Fusion Genes in Myeloid Malignancies

Abstract

Simple Summary

Oncogenic fusion genes are instrumental in the pathogenesis, diagnosis, prognostication and treatment of myeloid malignancies. This review provides an overview of the clinical significance of fusion genes and discusses the strengths and limitations of available methods that can be used to detect these genes. Through a series of clinical cases, we highlight potential challenges that may be encountered in the management of myeloid malignancies with fusion genes.

1. Introduction

Myeloid malignancies encompass a broad spectrum of disease entities, which include myelodysplastic syndrome (MDS), myeloproliferative neoplasms (MPNs), and acute myeloid leukemia (AML). They are a group of vastly heterogeneous diseases which arise from genetic and epigenetic alterations, and are characterized by the clonal proliferation of abnormal hematopoietic progenitor cells [1,2,3]. Amongst the diverse range of acquired mutations with which myeloid malignancies are associated, chromosomal rearrangements that give rise to fusion genes represent an important class of somatic alterations that are pivotal in oncogenesis.

Fusion genes occur in approximately 30–40% of patients with AML [4,5,6] and 3% of MDS patients [7]. While the prevalence of fusion genes in BCR::ABL1-negative MPNs has not been well characterized, an increase in the frequency of acquired chromosomal aberrations has been observed in accelerated-phase MPNs and likely contributes to the pathogenesis of leukemic transformation [8].

Over the past decade, the topic of fusion genes in myeloid malignancies has garnered increasing attention in the community. Many of these chimeric genes are found to be oncogenic drivers, which are not only disease-defining and prognostic determinants, but also potential therapeutic targets and biomarkers of disease activity. In the field of MPNs, the discovery of BCR::ABL1 fusion (arising from a reciprocal translocation between chromosomes 9 and 22), which is the hallmark of chronic myeloid leukemia (CML), is an archetypal example of how the diagnosis of a fusion gene has successfully transformed clinical outcomes through the development of effective targeted therapy [9]. With advances in state-of-the-art technologies such as high-throughput RNA sequencing, the number of detected fusion genes with pathogenic significance has grown exponentially. Outside of the realm of hematological malignancies, fusion genes have also been recognized as oncogenic drivers in solid-organ cancers [10].

This review outlines the molecular and clinical significance of fusion genes in myeloid malignancies and critically dissects the utility of current diagnostic modalities that can be employed to detect fusion genes. We conclude by sharing clinical insights and reflections based on real-world experience.

2. Mechanisms of Action of Fusion Genes

Fusion genes can arise from chromosomal translocations, insertions, deletions, inversions, tandem duplication, and chromothripsis. Figure 1 illustrates the various mechanisms of chromosomal aberrations that can lead to the formation of fusion genes.

Fusion genes exert their oncogenic effects through one or more mechanisms, including the disruption of normal gene function, altered protein function, dysregulated protein expression, constitutive activation of signaling pathways, and epigenetic changes.

In the classical example of CML, the BCR::ABL1 fusion gene encodes a multi-domain, constitutively active chimeric tyrosine kinase [13], in turn activating downstream signaling pathways that result in abnormal cellular adhesion, inhibition of apoptosis through inactivation of tumor suppressor genes [14,15], and eventually, uncontrolled proliferation of myeloid cells.

The orchestration of epigenetic alterations by fusion genes is well exemplified by the downstream molecular consequences of KMT2A fusion genes. By virtue of the role of KMT2A in histone methylation and transcriptional activation of HOX genes, KMT2A fusion proteins can induce broad changes in histone modification patterns, leading to aberrant gene expression profiles that favor leukemogenesis [16,17,18]. This is a recurrent theme that is similarly observed in AML with RUNX1::RUNX1T1 fusion, as the resultant chimeric fusion proteins have also been shown to mediate the recruitment of chromatin-modifying enzymes to target genes [19].

3. Clinical Implications of Fusion Genes in Myeloid Malignancies

3.1. Establishment of Diagnosis

A strong association exists between specific fusion genes and particular subtypes of leukemia, resulting in genetically defined disease entities. The latest World Health Organization (WHO) classification [20] and International Consensus Classification (ICC) [21] have demonstrated unanimity in their retention of “AML with defining or recurrent genetic abnormalities” as a subcategory of AML, reflecting the essence of molecular classification in the diagnosis of AML (Table 1).

A prototypic example is acute promyelocytic leukemia (APML), a distinct subtype of AML that is defined by the presence of the PML::RARA fusion gene, which results from t(15;17)(q22;q12). Aside from its characteristic morphology, it is uniquely treated with a combination of all-trans-retinoic acid and arsenic trioxide with high response rates [22]. AML with RUNX1::RUNX1T1 fusion represents another paradigm of how a single cytogenetic abnormality of t(8;21)(q22;q22) suffices in delineating a distinct leukemia subtype, in terms of morphology, immunophenotype, association with recurrent cooperating mutations, and favorable clinical outcomes.

AML classification aside, the detection of fusion genes supports the refinement of the diagnosis of rare disease subtypes, such as cases of undifferentiated hypereosinophilia with morphological findings that are not specific for a unifying diagnosis. This is especially relevant in the early phases of disease when the bone marrow shows only moderate histomorphologic changes [23].

3.2. Prognostication

In the prognostication of AML, cooperative groups such as the European Leukemia Net (ELN) have incorporated the presence of chromosomal aberrations and genetic mutations as part of their criteria for risk stratification (Table 2) [24]. This underscores the critical role that fusion genes play in facilitating risk-directed therapy.

3.3. Therapeutic Targets

Taking the molecular basis of fusion genes into account, fusion genes that encode tyrosine kinases, transcription factors, or proteins that interact with transcription factors and oncoproteins can give rise to the development of potential therapeutic targets [25]. Table 3 lists several targets that have been identified and translated into pharmacological interventions.

3.4. Monitoring of Disease Activity

Given that fusion genes may act as oncogenic drivers and are involved in canonical pathways in the pathogenesis of myeloid malignancies, they serve as surrogates of disease activity, and several (such as CBFB::MYH11 and RUNX1::RUNX1T1) [24] have been identified as robust candidates of response biomarkers for monitoring of measurable residual disease (MRD).

4. Diagnostic Methods for the Detection of Fusion Genes

4.1. Karyotyping

Following the discovery of the Philadelphia chromosome in 1960 [38], conventional karyotyping has been adopted as a fundamental and routine investigation for the detection of chromosomal aberrations and structural variants in various hematological malignancies. Karyotyping involves culturing dividing cells, followed by the application of a mitosis inhibitor to arrest cells in metaphase so that chromosomes can be examined in a condensed form. Cells are then subjected to Giemsa banding and studied on slides with an image analyzer by a cytogeneticist. The resolution of karyotyping is approximately 5–7Mb of DNA [39], depending on the banding technique and the type and location of structural variant. The turnaround time is in the range of one to two weeks, contingent upon the complexity of the case and yield of metaphases.

This is a cost-effective but laborious method that is heavily reliant on the expertise of trained technicians and subjective analysis. In cases of complex cytogenetics, not all aberrations can be fully resolved by conventional karyotyping [40,41]. Recent advances in whole transcriptome sequencing revealed a high number of novel fusions in AML and MDS patients with complex cytogenetics [7], which may potentially be missed by conventional karyotyping. In addition, karyotyping is unable to detect cryptic translocations, which inherently involve chromosomal regions with similar G-banding patterns [42,43]. Fusion genes that lie close to telomeric ends of chromosomes also pose technical challenges for detection by conventional karyotyping [44].

4.2. Fluorescence In Situ Hybridization (FISH)

This is a technique that identifies chromosomal abnormalities with the use of fluorescently labeled DNA probes that hybridize complementary DNA of specified genetic sequences. Cells are then analyzed under fluorescence microscopy. FISH can be performed on interphase cells and does not require actively dividing cells. An apparent limitation of FISH is that it does not provide whole genome coverage and is a targeted assay that is only able to interrogate a single genomic region of interest. For genes with multiple fusion partners, such as KMT2A or NUP98, FISH can identify the gene rearrangement of interest but is unable to detect corresponding partner genes. To assess multiple aberrancies, several independent assays are required, which evokes additional cost and complexity in the assay. The turnaround time can be as fast as 24 to 48 h, which enables rapid confirmation of clinical diagnosis, such as in cases of suspected APML [45].

4.3. Optical Genome Mapping (OGM)

OGM, also referred to as next-generation cytogenomics, is a novel technique that maps optically imaged linearized DNA fragments to reference genome sequences. It is performed by the extraction of ultra-high molecular weight DNA, enzymatic labeling of DNA at specific motifs, followed by imaging of individual DNA molecules as they pass through a nanochannel flow cell. This method is centered upon DNA labeling as opposed to sequencing and directly visualizes DNA molecules in their native state, enabling rapid and efficient large-scale structural and copy number evaluation of the genome [46].

Potential drawbacks of this technique include the requirement for high-molecular-weight DNA isolated by specialized kits, and hence it cannot be performed on formalin-fixed paraffin-embedded (FFPE) tissues or on previously extracted DNA using conventional methods. OGM is also unable to detect variants located in centromeric or telomeric regions.

Recent studies have demonstrated that OGM has equal or better sensitivity compared to conventional karyotyping in the diagnostic workup of hematological malignancies [47,48,49]. In 2021, an international consortium for OGM in hematological malignancies was established for the creation of a consensus framework for the implementation of OGM in a clinical setting [50]. Though bioinformatic processing is provided by the manufacturer, one main challenge that was encountered with the use of this technology is the lack of harmonization of criteria for the reporting of clinical variants.

4.4. Polymerase Chain Reaction (PCR)

PCR is an enzymatic assay that allows for rapid amplification of a specific DNA fragment from a complex pool. Reverse transcriptase quantitative PCR (RT-qPCR) enables the detection and quantification of oncogenic fusion transcripts by transcribing RNA into complementary DNA, which is then used as the template for amplification. The quantitative nature of this assay facilitates monitoring of MRD status, which can guide clinical decisions.

RT-qPCR is a sensitive technique that can identify submicroscopic genetic aberrations with a limit of detection that can go as low as 1 target cell in 100,000 non-target cells. It also has a fast turnaround time of three to five days. However, the prerequisite is that it mandates prior knowledge of both fusion partners and a standard curve. As such, it is not capable of providing pan-genomic coverage, nor can it identify novel fusion genes.

Innovation in PCR methodology has led to the development of droplet digital PCR (ddPCR). This is an improvisation of digital PCR, which was first described in 1999 [51], but has only been increasingly adopted as a diagnostic tool in hematological malignancies in recent years [52]. In ddPCR, a sample is partitioned into several thousand droplets of individual PCR reactions to generate a limiting dilution, allowing for the quantification of target nucleic acids at single-molecule resolution and abrogating the need for a standard curve. The sensitivity of ddPCR has been demonstrated to be at least comparable to qPCR in the quantification of JAK2 V617F mutation in MPNs, with its ability to detect mutations at low allelic burdens [53]. This is a result of absolute quantification, which circumvents the difficulties of interpreting results when they fall below the limit of quantification or over the limit of detection [54]. Consequently, the field of molecular diagnostics has witnessed a sharp rise in the development of ddPCR assays for the detection of fusion genes.

4.5. Next-Generation Sequencing (NGS)

NGS refers to high-throughput and massively parallel sequencing, during which numerous small DNA fragments are sequenced and assembled [55]. Common NGS techniques employed to characterize the molecular landscape of malignant conditions include targeted gene panels, whole-genome sequencing, whole-exome sequencing and transcriptome sequencing (RNA-seq). Targeted gene panels, which are extensively employed in clinical laboratories, focus on established mutations and fusion genes recurrently described in myeloid malignancies. Whole-genome sequencing is performed de novo without prior knowledge of sequencing data, while exome sequencing refers to selective sequencing of coding regions of the genome after capturing of the exome. In contrast, RNA-seq interrogates regions of the genome that are transcribed and spliced into mature mRNA, providing information on alternative splicing, mutations, and changes in gene expression, facilitating an unbiased profiling of the target cells of interest.

The detection of fusion genes via DNA sequencing is challenging due to the need to cover a long intron region with many repeated sequences for precise identification of the fusion breakpoint. Also, complex transcriptional or post-transcriptional splicing processes can affect fusion gene detection at the DNA level [56]. As such, RNA-seq is recognized as a more suitable NGS-based approach for the detection of fusion genes.

Several groups have utilized RNA-seq to achieve unprecedented comprehensive profiling of somatic fusion genes in myeloid malignancies [2,7,57,58,59,60]. However, owing to the sheer enormity of the transcriptome, RNA-seq has poor sensitivity for the detection of fusion genes that are expressed at low levels or are diluted by the presence of accompanying non-malignant cell populations. Also, most non-recurrent fusion transcripts detected are stochastic events and are not pathogenic. While this is a powerful methodology, drawbacks associated with RNA-seq include the complexity of sequence library preparation and the need for a robust bioinformatics pipeline with integrated filtering strategies to ensure accurate analysis of results [61]. Though several algorithms for fusion gene selection have been described [62], to date there is no standardized way to interpret data from RNA-seq.

There is also variability with regard to practice and testing capabilities across different molecular laboratories. One major issue relates to the turnaround time of NGS results, which can range from one to three weeks, depending on the design and size of the panel, operational workflow, individual laboratory capability and resources.

4.6. Comparison of Diagnostic Methods

Table 4 summarizes the characteristics of currently available diagnostic methods for the detection of fusion genes.

5. Our Experience: Case Studies and Clinical Insights

In our institution, NGS has been established as an integral investigation in our diagnostic workflow for all patients presenting with suspected myeloid malignancies. We utilize a targeted and combined DNA and RNA panel that enables simultaneous detection of both DNA alterations and RNA fusions in a single run. Here, we share our experience in the form of three clinical vignettes that shed light on various real-world challenges and pitfalls in the detection of fusion genes. They also serve to illustrate the translational impact of fusion genes on clinical applications and therapeutic outcomes.

5.1. Detection of a Cryptic Translocation

A bone marrow aspirate from a 60-year-old lady presenting with skin rash and leukocytosis showed myeloblast infiltrate (25%), increased monocytic component, and dysgranulopoiesis. Interphase FISH using MLL breakapart probes revealed two fusion signals and one 5′MLL gene signal in 60% of 200 nuclei—suggesting that there was insertion of 5′MLL gene in a derivative chromosome. However, her karyotype demonstrated only trisomy 8, which provided no further insight into the underlying mechanism of MLL rearrangement.

She was treated with standard anthracycline-based ‘3+7’ induction chemotherapy, given clinical urgency in the setting of significant leukocytosis and extramedullary involvement. Subsequently, NGS returned positive for mutations in U2AF1, ASXL1, and CBL genes, together with KMT2A::SORBS2 fusion. We were then able to clinch a comprehensive diagnosis of KMT2A-rearranged acute myelomonocytic leukemia, on a background of MDS. After induction chemotherapy, she achieved morphological remission but remained profoundly cytopenic. Given the presumptive diagnosis of underlying MDS, the decision was made to switch her consolidation therapy to azacitidine and venetoclax, as opposed to high-dose cytarabine. She was worked up for allogeneic stem cell transplantation but unfortunately succumbed to infective complications.

Clinical Pearls

This vignette showcases the utility of NGS in the detection of fusion genes in cases of cryptic translocations. Karyotyping was unable to detect the translocation between chromosomes 4 and 11, which gives rise to KMT2A::SORBS2 fusion, a rare fusion that has been reported in pediatric AML [63,64]. The KMT2A gene, once termed a mixed-lineage leukemia (MLL) gene, is well known for its promiscuity, as it fuses with over 100 partner genes with numerous breakpoints in the context of hematological malignancies [64], giving rise to challenges in detection with conventional diagnostic methods. Through this case, we can appreciate the complementary roles of FISH and NGS in elucidating the mechanism of KMT2A rearrangement that underpins the pathophysiology of disease.

While our combined DNA and RNA NGS panel was useful in comprehensive molecular profiling, it had little impact on the choice of initial therapy due to the long turnaround time of two to three weeks. This highlights the importance of robust turnaround time for clinical NGS tests.

5.2. Unravelling a Potential Therapeutic Target

A 38-year-old man presented with generalized bone and joint pains and was found to have peripheral blood leukoerythroblastosis. His initial bone marrow findings were suggestive of BCR::ABL1 fusion and JAK2 mutation negative chronic MPN with fibrosis (reticulin score 4/4). He was started on hydroxyurea for cytoreduction, but deteriorated rapidly with fever and arthralgia and accelerated into leukemic phase (13% myeloblasts with an increased monocytic component comprising 6% promonocytes and 2% monoblasts) in his repeat bone marrow assessment three weeks later. Karyotyping showed complex cytogenetics with t(5;13)(q14;q12), t(10;18)(p15;q21), and t(11;19)(q23;p13.3). NGS panel detected KMT2A::MLLT1 fusion. He was treated with cytarabine and venetoclax, and achieved hematological response with persistence of t(5;13). This prompted an exploratory RNA-seq analysis, which revealed a novel fusion MEF2C::FLT3, consistent with his karyotype t(5;13)(q14;q12). He was switched to maintenance therapy with gilteritinib and venetoclax, and was successfully bridged to allogeneic stem cell transplantation in an MRD-negative state. He remained in complete cytogenetic and molecular remission post-transplant with complete donor chimerism.

Clinical Pearls

This case highlights the challenges with a fibrotic marrow that can mask the findings of initial diagnostics, leading to false negative results and a potential delay in therapeutic management. Given the purported feasibility of the detection of fusion genes in bone marrow trephines [65,66], due consideration should be given to the option of performing FISH and NGS on FFPE sections of bone marrow trephine biopsies when encountering cases of fibrotic marrow with suboptimal or hemodiluted bone marrow aspirates, in order to optimize diagnostic yield.

FLT3 fusion genes are a rare occurrence mostly described in cases of MPNs and myeloid/lymphoid neoplasms associated with eosinophilia [67,68]. The majority of cases with FLT3 fusions exhibited a poor response to conventional chemotherapy, while patients who received a FLT3 inhibitor demonstrated at least a partial or transient response [69,70,71,72]. To our knowledge, MEF2C::FLT3 fusion has not been previously reported in the literature. In our case, the identification of this novel fusion facilitated the use of a targeted FLT3 inhibitor (gilteritinib), which contributed to disease control while sparing excessive toxicity before transplant. The achievement of MRD negativity prior to allogeneic stem cell transplantation predicted the reduced risks of relapse post-transplant [73].

5.3. Resolution of a Diagnostic Dilemma and Impact on the Choice of Therapy

A 48-year-old lady with fever and severe abdominal pain was found to have 84% circulating blasts. Bone marrow aspiration demonstrated a leukemic infiltrate, which morphologically favored the diagnosis of AML without maturation. On immunophenotyping, blasts were found to express both myeloid and B-lymphoid markers including CD19 and CD22. Differentials entertained at that point included AML with aberrant B-lymphoid markers, or B/myeloid mixed phenotypic acute leukemia. As the patient was critically ill with concurrent infective complications and unfit for intensive chemotherapy, she was initially treated with a novel combination of inotuzumab ozogamicin, venetoclax, and dexamethasone, aimed at targeting both myeloblasts and CD22-expressing blasts. Subsequently, her karyotype showed trisomy 8 and interphase FISH was positive for TP53 gene deletion at 17p13.1. FLT3-ITD PCR was also positive. No fusion genes were detected on the initial NGS panel. An additional RNA-seq study returned positive for NUP98::NSD1 fusion.

She was then switched to azacitidine, venetoclax, and gilteritinib as she stabilized. Repeat bone marrow aspiration showed complete remission with flow cytometry demonstrating positive MRD with aberrant CD19+ expression. She was then treated with blinatumomab, a CD19/CD3 bispecific T-cell engager, as a bridge to allogeneic stem cell transplantation. She achieved complete molecular remission with complete donor chimerism following transplantation and was managed with prophylactic strategies to prevent disease relapse, including the use of donor lymphocyte infusions and maintenance with azacitidine in combination with a FLT3 inhibitor (sorafenib).

Clinical Pearls

NUP98::NSD1 is a rare fusion that is more commonly associated with pediatric AML. In a comprehensive transcriptomic study of a large cohort of children and young adults with AML, 4.8% were found to have NUP98::NSD1 fusion [74]. This fusion is recognized as one of the frequently underestimated fusion genes by conventional karyotyping [75], as both partner genes lie close to the telomeric ends of chromosomes 11 (NUP98) and 5 (NSD1) [76]. A high incidence of FLT3-ITD mutations has been reported in AML patients with NUP98::NSD1 fusion, and it has been hypothesized that the interaction between NUP98::NSD1 and FLT3-ITD confers a poor prognosis in this group [76,77].

In this case, the detection of NUP98::NSD1 fusion enabled prompt diagnosis of a rare disease entity that portends an aggressive clinical course with poor outcomes. As NUP98::NSD1 AML tends to respond poorly to conventional chemotherapy, there was greater impetus for the use of targeted therapy and immunotherapy for this patient. Regular monitoring of the fusion was practiced as its resurgence will enable the detection of an early molecular relapse and allow for pre-emptive intervention.

6. Future Directions

As RNA sequencing platforms become more widely accessible and affordable, they will be increasingly incorporated in routine diagnostics for hematological malignancies, refining our understanding of the role of fusion genes in pathogenesis. In an era of precision medicine, this will undoubtedly open new doors to novel therapeutic strategies. Genomic knowledge will also facilitate the repurposing of different chemotherapeutics in other cancers for their use in myeloid malignancies. One such example is the potential adaptation of TRK inhibitors, which were originally developed for the treatment of solid organ cancers expressing the NTRK fusion, as they may exhibit efficacy in rare cases of AML with NTRK fusion [78].

With greater utilization of NGS, concerted efforts towards the generation of a standardized and validated bioinformatics algorithm for the analysis of RNA-seq data are necessary in a bid to improve the clinical utility of this methodology. Artificial intelligence technologies may be the missing piece to this puzzle, given its powerful machine-learning capabilities and promising applications in annotation of sequencing data [79,80].

We foresee that with further advancements in sequencing approaches, techniques will also be geared towards a shorter turnaround time. Strategies that are currently being employed by molecular laboratories to achieve faster results include case prioritization and the design of optimized NGS panels [81]. Prompt identification of actionable fusion genes is especially relevant to the time-sensitive treatment of AML patients, since there is now an expanded spectrum of upfront novel treatment options potentially available. However, high costs associated with NGS-based approaches remain a practical issue that poses a conundrum in the clinical setting.

By nature of their cancer-specific expression, fusion genes serve as reliable markers of MRD and surrogates of disease activity. Given the importance of MRD monitoring in the management of myeloid malignancies (in particular AML) [82], the future will witness an increase in the development, validation, and availability of high-sensitivity assays (ddPCR and NGS-based) for monitoring fusion genes as MRD markers. This may eventually revolutionize the way we treat conventionally adverse-risk patients, especially if a deep molecular remission can be achieved with targeted therapy.

7. Conclusions

Fusion genes are paramount to the pathogenesis, diagnosis, and treatment of myeloid malignancies, with a profound impact on therapeutic approaches and clinical outcomes. With the advent of NGS, while we embrace its benefits and harness the prowess of RNA-seq in the detection of fusion genes, it is essential to recognize its limitations and shortfalls. To date, there is not a single diagnostic assay that can achieve high specificity and sensitivity of detection while being comprehensive (or targeted when necessary) and cost-effective with a rapid turnaround time. As such, there remains a place for conventional diagnostic methods, such as karyotyping, FISH, and PCR, in current clinical practice. Nevertheless, we believe that the standard of care in the diagnosis of myeloid malignancies will continue to evolve with the incorporation of pan-genomic platforms and improved access to novel technologies.

Author Contributions

Conceptualization, C.H.A. and H.T.; writing—original draft preparation, C.H.A.; writing—review and editing, C.H.A., T.T.T., H.T. and Y.T.G.; supervision, Y.T.G. All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figure and Tables

Figure 1. Structural chromosome rearrangements that can lead to the formation of fusion genes [11,12]. Examples of chromosomal aberrations with respective fusion gene products and clinical associations are listed in the panel on the right. Abbreviations: APML; acute promyelocytic leukemia, AML-MRC; acute myeloid leukemia with myelodysplasia-related changes, Ph+ ALL; Philadelphia-positive acute lymphoblastic leukemia.

Table 1

Leukemic fusion genes in the fifth edition of the World Health Organization (WHO) Classification of Hematolymphoid Tumors and International Consensus Classification (2022).

WHO (5th Edition)	ICC (2022)
PML::RARA
RUNX1::RUNX1T1
CBFB::MYH11
DEK::NUP214
RBM15::MRTFA *
BCR::ABL1
KMT2A rearrangement	MLLT3::KMT2AOther KMT2A rearrangement
MECOM rearrangement	GATA2::MECOM(EVI1)Other MECOM rearrangement
NUP98 rearrangement	NUP98::NSD1 *NUP98::KDM5A *NUP98 and other partners *
AML with other defined genetic alterations	PRDM16::RPN1 * NPM1::MLF1 * KAT6A::CREBBP * ETV6::MNX * PICALM::MLLT10 * FUS::ERG * RUNX1::CBFA2T3 * CBFA2T3::GLIS2 *

Fusion genes listed under WHO classification belong to the category of “AML with defining genetic abnormalities”. Fusion genes listed under ICC are categorized under “AML with recurrent genetic abnormalities”, aside from those marked with *, which are classified under “AML with other rare recurring translocations”.

Table 2

Leukemic fusion genes incorporated in European Leukemia Net (ELN) 2022 risk stratification criteria for acute myeloid leukemia.

Risk Category	ELN 2022
Favorable	t(8;21)(q22;q22.1); RUNX1::RUNX1T1inv(16)(p13.1;q22) or t(16;16)(p13.1;q22); CBFB::MYH11
Intermediate	t(9;11)(p21.3;q23.3); MLLT3::KMT2A
Adverse	t(6;9)(p23;q34.1); DEK::NUP214t(v;11q23.3); KMT2A rearrangedt(9;22)(q34.1;q11.2); BCR::ABL1t(8;16)(p11.2;p13.3); KAT6A::CREBBPinv(3)(q21.3;q26.2) or t(3;3)(q21.3;q26.2); GATA2::MECOM (EVI1)

Table 3

Examples of targeted therapy in myeloid malignancies with fusion genes.

Fusion Gene(s) of Interest	Target Category	Drug	Mechanism of Action
BCR::ABL1 FIP1L1::PDGFRA	Tyrosine kinase	Tyrosine kinase inhibitors (targets) [26] ○ Imatinib (BCR-ABL, PDGFR, KIT) ○ Dasatinib (BCR-ABL, PDGFR, KIT, VEGFR, SRC) ○ Nilotinib (BCR-ABL, PDGFR, KIT, VEGF) ○ Bosutinib (BCR-ABL, SRC) ○ Ponatinib (BCR-ABL, PDGFR, KIT)	Inhibition of constitutive activation of tyrosine kinase activity prevents transmission of proliferative signals to the nucleus and induces apoptosis of malignant cells [27].
PML::RARA	Transcription factors	All-trans-retinoic acid (ATRA) [28]	In the absence of retinoic acid, RARA represses the transcription of target genes involved in myeloid differentiation. ATRA binds PML-RARA and converts it to an effective transcriptional activator, inducing differentiation [29].
CBFB::MYH11	Downstream protein-protein interactions	AI-10-49 [30]	Inhibition of protein-protein interactions of CBFB-MYH11 with RUNX1 restores RUNX1 transcriptional activity and prevents recruitment of DNMT3A to target genes [31].
KMT2A rearranged fusion genes	Complexes associated with transcription factors	Menin inhibitors [32] ○ Revumenib [33] ○ Ziftomenib [34]	KMT2A fusion interacts directly with tumor suppressor Menin and leads to hypermethylation and aberrant gene expression including upregulation of HOX gene expression, enhanced proliferation, and blocking differentiation [35]. Disruption of KMT2A-menin complex inhibits leukemogenic transcriptional activity.
DEK::NUP214 NUP98::NSD1	Oncoprotein interactors	Chromosome region maintenance 1; exportin 1 (CRM1) inhibitor ○ Selinexor [36]	Nucleophorin fusions inhibit CRM1-mediated nuclear export of substrates, resulting in aberrant transcriptional activity from the accumulation of CRM1-regulated transcription factors [37]. CRM1 inhibitors disrupt protein complexes formed by nucleophorin fusions with other transcription factors.

Table 4

Characteristics of diagnostic methods for the detection of fusion genes.

	Karyotyping	FISH	PCR	OGM	NGS
Relative specificity	++	+++	+++	+++	+++
Relative sensitivity	+	++	+++	+++	++
Turnaround time	1 to 2 weeks	1 to 2 days	3 to 5 days	3 to 5 days	1 to 3 weeks
Sample requirements	Actively dividing cells, cell culture	Can be performed on interphase cells and FFPE		Ultra-high molecular weight DNA isolated by specialized kits	DNA or RNA isolated by conventional kits
Interrogation approach	Unbiased	Need for pre-defined targets		Unbiased	Unbiased(Option of pre-defined targets in targeted NGS panels)
Demands for logistics and manpower	Trained cytogeneticist		Trained lab scientist	Laboratory infrastructureBioinformatics pipeline
Cost	Low	Moderate	Moderate	High	High
Blind spots	Cryptic structural variants	Unable to identify novel fusion partners		Variant reporting not standardized—detection of variants of undetermined significance

A subjective score is assigned to the relative specificity and sensitivity of each diagnostic method, denoted by the number of ‘+’ symbols.

References

1. Murati, A.; Brecqueville, M.; Devillier, R.; Mozziconacci, M.J.; Gelsi-Boyer, V.; Birnbaum, D. Myeloid malignancies: Mutations, models and management. BMC Cancer; 2012; 12, 304. [DOI: https://dx.doi.org/10.1186/1471-2407-12-304] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22823977]

2. Cancer Genome Atlas Research Network Ley, T.J.; Miller, C.; Ding, L.; Raphael, B.J.; Mungall, A.J.; Robertson, A.; Hoadley, K.; Triche, T.J., Jr.; Laird, P.W. et al. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med.; 2013; 368, pp. 2059-2074. [DOI: https://dx.doi.org/10.1056/NEJMoa1301689] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23634996]

3. Venney, D.; Mohd-Sarip, A.; Mills, K.I. The Impact of Epigenetic Modifications in Myeloid Malignancies. Int. J. Mol. Sci.; 2021; 22, 5013. [DOI: https://dx.doi.org/10.3390/ijms22095013]

4. Grimwade, D.; Hills, R.K.; Moorman, A.V.; Walker, H.; Chatters, S.; Goldstone, A.H.; Wheatley, K.; Harrison, C.J.; Burnett, A.K. Cancer Research Institute Adult Leukaemia Working Group. Refinement of cytogenetic classification in acute myeloid leukemia: Determination of prognostic significance of rare recurring chromosomal abnormalities among 5876 younger adult patients treated in the United Kingdom Medical Research Council trials. Blood; 2010; 116, pp. 354-365. [DOI: https://dx.doi.org/10.1182/blood-2009-11-254441] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20385793]

5. Tyner, J.W.; Tognon, C.E.; Bottomly, D.; Wilmot, B.; Kurtz, S.E.; Savage, S.L.; Long, N.; Schultz, A.R.; Traer, E.; Abel, M. et al. Functional genomic landscape of acute myeloid leukaemia. Nature; 2018; 562, pp. 526-531. [DOI: https://dx.doi.org/10.1038/s41586-018-0623-z]

6. Chen, X.; Wang, F.; Zhang, Y.; Wang, M.; Tian, W.; Teng, W.; Ma, X.; Guo, L.; Fang, J.; Zhang, Y. et al. Panoramic view of common fusion genes in a large cohort of Chinese de novo acute myeloid leukemia patients. Leuk. Lymphoma; 2019; 60, pp. 1071-1078. [DOI: https://dx.doi.org/10.1080/10428194.2018.1516876] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30277115]

7. Stengel, A.; Shahswar, R.; Haferlach, T.; Walter, W.; Hutter, S.; Meggendorfer, M.; Kern, W.; Haferlach, C. Whole transcriptome sequencing detects a large number of novel fusion transcripts in patients with AML and MDS. Blood Adv.; 2020; 4, pp. 5393-5401. [DOI: https://dx.doi.org/10.1182/bloodadvances.2020003007] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33147338]

8. Milosevic, J.D.; Kralovics, R. Genetic and epigenetic alterations of myeloproliferative disorders. Int. J. Hematol.; 2013; 97, pp. 183-197. [DOI: https://dx.doi.org/10.1007/s12185-012-1235-2]

9. Druker, B.J. Translation of the Philadelphia chromosome into therapy for CML. Blood; 2008; 112, pp. 4808-4817. [DOI: https://dx.doi.org/10.1182/blood-2008-07-077958]

10. Gao, Q.; Liang, W.W.; Foltz, S.M.; Mutharasu, G.; Jayasinghe, R.G.; Cao, S.; Liao, W.W.; Reynolds, S.M.; Wyczalkowski, M.A.; Yao, L. et al. Driver Fusions and Their Implications in the Development and Treatment of Human Cancers. Cell Rep.; 2018; 23, pp. 227-238.e223. [DOI: https://dx.doi.org/10.1016/j.celrep.2018.03.050]

11. Gao, J.; Chen, Y.H.; Mina, A.; Altman, J.K.; Kim, K.Y.; Zhang, Y.; Lu, X.; Jennings, L.; Sukhanova, M. Unique morphologic and genetic characteristics of acute myeloid leukemia with chromothripsis: A clinicopathologic study from a single institution. Hum. Pathol.; 2020; 98, pp. 22-31. [DOI: https://dx.doi.org/10.1016/j.humpath.2020.02.003] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32088209]

12. Lafage-Pochitaloff, M.; Alcalay, M.; Brunel, V.; Longo, L.; Sainty, D.; Simonetti, J.; Birg, F.; Pelicci, P.G. Acute promyelocytic leukemia cases with nonreciprocal PML/RARa or RARa/PML fusion genes. Blood; 1995; 85, pp. 1169-1174. [DOI: https://dx.doi.org/10.1182/blood.V85.5.1169.1169] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/7858248]

13. Lugo, T.G.; Pendergast, A.M.; Muller, A.J.; Witte, O.N. Tyrosine kinase activity and transformation potency of bcr-abl oncogene products. Science; 1990; 247, pp. 1079-1082. [DOI: https://dx.doi.org/10.1126/science.2408149] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/2408149]

14. Ren, R. Mechanisms of BCR-ABL in the pathogenesis of chronic myelogenous leukaemia. Nat. Rev. Cancer; 2005; 5, pp. 172-183. [DOI: https://dx.doi.org/10.1038/nrc1567] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/15719031]

15. Crivellaro, S.; Carra, G.; Panuzzo, C.; Taulli, R.; Guerrasio, A.; Saglio, G.; Morotti, A. The non-genomic loss of function of tumor suppressors: An essential role in the pathogenesis of chronic myeloid leukemia chronic phase. BMC Cancer; 2016; 16, 314. [DOI: https://dx.doi.org/10.1186/s12885-016-2346-6]

16. Krivtsov, A.V.; Armstrong, S.A. MLL translocations, histone modifications and leukaemia stem-cell development. Nat. Rev. Cancer; 2007; 7, pp. 823-833. [DOI: https://dx.doi.org/10.1038/nrc2253]

17. Chan, A.K.N.; Chen, C.W. Rewiring the Epigenetic Networks in MLL-Rearranged Leukemias: Epigenetic Dysregulation and Pharmacological Interventions. Front. Cell Dev. Biol.; 2019; 7, 81. [DOI: https://dx.doi.org/10.3389/fcell.2019.00081]

18. Milan, T.; Celton, M.; Lagace, K.; Roques, E.; Safa-Tahar-Henni, S.; Bresson, E.; Bergeron, A.; Hebert, J.; Meshinchi, S.; Cellot, S. et al. Epigenetic changes in human model KMT2A leukemias highlight early events during leukemogenesis. Haematologica; 2022; 107, pp. 86-99. [DOI: https://dx.doi.org/10.3324/haematol.2020.271619]

19. Rejeski, K.; Duque-Afonso, J.; Lubbert, M. AML1/ETO and its function as a regulator of gene transcription via epigenetic mechanisms. Oncogene; 2021; 40, pp. 5665-5676. [DOI: https://dx.doi.org/10.1038/s41388-021-01952-w]

20. Khoury, J.D.; Solary, E.; Abla, O.; Akkari, Y.; Alaggio, R.; Apperley, J.F.; Bejar, R.; Berti, E.; Busque, L.; Chan, J.K.C. et al. The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Myeloid and Histiocytic/Dendritic Neoplasms. Leukemia; 2022; 36, pp. 1703-1719. [DOI: https://dx.doi.org/10.1038/s41375-022-01613-1]

21. Arber, D.A.; Orazi, A.; Hasserjian, R.P.; Borowitz, M.J.; Calvo, K.R.; Kvasnicka, H.M.; Wang, S.A.; Bagg, A.; Barbui, T.; Branford, S. et al. International Consensus Classification of Myeloid Neoplasms and Acute Leukemias: Integrating morphologic, clinical, and genomic data. Blood; 2022; 140, pp. 1200-1228. [DOI: https://dx.doi.org/10.1182/blood.2022015850] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35767897]

22. Lo-Coco, F.; Avvisati, G.; Vignetti, M.; Thiede, C.; Orlando, S.M.; Iacobelli, S.; Ferrara, F.; Fazi, P.; Cicconi, L.; Di Bona, E. et al. Retinoic acid and arsenic trioxide for acute promyelocytic leukemia. N. Engl. J. Med.; 2013; 369, pp. 111-121. [DOI: https://dx.doi.org/10.1056/NEJMoa1300874] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23841729]

23. Wang, S.A.; Hasserjian, R.P.; Tam, W.; Tsai, A.G.; Geyer, J.T.; George, T.I.; Foucar, K.; Rogers, H.J.; Hsi, E.D.; Rea, B.A. et al. Bone marrow morphology is a strong discriminator between chronic eosinophilic leukemia, not otherwise specified and reactive idiopathic hypereosinophilic syndrome. Haematologica; 2017; 102, pp. 1352-1360. [DOI: https://dx.doi.org/10.3324/haematol.2017.165340] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28495918]

24. Dohner, H.; Wei, A.H.; Appelbaum, F.R.; Craddock, C.; DiNardo, C.D.; Dombret, H.; Ebert, B.L.; Fenaux, P.; Godley, L.A.; Hasserjian, R.P. et al. Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN. Blood; 2022; 140, pp. 1345-1377. [DOI: https://dx.doi.org/10.1182/blood.2022016867]

25. Pikman, Y.; Stegmaier, K. Targeted therapy for fusion-driven high-risk acute leukemia. Blood; 2018; 132, pp. 1241-1247. [DOI: https://dx.doi.org/10.1182/blood-2018-04-784157]

26. Gianfelici, V.; Lahortiga, I.; Cools, J. Chromosomal aberrations and fusion genes in myeloid malignancies. Expert. Rev. Hematol.; 2012; 5, pp. 381-393. [DOI: https://dx.doi.org/10.1586/ehm.12.30]

27. Megias-Vericat, J.E.; Ballesta-Lopez, O.; Barragan, E.; Martinez-Cuadron, D.; Montesinos, P. Tyrosine kinase inhibitors for acute myeloid leukemia: A step toward disease control?. Blood Rev.; 2020; 44, 100675. [DOI: https://dx.doi.org/10.1016/j.blre.2020.100675]

28. de The, H.; Pandolfi, P.P.; Chen, Z. Acute Promyelocytic Leukemia: A Paradigm for Oncoprotein-Targeted Cure. Cancer Cell; 2017; 32, pp. 552-560. [DOI: https://dx.doi.org/10.1016/j.ccell.2017.10.002]

29. Zhu, J.; Lallemand-Breitenbach, V.; de The, H. Pathways of retinoic acid- or arsenic trioxide-induced PML/RARalpha catabolism, role of oncogene degradation in disease remission. Oncogene; 2001; 20, pp. 7257-7265. [DOI: https://dx.doi.org/10.1038/sj.onc.1204852]

30. Illendula, A.; Pulikkan, J.A.; Zong, H.; Grembecka, J.; Xue, L.; Sen, S.; Zhou, Y.; Boulton, A.; Kuntimaddi, A.; Gao, Y. et al. Chemical biology. A small-molecule inhibitor of the aberrant transcription factor CBFbeta-SMMHC delays leukemia in mice. Science; 2015; 347, pp. 779-784. [DOI: https://dx.doi.org/10.1126/science.aaa0314]

31. Liu, P.; Liu, J.P.; Sun, S.J.; Gao, Y.; Ai, Y.; Chen, X.; Sun, Y.; Zhou, M.; Liu, Y.; Xiong, Y. et al. CBFB-MYH11 Fusion Sequesters RUNX1 in Cytoplasm to Prevent DNMT3A Recruitment to Target Genes in AML. Front. Cell Dev. Biol.; 2021; 9, 675424. [DOI: https://dx.doi.org/10.3389/fcell.2021.675424] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34336831]

32. Candoni, A.; Coppola, G. A 2024 Update on Menin Inhibitors. A New Class of Target Agents against KMT2A-Rearranged and NPM1-Mutated Acute Myeloid Leukemia. Hematol. Rep.; 2024; 16, pp. 244-254. [DOI: https://dx.doi.org/10.3390/hematolrep16020024] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38651453]

33. Issa, G.C.; Aldoss, I.; Thirman, M.J.; DiPersio, J.; Arellano, M.; Blachly, J.S.; Mannis, G.N.; Perl, A.; Dickens, D.S.; McMahon, C.M. et al. Menin Inhibition With Revumenib for KMT2A-Rearranged Relapsed or Refractory Acute Leukemia (AUGMENT-101). J. Clin. Oncol.; 2024; JCO2400826. [DOI: https://dx.doi.org/10.1200/JCO.24.00826] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/39121437]

34. Wang, E.S.; Issa, G.C.; Erba, H.P.; Altman, J.K.; Montesinos, P.; DeBotton, S.; Walter, R.B.; Pettit, K.; Savona, M.R.; Shah, M.V. et al. Ziftomenib in relapsed or refractory acute myeloid leukaemia (KOMET-001): A multicentre, open-label, multi-cohort, phase 1 trial. Lancet Oncol.; 2024; 25, pp. 1310-1324. [DOI: https://dx.doi.org/10.1016/S1470-2045(24)00386-3]

35. Caslini, C.; Yang, Z.; El-Osta, M.; Milne, T.A.; Slany, R.K.; Hess, J.L. Interaction of MLL amino terminal sequences with menin is required for transformation. Cancer Res.; 2007; 67, pp. 7275-7283. [DOI: https://dx.doi.org/10.1158/0008-5472.CAN-06-2369]

36. Alexander, T.B.; Lacayo, N.J.; Choi, J.K.; Ribeiro, R.C.; Pui, C.H.; Rubnitz, J.E. Phase I Study of Selinexor, a Selective Inhibitor of Nuclear Export, in Combination With Fludarabine and Cytarabine, in Pediatric Relapsed or Refractory Acute Leukemia. J. Clin. Oncol.; 2016; 34, pp. 4094-4101. [DOI: https://dx.doi.org/10.1200/JCO.2016.67.5066]

37. Takeda, A.; Sarma, N.J.; Abdul-Nabi, A.M.; Yaseen, N.R. Inhibition of CRM1-mediated nuclear export of transcription factors by leukemogenic NUP98 fusion proteins. J. Biol. Chem.; 2010; 285, pp. 16248-16257. [DOI: https://dx.doi.org/10.1074/jbc.M109.048785]

38. Nowell, P.C.; Hungerford, D.A. Chromosome studies on normal and leukemic human leukocytes. J. Natl. Cancer Inst.; 1960; 25, pp. 85-109.

39. Vorsanova, S.G.; Yurov, Y.B.; Iourov, I.Y. Human interphase chromosomes: A review of available molecular cytogenetic technologies. Mol. Cytogenet.; 2010; 3, 1. [DOI: https://dx.doi.org/10.1186/1755-8166-3-1]

40. Haferlach, C.; Dicker, F.; Herholz, H.; Schnittger, S.; Kern, W.; Haferlach, T. Mutations of the TP53 gene in acute myeloid leukemia are strongly associated with a complex aberrant karyotype. Leukemia; 2008; 22, pp. 1539-1541. [DOI: https://dx.doi.org/10.1038/leu.2008.143] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18528419]

41. He, P.; Wei, X.; Xu, Y.; Huang, J.; Tang, N.; Yan, T.; Yang, C.; Lu, K. Analysis of complex chromosomal rearrangements using a combination of current molecular cytogenetic techniques. Mol. Cytogenet.; 2022; 15, 20. [DOI: https://dx.doi.org/10.1186/s13039-022-00597-y]

42. Krauter, J.; Peter, W.; Pascheberg, U.; Heinze, B.; Bergmann, L.; Hoelzer, D.; Lubbert, M.; Schlimok, G.; Arnold, R.; Kirchner, H. et al. Detection of karyotypic aberrations in acute myeloblastic leukaemia: A prospective comparison between PCR/FISH and standard cytogenetics in 140 patients with de novo AML. Br. J. Haematol.; 1998; 103, pp. 72-78. [DOI: https://dx.doi.org/10.1046/j.1365-2141.1998.00926.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/9792292]

43. King, R.L.; Naghashpour, M.; Watt, C.D.; Morrissette, J.J.; Bagg, A. A comparative analysis of molecular genetic and conventional cytogenetic detection of diagnostically important translocations in more than 400 cases of acute leukemia, highlighting the frequency of false-negative conventional cytogenetics. Am. J. Clin. Pathol.; 2011; 135, pp. 921-928. [DOI: https://dx.doi.org/10.1309/AJCPJCW6BY0CNIHD] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21571965]

44. Brown, J.; Jawad, M.; Twigg, S.R.; Saracoglu, K.; Sauerbrey, A.; Thomas, A.E.; Eils, R.; Harbott, J.; Kearney, L. A cryptic t(5;11)(q35;p15.5) in 2 children with acute myeloid leukemia with apparently normal karyotypes, identified by a multiplex fluorescence in situ hybridization telomere assay. Blood; 2002; 99, pp. 2526-2531. [DOI: https://dx.doi.org/10.1182/blood.V99.7.2526] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/11895789]

45. Kolhe, R.; Mangaonkar, A.; Mansour, J.; Clemmons, A.; Shaw, J.; Dupont, B.; Walczak, L.; Mondal, A.; Rojiani, A.; Jillella, A. et al. Utility and impact of early t(15;17) identification by Fluorescence In Situ Hybridization (FISH) in clinical decision making for patients in Acute Promyelocytic Leukemia (APL). Int. J. Lab. Hematol.; 2015; 37, pp. 515-520. [DOI: https://dx.doi.org/10.1111/ijlh.12326] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25639817]

46. Dremsek, P.; Schwarz, T.; Weil, B.; Malashka, A.; Laccone, F.; Neesen, J. Optical Genome Mapping in Routine Human Genetic Diagnostics-Its Advantages and Limitations. Genes; 2021; 12, 1958. [DOI: https://dx.doi.org/10.3390/genes12121958]

47. Levy, B.; Baughn, L.B.; Akkari, Y.; Chartrand, S.; LaBarge, B.; Claxton, D.; Lennon, P.A.; Cujar, C.; Kolhe, R.; Kroeger, K. et al. Optical genome mapping in acute myeloid leukemia: A multicenter evaluation. Blood Adv.; 2023; 7, pp. 1297-1307. [DOI: https://dx.doi.org/10.1182/bloodadvances.2022007583]

48. Neveling, K.; Mantere, T.; Vermeulen, S.; Oorsprong, M.; van Beek, R.; Kater-Baats, E.; Pauper, M.; van der Zande, G.; Smeets, D.; Weghuis, D.O. et al. Next-generation cytogenetics: Comprehensive assessment of 52 hematological malignancy genomes by optical genome mapping. Am. J. Hum. Genet.; 2021; 108, pp. 1423-1435. [DOI: https://dx.doi.org/10.1016/j.ajhg.2021.06.001]

49. Balducci, E.; Kaltenbach, S.; Villarese, P.; Duroyon, E.; Zalmai, L.; Friedrich, C.; Suarez, F.; Marcais, A.; Bouscary, D.; Decroocq, J. et al. Optical genome mapping refines cytogenetic diagnostics, prognostic stratification and provides new molecular insights in adult MDS/AML patients. Blood Cancer J.; 2022; 12, 126. [DOI: https://dx.doi.org/10.1038/s41408-022-00718-1]

50. Levy, B.; Kanagal-Shamanna, R.; Sahajpal, N.S.; Neveling, K.; Rack, K.; Dewaele, B.; Olde Weghuis, D.; Stevens-Kroef, M.; Puiggros, A.; Mallo, M. et al. A framework for the clinical implementation of optical genome mapping in hematologic malignancies. Am. J. Hematol.; 2024; 99, pp. 642-661. [DOI: https://dx.doi.org/10.1002/ajh.27175]

51. Vogelstein, B.; Kinzler, K.W. Digital PCR. Proc. Natl. Acad. Sci. USA; 1999; 96, pp. 9236-9241. [DOI: https://dx.doi.org/10.1073/pnas.96.16.9236] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/10430926]

52. Galimberti, S.; Balducci, S.; Guerrini, F.; Del Re, M.; Cacciola, R. Digital Droplet PCR in Hematologic Malignancies: A New Useful Molecular Tool. Diagnostics; 2022; 12, 1305. [DOI: https://dx.doi.org/10.3390/diagnostics12061305]

53. Link-Lenczowska, D.; Pallisgaard, N.; Cordua, S.; Zawada, M.; Czekalska, S.; Krochmalczyk, D.; Kandula, Z.; Sacha, T. A comparison of qPCR and ddPCR used for quantification of the JAK2 V617F allele burden in Ph negative MPNs. Ann. Hematol.; 2018; 97, pp. 2299-2308. [DOI: https://dx.doi.org/10.1007/s00277-018-3451-1] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30056580]

54. Waterhouse, M.; Follo, M.; Pfeifer, D.; von Bubnoff, N.; Duyster, J.; Bertz, H.; Finke, J. Sensitive and accurate quantification of JAK2 V617F mutation in chronic myeloproliferative neoplasms by droplet digital PCR. Ann. Hematol.; 2016; 95, pp. 739-744. [DOI: https://dx.doi.org/10.1007/s00277-016-2623-0]

55. Schuster, S.C. Next-generation sequencing transforms today’s biology. Nat. Methods; 2008; 5, pp. 16-18. [DOI: https://dx.doi.org/10.1038/nmeth1156]

56. Zheng, Z.; Liebers, M.; Zhelyazkova, B.; Cao, Y.; Panditi, D.; Lynch, K.D.; Chen, J.; Robinson, H.E.; Shim, H.S.; Chmielecki, J. et al. Anchored multiplex PCR for targeted next-generation sequencing. Nat. Med.; 2014; 20, pp. 1479-1484. [DOI: https://dx.doi.org/10.1038/nm.3729] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25384085]

57. Engvall, M.; Cahill, N.; Jonsson, B.I.; Hoglund, M.; Hallbook, H.; Cavelier, L. Detection of leukemia gene fusions by targeted RNA-sequencing in routine diagnostics. BMC Med. Genom.; 2020; 13, 106. [DOI: https://dx.doi.org/10.1186/s12920-020-00739-4]

58. Kerbs, P.; Vosberg, S.; Krebs, S.; Graf, A.; Blum, H.; Swoboda, A.; Batcha, A.M.N.; Mansmann, U.; Metzler, D.; Heckman, C.A. et al. Fusion gene detection by RNA-sequencing complements diagnostics of acute myeloid leukemia and identifies recurring NRIP1-MIR99AHG rearrangements. Haematologica; 2022; 107, pp. 100-111. [DOI: https://dx.doi.org/10.3324/haematol.2021.278436] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34134471]

59. Kandarpa, M.; Robinson, D.; Wu, Y.M.; Qin, T.; Pettit, K.; Li, Q.; Luker, G.; Sartor, M.; Chinnaiyan, A.; Talpaz, M. Broad Next-Generation Integrated Sequencing of Myelofibrosis Identifies Disease-Specific and Age-Related Genomic Alterations. Clin. Cancer Res.; 2024; 30, pp. 1972-1983. [DOI: https://dx.doi.org/10.1158/1078-0432.CCR-23-0372]

60. Docking, T.R.; Parker, J.D.K.; Jadersten, M.; Duns, G.; Chang, L.; Jiang, J.; Pilsworth, J.A.; Swanson, L.A.; Chan, S.K.; Chiu, R. et al. A clinical transcriptome approach to patient stratification and therapy selection in acute myeloid leukemia. Nat. Commun.; 2021; 12, 2474. [DOI: https://dx.doi.org/10.1038/s41467-021-22625-y]

61. Byron, S.A.; Van Keuren-Jensen, K.R.; Engelthaler, D.M.; Carpten, J.D.; Craig, D.W. Translating RNA sequencing into clinical diagnostics: Opportunities and challenges. Nat. Rev. Genet.; 2016; 17, pp. 257-271. [DOI: https://dx.doi.org/10.1038/nrg.2016.10] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26996076]

62. Haas, B.J.; 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, 213. [DOI: https://dx.doi.org/10.1186/s13059-019-1842-9] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31639029]

63. Pession, A.; Lo Nigro, L.; Montemurro, L.; Serravalle, S.; Fazzina, R.; Izzi, G.; Nucifora, G.; Slany, R.; Tonelli, R. ArgBP2, encoding a negative regulator of ABL, is fused to MLL in a case of infant M5 acute myeloid leukemia involving 4q35 and 11q23. Leukemia; 2006; 20, pp. 1310-1313. [DOI: https://dx.doi.org/10.1038/sj.leu.2404222] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16628191]

64. Meyer, C.; Burmeister, T.; Groger, D.; Tsaur, G.; Fechina, L.; Renneville, A.; Sutton, R.; Venn, N.C.; Emerenciano, M.; Pombo-de-Oliveira, M.S. et al. The MLL recombinome of acute leukemias in 2017. Leukemia; 2018; 32, pp. 273-284. [DOI: https://dx.doi.org/10.1038/leu.2017.213]

65. Bartels, S.; Hasemeier, B.; Vogtmann, J.; Schipper, E.; Busche, G.; Schlue, J.; Kreipe, H.; Lehmann, U. Feasibility of Combined Detection of Gene Mutations and Fusion Transcripts in Bone Marrow Trephines from Leukemic Neoplasms. J. Mol. Diagn.; 2020; 22, pp. 591-598. [DOI: https://dx.doi.org/10.1016/j.jmoldx.2020.01.004]

66. Tansatit, M. Applications of Fluorescence In Situ Hybridization Technology in Malignancies. Methods Mol. Biol.; 2017; 1541, pp. 75-90. [DOI: https://dx.doi.org/10.1007/978-1-4939-6703-2_8]

67. Venable, E.R.; Gagnon, M.F.; Pitel, B.A.; Palmer, J.M.; Peterson, J.F.; Baughn, L.B.; Hoppman, N.L.; Greipp, P.T.; Ketterling, R.P.; Patnaik, M.S. et al. A TRIP11:: FLT3 gene fusion in a patient with myeloid/lymphoid neoplasm with eosinophilia and tyrosine kinase gene fusions: A case report and review of the literature. Cold Spring Harb. Mol. Case Stud.; 2023; 9, a006243. [DOI: https://dx.doi.org/10.1101/mcs.a006243]

68. Zhang, H.; Paliga, A.; Hobbs, E.; Moore, S.; Olson, S.; Long, N.; Dao, K.T.; Tyner, J.W. Two myeloid leukemia cases with rare FLT3 fusions. Cold Spring Harb. Mol. Case Stud.; 2018; 4, a003079. [DOI: https://dx.doi.org/10.1101/mcs.a003079]

69. Walz, C.; Erben, P.; Ritter, M.; Bloor, A.; Metzgeroth, G.; Telford, N.; Haferlach, C.; Haferlach, T.; Gesk, S.; Score, J. et al. Response of ETV6-FLT3-positive myeloid/lymphoid neoplasm with eosinophilia to inhibitors of FMS-like tyrosine kinase 3. Blood; 2011; 118, pp. 2239-2242. [DOI: https://dx.doi.org/10.1182/blood-2011-03-343426]

70. Falchi, L.; Mehrotra, M.; Newberry, K.J.; Lyle, L.M.; Lu, G.; Patel, K.P.; Luthra, R.; Popat, U.; Verstovsek, S. ETV6-FLT3 fusion gene-positive, eosinophilia-associated myeloproliferative neoplasm successfully treated with sorafenib and allogeneic stem cell transplant. Leukemia; 2014; 28, pp. 2090-2092. [DOI: https://dx.doi.org/10.1038/leu.2014.168]

71. Chao, A.K.; Meyer, J.A.; Lee, A.G.; Hecht, A.; Tarver, T.; Van Ziffle, J.; Koegel, A.K.; Golden, C.; Braun, B.S.; Sweet-Cordero, E.A. et al. Fusion driven JMML: A novel CCDC88C-FLT3 fusion responsive to sorafenib identified by RNA sequencing. Leukemia; 2020; 34, pp. 662-666. [DOI: https://dx.doi.org/10.1038/s41375-019-0549-y] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31511612]

72. Tang, G.; Tam, W.; Short, N.J.; Bose, P.; Wu, D.; Hurwitz, S.N.; Bagg, A.; Rogers, H.J.; Hsi, E.D.; Quesada, A.E. et al. Myeloid/lymphoid neoplasms with FLT3 rearrangement. Mod. Pathol.; 2021; 34, pp. 1673-1685. [DOI: https://dx.doi.org/10.1038/s41379-021-00817-7]

73. Jentzsch, M.; Bischof, L.; Backhaus, D.; Brauer, D.; Schulz, J.; Franke, G.N.; Vucinic, V.; Niederwieser, D.; Platzbecker, U.; Schwind, S. Impact of MRD status in patients with AML undergoing allogeneic stem cell transplantation in the first vs. the second remission. Blood Adv.; 2022; 6, pp. 4570-4580. [DOI: https://dx.doi.org/10.1182/bloodadvances.2022007168]

74. Bertrums, E.J.M.; Smith, J.L.; Harmon, L.; Ries, R.E.; Wang, Y.J.; Alonzo, T.A.; Menssen, A.J.; Chisholm, K.M.; Leonti, A.R.; Tarlock, K. et al. Comprehensive molecular and clinical characterization of NUP98 fusions in pediatric acute myeloid leukemia. Haematologica; 2023; 108, pp. 2044-2058. [DOI: https://dx.doi.org/10.3324/haematol.2022.281653]

75. Shiba, N.; Ichikawa, H.; Taki, T.; Park, M.J.; Jo, A.; Mitani, S.; Kobayashi, T.; Shimada, A.; Sotomatsu, M.; Arakawa, H. et al. NUP98-NSD1 gene fusion and its related gene expression signature are strongly associated with a poor prognosis in pediatric acute myeloid leukemia. Genes. Chromosomes Cancer; 2013; 52, pp. 683-693. [DOI: https://dx.doi.org/10.1002/gcc.22064] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23630019]

76. Hollink, I.H.; van den Heuvel-Eibrink, M.M.; Arentsen-Peters, S.T.; Pratcorona, M.; Abbas, S.; Kuipers, J.E.; van Galen, J.F.; Beverloo, H.B.; Sonneveld, E.; Kaspers, G.J. et al. NUP98/NSD1 characterizes a novel poor prognostic group in acute myeloid leukemia with a distinct HOX gene expression pattern. Blood; 2011; 118, pp. 3645-3656. [DOI: https://dx.doi.org/10.1182/blood-2011-04-346643] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21813447]

77. Akiki, S.; Dyer, S.A.; Grimwade, D.; Ivey, A.; Abou-Zeid, N.; Borrow, J.; Jeffries, S.; Caddick, J.; Newell, H.; Begum, S. et al. NUP98-NSD1 fusion in association with FLT3-ITD mutation identifies a prognostically relevant subgroup of pediatric acute myeloid leukemia patients suitable for monitoring by real time quantitative PCR. Genes. Chromosomes Cancer; 2013; 52, pp. 1053-1064. [DOI: https://dx.doi.org/10.1002/gcc.22100]

78. Joshi, S.K.; Qian, K.; Bisson, W.H.; Watanabe-Smith, K.; Huang, A.; Bottomly, D.; Traer, E.; Tyner, J.W.; McWeeney, S.K.; Davare, M.A. et al. Discovery and characterization of targetable NTRK point mutations in hematologic neoplasms. Blood; 2020; 135, pp. 2159-2170. [DOI: https://dx.doi.org/10.1182/blood.2019003691] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32315394]

79. Del Giudice, M.; Peirone, S.; Perrone, S.; Priante, F.; Varese, F.; Tirtei, E.; Fagioli, F.; Cereda, M. Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology. Int. J. Mol. Sci.; 2021; 22, 4563. [DOI: https://dx.doi.org/10.3390/ijms22094563]

80. Tang, M.; Antic, Z.; Fardzadeh, P.; Pietzsch, S.; Schroder, C.; Eberhardt, A.; van Bommel, A.; Escherich, G.; Hofmann, W.; Horstmann, M.A. et al. An artificial intelligence-assisted clinical framework to facilitate diagnostics and translational discovery in hematologic neoplasia. EBioMedicine; 2024; 104, 105171. [DOI: https://dx.doi.org/10.1016/j.ebiom.2024.105171]

81. Lee, T.D.; Aisner, D.L.; David, M.P.; Eno, C.C.; Gagan, J.; Gocke, C.D.; Guseva, N.V.; Haley, L.; Jajosky, A.N.; Jones, D. et al. Current clinical practices and challenges in molecular testing: A GOAL Consortium Hematopathology Working Group report. Blood Adv.; 2023; 7, pp. 4599-4607. [DOI: https://dx.doi.org/10.1182/bloodadvances.2023010149] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37236162]

82. Heuser, M.; Freeman, S.D.; Ossenkoppele, G.J.; Buccisano, F.; Hourigan, C.S.; Ngai, L.L.; Tettero, J.M.; Bachas, C.; Baer, C.; Bene, M.C. et al. 2021 Update on MRD in acute myeloid leukemia: A consensus document from the European LeukemiaNet MRD Working Party. Blood; 2021; 138, pp. 2753-2767. [DOI: https://dx.doi.org/10.1182/blood.2021013626] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34724563]