Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-22T20:55:08.689Z Has data issue: false hasContentIssue false

Chapter 9 - Ancillary Tests

from Section IV - Neoplastic Disorders of Bone Marrow

Published online by Cambridge University Press:  25 January 2024

By
Bradford Siegele ,
Liming Bao ,
Amy L. Treece  and
Xiayuan Liang
Edited by
Xiayuan Liang ,
Bradford Siegele  and
Jennifer Picarsic
Xiayuan Liang
Affiliation:
Children’s Hospital of Colorado
Bradford Siegele
Affiliation:
Children’s Hospital of Colorado
Jennifer Picarsic
Affiliation:
Cincinnati Childrens Hospital Medicine Center
Get access

Summary

Hematolymphoid malignancies represent an area in which ancillary studies offer particularly valuable information for diagnosis, classification, and prognosis, as well as risk-stratified and targeted therapy. The results of multiple test modalities, including flow cytometry, immunohistochemistry, and cytogenetic and molecular genetic analyses, should be integrated and interpreted within the context of morphologic evaluation. In this chapter, the principles, general technical aspects, and clinical applications of these ancillary tests are discussed.

Type
Chapter
Information
Pediatric Pathology of Hematopoietic and Histiocytic Disorders , pp. 97 - 120
Publisher: Cambridge University Press
Print publication year: 2024

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Swerdlow, S, Campo, E, Harris, N, et al., eds. WHO classification of tumors of haematopoietic and lymphoid tissues. Rev. 4th ed. Lyon, France: IARC Press; 2017.Google Scholar
Wood, B, Wu, D, Crossley, B, et al. Measurable residual disease detection by high-throughput sequencing improves risk stratification for pediatric B-ALL. Blood. 2018;131:1350–9.Google Scholar
Borowitz, MJ, Wood, BL, Devidas, M, et al. Prognostic significance of minimal residual disease in high risk B-ALL: a report from Children’s Oncology Group study AALL0232. Blood. 2015;126:964–71.CrossRefGoogle Scholar
Wood, BL. Principles of minimal residual disease detection for hematopoietic neoplasms by flow cytometry. Cytometry B Clin Cytom. 2016;90:47–53.Google Scholar
Berry, DA, Zhou, S, Higley, H, et al. Association of minimal residual disease with clinical outcome in pediatric and adult acute lymphoblastic leukemia: a meta-analysis. JAMA Oncol. 2017;3(7):e170580.Google Scholar
Brown, PA, Wieduwilt, M, Logan, A, et al. Guidelines insights: acute lymphoblastic leukemia, version 1.2019. J Natl Compr Canc Netw. 2019;17:414–23.CrossRefGoogle ScholarPubMed
Theunissen, P, Mejstrikova, E, Sedek, L, et al. Standardized flow cytometry for highly sensitive MRD measurements in B-cell acute lymphoblastic leukemia. Blood. 2017;129:347–57.Google Scholar
Brando, B, Gatti, A, Preijers, F. Flow cytometric diagnosis of paroxysmal nocturnal hemoglobinuria: pearls and pitfalls – a critical review article. EJIFCC. 2019;30:355–70.Google Scholar
Illingworth, A, Marinov, I, Sutherland, DR, et al. ICCS/ESCCA consensus guidelines to detect GPI-deficient cells in paroxysmal nocturnal hemoglobinuria (PNH) and related disorders part 3-data analysis, reporting and case studies. Cytometry B Clin Cytom. 2018;94:49–66.Google Scholar
Fisher, GH, Rosenberg, FJ, Straus, SE, et al. Dominant interfering Fas gene mutations impair apoptosis in a human autoimmune lymphoproliferative syndrome. Cell. 1995;81:935–46.Google Scholar
Oliveira, JB, Bleesing, JJ, Dianzani, U, et al. Revised diagnostic criteria and classification for the autoimmune lymphoproliferative syndrome (ALPS): report from the 2009 NIH International Workshop. Blood. 2010;116:e35–40.CrossRefGoogle ScholarPubMed
Chisholm, KM, Xu, M, Davis, B, et al. Evaluation of the utility of bone marrow morphology and ancillary studies in pediatric patients under surveillance for myelodysplastic syndrome. Am J Clin Pathol. 2018;149:499–513.CrossRefGoogle ScholarPubMed
Veltroni, M, Sainati, L, Zecca, M, et al. Advanced pediatric myelodysplastic syndromes: can immunophenotypic characterization of blast cells be a diagnostic and prognostic tool? Pediatr Blood Cancer. 2009;52:357–63.Google Scholar
Pittaluga, S, Barry, TS, Raffeld, M. Immunohistochemistry for the hematopathology laboratory. In: Jaffe, ES, Arber, DA, Campo, E, et al., eds. Hematopathology. 2nd ed. Philadelphia, PA: Elsevier; 2017:41–52.Google Scholar
Taylor, CR, Rudbeck, L. Immunohistochemical staining methods. 6th ed. Glostrup, Denmark: Dako Denmark; 2013.Google Scholar
Heim, S, Mitelman, F. Primary chromosome abnormalities in human neoplasia. Adv Cancer Res. 1989;52:1–43.Google Scholar
Dohner, H, Estey, E, Grimwade, D, et al. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood. 2017;129:424–47.Google Scholar
Hunger, SP, Mullighan, CG. Redefining ALL classification: toward detecting high-risk ALL and implementing precision medicine. Blood. 2015;125:3977–87.Google Scholar
Vujkovic, M, Attiyeh, EF, Ries, RE, et al. Genomic architecture and treatment outcome in pediatric acute myeloid leukemia: a Children’s Oncology Group report. Blood. 2017;129:3051–8.Google Scholar
Greenberg, PL, Tuechler, H, Schanz, J, et al. Revised international prognostic scoring system for myelodysplastic syndromes. Blood. 2012;120:2454–65.Google Scholar
Dou, H, Chen, X, Huang, Y, et al. Prognostic significance of P2RY8-CRLF2 and CRLF2 overexpression may vary across risk subgroups of childhood B-cell acute lymphoblastic leukemia. Genes Chromosomes Cancer. 2017;56:135–46.CrossRefGoogle ScholarPubMed
Lange, BJ, Kobrinsky, N, Barnard, DR, et al. Distinctive demography, biology, and outcome of acute myeloid leukemia and myelodysplastic syndrome in children with Down syndrome: Children’s Cancer Group Studies 2861 and 2891. Blood. 1998;91:608–15.Google Scholar
Choi, SE, Hong, SW, Yoon, SO. Proposal of an appropriate decalcification method of bone marrow biopsy specimens in the era of expanding genetic molecular study. J Pathol Transl Med. 2015;49:236–42.Google Scholar
Al Hinai, ASA, Grob, T, Kavelaars, FG, et al. Archived bone marrow smears are an excellent source for NGS-based mutation detection in acute myeloid leukemia. Leukemia. 2020;34:2220–4.Google Scholar
Czuchlewski, DR, Peterson, LC. Myeloid neoplasms with germline predisposition: a new provisional entity within the World Health Organization classification. Surg Pathol Clin. 2016;9:165–76.Google Scholar
Chaudhary, G, Dogra, TD, Raina, A. Evaluation of blood, buccal swabs, and hair follicles for DNA profiling technique using STR markers. Croat Med J. 2015;56:239–45.Google Scholar
Preuner, S, Danzer, M, Pröll, J, et al. High-quality DNA from fingernails for genetic analysis. J Mol Diagn. 2014;16:459–66.Google Scholar
Murphy, KM, Levis, M, Hafez, MJ, et al. Detection of FLT3 internal tandem duplication and D835 mutations by a multiplex polymerase chain reaction and capillary electrophoresis assay. J Mol Diagn. 2003;5(2):96–102.Google Scholar
`National Comprehensive Cancer Network. NCCN clinical practice guidelines in oncology: acute myeloid leukemia. Version 3. 2021.Google Scholar
`Tozzo, P, Delicati, A, Zambello, R, et al. Chimerism monitoring techniques after hematopoietic stem cell transplantation: an overview of the last 15 years of innovations. Diagnostics (Basel). 2021;11:621.Google Scholar
Sanger, F, Nicklen, S, Coulson, AR. DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci USA. 1977;74:5463–7.Google Scholar
Liu, W, Saint, DA. A new quantitative method of real time reverse transcription polymerase chain reaction assay based on simulation of polymerase chain reaction kinetics. Anal Biochem. 2002;302:52–9.Google Scholar
Schuurhuis, GJ, Heuser, M, Freeman, S, et al. Minimal/measurable residual disease in AML: a consensus document from the European Leukemia Net MRD Working Party. Blood. 2018;131:1275–91.Google Scholar
Hughes, T, Deininger, M, Hochhaus, A, et al. Monitoring CML patients responding to treatment with tyrosine kinase inhibitors: review and recommendations for harmonizing current methodology for detecting BCR-ABL transcripts and kinase domain mutations and for expressing results. Blood. 2006;108:28–37.Google Scholar
National Comprehensive Cancer Network. NCCN clinical practice guidelines in oncology: chronic myelogenous leukemia. Version 3. 2020.Google Scholar
Bentley, DR, Balasubramanian, S, Swerdlow, HP, et al. Accurate whole human genome sequencing using reversible terminator chemistry. Nature. 2008;456:53–9.Google Scholar
Zheng, Z, Liebers, M, Zhelyazkova, B, et al. Anchored multiplex PCR for targeted next-generation sequencing. Nat Med. 2014;20:1479–84.Google Scholar
van Dongen, JJ, Langerak, AW, Brüggemann, M, 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.Google Scholar
van der Velden, VH, van Dongen, JJ. MRD detection in acute lymphoblastic leukemia patients using Ig/TCR gene rearrangements as targets for real-time quantitative PCR. Methods Mol Biol. 2009;538:115–50.Google Scholar
Mahe, E, Pugh, T, Kamel-Reid, S. T cell clonality assessment: past, present and future. J Clin Pathol. 2018;71:195–200.Google Scholar
Medina, A, Puig, N, Flores-Montero, J, et al. Comparison of next-generation sequencing (NGS) and next-generation flow (NGF) for minimal residual disease (MRD) assessment in multiple myeloma. Blood Cancer J. 2020;10:108.Google Scholar
Schmitt, MW, Kennedy, SR, Salk, JJ, et al. Detection of ultra-rare mutations by next-generation sequencing. Proc Natl Acad Sci USA. 2012;109:14508–13.Google Scholar
Yoest, JM, Shirai, CL, Duncavage, EJ. Sequencing-based measurable residual disease testing in acute myeloid leukemia. Front Cell Dev Biol. 2020;8:249.Google Scholar
Quan, PL, Sauzade, M, Brouzes, E. dPCR: a technology review. Sensors (Basel). 2018;18:1271.Google Scholar
Pettersson, L, Alm, JS, Almstedt, A, et al. Comparison of RNA- and DNA-based methods for measurable residual disease analysis in NPM1-mutated acute myeloid leukemia. Int J Lab Hematol. 2021;43:664–74.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×