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Establishment of a Typing Model for Diffuse Large B-Cell Lymphoma Based on B-Cell Receptor Repertoire Sequencing

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Jiang et al. BMC Cancer (2021) 21:297 https://doi.org/10.1186/s12885-021-08015-z RESEARCH ARTICLE Open Access Establishment of a typing model for diffuse large B-cell lymphoma based on B-cell receptor repertoire sequencing Wenhua Jiang1†, Hailong Wang2†, Shiyong Zhou3†, Guoqing Zhu4, Mingyou Gao5, Kuo Zhao5, Limeng Zhang5, Xiaojing Xie5, Ning Zhao5, Caijuan Tian6, Zhenzhen Zhang6, Fang Yan6, Yi Pan7* and Pengfei Liu2* Abstract Background: The purpose of this study was to construct a new typing model for diffuse large B-cell lymphoma (DLBCL) patients based on the B-cell receptor (BCR) and explore its potential molecular mechanism. Methods: BCR repertoire sequencing and whole-exome sequencing were performed on formalin-fixed paraffin- embedded samples from 12 DLBCL patients. Subsequently, a typing model was built with cluster analysis, and prognostic indicators between the two groups were compared to verify the typing model. Then, mutation and bioinformatics analyses were conducted to investigate the potential biomarkers of prognostic differences between the two groups. Results: Based on BCR sequencing data, we divided patients into two clusters (cluster 1 and cluster 2); this classification differed from the traditional typing method (GCB and non-GCB), in which cluster 1 included some non-GCB patients. The progression-free survival (PFS), overall survival (OS), metastasis and Shannon diversity index of IGH V-J and survival after chemotherapy were significantly different (P < 0.05) between the two clusters, but no statistical significance was found between the GCB and non-GCB groups. The mutation status of 248 genes was significantly different between cluster 1 and cluster 2. Among them, FTSJ3, MAGED2, and ODF3L2 were the specific mutated genes in all patients in cluster 2, and these genes could be considered critical to the different prognoses of the two clusters of DLBCL patients. Conclusion: We constructed a new typing model of DLBCL based on BCR repertoire sequencing that can better predict the survival time after chemotherapy. FTSJ3, MAGED2, and ODF3L2 may represent key genes for the difference in prognosis between the two clusters. Keywords: Diffuse large B-cell lymphoma, B-cell receptor repertoire, Typing, Prognosis * Correspondence: [email protected]; [email protected] †Wenhua Jiang, Hailong Wang and Shiyong Zhou contributed equally to this work. 7Department of Pathology, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin Medical University Cancer Institute and Hospital, Huanhu West Road, Tiyuanbei, Hexi District, Tianjin 300060, China 2Department of Oncology, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital, No.354 Beima Road, Hongqiao District, Tianjin 300120, China Full list of author information is available at the end of the article © The Author(s). 2021 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Jiang et al. BMC Cancer (2021) 21:297 Page 2 of 10 Background BCR diversity comprises one of the core compo- Diffuse large B-cell lymphoma (DLBCL) is a moderately nents of the complicated immune system and serves aggressive lymphoma originating from B cells and is the as a pivotal defensive component to protect the body most common type of non-Hodgkin lymphoma (NHL). against invading viruses, bacteria, etc. [7]. The diver- DLBCL is a heterogeneous disease due to its clinical sity of the BCR repertoire may vary greatly among manifestations and morphological and genetic character- different health conditions. Although this type of istics [1]. DLBCL accounts for approximately 30% of antigenic specificity ensures protection against infec- adult NHL in developed countries, while the incidence tious diseases, the imbalance between exogenous and rate in developing China is estimated to be higher [2, 3]. autoantigen discrimination may lead to immunodefi- Typically, patients with DLBCL are divided into two ciency or even malignant tumours [8]. Under differ- subtypes according to gene expression profiling (GEP) ent phenotypic statuses, BCRs may change technology, namely germinal centre B cell (GCB) type dynamically. Even under the same phenotype, the and activated B cell (ABC) type. Given that mutations in BCR repertoire of different patients exhibit great dif- Toll-like receptor (TLR) and B-cell receptor (BCR) sig- ferences. BCR sequencing provides an opportunity to nalling molecules play a key role in the pathogenesis and monitor the evolution of the B-cell response by de- progression of DLBCL, various downstream targets are scribing the diversity of BCR gene sequences. Re- being developed to obtain clinical therapeutic benefits searchers have demonstrated the utility of BCR [4]. sequencing in adaptive immune responses [9–11]. The immunohistochemistry classification method is With the development of next-generation sequencing the gold standard for DLBCL typing. CD10, Bcl-6, (NGS) technology, BCR sequencing has the potential MUM1, GCET1, and FOXP1 are used as markers to div- to reliably quantify all aspects of the adaptive im- ide DLBCL into 2 subtypes: GCB and non-GCB. How- mune response. BCR repertoire sequencing in ever, a number of studies have found that DLBCL DLBCL patients may help us to understand the de- histological morphology, immunophenotype, genetic velopmental mechanism of different DLBCLs and characteristics, and clinical incidence vary greatly among predict the prognosis of patients. However, most different cases. The heterogeneity of DLBCL and the studies based on BCR sequencing have focused on limitations of the international prognostic index (IPI) in theuseofthehumanimmunoglobulinheavychain assessing prognosis indicate that DLBCL may have dif- (IGH) V-(D)-J gene as a measure of BCR diversity ferent pathological subtypes. Therefore, accurate classifi- and clonal evolution to describe B-cell responses in cation of DLBCL and clarification of its characteristics health and disease [12]. are particularly important in the exploration of new Here, we constructed a typing model in DLBCL pa- therapeutic schemes. tients based on BCR repertoire sequencing to contribute B cells are key participants in the adaptive immune to accurate prognosis prediction to achieve precision system. These cells mediate fluid immunity through medicine in DLBCL. the production of antibodies, thus protecting the body from infection. Each B cell expresses a unique Methods membrane-binding antibody or B-cell receptor (BCR). Patients and samples Other parts of the immune system, such as antigen- A total of 12 patients with DLBCL in our hospital who presenting and follicular helper T (Tfh) cells, also had not received immunotherapy were enrolled in this need to function effectively and interact with B cells study. Formalin-fixed paraffin-embedded (FFPE) tumour toproduceavarietyoffunctionalBCRcomponents samples from all patients were collected. The clinical stimulated by antigens [5]. The various BCRs pro- characteristics of these patients are listed in Table 1. duced in individuals must be sufficient for the im- mune system to recognize a large number of different DNA isolation, CDR3 amplification and sequencing library antigens. This large variation is produced by DNA re- construction combination of different BCR variable (V), diversity Genomic DNA was extracted from FFPE samples using (D) and joining (J) genes (called VDJ recombination) an AllPrep FFPE DNA Extraction Kit (Qiagen, Hildon, [5]. These genes are highly polymorphic and consti- Germany) according to the manufacturer’s instructions. tute the basis of BCR allele diversity in individuals. BCR complementarity determining region 3 (CDR3) was During B–cell mutation, V, D and J (or V and J in amplified using a Multiplex PCR kit (Qiagen, Hilden, the light chain) gene fragments are recombined to Germany), and a 100 to 200-bp fragment was selected form functional BCR proteins. The diversity of V/(D)/ and purified using a QIAquick gel purification kit (Qia- J alleles and V-(D)-J combinations yield a highly di- gen, Hilden, Germany). The DNA was then subjected to versified BCR repertoire [6]. end repair. The A tail was added, ligated, adapte, Jiang et al. BMC Cancer (2021) 21:297 Page 3 of 10 Table 1 Clinical characteristics of patients conducted with the pheatmap package (version 1.0.12) Parameters Group n (%) Parameters Group n (%) using hierarchical clustering method. The Shannon Sex male 8 (66.67) KPS ≤ 80 3 (25.00) index was calculated using the following formula: – female 4 (33.33) 80 90 8 (66.67) Xs ≤ ¼ ‐ ðÞp p Age 60 6 (50.00) > 90 1 (8.33) H i log2 i ¼ > 60 6 (50.00) Recurrence no 12 (100) i 1 Stage I 1 (8.33) yes 0 (0) where s is the count of V/J sequences, and pi is the ratio II 5 (41.67) Metastasis no 8 (66.67) of the i-th operational taxonomic unit. III 0 (0) yes 4 (33.33) IV 6 (50.00) OS ≤ 24 months 8 (66.67) Whole-exome sequencing DNA from FFPE samples was extracted using the Infection HAV 12 (100) > 24 months 4 (33.33) AllPrep-FFPE-DNA Extraction Kit (Qiagen). Library ≤ HBV 9 (75.00) PFS 6 months 2 (16.67) preparation for each sample was performed according to HCV 0 (0) > 6 months 2 (16.67) the instructions provided by the manufacturer.
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  • A Temporally Controlled Sequence of X-Chromosome Inactivation and Reactivation Defines Female Mouse in Vitro Germ Cells with Meiotic Potential

    A Temporally Controlled Sequence of X-Chromosome Inactivation and Reactivation Defines Female Mouse in Vitro Germ Cells with Meiotic Potential

    bioRxiv preprint doi: https://doi.org/10.1101/2021.08.11.455976; this version posted August 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. A temporally controlled sequence of X-chromosome inactivation and reactivation defines female mouse in vitro germ cells with meiotic potential Jacqueline Severino1†, Moritz Bauer1,9†, Tom Mattimoe1, Niccolò Arecco1, Luca Cozzuto1, Patricia Lorden2, Norio Hamada3, Yoshiaki Nosaka4,5,6, So Nagaoka4,5,6, Holger Heyn2, Katsuhiko Hayashi7, Mitinori Saitou4,5,6 and Bernhard Payer1,8* Abstract The early mammalian germ cell lineage is characterized by extensive epigenetic reprogramming, which is required for the maturation into functional eggs and sperm. In particular, the epigenome needs to be reset before parental marks can be established and then transmitted to the next generation. In the female germ line, reactivation of the inactive X- chromosome is one of the most prominent epigenetic reprogramming events, and despite its scale involving an entire chromosome affecting hundreds of genes, very little is known about its kinetics and biological function. Here we investigate X-chromosome inactivation and reactivation dynamics by employing a tailor-made in vitro system to visualize the X-status during differentiation of primordial germ cell-like cells (PGCLCs) from female mouse embryonic stem cells (ESCs). We find that the degree of X-inactivation in PGCLCs is moderate when compared to somatic cells and characterized by a large number of genes escaping full inactivation.