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      Leukemia relapse via genetic immune escape after allogeneic hematopoietic cell transplantation

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          Abstract

          Graft-versus-leukemia (GvL) reactions are responsible for the effectiveness of allogeneic hematopoietic cell transplantation as a treatment modality for myeloid neoplasia, whereby donor T- effector cells recognize leukemia neoantigens. However, a substantial fraction of patients experiences relapses because of the failure of the immunological responses to control leukemic outgrowth. Here, through a broad immunogenetic study, we demonstrate that germline and somatic reduction of human leucocyte antigen (HLA) heterogeneity enhances the risk of leukemic recurrence. We show that preexistent germline-encoded low evolutionary divergence of class II HLA genotypes constitutes an independent factor associated with disease relapse and that acquisition of clonal somatic defects in HLA alleles may lead to escape from GvL control. Both class I and II HLA genes are targeted by somatic mutations as clonal selection factors potentially impairing cellular immune responses and response to immunomodulatory strategies. These findings define key molecular modes of post-transplant leukemia escape contributing to relapse.

          Abstract

          Graft-versus-leukemia reactions are required for the eradication of myeloid malignancies after allogeneic hematopoietic cell transplantation. However, treatment efficacy is variable, depending on the immunological response. Here the authors show that dysfunction of HLA heterogeneity is associated with post-transplant leukemia relapse.

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            Fast and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Contributors
                maciejj@ccf.org
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                31 May 2023
                31 May 2023
                2023
                : 14
                : 3153
                Affiliations
                [1 ]GRID grid.239578.2, ISNI 0000 0001 0675 4725, Department of Translational Hematology and Oncology Research, , Taussig Cancer Institute, Cleveland Clinic, ; Cleveland, OH USA
                [2 ]GRID grid.410527.5, ISNI 0000 0004 1765 1301, Department of Hematology, , CHRU de Nancy, ; Vandœuvre-lès-Nancy, France
                [3 ]GRID grid.463896.6, ISNI 0000 0004 1758 9034, CNRS UMR 7365, IMoPA, Biopole of University of Lorraine, ; Vandœuvre-lès-Nancy, France
                [4 ]GRID grid.6530.0, ISNI 0000 0001 2300 0941, Department of Biomedicine and Prevention, PhD in Immunology, Molecular Medicine and Applied Biotechnology, , University of Rome Tor Vergata, ; Rome, Italy
                [5 ]Novocraft Technologies Sdn Bhd, Kuala Lumpur, Malaysia
                [6 ]GRID grid.29172.3f, ISNI 0000 0001 2194 6418, Inserm UMR-S 1256 Nutrition-Genetics-Environmental Risk Exposure, , University of Lorraine, ; 54500 Vandœuvre-lès-Nancy, France
                [7 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Division of Hematologic Malignancies and Cellular Therapy, Department of Medicine, , Duke University School of Medicine, ; Durham, NC USA
                [8 ]GRID grid.410527.5, ISNI 0000 0004 1765 1301, Histocompatibility Department, , CHRU de Nancy, ; Vandœuvre-lès-Nancy, France
                [9 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Division of Oncology, Department of Medicine, , Washington University School of Medicine in St. Louis, ; St. Louis, MO USA
                [10 ]GRID grid.239578.2, ISNI 0000 0001 0675 4725, Leukemia Program, Hematology Department, , Taussig Cancer Institute, Cleveland Clinic, ; Cleveland, OH USA
                [11 ]GRID grid.239578.2, ISNI 0000 0001 0675 4725, Blood and Marrow Transplant Program, , Taussig Cancer Institute, Cleveland Clinic, ; Cleveland, OH USA
                [12 ]GRID grid.419513.b, ISNI 0000 0004 0459 5478, Sarah Cannon Transplant and Cellular Therapy Network, ; Nashville, TN USA
                Author information
                http://orcid.org/0000-0003-4688-2478
                http://orcid.org/0000-0001-6829-5544
                http://orcid.org/0000-0001-8456-7992
                http://orcid.org/0000-0003-0098-9819
                http://orcid.org/0000-0001-5241-3614
                http://orcid.org/0000-0003-1252-6539
                http://orcid.org/0000-0002-2993-1509
                http://orcid.org/0000-0002-6837-4346
                Article
                38113
                10.1038/s41467-023-38113-4
                10232425
                37258544
                9b6151a4-b5b2-4070-bcbe-8e0469b3dcbc
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 9 October 2022
                : 13 April 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100004097, Fondation ARC pour la Recherche sur le Cancer (ARC Foundation for Cancer Research);
                Funded by: FundRef https://doi.org/10.13039/100008884, Edward P. Evans Foundation;
                Funded by: FundRef https://doi.org/10.13039/100005410, American-Italian Cancer Foundation (AICF);
                Funded by: FundRef https://doi.org/10.13039/100005189, Leukemia and Lymphoma Society (Leukemia & Lymphoma Society);
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                © Springer Nature Limited 2023

                Uncategorized
                cancer genetics,genetics research,leukaemia
                Uncategorized
                cancer genetics, genetics research, leukaemia

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