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Blood Res (2024) 59:1
Published online February 13, 2024
https://doi.org/10.1007/s44313-024-00001-1
© The Korean Society of Hematology
Genomic technologies for detecting structural variations in hematologic malignancies
Mi‑Ae Jang1* 
Correspondence to : * Mi‑Ae Jang
miaeyaho@gmail.com
© The Author(s) 2024. 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/.
Genomic structural variations in myeloid, lymphoid, and plasma cell neoplasms can provide key diagnostic, prognostic, and therapeutic information while elucidating the underlying disease biology. Several molecular diagnostic approaches play a central role in evaluating hematological malignancies. Traditional cytogenetic diagnostic assays, such as chromosome banding and fluorescence in situ hybridization, are essential components of the current diagnostic workup that guide clinical care for most hematologic malignancies. However, each assay has inherent limitations, including limited resolution for detecting small structural variations and low coverage, and can only detect alterations in the target regions. Recently, the rapid expansion and increasing availability of novel and comprehensive genomic technologies have led to their use in clinical laboratories for clinical management and translational research. This review aims to describe the clinical relevance of structural variations in hematologic malignancies and introduce genomic technologies that may facilitate personalized tumor characterization and treatment.
Keywords Molecular diagnostics, Next-generation sequencing, Leukemia, Lymphoma, Myeloma, Myeloid, Lymphoid
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REVIEW
Blood Res 2024; 59():
Published online February 13, 2024 https://doi.org/10.1007/s44313-024-00001-1
Copyright © The Korean Society of Hematology.
Genomic technologies for detecting structural variations in hematologic malignancies
Mi‑Ae Jang1*
1 Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon‑Ro, Gangnam‑Gu, Seoul 06351, Korea
Correspondence to:* Mi‑Ae Jang
miaeyaho@gmail.com
© The Author(s) 2024. 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/.
Abstract
Genomic structural variations in myeloid, lymphoid, and plasma cell neoplasms can provide key diagnostic, prognostic, and therapeutic information while elucidating the underlying disease biology. Several molecular diagnostic approaches play a central role in evaluating hematological malignancies. Traditional cytogenetic diagnostic assays, such as chromosome banding and fluorescence in situ hybridization, are essential components of the current diagnostic workup that guide clinical care for most hematologic malignancies. However, each assay has inherent limitations, including limited resolution for detecting small structural variations and low coverage, and can only detect alterations in the target regions. Recently, the rapid expansion and increasing availability of novel and comprehensive genomic technologies have led to their use in clinical laboratories for clinical management and translational research. This review aims to describe the clinical relevance of structural variations in hematologic malignancies and introduce genomic technologies that may facilitate personalized tumor characterization and treatment.
Keywords: Molecular diagnostics, Next-generation sequencing, Leukemia, Lymphoma, Myeloma, Myeloid, Lymphoid
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Table 1 . Key somatic structural variations of diagnostic and/or prognostic values in selected hematologic malignancies.
Disease subtype Prognostic implication Chromosomal/molecular abnormality Myeloid neoplasms AML [11] Good.
Intermediate.
Poor.
t(8;21)(q22;q22.1), inv(16)(p13.1q22) or t(16;16)(p13.1;q22).
t(9;11)(p21.3;q23.3).
t(6;9)(p23;q34.1), t(v;11q23.3), t(9;22)(q34.1;q11.2), inv(3)(q21.3q26.2) or t(3;3)(q21.3q26.2), -5 or del(5q), -7, -17/abn(17p), complex karyotype, monosomal karyotype.
MDS [16] Very good.
Good.
Intermediate.
Poor.
Very poor.
-Y, del(11q).
normal karyotype, del(5q), del(12p), del(20q), double including.
del(5q) del(7q), + 8, + 19, i(17q), any other single or double independent clones.
-7, inv(3)/t(3q)/del(3q), double including -7/del(7q), complex: 3 abnormalities.
complex karyotype > 3 abnormalities.
CML [17] Poor + 8, + Ph, i(17q), + 19, -7/7q-, 11q23 or 3q26.2 aberrations, complex karyotype Lymphoid neoplasms B-ALL [10] Good.
Poor.
t(12;21)(p13;q22), high hyperdiploidy (51–65 chromosomes).
t(9;22)(q34.1;q11.2), hypodiploidy (≤ 45 chromosomes), iAMP 21, t(17;19)(q22;p13), t(v;11q23.3).
CLL [18] Favorable.
Intermediate.
Unfavorable.
del(13q) (as a sole abnormality).
normal karyotype, + 12.
del(17p), del(11q).
MM [19] High risk del(17p), t(4;14)(p16;q32), t(14;16)(q32;q23) FL [20] t(14;18)(q32;q21) MCL [20] t(11;14)(q13;q32) Burkitt [20] t(8;14)(q24;q32) Abbreviations: AML acute myeloid leukemia, MDS myelodysplastic syndrome, CML chronic myeloid leukemia, B-ALL B-lymphoblastic leukemia/lymphoma, CLL chronic lymphocytic leukemia, MM multiple myeloma, FL follicular lymphoma, MCL mantle cell lymphoma, + Ph second or extra copy of the Ph chromosome, iAMP21 intrachromosomal amplification of chromosome 21.
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Table 2 . Comparative characteristics of available genomic technologies for hematologic malignancies.
Method CBA FISH CMA RT-PCR Targeted sequencing WGS WTS OGM Analyte Living cells DNA in interphase and metaphase DNA RNA DNA RNA DNA RNA DNA Coverage Genome-wide Targeted Genome-wide Targeted Targeted Targeted Genome-wide Genome-wide Genome-wide Individual cell clone identification Yes Yes No No No No No No No Ability to multiplex Low Low High High High High High High Low to medium Resolution > 5–10 Mb > 100 Kb > 15 Kb NA Single base Single base Single base Single base 0.5–5 kb (for SV), 500 kb (for CNV) Detection range SVs Yes Yes No Limited Limited Yes Yes Yes Yes CNVs Yes Yes Yes Limited Limited Limited Yes Limited Yes SNVs No No No No Yes Yes Yes Yes No Abbreviations: CBA chromosome banding analysis, FISH fluorescence in situ hybridization, CMA chromosomal microarray, RT-PCR reverse transcriptase PCR, WGS whole genome sequencing, WTS whole transcriptome sequencing, OGM optical genomic mapping, NA not available, SV structural variation, CNV copy number variation, SNV single-nucleotide variant.
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