臺灣博碩士論文加值系統

免疫組庫被定義為在人體後天免疫系統中T細胞受體和B細胞受體的總和。免疫庫的多樣性首先源於T細胞受體（TCR）和B細胞受體（BCR）的V(D)J重排、隨機插入、缺失和替換。在受到病原體感染、自身免疫性疾病和癌症等疾病的抗原刺激後，免疫組庫的組成也會被改變。因此，研究免疫組庫可以讓我們更深入地了解疾病的進展或治療。免疫組庫定序平台的起始材料包括DNA和RNA，因此又分為以DNA為原料和以RNA為原料的方法。然而，目前的知識差距是我們需要一種工具來構建免疫組庫，並且對A型免疫球蛋白腎病變和胰臟癌中疾病和免疫的關聯仍然缺乏了解。
在我們的研究中，我們有三個目標。首先，我們旨在建立一個以DNA為原料的T細胞受體定序(TCR-seq)平台和一個以RNA為原料的B細胞受體定序(BCR-seq)平台，並將這些方法分別應用於A型免疫球蛋白腎病和胰臟癌的臨床研究。其次，我們旨在分析 A型免疫球蛋白腎病中的B細胞受體組庫。第三，我們旨在探討胰臟癌中的T細胞受體組庫。
結果顯示，以RNA為原料的B細胞受體定序平台具有組裝率大於60%、通用性和再現性的特色。結果顯示，以DNA為原料的T細胞受體定序平台具有7.93%至74.88%的組裝率且與以RNA為原料的方法相比有0.109至0.575的相似性。在對IgAN和非IgAN患者的B細胞受體組庫進行分析後，我們有幾個重要的發現，例如IgAN患者中IgA、IgD、IgM、IgL和IgK的多樣性較高、IgAN患者中IgK/IgL的比率較低、我們發現IgAN和非IgAN 患者之間的幾個基因使用頻率不同，包括IGLV2-18、IGLV5-48、IGLV8-61、IGLJ1、IGLJ5和IGKV1-13。在對T細胞受體組庫進行探討後，我們有幾個顯著的發現，例如周邊血的多樣性高於腫瘤和正常組織、早期患者的腫瘤組織多樣性高於晚期患者、腫瘤組織多樣性高的患者整體存活比低的患者更好以及我們使用公共克隆型參數的中位數可以顯著區分整體存活率。
總結來說，我們成功開發了兩個免疫組庫定序平台，並將其應用於臨床研究，發現了一些可能有利於A型免疫球蛋白腎病變和胰臟癌之預測和診斷的重要結果。
關鍵詞: 免疫基因定序;免疫基因組庫; A型免疫球蛋白腎病變;胰臟癌; B細胞受體;T細胞受體

Immune repertoire is defined as the sum of T cell receptors and B cell receptors that make the organism’s adaptive immune system. The diversity of immune repertoire is first originated from V(D)J rearrangement, random insertion, deletion, and substitution of T cell receptor (TCR) and B cell receptor (BCR). The repertoire is shaped by antigen-driven immune responses in the inflammatory of pathogen infections, autoimmune diseases, and cancer. Thus, investigating the immune repertoire might give us a deeper insight into disease developments or treatments. The starting materials of immune repertoire sequencing include DNA and RNA, which are classified as DNA-based and RNA-based methods. However, the knowledge gaps are that we need a tool to construct immune repertoire and there are still lack of understandings in disease and immunity of Immunoglobin A nephropathy and pancreatic cancer.
In our study, we had three aims. First, we aimed to establish a DNA-based TCR-seq platform and a RNA-based BCR-seq platform, and applied these methods to clinical studies of IgA nephropathy and pancreatic cancer, respectively. Second, we aimed to profile BCR repertoire in IgA nephropathy (IgAN). Third, we aimed to characterize TCR repertoire in pancreatic cancer (PC).
The results showed the RNA-based BCR-seq platform had performance with assembled rate > 60%, generalizability, and reproductivity. The results showed the DNA-based TCR-seq platform had performance with assembled rates ranging from 7.93% to 74.88% and repertoire similarity compared with RNA-based method ranging from 0.109 to 0.575. After profiling BCR repertoire of IgAN and non-IgAN patients, we had several significant findings such as diversity in IgA, IgD, IgM, IgL, and IgK were higher in IgAN patients, the ratio of IgK/IgL was lower in IgAN patients, and we found several gene frequency different between IgAN and non-IgAN patients, including IGLV2-18, IGLV5-48, IGLV8-61, IGLJ1, IGLJ5, and IGKV1-13.
After characterizing the TCR repertoire, we had several significant findings such as diversity of peripheral blood was higher than in tumor and normal tissues, early-stage patients had significantly higher diversity in tumor tissue than late-stage patients, patients with high diversity in tumor tissue had better overall survival than low ones, and the score defined by us using public clonotypes could significantly separate overall survival by the median value.
In conclusion, we successfully developed two immune repertoire sequencing platforms and applied them to clinical studies by finding several significant results that may benefit the prediction and diagnosis of IgA nephropathy and pancreatic cancer.
Keywords: immunosequencing; immune repertoire; IgA nephropathy; pancreatic cancer; B-cell receptor; T-cell receptor.

中文摘要 vii
ABSTRACT ix
ABBREVIATIONS xi
CHAPTER 1 Introduction 1
1.1 Adaptive immunity 1
1.2 Immune repertoire 1
1.3 Immune repertoire sequencing 2
1.4 Immune repertoire and human diseases 3
1.4.1 B cell immunity in IgA nephropathy 3
1.4.2 T cell immunity in pancreatic cancer 4
CHAPTER 2 Aim 6
2.1 Immune repertoire platform development 6
2.1.1 To develop an RNA-based platform for B-cell receptor (BCR) library preparation 6
2.1.2 To develop a DNA-based platform for T-cell receptor (TCR) library preparation 6
2.2 Profiling of BCR repertoire in IgA nephropathy (IgAN) 6
2.2.1 To explore the difference in characteristics of BCR repertoire between IgAN and non-IgAN patients 6
2.2.2 To identify the difference in BCR isotype usage between IgAN and non-IgAN patients 6
2.3 Characterization of TCR repertoire in pancreatic cancer (PC) 6
2.3.1 To profile TCR repertoire in tumor tissue, normal tissue, and peripheral blood of PC patients 6
2.3.2 To find a connection between TCR repertoire characteristics and clinical outcomes of PC patients 6
2.3.3 To construct a Taiwan Pancreatic Cancer TCR Repertoire Database 6
CHAPTER 3 Material and methods 7
3.1 Study subjects 7
3.2 PBMCs isolation and nucleic acid extraction 7
3.3 Immune repertoire library preparation 8
3.4 Immune repertoire sequencing 9
3.5 Immune repertoire analysis 10
3.5.1 Processing of raw sequencing data 10
3.5.2 Diversity index and profile of immune repertoire 10
3.5.3 V/J gene usage of immune repertoire 11
3.5.4 The similarity of immune repertoire 11
3.5.5 Survival analysis 11
3.5.6 Statistics and visualization 12
CHAPTER 4 Results 13
4.1 Development of BCR-seq platform 13
4.1.1 To design BCR-seq platform 13
4.1.2 To examine the performance of the BCR-seq platform 13
4.2 Profiling of B-cell repertoire in IgA nephropathy 15
4.2.1 The summary of patient baseline characteristics and BCR-seq data 15
4.2.2 To investigate the difference in BCR repertoire diversity between IgAN and non-IgAN patients 15
4.2.3 To identify the difference in the proportion of BCR clonotype with a specific frequency between IgAN and non-IgAN patients 16
4.2.4 To explore the difference in BCR isotype frequency between IgAN and non-IgAN patients 16
4.2.5 To interrogate the difference in V/J gene usage between IgAN and non-IgAN patients 17
4.3 Development of multiplex PCR based TCR-seq platform 18
4.3.1 To design TRBV and TRBJ primers for multiplex PCR based TCR-seq platform 18
4.3.2 To assess the amplification of TCR gene fragments by self-designed TRBV-TRBJ primers 18
4.3.3 To test conditions of size selection for TCR library preparation 18
4.3.4 To examine the performance of multiplex PCR based TCR-seq platform 19
4.4 Characterization of T-cell repertoire in pancreatic cancer 20
4.4.1 Baseline characteristics and TCR-seq summary 20
4.4.2 To identify TCR repertoire diversity profiling of tumor, normal and peripheral tissues in PC patients 20
4.4.3 To interrogate TCR repertoire similarity between tumor and normal and peripheral tissues in PC patients 21
4.4.4 To study abundant/public tumor-specific TCR clonotypes in PC patients 21
4.4.5 To investigate TCR repertoire features and clinical outcomes 24
CHAPTER 5 Discussion 25
5.1 Development of BCR-seq platform 25
5.2 Profiling of B-cell repertoire in IgA nephropathy 26
5.3 Development of multiplex PCR based TCR-seq platform 28
5.4 Characterization of T-cell repertoire in pancreatic cancer 29
Chapter 6 Conclusion and Perspective 31
TABLES 33
Table 1. BCR-seq summary of 1st BCR-seq platform construction 33
Table 2. BCR-seq summary of 2nd BCR-seq platform construction 34
Table 3. BCR-seq summary of different methods in 3rd BCR-seq platform construction 35
Table 4. BCR-seq summary of different samples using BCR mixed method in 3rd BCR-seq platform construction 36
Table 5. Repertoire similarity of different methods or batches in IGLC 37
Table 6. Repertoire similarity of different methods or batches in IGKC 38
Table 7. Repertoire similarity of different methods or batches in IGHA 39
Table 8. Repertoire similarity of different methods or batches in IGHD 40
Table 9. Repertoire similarity of different methods or batches in IGHE 41
Table 10. Repertoire similarity of different methods or batches in IGHG 42
Table 11. Repertoire similarity of different methods or batches in IGHM 43
Table 12. BCR-seq summary of different times of RNA extraction and IGHM primers in 4th BCR-seq platform construction 44
Table 13. BCR-seq summary of 4th BCR-seq platform construction 45
Table 14. TCR-seq summary of multiplex PCR based TCR-seq platform construction 46
Table 15. Summary of baseline characteristics of study patients in IgAN study 47
Table 16. BCR-seq summary of IgAN patients 48
Table 17. BCR-seq summary of NIgAN patients 49
Table 18. Summary of baseline characteristics of PC patients 50
Table 19. TCR-seq summary of PC patients -1 51
Table 20. TCR-seq summary of PC patients -2 52
Table 21. TCR-seq summary of PC patients -3 53
Table 22. TCR-seq summary of PC patients -4 54
FIGURES 55
Figure 1. Heatmaps of repertoire similarity between different times of RNA extraction in 4th BCR-seq platform construction 55
Figure 2. MIG size distribution of 7 BCRs in 4th BCR-seq platform construction 56
Figure 3. Performance of V-J primer combination in multiplex PCR based TCR-seq platform construction 57
(Loose criteria) 57
Figure 4. Performance of V-J primer combination in multiplex PCR based TCR-seq platform construction 58
(Strict criteria) 58
Figure 5. Validation of index PCR products in multiplex PCR based TCR-seq platform construction 59
Figure 6. Repertoire similarity between DNA based and RNA based methods in multiplex PCR based TCR-seq platform construction 60
Figure 7. Comparisons of Inverse Simpson index between IgAN and NIgAN patients in separate BCR isotypes and all BCRs 61
Figure 8. Comparisons of Shannon index between IgAN and NIgAN patients in separate BCR isotypes and all BCRs 62
Figure 9. Comparisons of Rényi entropy of alpha scaled from 0 to 10 between IgAN and NIgAN patients in separate BCR isotypes and all BCRs 63
Figure 10. Comparisons of relative abundance of different clonotype groups between IgAN and NIgAN patients in BCR heavy chain 64
Figure 11. Comparisons of relative abundance of different clonotype groups between IgAN and NIgAN patients in BCR light chain 65
Figure 12. Comparisons of isotype frequency between IgAN and NIgAN patients in BCR heavy chain 66
Figure 13. Comparisons of frequency and IgK/IgL ratio between IgAN and NIgAN patients in BCR light chain 67
Figure 14. Comparisons of IgHV gene usage between IgAN and NIgAN patients 68
Figure 15. Comparisons of IgHJ gene usage between IgAN and NIgAN patients 69
Figure 16. Comparisons of IgLV gene usage between IgAN and NIgAN patients 70
Figure 17. Comparisons of IgLJ gene usage between IgAN and NIgAN patients 71
Figure 18. Comparisons of IgKV gene usage between IgAN and NIgAN patients 72
Figure 19. Comparisons of IgKJ gene usage between IgAN and NIgAN patients 73
Figure 20. Gene of significant difference between IgAN and NIgAN patients 74
Figure 21. Comparisons of Shannon index between tissues in PC patients 75
Figure 22. Comparison of Rényi entropy of alpha scaled from 0 to 10 between tissues in PC patients 76
Figure 23. Comparison of diversity between different stages (E: early, stage Iⅈ L: late, stage III&IV) among tissues in PC patients 77
Figure 24. Comparisons of repertoire similarity between stages in PC patients 78
Figure 25. Relative abundance of clonotypes with specific frequencies in tumor tissue of PC patients 79
Figure 26. Relative abundance of clonotypes with specific frequencies in normal tissue of PC patients 80
Figure 27. Relative abundance of clonotypes with specific frequencies in peripheral blood of PC patients 81
Figure 28. Comparisons of relative abundance of different clonotype groups between tissues in PC patients 82
Figure 29. Frequency of shared public TCR clonotypes in tumor tissue of PC patients 83
Figure 30. Frequency of shared public TCR clonotypes in normal tissue of PC patients 84
Figure 31. Frequency of shared public TCR clonotypes in peripheral blood of PC patients 85
Figure 32. Number of shared samples in public TCR clonotypes of tumor tissue in PC patients 86
Figure 33. Number of shared samples in public TCR clonotypes of normal tissue in PC patients 87
Figure 34. Number of shared samples in public TCR clonotypes of peripheral blood in PC patients 88
Figure 35. Frequency of shared public TCR clonotypes in tumor tissue of good survival (> 24 months) PC patients (n=18) 89
Figure 36. Frequency of shared public TCR clonotypes in tumor tissue of poor survival (< 24 months) PC patients (n=26) 90
Figure 37. Clustering of public clonotypes in tumor tissue of good and bad survival patients 91
Figure 38. Forest plot of baseline characteristics and overall survival 92
Figure 39. Survival analysis according to median Shannon index in tumor tissue 93
Figure 40. Survival analysis according to median Shannon index in normal tissue 94
Figure 41. Survival analysis according to median Shannon index in peripheral blood 95
Figure 42. Survival analysis according to median score of public clonotypes 96
REFERENCE 97

1.Marshall JS, Warrington R, Watson W, Kim HL: An introduction to immunology and immunopathology. Allergy, Asthma & Clinical Immunology 2018, 14(2):49.
2.Kumar BV, Connors TJ, Farber DL: Human T Cell Development, Localization, and Function throughout Life. Immunity 2018, 48(2):202-213.
3.Clambey ET, Davenport B, Kappler JW, Marrack P, Homann D: Molecules in medicine mini review: the αβ T cell receptor. J Mol Med (Berl) 2014, 92(7):735-741.
4.Patel AM, Liu YS, Davies SP, Brown RM, Kelly DA, Scheel-Toellner D, Reynolds GM, Stamataki Z: The Role of B Cells in Adult and Paediatric Liver Injury. Front Immunol 2021, 12:729143.
5.Bai Y, Orfao A, Chim CS: Molecular detection of minimal residual disease in multiple myeloma. Br J Haematol 2018, 181(1):11-26.
6.Liu X, Wu J: History, applications, and challenges of immune repertoire research. Cell Biol Toxicol 2018, 34(6):441-457.
7.Zhang W, Wang L, Liu K, Wei X, Yang K, Du W, Wang S, Guo N, Ma C, Luo L et al: PIRD: Pan Immune Repertoire Database. Bioinformatics 2019, 36(3):897-903.
8.Laydon DJ, Bangham CR, Asquith B: Estimating T-cell repertoire diversity: limitations of classical estimators and a new approach. Philos Trans R Soc Lond B Biol Sci 2015, 370(1675).
9.Six A, Mariotti-Ferrandiz E, Chaara W, Magadan S, Pham H-P, Lefranc M-P, Mora T, Thomas-Vaslin V, Walczak A, Boudinot P: The Past, Present, and Future of Immune Repertoire Biology – The Rise of Next-Generation Repertoire Analysis. Frontiers in Immunology 2013, 4.
10.Chaudhary N, Wesemann DR: Analyzing Immunoglobulin Repertoires. Frontiers in Immunology 2018, 9.
11.Hou X, Wang M, Lu C, Xie Q, Cui G, Chen J, Du Y, Dai Y, Diao H: Analysis of the Repertoire Features of TCR Beta Chain CDR3 in Human by High-Throughput Sequencing. Cellular Physiology and Biochemistry 2016, 39(2):651-667.
12.Hou D, Chen C, Seely EJ, Chen S, Song Y: High-Throughput Sequencing-Based Immune Repertoire Study during Infectious Disease. Frontiers in Immunology 2016, 7.
13.Rosati E, Dowds CM, Liaskou E, Henriksen EKK, Karlsen TH, Franke A: Overview of methodologies for T-cell receptor repertoire analysis. BMC Biotechnology 2017, 17(1):61.
14.Barennes P, Quiniou V, Shugay M, Egorov ES, Davydov AN, Chudakov DM, Uddin I, Ismail M, Oakes T, Chain B et al: Benchmarking of T cell receptor repertoire profiling methods reveals large systematic biases. Nature Biotechnology 2021, 39(2):236-245.
15.Woodsworth DJ, Castellarin M, Holt RA: Sequence analysis of T-cell repertoires in health and disease. Genome Med 2013, 5(10):98.
16.Magistroni R, D'Agati VD, Appel GB, Kiryluk K: New developments in the genetics, pathogenesis, and therapy of IgA nephropathy. Kidney Int 2015, 88(5):974-989.
17.Chiu HF, Chen HC, Lu KC, Shu KH: Distribution of glomerular diseases in Taiwan: preliminary report of National Renal Biopsy Registry-publication on behalf of Taiwan Society of Nephrology. BMC Nephrol 2018, 19(1):6.
18.Wu M-Y, Chen C-S, Yiang G-T, Cheng P-W, Chen Y-L, Chiu H-C, Liu K-H, Lee W-C, Li C-J: The Emerging Role of Pathogenesis of IgA Nephropathy. Journal of Clinical Medicine 2018, 7(8):225.
19.Coppo R: Treatment of IgA nephropathy: Recent advances and prospects. Nephrol Ther 2018, 14 Suppl 1:S13-s21.
20.Barbour SJ, Espino-Hernandez G, Reich HN, Coppo R, Roberts IS, Feehally J, Herzenberg AM, Cattran DC: The MEST score provides earlier risk prediction in lgA nephropathy. Kidney Int 2016, 89(1):167-175.
21.Huang C, Li X, Wu J, Zhang W, Sun S, Lin L, Wang X, Li H, Wu X, Zhang P et al: The landscape and diagnostic potential of T and B cell repertoire in Immunoglobulin A Nephropathy. J Autoimmun 2019, 97:100-107.
22.Lin Y, Jia J, Guo Y, He D, Zhang Y, Wang F, Yan T, Liu Y, Lin S: Corticosteroid for IgA Nephropathy: Are They Really Therapeutic? Am J Nephrol 2018, 47(6):385-394.
23.Yang P, Wang Q, Xie C, Xu G, Wu Q: Efficacy and Safety of Agents in IgA Nephropathy: An Update Network Meta-Analysis. Kidney Blood Press Res 2018, 43(6):1890-1897.
24.Chang S, Li X-K: The Role of Immune Modulation in Pathogenesis of IgA Nephropathy. Frontiers in Medicine 2020, 7.
25.Zhai YL, Zhu L, Shi SF, Liu LJ, Lv JC, Zhang H: Increased APRIL Expression Induces IgA1 Aberrant Glycosylation in IgA Nephropathy. Medicine (Baltimore) 2016, 95(11):e3099.
26.Zheng N, Fan J, Wang B, Wang D, Feng P, Yang Q, Yu X: Expression profile of BAFF in peripheral blood from patients of IgA nephropathy: Correlation with clinical features and Streptococcus pyogenes infection. Mol Med Rep 2017, 15(4):1925-1935.
27.Huang X, Xu G: An Update on Targeted Treatment of IgA Nephropathy: An Autoimmune Perspective. Frontiers in Pharmacology 2021, 12.
28.Klein AP: Pancreatic cancer epidemiology: understanding the role of lifestyle and inherited risk factors. Nature Reviews Gastroenterology & Hepatology 2021, 18(7):493-502.
29.Hu JX, Zhao CF, Chen WB, Liu QC, Li QW, Lin YY, Gao F: Pancreatic cancer: A review of epidemiology, trend, and risk factors. World J Gastroenterol 2021, 27(27):4298-4321.
30.Rawla P, Sunkara T, Gaduputi V: Epidemiology of Pancreatic Cancer: Global Trends, Etiology and Risk Factors. World J Oncol 2019, 10(1):10-27.
31.Chang JS, Chen LT, Shan YS, Chu PY, Tsai CR, Tsai HJ: The incidence and survival of pancreatic cancer by histology, including rare subtypes: a nation-wide cancer registry-based study from Taiwan. Cancer Med 2018, 7(11):5775-5788.
32.Conroy T, Hammel P, Hebbar M, Ben Abdelghani M, Wei AC, Raoul J-L, Choné L, Francois E, Artru P, Biagi JJ et al: FOLFIRINOX or Gemcitabine as Adjuvant Therapy for Pancreatic Cancer. New England Journal of Medicine 2018, 379(25):2395-2406.
33.Mavros MN, Moris D, Karanicolas PJ, Katz MHG, O’Reilly EM, Pawlik TM: Clinical Trials of Systemic Chemotherapy for Resectable Pancreatic Cancer: A Review. JAMA Surgery 2021, 156(7):663-672.
34.Tempero MA, Malafa MP, Al-Hawary M, Behrman SW, Benson AB, Cardin DB, Chiorean EG, Chung V, Czito B, Del Chiaro M et al: Pancreatic Adenocarcinoma, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 2021, 19(4):439-457.
35.Gbolahan OB, Tong Y, Sehdev A, O’Neil B, Shahda S: Overall survival of patients with recurrent pancreatic cancer treated with systemic therapy: a retrospective study. BMC Cancer 2019, 19(1):468.
36.Tanaka M, Mihaljevic AL, Probst P, Heckler M, Klaiber U, Heger U, Büchler MW, Hackert T: Meta-analysis of recurrence pattern after resection for pancreatic cancer. British Journal of Surgery 2019, 106(12):1590-1601.
37.Waldman AD, Fritz JM, Lenardo MJ: A guide to cancer immunotherapy: from T cell basic science to clinical practice. Nature Reviews Immunology 2020, 20(11):651-668.
38.Hall M, Liu H, Malafa M, Centeno B, Hodul PJ, Pimiento J, Pilon-Thomas S, Sarnaik AA: Expansion of tumor-infiltrating lymphocytes (TIL) from human pancreatic tumors. Journal for ImmunoTherapy of Cancer 2016, 4(1):61.
39.Saka D, Gökalp M, Piyade B, Cevik NC, Arik Sever E, Unutmaz D, Ceyhan GO, Demir IE, Asimgil H: Mechanisms of T-Cell Exhaustion in Pancreatic Cancer. Cancers (Basel) 2020, 12(8).
40.Sivakumar S, Abu-Shah E, Ahern DJ, Arbe-Barnes EH, Jainarayanan AK, Mangal N, Reddy S, Rendek A, Easton A, Kurz E et al: Activated Regulatory T-Cells, Dysfunctional and Senescent T-Cells Hinder the Immunity in Pancreatic Cancer. Cancers 2021, 13(8):1776.
41.Turchaninova MA, Davydov A, Britanova OV, Shugay M, Bikos V, Egorov ES, Kirgizova VI, Merzlyak EM, Staroverov DB, Bolotin DA et al: High-quality full-length immunoglobulin profiling with unique molecular barcoding. Nature Protocols 2016, 11(9):1599-1616.
42.Rubelt F, Bolen CR, McGuire HM, Heiden JAV, Gadala-Maria D, Levin M, M. Euskirchen G, Mamedov MR, Swan GE, Dekker CL et al: Individual heritable differences result in unique cell lymphocyte receptor repertoires of naïve and antigen-experienced cells. Nature Communications 2016, 7(1):11112.
43.Mamedov IZ, Britanova OV, Zvyagin IV, Turchaninova MA, Bolotin DA, Putintseva EV, Lebedev YB, Chudakov DM: Preparing unbiased T-cell receptor and antibody cDNA libraries for the deep next generation sequencing profiling. Front Immunol 2013, 4:456.
44.Kitaura K, Yamashita H, Ayabe H, Shini T, Matsutani T, Suzuki R: Different Somatic Hypermutation Levels among Antibody Subclasses Disclosed by a New Next-Generation Sequencing-Based Antibody Repertoire Analysis. Front Immunol 2017, 8:389.
45.Vázquez Bernat N, Corcoran M, Hardt U, Kaduk M, Phad GE, Martin M, Karlsson Hedestam GB: High-Quality Library Preparation for NGS-Based Immunoglobulin Germline Gene Inference and Repertoire Expression Analysis. Frontiers in Immunology 2019, 10.
46.Niu X, Li S, Li P, Pan W, Wang Q, Feng Y, Mo X, Yan Q, Ye X, Luo J et al: Longitudinal Analysis of T and B Cell Receptor Repertoire Transcripts Reveal Dynamic Immune Response in COVID-19 Patients. Frontiers in Immunology 2020, 11.
47.Wu TD, Madireddi S, de Almeida PE, Banchereau R, Chen YJ, Chitre AS, Chiang EY, Iftikhar H, O'Gorman WE, Au-Yeung A et al: Peripheral T cell expansion predicts tumour infiltration and clinical response. Nature 2020, 579(7798):274-278.
48.Bashford-Rogers RJM, Bergamaschi L, McKinney EF, Pombal DC, Mescia F, Lee JC, Thomas DC, Flint SM, Kellam P, Jayne DRW et al: Analysis of the B cell receptor repertoire in six immune-mediated diseases. Nature 2019, 574(7776):122-126.
49.Lai K-N, Chui S-H, Lai FM-M, Lam CWK: Predominant synthesis of IgA with lambda light chain in IgA nephropathy. Kidney International 1988, 33(2):584-589.
50.Uppin M, Prayaga A, Srinivas B, Rapur R, Desai M, Dakshina Murthy K: Light chain immunofluorescence in various nephropathies. Indian Journal of Pathology and Microbiology 2011, 54(1):55-58.
51.Rai R, Chauhan SK, Singh VV, Rai M, Rai G: RNA-seq Analysis Reveals Unique Transcriptome Signatures in Systemic Lupus Erythematosus Patients with Distinct Autoantibody Specificities. PLoS One 2016, 11(11):e0166312.
52.Brynedal B WH, Folkersen L, Padyukov L, Vivar N, Catrina AI, Klareskog L, Berg L.: Antibody Mediated Immunity Drives Response to Methotrexate Treatment in Rheumatoid Arthritis Patients. Arthritis Rheumatol 2016.
53.Hou X, Wang G, Fan W, Chen X, Mo C, Wang Y, Gong W, Wen X, Chen H, He D et al: T-cell receptor repertoires as potential diagnostic markers for patients with COVID-19. International Journal of Infectious Diseases 2021, 113:308-317.
54.Schultheiß C, Simnica D, Willscher E, Oberle A, Fanchi L, Bonzanni N, Wildner NH, Schulze Zur Wiesch J, Weiler-Normann C, Lohse AW et al: Next-Generation Immunosequencing Reveals Pathological T-Cell Architecture in Autoimmune Hepatitis. Hepatology 2021, 73(4):1436-1448.
55.Porritt RA, Paschold L, Rivas MN, Cheng MH, Yonker LM, Chandnani H, Lopez M, Simnica D, Schultheiß C, Santiskulvong C et al: HLA class I–associated expansion of TRBV11-2 T cells in multisystem inflammatory syndrome in children. The Journal of Clinical Investigation 2021, 131(10).
56.Yang J, Chen J, He J, Xie Y, Zhu Y, Cao H, Li L: Profiling the repertoire of T-cell receptor beta-chain variable genes in peripheral blood lymphocytes from subjects who have recovered from acute hepatitis B virus infection. Cellular & Molecular Immunology 2014, 11(4):332-342.
57.Wang Z, Zhong Y, Zhang Z, Zhou K, Huang Z, Yu H, Liu L, Liu S, Yang H, Zhou J et al: Characteristics and Clinical Significance of T-Cell Receptor Repertoire in Hepatocellular Carcinoma. Frontiers in Immunology 2022, 13.
58.Wang X, Zhang B, Yang Y, Zhu J, Cheng S, Mao Y, Feng L, Xiao T: Characterization of Distinct T Cell Receptor Repertoires in Tumor and Distant Non-tumor Tissues from Lung Cancer Patients. Genomics Proteomics Bioinformatics 2019, 17(3):287-296.
59.Song Z, Chen X, Shi Y, Huang R, Wang W, Zhu K, Lin S, Wang M, Tian G, Yang J et al: Evaluating the Potential of T Cell Receptor Repertoires in Predicting the Prognosis of Resectable Non-Small Cell Lung Cancers. Molecular Therapy - Methods & Clinical Development 2020, 18:73-83.
60.Braun DA, Street K, Burke KP, Cookmeyer DL, Denize T, Pedersen CB, Gohil SH, Schindler N, Pomerance L, Hirsch L et al: Progressive immune dysfunction with advancing disease stage in renal cell carcinoma. Cancer Cell 2021, 39(5):632-648.e638.
61.Murthy P, Saini P, Russell K, Pan W, Weber D, Byrne-Steele M, Han J, Espina V, Liotta L, Zeh H et al: 260 T cell infiltrating repertoire diversity is associated with enhanced survival following neoadjuvant therapy in patients with resectable pancreatic cancer. Journal for ImmunoTherapy of Cancer 2020, 8(Suppl 3):A158-A159.
62.Valpione S, Mundra PA, Galvani E, Campana LG, Lorigan P, De Rosa F, Gupta A, Weightman J, Mills S, Dhomen N et al: The T cell receptor repertoire of tumor infiltrating T cells is predictive and prognostic for cancer survival. Nature Communications 2021, 12(1):4098.