossover event was lower in patients with different leukemia malignancies than in healthy family members. The finding suggests that leukemia patients were not disadvantaged from recombination in HLA regions.