Top

Published in:

Open Access 01-12-2021 | Acute Lymphoblastic Leukemia | Research

Clinical application of whole transcriptome sequencing for the classification of patients with acute lymphoblastic leukemia

Authors: Wencke Walter, Rabia Shahswar, Anna Stengel, Manja Meggendorfer, Wolfgang Kern, Torsten Haferlach, Claudia Haferlach

Published in: BMC Cancer | Issue 1/2021

Abstract

Background

Considering the clinical and genetic characteristics, acute lymphoblastic leukemia (ALL) is a rather heterogeneous hematological neoplasm for which current standard diagnostics require various analyses encompassing morphology, immunophenotyping, cytogenetics, and molecular analysis of gene fusions and mutations. Hence, it would be desirable to rely on a technique and an analytical workflow that allows the simultaneous analysis and identification of all the genetic alterations in a single approach. Moreover, based on the results with standard methods, a significant amount of patients have no established abnormalities and hence, cannot further be stratified.

Methods

We performed WTS and WGS in 279 acute lymphoblastic leukemia (ALL) patients (B-cell: n = 211; T-cell: n = 68) to assess the accuracy of WTS, to detect relevant genetic markers, and to classify ALL patients.

Results

DNA and RNA-based genotyping was used to ensure correct WTS-WGS pairing. Gene expression analysis reliably assigned samples to the B Cell Precursor (BCP)-ALL or the T-ALL group. Subclassification of BCP-ALL samples was done progressively, assessing first the presence of chromosomal rearrangements by the means of fusion detection. Compared to the standard methods, 97% of the recurrent risk-stratifying fusions could be identified by WTS, assigning 76 samples to their respective entities. Additionally, read-through fusions (indicative of CDKN2A and RB1 gene deletions) were recurrently detected in the cohort along with 57 putative novel fusions, with yet untouched diagnostic potentials. Next, copy number variations were inferred from WTS data to identify relevant ploidy groups, classifying an additional of 31 samples. Lastly, gene expression profiling detected a BCR-ABL1-like signature in 27% of the remaining samples.

Conclusion

As a single assay, WTS allowed a precise genetic classification for the majority of BCP-ALL patients, and is superior to conventional methods in the cases which lack entity defining genetic abnormalities.

Available only for authorised users

Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 2016;17(1):13. https://doi.org/10.1186/s13059-016-0881-8.CrossRefPubMedPubMedCentral

Li J-F, Dai YT, Lilljebjörn H, Shen SH, Cui BW, Bai L, et al. Transcriptional landscape of B cell precursor acute lymphoblastic leukemia based on an international study of 1,223 cases. Proc Natl Acad Sci U S A. 2018;115(50):E11711–20. https://doi.org/10.1073/pnas.1814397115.CrossRefPubMedPubMedCentral

Brown LM, et al. The application of RNA sequencing for the diagnosis and genomic classification of pediatric acute lymphoblastic leukemia. Blood Adv 2020;4(5):930–942. Blood Adv. 2020;4:1217.CrossRef

Arindrarto W, Borràs DM, de Groen RAL, van den Berg RR, Locher IJ, van Diessen SAME, et al. Comprehensive diagnostics of acute myeloid leukemia by whole transcriptome RNA sequencing. Leukemia. 2020;35(1):47–61. https://doi.org/10.1038/s41375-020-0762-8.CrossRefPubMedPubMedCentral

Byron SA, Van Keuren-Jensen KR, Engelthaler DM, Carpten JD, Craig DW. Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Nat Rev Genet. 2016;17(5):257–71. https://doi.org/10.1038/nrg.2016.10.CrossRefPubMedPubMedCentral

Marco-Puche G, Lois S, Benítez J, Trivino JC. RNA-seq perspectives to improve clinical diagnosis. Front Genet. 2019;10:1152. https://doi.org/10.3389/fgene.2019.01152.CrossRefPubMedPubMedCentral

Mullighan CG, Downing JR. Global genomic characterization of acute lymphoblastic leukemia. Semin Hematol. 2009;46(1):3–15. https://doi.org/10.1053/j.seminhematol.2008.09.005.CrossRefPubMedPubMedCentral

Swerdlow SH, Campo E, Pileri SA, Harris NL, Stein H, Siebert R, et al. The 2016 revision of the World Health Organization classification of lymphoid neoplasms. Blood. 2016;127(20):2375–90. https://doi.org/10.1182/blood-2016-01-643569.CrossRefPubMedPubMedCentral

Mullighan CG, Su X, Zhang J, Radtke I, Phillips LA, Miller CB, et al. Deletion of IKZF1 and prognosis in acute lymphoblastic leukemia. N Engl J Med. 2009;360(5):470–80. https://doi.org/10.1056/NEJMoa0808253.CrossRefPubMedPubMedCentral

10.

Den Boer ML, et al. A subtype of childhood acute lymphoblastic leukaemia with poor treatment outcome: a genome-wide classification study. Lancet Oncol. 2009;10(2):125–34. https://doi.org/10.1016/S1470-2045(08)70339-5.CrossRef

11.

Lilljebjörn H, Henningsson R, Hyrenius-Wittsten A, Olsson L, Orsmark-Pietras C, von Palffy S, et al. Identification of ETV6-RUNX1-like and DUX4-rearranged subtypes in paediatric B-cell precursor acute lymphoblastic leukaemia. Nat Commun. 2016;7(1):11790. https://doi.org/10.1038/ncomms11790.CrossRefPubMedPubMedCentral

12.

Hoelzer D, Bassan R, Dombret H, Fielding A, Ribera JM, Buske C, et al. Acute lymphoblastic leukaemia in adult patients: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2016;27(suppl 5):v69–82. https://doi.org/10.1093/annonc/mdw025.CrossRefPubMed

13.

Schoch C, Schnittger S, Bursch S, Gerstner D, Hochhaus A, Berger U, et al. Comparison of chromosome banding analysis, interphase- and hypermetaphase-FISH, qualitative and quantitative PCR for diagnosis and for follow-up in chronic myeloid leukemia: a study on 350 cases. Leukemia. 2002;16(1):53–9. https://doi.org/10.1038/sj.leu.2402329.CrossRefPubMed

14.

Haferlach T, Schoch C, Löffler H, Gassmann W, Kern W, Schnittger S, et al. Morphologic dysplasia in de novo acute myeloid leukemia (AML) is related to unfavorable cytogenetics but has no independent prognostic relevance under the conditions of intensive induction therapy: results of a multiparameter analysis from the German AML cooperative group studies. J Clin Oncol. 2003;21(2):256–65. https://doi.org/10.1200/JCO.2003.08.005.CrossRefPubMed

15.

Mühlbacher V, Zenger M, Schnittger S, Weissmann S, Kunze F, Kohlmann A, et al. Acute lymphoblastic leukemia with low hypodiploid/near triploid karyotype is a specific clinical entity and exhibits a very high TP53 mutation frequency of 93%: characterization of low Hypodiploid ALL. Genes Chromosomes Cancer. 2014;53(6):524–36. https://doi.org/10.1002/gcc.22163.CrossRefPubMed

16.

McGowan-Jordan J, Simons A, Schmid M. ISCN 2016: an international system for human cytogenomic nomenclature (2016). Reprint of: Cytogenetic and genome research 2016, vol. 149, no. 1–2. Basel: S Karger AG; 2016.CrossRef

17.

Stengel A, Shahswar R, Haferlach T, Walter W, Hutter S, Meggendorfer M, et al. Whole transcriptome sequencing detects a large number of novel fusion transcripts in patients with AML and MDS. Blood Adv. 2020;4(21):5393–401. https://doi.org/10.1182/bloodadvances.2020003007.CrossRefPubMedPubMedCentral

18.

Yousefi S, et al. A SNP panel for identification of DNA and RNA specimens. BMC Genomics. 2018;19(1):90. https://doi.org/10.1186/s12864-018-4482-7.CrossRefPubMedPubMedCentral

19.

Poplin R, et al. Scaling accurate genetic variant discovery to tens of thousands of samples. bioRxiv. 2017. https://doi.org/10.1101/201178.

20.

Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9. https://doi.org/10.1093/bioinformatics/btp352.CrossRefPubMedPubMedCentral

21.

Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11(3):R25. https://doi.org/10.1186/gb-2010-11-3-r25.CrossRefPubMedPubMedCentral

22.

Zhang H, Meltzer P, Davis S. RCircos: an R package for Circos 2D track plots. BMC Bioinformatics. 2013;14(1):244. https://doi.org/10.1186/1471-2105-14-244.CrossRefPubMedPubMedCentral

23.

Hulsen T, de Vlieg J, Alkema W. BioVenn - a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams. BMC Genomics. 2008;9(1):488. https://doi.org/10.1186/1471-2164-9-488.CrossRefPubMedPubMedCentral

24.

Wickham H. Ggplot2: elegant graphics for data analysis. 1st ed. New York: Springer; 2010.

25.

Haas BJ, et al. STAR-fusion: fast and accurate fusion transcript detection from RNA-Seq. bioRxiv. 2017. https://doi.org/10.1101/120295.

26.

Chen X, Schulz-Trieglaff O, Shaw R, Barnes B, Schlesinger F, Källberg M, et al. Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics. 2016;32(8):1220–2. https://doi.org/10.1093/bioinformatics/btv710.CrossRefPubMed

27.

Jang YE, Jang I, Kim S, Cho S, Kim D, Kim K, et al. ChimerDB 4.0: an updated and expanded database of fusion genes. Nucleic Acids Res. 2020;48(D1):D817–24. https://doi.org/10.1093/nar/gkz1013.CrossRefPubMed

28.

Talevich E, Shain AH, Botton T, Bastian BC. CNVkit: genome-wide copy number detection and visualization from targeted DNA sequencing. PLoS Comput Biol. 2016;12(4):e1004873. https://doi.org/10.1371/journal.pcbi.1004873.CrossRefPubMedPubMedCentral

29.

Chiaretti S, Zini G, Bassan R. Diagnosis and subclassification of acute lymphoblastic leukemia. Mediterr J Hematol Infect Dis. 2014;6(1):e2014073. https://doi.org/10.4084/mjhid.2014.073.CrossRefPubMedPubMedCentral

30.

Andersson AK, et al. The landscape of somatic mutations in infant MLL-rearranged acute lymphoblastic leukemias. Nat Genet. 2015;47(4):330–7. https://doi.org/10.1038/ng.3230.CrossRefPubMedPubMedCentral

31.

Xie H, Rachakonda PS, Heidenreich B, Nagore E, Sucker A, Hemminki K, et al. Mapping of deletion breakpoints at the CDKN2A locus in melanoma: detection of MTAP-ANRIL fusion transcripts. Oncotarget. 2016;7(13):16490–504. https://doi.org/10.18632/oncotarget.7503.CrossRefPubMedPubMedCentral

32.

Schwab CJ, Chilton L, Morrison H, Jones L, al-Shehhi H, Erhorn A, et al. Genes commonly deleted in childhood B-cell precursor acute lymphoblastic leukemia: association with cytogenetics and clinical features. Haematologica. 2013;98(7):1081–8. https://doi.org/10.3324/haematol.2013.085175.CrossRefPubMedPubMedCentral

33.

Ivanov Öfverholm I, Zachariadis V, Taylan F, Marincevic-Zuniga Y, Tran AN, Saft L, et al. Overexpression of chromatin remodeling and tyrosine kinase genes in iAMP21-positive acute lymphoblastic leukemia. Leuk Lymphoma. 2020;61(3):604–13. https://doi.org/10.1080/10428194.2019.1678153.CrossRefPubMed

34.

Harvey RC, et al. Development and validation of a highly sensitive and specific gene expression classifier to prospectively screen and identify B-precursor acute lymphoblastic leukemia (ALL) patients with a Philadelphia chromosome-like (“Ph-like” or “BCR-ABL1-like”) signature for therapeutic targeting and clinical intervention. Blood. 2013;122:826.CrossRef

35.

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(11):1005–15. https://doi.org/10.1056/NEJMoa1403088.CrossRefPubMedPubMedCentral

36.

Chiaretti S, Messina M, Grammatico S, Piciocchi A, Fedullo AL, di Giacomo F, et al. Rapid identification ofBCR/ABL1-like acute lymphoblastic leukaemia patients using a predictive statistical model based on quantitative real time-polymerase chain reaction: clinical, prognostic and therapeutic implications. Br J Haematol. 2018;181(5):642–52. https://doi.org/10.1111/bjh.15251.CrossRefPubMedPubMedCentral

37.

Sánchez R, Ribera J, Morgades M, Ayala R, Onecha E, Ruiz-Heredia Y, et al. A novel targeted RNA-Seq panel identifies a subset of adult patients with acute lymphoblastic leukemia with BCR-ABL1-like characteristics. Blood Cancer J. 2020;10(4):43. https://doi.org/10.1038/s41408-020-0308-3.CrossRefPubMedPubMedCentral

38.

Chiaretti, et al. Gene expression profiles of B-lineage adult acute lymphocytic leukemia reveal genetic patterns that identify lineage derivation and distinct mechanisms of transformation. Clin Cancer Res. 2005;11(20):7209–19. https://doi.org/10.1158/1078-0432.CCR-04-2165.CrossRefPubMed

39.

Marincevic-Zuniga Y, Dahlberg J, Nilsson S, Raine A, Nystedt S, Lindqvist CM, et al. Transcriptome sequencing in pediatric acute lymphoblastic leukemia identifies fusion genes associated with distinct DNA methylation profiles. J Hematol Oncol. 2017;10(1):148. https://doi.org/10.1186/s13045-017-0515-y.CrossRefPubMedPubMedCentral

40.

Winters JL, Davila JI, McDonald AM, Nair AA, Fadra N, Wehrs RN, et al. Development and verification of an RNA sequencing (RNA-seq) assay for the detection of gene fusions in tumors. J Mol Diagn. 2018;20(4):495–511. https://doi.org/10.1016/j.jmoldx.2018.03.007.CrossRefPubMed

41.

Schieck M, Lentes J, Thomay K, Hofmann W, Behrens YL, Hagedorn M, et al. Implementation of RNA sequencing and array CGH in the diagnostic workflow of the AIEOP-BFM ALL 2017 trial on acute lymphoblastic leukemia. Ann Hematol. 2020;99(4):809–18. https://doi.org/10.1007/s00277-020-03953-3.CrossRefPubMedPubMedCentral

42.

Haas BJ, Dobin A, Li B, Stransky N, Pochet N, Regev A. Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods. Genome Biol. 2019;20(1):213. https://doi.org/10.1186/s13059-019-1842-9.CrossRefPubMedPubMedCentral

43.

Gocho Y, et al. A novel recurrent EP300-ZNF384 gene fusion in B-cell precursor acute lymphoblastic leukemia. Leukemia. 2015;29(12):2445–8. https://doi.org/10.1038/leu.2015.111.CrossRefPubMed

44.

Qian M, Zhang H, Kham SKY, 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(2):185–95. https://doi.org/10.1101/gr.209163.116.CrossRefPubMedPubMedCentral

45.

Hirabayashi S, Ohki K, Nakabayashi K, Ichikawa H, Momozawa Y, Okamura K, et al. ZNF384 -related fusion genes define a subgroup of childhood B-cell precursor acute lymphoblastic leukemia with a characteristic immunotype. Haematologica. 2017;102(1):118–29. https://doi.org/10.3324/haematol.2016.151035.CrossRefPubMedPubMedCentral

46.

Zhang W, Kuang P, Liu T. Prognostic significance of CDKN2A/B deletions in acute lymphoblastic leukaemia: a meta-analysis. Ann Med. 2019;51(1):28–40. https://doi.org/10.1080/07853890.2018.1564359.CrossRefPubMedPubMedCentral

47.

Holmfeldt L, Wei L, Diaz-Flores E, Walsh M, Zhang J, Ding L, et al. The genomic landscape of hypodiploid acute lymphoblastic leukemia. Nat Genet. 2013;45(3):242–52. https://doi.org/10.1038/ng.2532.CrossRefPubMedPubMedCentral

48.

Talevich E, Shain AH. CNVkit-RNA: copy number inference from RNA-sequencing data. bioRxiv. 2018. https://doi.org/10.1101/408534.

49.

Harvey RC, Tasian SK. Clinical diagnostics and treatment strategies for Philadelphia chromosome-like acute lymphoblastic leukemia. Blood Adv. 2020;4(1):218–28. https://doi.org/10.1182/bloodadvances.2019000163.CrossRefPubMedPubMedCentral

50.

Frisch A, Ofran Y. How I diagnose and manage Philadelphia chromosome-like acute lymphoblastic leukemia. Haematologica. 2019;104(11):2135–43. https://doi.org/10.3324/haematol.2018.207506.CrossRefPubMedPubMedCentral

51.

Jain N, Roberts KG, Jabbour E, Patel K, Eterovic AK, Chen K, et al. Ph-like acute lymphoblastic leukemia: a high-risk subtype in adults. Blood. 2017;129(5):572–81. https://doi.org/10.1182/blood-2016-07-726588.CrossRefPubMedPubMedCentral

52.

Roberts KG, Gu Z, Payne-Turner D, McCastlain K, Harvey RC, Chen IM, et al. High frequency and poor outcome of Philadelphia chromosome-like acute lymphoblastic leukemia in adults. J Clin Oncol. 2017;35(4):394–401. https://doi.org/10.1200/JCO.2016.69.0073.CrossRefPubMed

53.

Herold T, Baldus CD, Gökbuget N. Ph-like acute lymphoblastic leukemia in older adults. N Engl J Med. 2014;371(23):2235. https://doi.org/10.1056/NEJMc1412123.CrossRefPubMed

54.

Tasian SK, Hurtz C, Wertheim GB, Bailey NG, Lim MS, Harvey RC, et al. High incidence of Philadelphia chromosome-like acute lymphoblastic leukemia in older adults with B-ALL. Leukemia. 2017;31(4):981–4. https://doi.org/10.1038/leu.2016.375.CrossRefPubMed

55.

Coccaro N, Anelli L, Zagaria A, Specchia G, Albano F. Next-generation sequencing in acute lymphoblastic leukemia. Int J Mol Sci. 2019;20(12):2929. https://doi.org/10.3390/ijms20122929.CrossRefPubMedCentral

56.

Palmi C, Vendramini E, Silvestri D, Longinotti G, Frison D, Cario G, et al. Poor prognosis for P2RY8-CRLF2 fusion but not for CRLF2 over-expression in children with intermediate risk B-cell precursor acute lymphoblastic leukemia. Leukemia. 2012;26(10):2245–53. https://doi.org/10.1038/leu.2012.101.CrossRefPubMed

57.

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(5):569–74. https://doi.org/10.1038/ng.3535.CrossRefPubMed

Title: Clinical application of whole transcriptome sequencing for the classification of patients with acute lymphoblastic leukemia
Authors: Wencke Walter
Rabia Shahswar
Anna Stengel
Manja Meggendorfer
Wolfgang Kern
Torsten Haferlach
Claudia Haferlach
Publication date: 01-12-2021
Publisher: BioMed Central
Keyword: Acute Lymphoblastic Leukemia
Published in: BMC Cancer / Issue 1/2021
Electronic ISSN: 1471-2407
DOI: https://doi.org/10.1186/s12885-021-08635-5

Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras

Prof. Fabrice Barlesi

Developed by: Springer Medicine

2024 ASCO Annual Meeting

Springer Medicine

Abstract

Background

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 1/2021

Identifying outcomes reported in exercise interventions in oesophagogastric cancer survivors: a systematic review

RGD4C peptide mediates anti-p21Ras scFv entry into tumor cells and produces an inhibitory effect on the human colon cancer cell line SW480

Molecular subtypes based on DNA methylation predict prognosis in lung squamous cell carcinoma

Reduction of metal artifacts from knee tumor prostheses on CT images: value of the single energy metal artifact reduction (SEMAR) algorithm

Systemic immune-inflammation index predicts prognosis of sequential therapy with sorafenib and regorafenib in hepatocellular carcinoma

Validity and reliability of the simplified Chinese patient-reported outcomes version of the common terminology criteria for adverse events

Keynote webinar | Spotlight on antibody–drug conjugates in cancer