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Minimal residual disease

Identifying IGH disease clones for MRD monitoring in childhood B-cell acute lymphoblastic leukemia using RNA-Seq

Leukemia volume 34, pages 2418–2429 (2020)Cite this article

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Abstract

Identifying patient-specific clonal IGH/TCR junctional sequences is critical for minimal residual disease (MRD) monitoring. Conventionally these junctional sequences are identified using laborious Sanger sequencing of excised heteroduplex bands. We found that the IGH is highly expressed in our diagnostic B-cell acute lymphoblastic leukemia (B-ALL) samples using RNA-Seq. Therefore, we used RNA-Seq to identify IGH disease clone sequences in 258 childhood B-ALL samples for MRD monitoring. The amount of background IGH rearrangements uncovered by RNA-Seq followed the Zipf’s law with IGH disease clones easily identified as outliers. Four hundred and ninety-seven IGH disease clones (median 2, range 0–7 clones/patient) are identified in 90.3% of patients. High hyperdiploid patients have the most IGH disease clones (median 3) while DUX4 subtype has the least (median 1) due to the rearrangements involving the IGH locus. In all, 90.8% of IGH disease clones found by Sanger sequencing are also identified by RNA-Seq. In addition, RNA-Seq identified 43% more IGH disease clones. In 69 patients lacking sensitive IGH targets, targeted NGS IGH MRD showed high correlation (R = 0.93; P = 1.3 × 10−14), better relapse prediction than conventional RQ-PCR MRD using non-IGH targets. In conclusion, RNA-Seq can identify patient-specific clonal IGH junctional sequences for MRD monitoring, adding to its usefulness for molecular diagnosis in childhood B-ALL.

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Fig. 1: Defining IGH disease clones.
Fig. 2: Comparison between RNA-Seq and Sanger sequencing in IGH disease clone identification.
Fig. 3: Comparison of MRD quantification using IGH-Seq based on RNA-Seq identified IGH disease clones and RQ-PCR using non-IGH markers.
Fig. 4: Unproductive rearrangements are mainly expressed in disease clones.

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References

  1. Coustan-Smith E, Behm FG, Sanchez J, Boyett JM, Hancock ML, Raimondi SC, et al. Immunological detection of minimal residual disease in children with acute lymphoblastic leukaemia. Lancet. 1998;351:550–4.

    Article  CAS  Google Scholar 

  2. van Dongen JJ, Seriu T, Panzer-Grumayer ER, Biondi A, Pongers-Willemse MJ, Corral L, et al. Prognostic value of minimal residual disease in acute lymphoblastic leukaemia in childhood. Lancet. 1998;352:1731–8.

    Article  Google Scholar 

  3. Borowitz MJ, Devidas M, Hunger SP, Bowman WP, Carroll AJ, Carroll WL, et al. Clinical significance of minimal residual disease in childhood acute lymphoblastic leukemia and its relationship to other prognostic factors: a Children’s Oncology Group study. Blood. 2008;111:5477–85.

    Article  CAS  Google Scholar 

  4. Flohr T, Schrauder A, Cazzaniga G, Panzer-Grumayer R, van der Velden V, Fischer S, et al. Minimal residual disease-directed risk stratification using real-time quantitative PCR analysis of immunoglobulin and T-cell receptor gene rearrangements in the international multicenter trial AIEOP-BFM ALL 2000 for childhood acute lymphoblastic leukemia. Leukemia. 2008;22:771–82.

    Article  CAS  Google Scholar 

  5. Yeoh AE, Ariffin H, Chai EL, Kwok CS, Chan YH, Ponnudurai K, et al. Minimal residual disease-guided treatment deintensification for children with acute lymphoblastic leukemia: results from the Malaysia-Singapore acute lymphoblastic leukemia 2003 study. J Clin Oncol. 2012;30:2384–92.

    Article  CAS  Google Scholar 

  6. Pongers-Willemse MJ, Seriu T, Stolz F, d’Aniello E, Gameiro P, Pisa P, et al. Primers and protocols for standardized detection of minimal residual disease in acute lymphoblastic leukemia using immunoglobulin and T cell receptor gene rearrangements and TAL1 deletions as PCR targets: report of the BIOMED-1 CONCERTED ACTION: investigation of minimal residual disease in acute leukemia. Leukemia. 1999;13:110–8.

    Article  CAS  Google Scholar 

  7. van Dongen JJ, Langerak AW, Bruggemann M, Evans PA, Hummel M, Lavender FL, et al. Design and standardization of PCR primers and protocols for detection of clonal immunoglobulin and T-cell receptor gene recombinations in suspect lymphoproliferations: report of the BIOMED-2 Concerted Action BMH4-CT98-3936. Leukemia. 2003;17:2257–317.

    Article  Google Scholar 

  8. Roberts KG, Li Y, Payne-Turner D, Harvey RC, Yang YL, Pei D, et al. Targetable kinase-activating lesions in Ph-like acute lymphoblastic leukemia. N. Engl J Med. 2014;371:1005–15.

    Article  Google Scholar 

  9. Zhang J, McCastlain K, Yoshihara H, Xu B, Chang Y, Churchman ML, et al. Deregulation of DUX4 and ERG in acute lymphoblastic leukemia. Nat Genet. 2016;48:1481–9.

    Article  CAS  Google Scholar 

  10. Yasuda T, Tsuzuki S, Kawazu M, Hayakawa F, Kojima S, Ueno T, et al. Recurrent DUX4 fusions in B cell acute lymphoblastic leukemia of adolescents and young adults. Nat Genet. 2016;48:569–74.

    Article  CAS  Google Scholar 

  11. Suzuki K, Okuno Y, Kawashima N, Muramatsu H, Okuno T, Wang X, et al. MEF2D-BCL9 fusion gene is associated with high-risk acute B-cell precursor lymphoblastic leukemia in adolescents. J Clin Oncol. 2016;34:3451–9.

    Article  CAS  Google Scholar 

  12. Qian M, Zhang H, Kham SK, Liu S, Jiang C, Zhao X, et al. Whole-transcriptome sequencing identifies a distinct subtype of acute lymphoblastic leukemia with predominant genomic abnormalities of EP300 and CREBBP. Genome Res. 2017;27:185–95.

    Article  CAS  Google Scholar 

  13. Gu Z, Churchman ML, Roberts KG, Moore I, Zhou X, Nakitandwe J, et al. PAX5-driven subtypes of B-progenitor acute lymphoblastic leukemia. Nat Genet. 2019;51:296–307.

    Article  CAS  Google Scholar 

  14. Arber DA, Orazi A, Hasserjian R, Thiele J, Borowitz MJ, Le Beau MM, et al. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood. 2016;127:2391–405.

    Article  CAS  Google Scholar 

  15. Inaba H, Azzato EM, Mullighan CG. Integration of next-generation sequencing to treat acute lymphoblastic leukemia with targetable lesions: the St. Jude Children’s Research Hospital approach. Front Pediatr. 2017;5:258.

  16. Yeoh AEJ, Lu Y, Chin WHN, Chiew EKH, Lim EH, Li Z, et al. Intensifying treatment of childhood B-lymphoblastic leukemia with IKZF1 deletion reduces relapse and improves overall survival: results of Malaysia-Singapore ALL 2010 Study. J Clin Oncol. 2018;36:2726–35.

    Article  CAS  Google Scholar 

  17. Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013;14:R36.

    Article  Google Scholar 

  18. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30:923–30.

    Article  CAS  Google Scholar 

  19. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

    Article  Google Scholar 

  20. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28:882–3.

    Article  CAS  Google Scholar 

  21. Nicorici D, Satalan M, Edgren H, Kangaspeska S, Murumagi A, Kallioniemi O, et al. FusionCatcher—a tool for finding somatic fusion genes in paired-end RNA-sequencing data. 2014. https://www.biorxiv.org/content/10.1101/011650v1.

  22. Bolotin DA, Poslavsky S, Mitrophanov I, Shugay M, Mamedov IZ, Putintseva EV, et al. MiXCR: software for comprehensive adaptive immunity profiling. Nat Methods. 2015;12:380–1.

    Article  CAS  Google Scholar 

  23. Giraud M, Salson M, Duez M, Villenet C, Quief S, Caillault A, et al. Fast multiclonal clusterization of V(D)J recombinations from high-throughput sequencing. BMC Genomics. 2014;15:409.

    Article  Google Scholar 

  24. Shannon CE. The mathematical theory of communication. 1963. MD Comput. 1997;14:306–17.

    CAS  PubMed  Google Scholar 

  25. Faham M, Zheng J, Moorhead M, Carlton VE, Stow P, Coustan-Smith E, et al. Deep-sequencing approach for minimal residual disease detection in acute lymphoblastic leukemia. Blood. 2012;120:5173–80.

    Article  CAS  Google Scholar 

  26. Brochet X, Lefranc MP, Giudicelli V. IMGT/V-QUEST: the highly customized and integrated system for IG and TR standardized V-J and V-D-J sequence analysis. Nucleic Acids Res. 2008;36:W503–508.

    Article  CAS  Google Scholar 

  27. Donovan JW, Ladetto M, Zou G, Neuberg D, Poor C, Bowers D, et al. Immunoglobulin heavy-chain consensus probes for real-time PCR quantification of residual disease in acute lymphoblastic leukemia. Blood. 2000;95:2651–8.

    Article  CAS  Google Scholar 

  28. Verhagen OJ, Willemse MJ, Breunis WB, Wijkhuijs AJ, Jacobs DC, Joosten SA, et al. Application of germline IGH probes in real-time quantitative PCR for the detection of minimal residual disease in acute lymphoblastic leukemia. Leukemia. 2000;14:1426–35.

    Article  CAS  Google Scholar 

  29. van der Velden VH, Cazzaniga G, Schrauder A, Hancock J, Bader P, Panzer-Grumayer ER, et al. Analysis of minimal residual disease by Ig/TCR gene rearrangements: guidelines for interpretation of real-time quantitative PCR data. Leukemia. 2007;21:604–11.

    Article  Google Scholar 

  30. Darlow JM, Stott DI. V(H) replacement in rearranged immunoglobulin genes. Immunology. 2005;114:155–65.

    Article  CAS  Google Scholar 

  31. Geng H, Hurtz C, Lenz KB, Chen Z, Baumjohann D, Thompson S, et al. Self-enforcing feedback activation between BCL6 and pre-B cell receptor signaling defines a distinct subtype of acute lymphoblastic leukemia. Cancer Cell. 2015;27:409–25.

    Article  CAS  Google Scholar 

  32. Chang YF, Imam JS, Wilkinson MF. The nonsense-mediated decay RNA surveillance pathway. Annu Rev Biochem. 2007;76:51–74.

    Article  CAS  Google Scholar 

  33. Zarnitsyna VI, Evavold BD, Schoettle LN, Blattman JN, Antia R. Estimating the diversity, completeness, and cross-reactivity of the T cell repertoire. Front Immunol. 2013;4:485.

    Article  Google Scholar 

  34. Desponds J, Mora T, Walczak AM. Fluctuating fitness shapes the clone-size distribution of immune repertoires. Proc Natl Acad Sci USA. 2016;113:274–9.

    Article  CAS  Google Scholar 

  35. Wood B, Wu D, Crossley B, Dai Y, Williamson D, Gawad C, et al. Measurable residual disease detection by high-throughput sequencing improves risk stratification for pediatric B-ALL. Blood. 2018;131:1350–9.

    Article  CAS  Google Scholar 

  36. Theunissen PMJ, van Zessen D, Stubbs AP, Faham M, Zwaan CM, van Dongen JJM, et al. Antigen receptor sequencing of paired bone marrow samples shows homogeneous distribution of acute lymphoblastic leukemia subclones. Haematologica. 2017;102:1869–77.

    Article  CAS  Google Scholar 

  37. Ma X, Edmonson M, Yergeau D, Muzny DM, Hampton OA, Rusch M, et al. Rise and fall of subclones from diagnosis to relapse in pediatric B-acute lymphoblastic leukaemia. Nat Commun. 2015;6:6604.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The computational work for this article was partially performed on resources of the National Supercomputing Centre, Singapore (https://www.nscc.sg). This study was supported by the Singapore National Medical Research Council Clinician Scientist Investigator Awards (NMRC/CSA/0053/2013); Cancer Science Institute, Singapore; VIVA Foundation for Children with Cancer; Children’s Cancer Foundation; Singapore Tote Board and Goh Foundation.

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Authors and Affiliations

  1. VIVA-NUS Centre for Translational Research in Acute Leukaemia, Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Zhenhua Li, Nan Jiang, Evelyn Huizi Lim, Winnie Hui Ni Chin, Yi Lu, Kean Hui Chiew, Shirley Kow Yin Kham, Thuan Chong Quah & Allen Eng-Juh Yeoh

  2. Department of Pharmaceutical Sciences, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA

    Wentao Yang & Jun J. Yang

  3. Viva-University Children’s Cancer Centre, Khoo Teck Puat-National University Children’s Medical Institute, National University Hospital, National University Health System, Singapore, Singapore

    Thuan Chong Quah & Allen Eng-Juh Yeoh

  4. Subang Jaya Medical Centre, Subang Jaya, Malaysia

    Hai Peng Lin

  5. Department of Paediatrics, KK Women’s & Children’s Hospital, Singapore, Singapore

    Ah Moy Tan

  6. University of Malaya Cancer Research Institute, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

    Hany Ariffin

  7. Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore

    Allen Eng-Juh Yeoh

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Li, Z., Jiang, N., Lim, E.H. et al. Identifying IGH disease clones for MRD monitoring in childhood B-cell acute lymphoblastic leukemia using RNA-Seq. Leukemia 34, 2418–2429 (2020). https://doi.org/10.1038/s41375-020-0774-4

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