scholarly journals Next generation sequencing HLA-typing of recipients and donors of allogeneic haematopoietic stem cells

2021 ◽  
Vol 66 (2) ◽  
pp. 206-217
Author(s):  
E. G. Khamaganova ◽  
A. R. Abdrakhimova ◽  
E. A. Leonov ◽  
S. P. Khizhinskiy ◽  
T. V. Gaponova ◽  
...  

Introduction. The patient survival after allogeneic haematopoietic stem cell transplantation (allo-HSCT) from an unrelated or related haploidentical donor is improved in a donor–recipient match resolution at the level of non-coding region identity of HLA genes. Next-generation sequencing (NGS) allows detection of point substitutions in HLA non-coding regions.Aim — assessment of the NGS-based HLA-typing performance.Materials and methods. An NGS-based HLA-typing of 1,056 DNA samples from allo-HSCT recipients, their related and registry donors was performed with AllTypekit chemistry (OneLambda, USA). A parallel HLA-typing assay of 96 samples by 8 genes (A/B/C/DRB1/DRB3/DRB4/DRB5/DQB1) was accomplished within one working week.Results. HLA class I genes were typed at a 4-field (allelic), and HLA class II genes — 2–4-field (high to allelic) resolution. An allelic-resolution typing of HLA class I genes in a Russian population (657 registry donors) was conducted for the first time. The most frequent HLA alleles have been identified: А*02:01:01:01 in HLA-A (26.9 %), B*07:02:01:01 in HLA-B (12.5 %) and C*07:02:01:03 in HLA-C (12.6 %). The most frequent HLA class II variants were DRB1*07:01:01 (14.1 %), DRB3*02:01:01 (18.0 %), DRB4*01:03:01 (18.9  %), DRB5*01:01:01 (13.5  %), DQB1*03:01P (17.4  %).Conclusion. An NGS-geared HLA-typing has yielded low-ambiguity allelic and high-level resolution results in a parallel sequencing assay with a large number of samples. The method implemented detects genetic polymorphisms also in non-exonic non-coding regions of HLA genes and facilitates typing in candidate HSCT recipients, related and unrelated donors.

HLA ◽  
2019 ◽  
Vol 94 (6) ◽  
pp. 504-513 ◽  
Author(s):  
Angelina Sverchkova ◽  
Irantzu Anzar ◽  
Richard Stratford ◽  
Trevor Clancy

BMC Genomics ◽  
2011 ◽  
Vol 12 (1) ◽  
Author(s):  
Rachel L Erlich ◽  
Xiaoming Jia ◽  
Scott Anderson ◽  
Eric Banks ◽  
Xiaojiang Gao ◽  
...  

2017 ◽  
Vol 78 ◽  
pp. 195
Author(s):  
Jennifer Gerfen ◽  
Ting F. Tang ◽  
Lihua Hou ◽  
Elizabeth Enriquez ◽  
Ana Lazaro-Shiben ◽  
...  

2016 ◽  
Vol 77 ◽  
pp. 90
Author(s):  
Nathaniel T. Smith ◽  
Vinh Ngo ◽  
Yudith Carmazzi ◽  
Sujatha Krishnakumar ◽  
Ming Li ◽  
...  

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A61-A61
Author(s):  
Lee McDaniel ◽  
Rachel Pyke ◽  
Charles Abbott ◽  
Gabor Bartha ◽  
John West ◽  
...  

BackgroundPrecision immuno-oncology is increasingly relevant to cancer therapy given the ascendance of immunotherapy. While next-generation sequencing (NGS) based algorithms may elucidate immunotherapeutic response, many such algorithms require highly accurate Class I HLA typing. One major challenge of HLA type derivation resides in highly polymorphic HLA allelic diversity, which conventional exome sequencing technologies poorly capture. Further, accurate HLA typing requires definitive distinction between thousands of potential HLA alleles. These challenges may cause widely used NGS HLA typing tools, such as Polysolver and Optitype, to perform inaccurate HLA typing. Poor HLA coverage poses the risk of silently mistyping HLA alleles, yielding inaccurate downstream HLA loss of heterozygosity (LOH) detection and neoepitope predictions.MethodsWe designed the ImmunoID NeXT Platform® to more comprehensively profile the HLA region. To evaluate the accuracy of conventional NGS-based Class I HLA typing, a widely used dbGaP project (phs000452, n=160) of melanoma NGS data was evaluated alongside a set of over 500 solid tumor cancer patient samples sequenced on the ImmunoID NeXT Platform. Read coverage was derived from both GRCh38 and HLA allele database alignments. To test whether Polysolver over represents specific HLA alleles under reduced read conditions, a Monte Carlo bootstrap approach predicted theoretical allele frequency ranges.ResultsBelow 20x read coverage, nearly 50% of Polysolver HLA calls (phs000452) are homozygous, representing a divergence from typical HLA homozygous rates of between 10–20%, with p<10-15 (Fisher’s Exact) compared to reference 1000 Genomes homozygous rates. Polysolver’s homozygous, heterozygous, and no-calls demonstrated a statistically significant difference in coverage (p<10-6, Kruskal-Wallis) across all Class I HLA genes per Polysolver and public exome data (phs000452). The Personalis ImmunoID NeXT™ cohort did not demonstrate such a trend despite a similar exome-wide sequencing depth. Further, sixteen rare HLA alleles were identified with sample frequencies greater than expected from the dbGaP data set, with no such alleles identified from the Personalis ImmunoID NeXT data set.ConclusionsHLA typing may silently fail in the context of reduced read coverage without HLA-specific platform augmentation. This silent failure can have large implications for accurate neoantigen prediction and HLA LOH detection, both of which are becoming increasingly important for immuno-oncology treatment modalities such as personalized cancer vaccines, adoptive cell therapies, and blockade therapy response biomarkers. Studies utilizing neoepitope and HLA LOH prediction require careful validation for HLA calls, including assessments of coverage and homozygous rates, and may benefit from increased HLA locus coverage.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 42-43
Author(s):  
Andrew E. O. Hughes ◽  
Maureen C. Montgomery ◽  
Chang Liu ◽  
Eric T. Weimer

Human leukocyte antigen (HLA) typing plays a critical role in evaluating donor-recipient compatibility prior to hematopoietic cell transplantation (HCT) to minimize the risk of rejection and graft versus host disease (GVHD). Compared to traditional sequence-based methods for HLA typing, next-generation sequencing offers significant advantages in terms of accuracy, turnaround time, and cost (Weimer et al., JMD, 2016). Nevertheless, an intrinsic limitation of DNA-based typing is that it does not quantify HLA gene expression, which has been implicated in clinical outcomes (Petersdorf et al., Blood, 2014; Petersdorf et al., NEJM, 2015). Previously, we demonstrated simultaneous HLA class I genotyping and gene-level expression analysis by RNA-seq using nanopore long-read sequencing (Montgomery et al., JMD, 2020). Given that mismatches in both class I and class II HLA genes-as well as the relative expression of individual alleles-impact donor-recipient compatibility, we sought to build on our previous work by quantifying allele-specific expression of both class I and class II HLA loci in donor lymphocytes. For this study, mRNA was isolated from peripheral blood lymphocytes from 12 donors. Barcoded cDNA libraries were prepared and sequenced on MinION flow cells (R9.4.1) using MinKNOW (v3.1.13) to a median depth of 1.6x106reads. Basecalling and demultiplexing were performed with Albacore (v2.3.4) or Guppy (v2.3.1), and adapter trimming was performed with Porechop (v0.2.3). Processed reads were aligned to the international ImMunoGeneTics project (IMGT) HLA database (v3.41.0) using minimap2 (v2.17). Reads mapping to individual HLA loci were realigned to allele-specific references using subject HLA types determined by Athlon (v1.0) or Illumina sequencing. In parallel, library size factors were estimated by aligning reads to GRCh38, counting reads in genes with HTseq (v0.12.4), and using trimmed mean of M-values normalization. As shown in Fig. 1, we observed higher expression of HLA class I genes compared to class II (median 593 vs. 150, p &lt; 0.001, Mann-Whitney U test), a pattern consistent with a mixture of primarily T cells, which express class I genes, as well as B cells, which express both class I and II. Within class I genes, we observed the highest expression of HLA-B, followed by HLA-A, and HLA-C (median 663, 578, and 459, respectively). Within class II, we observed the highest expression of HLA-DPB1, followed by HLA-DRB1, and HLA-DQB1 (median 281, 266, and 104, respectively). Importantly, we observed significant variation in expression both between and within alleles of individual HLA genes, suggesting that HLA type alone does not accurately predict HLA expression. We next analyzed HLA-DPB1 specifically, given reports that the risk of GVHD in HCT recipients with HLA-DPB1mismatched donors is modulated by HLA-DPB1 expression (Petersdorf et al., NEJM, 2015). Of note, HLA-DPB1 expression is linked to a single nucleotide polymorphism, rs9277534, which can be imputed from HLA-DPB1 type (Meurer et al., Front Immunol, 2018). Accordingly, we analyzed HLA-DPB1 expression conditioned on rs9277534 genotype. Although we observed lower HLA-DPB1 expression for the 'A' allele compared to 'G' (median 220 vs. 265), consistent with the reported association, this difference was not statistically significant (p = 0.22, Mann-Whitney U test). Furthermore, we observed significant variation in expression among 'A' alleles, with normalized counts ranging from 57 to 408 (vs. 191 to 367 for 'G' alleles). In this study, we demonstrate the feasibility of quantifying allele-specific expression of both class I and class II HLA genes with nanopore long-read sequencing. Taken together, our results reveal extensive variation in the expression of class I and class II HLA loci, even after accounting for individual allele types and known markers of expression. These results emphasize the potential value of methods, such as nanopore sequencing, for directly quantifying allele-specific HLA expression to develop improved risk prediction models that can inform the evaluation of donor-recipient immunocompatibility. Disclosures No relevant conflicts of interest to declare.


2018 ◽  
Vol 79 (11) ◽  
pp. 773-780 ◽  
Author(s):  
Aviva Geretz ◽  
Philip K. Ehrenberg ◽  
Alain Bouckenooghe ◽  
Marcelo A. Fernández Viña ◽  
Nelson L. Michael ◽  
...  

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