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Genomic profiling for clinical decision making in myeloid neoplasms and acute leukemia
Published online 2022 Sep 24. doi: 10.1182/blood.2022015853

Abstract
Myeloid neoplasms and acute leukemias derive from the clonal expansion of hematopoietic cells driven by somatic gene mutations. Although assessment of morphology plays a crucial role in the diagnostic evaluation of patients with these malignancies, genomic characterization has become increasingly important for accurate diagnosis, risk assessment, and therapeutic decision making. Conventional cytogenetics, a comprehensive and unbiased method for assessing chromosomal abnormalities, has been the mainstay of genomic testing over the past several decades and remains relevant today. However, more recent advances in sequencing technology have increased our ability to detect somatic mutations through the use of targeted gene panels, whole-exome sequencing, whole-genome sequencing, and whole-transcriptome sequencing or RNA sequencing. In patients with myeloid neoplasms, whole-genome sequencing represents a potential replacement for both conventional cytogenetic and sequencing approaches, providing rapid and accurate comprehensive genomic profiling. DNA sequencing methods are used not only for detecting somatically acquired gene mutations but also for identifying germline gene mutations associated with inherited predisposition to hematologic neoplasms. The 2022 International Consensus Classification of myeloid neoplasms and acute leukemias makes extensive use of genomic data. The aim of this report is to help physicians and laboratorians implement genomic testing for diagnosis, risk stratification, and clinical decision making and illustrates the potential of genomic profiling for enabling personalized medicine in patients with hematologic neoplasms.

Complementing the recently published Blood articles outlining the 2022 International Consensus Classifications for hematological malignancies (Vol. 140, Issue 11), this pair of Special Reports illustrates how molecular pathology can be applied to precision medicine. de Leval and colleagues summarize the potential of DNA sequencing of tumors and cell-free plasma, epigenetic profiling, and single-cell analyses to inform clinical decision-making about diagnosis, prognosis, and treatment for patients with lymphoid neoplasms. Similarly, Duncavage and colleagues cover genomic profiling for myeloid neoplasms and the acute leukemias, focusing principally on somatic changes but also with emphasis on the emerging importance of germline gene mutations in certain diseases. Both articles provide up-to-date references for how to apply genomic information to practice.

Introduction
Genomic characterization is essential for the management of myeloid neoplasms and acute leukemia, providing critical information for diagnosis, risk assessment, therapeutic decisions, residual disease monitoring, progression, and treatment resistance (Figure 1). Chromosome banding analysis complemented by a variety of molecular studies are a central facet of evaluation, with new genomic techniques increasingly being used to improve characterization as described herein and in Table 1. This article is meant to be a practical guide for the application of genomic methods in the clinical evaluation of myeloid neoplasms and acute leukemia.

Conventional methods
Chromosome banding analysis Karyotyping remains the most widely used and unbiased method for assessing chromosomal abnormalities including numerical (amplifications and losses) and structural (translocations, deletions, and inversions) abnormalities.1, 2, 3 The main limitations are the requirement for live culturable cells, low resolution (5-10 Mb), and low sensitivity (abnormalities present in 5%-10% of cells or an analytical sensitivity of ∼10−1). Turnaround times are generally between 2 and 21 days and may vary considerably between laboratories.
FISH Fluorescence in situ hybridization (FISH) is often used to complement chromosome banding analysis and can be performed on both cultured dividing cells (metaphase) and fixed or nondividing cells (interphase). FISH probes can only identify genomic events at specific targeted regions but is more sensitive than cytogenetics (abnormalities in 1%-5% of cells or an analytical sensitivity of ∼10−2) and can detect cytogenetically cryptic abnormalities.4,5 Turnaround times are generally 1 to 3 days.
CMAs Chromosomal microarrays (CMAs) are typically used to identify small, unbalanced abnormalities or cryptic copy number alterations (CNAs) but do not detect balanced rearrangements and, unlike karyotype, cannot distinguish changes occurring in separate clones. In addition, CMAs including single nucleotide polymorphism (SNP) probes (SNP arrays) can detect LOH and facilitate the determination of chromosomal ploidy.6 Unlike FISH, CMAs are unbiased and can detect abnormalities genome wide. CMAs are run from tumor DNA without requiring live cells and can detect small abnormalities (20-100 kb) present in 20% to 30% of tumor cells (or an analytical sensitivity of >10−1). Turnaround times are generally between 3 and 14 days. Although not array-based, multiplex ligation-dependent probe amplification (MLPA) can also be used to detect specific CNAs (including single exon events) through the use of multiple sequence-specific probes spanning a specific region, which are then amplified to determine DNA copy state.

OGM Optical genome mapping (OGM) methods are an unbiased approach that use genome-wide high-resolution enzymatic restriction digests of high-molecular-weight genomic DNA to identify structural variants such as translocations, inversions, and CNAs.8 Although not widely used in the clinical laboratory today, turnaround times are typically between 4 and 7 days with maximum sensitivity of ∼5%.9
PCR Polymerase chain reaction (PCR) is a technique based on the enzymatic replication of DNA (or complementary DNA [cDNA] from reverse-transcribed RNA) and can generate tens of billions of copies of a particular small DNA or cDNA fragment (the sequence of interest), allowing gene mutations to be detected by various methods. Most clinical PCR applications use allele-specific PCR with primers for a specific mutation that only produce a PCR product when the mutation is present. Real-time, quantitative PCR (qPCR) can rapidly quantify specific fragments containing a sequence alteration and is used to detect specific single gene mutations or gene rearrangements. Digital PCR (dPCR) technologies enable absolute quantification through partitioning the reaction into thousands of independent PCRs to achieve high levels of sensitivity (1 mutation in 10 000 normal cells or a sensitivity of 10−4). qPCR and dPCR methods are generally suited to detect specific recurrent genetic alterations such as single gene mutations (eg, JAK2 p.V617F, KIT p.D816V) and distinct fusions (eg, BCR::ABL1) for diagnosis and disease monitoring. Turnaround times are generally 2 to 5 days.
Sanger sequencing Sanger sequencing detects small gene-level DNA variation from PCR-amplified DNA fragments (<1 kb). Individual DNA bases are detected by electrophoresis due to the random incorporation of fluorescently labeled chain-terminating dideoxynucleotides by DNA polymerase during in vitro DNA replication. Sanger sequencing is generally used to detect gene mutations confined to single exons (eg, CEBPA, CALR) and has a relatively low analytic sensitivity of ∼20% (>10−1) but can be turned around more rapidly than next-generation sequencing (NGS)–based methods.
NGS-based methods
General concepts Building upon the previously described conventional genomic methods, NGS, massive parallel sequencing, or high throughput sequencing uses millions or billions of parallel sequencing reactions to identify genomic abnormalities. NGS is highly scalable and can be coupled with enrichment technologies to interrogate a small subset of key genes (targeted gene panels), up to thousands of genes or genomic regions (whole-exome sequencing [WES]), or can be used without enrichment to detect full genome (whole genome sequencing [WGS]) or transcriptome-wide (whole-transcriptome sequencing [WTS] or RNA sequencing [RNA-seq]) genomic abnormalities. Depending on the design of the assay, NGS can be used to study the full range of genomic variation, including SNVs, small indels, structural changes (ie, CNAs), gene fusions or chromosomal translocations, gene expression, and DNA methylation, and may be used for initial diagnosis as well as monitoring.
The abundance of a detected variant is generally represented as a variant allele frequency (VAF, Figure 2A), which represents the ratio of sequencing reads that contain a variant at a given position in the genome divided by the total number of reads at that position. VAFs are considered a semiquantitative measure because the exact methods used to calculate VAFs, VAF precision, and VAF accuracy will differ slightly between laboratories. In addition, chromosomal aneuploidy, LOH, or gene amplification/deletion can skew both inherited and somatic variant VAFs either higher or lower. The methods used to separate somatic variants (those present in cancer cells) from germline variants (polymorphisms or pathogenic alterations in germline DNA) may also differ between laboratories. Although the gold standard for establishing somatic status of a variant involves sequencing both tumor and paired non-neoplastic tissue DNA, this approach is expensive and impractical for most clinical testing. Instead, many laboratories often infer variants with VAFs near 50% (in regions without CNAs) as germline; the use of population databases (1000 Genomes,10 gnomAD,11 dbSNP,12 etc) to filter out known polymorphisms is highly suggested to separate germline variants from somatic mutations. The exact methods used to filter polymorphisms varies widely between laboratories13 and may give rise to both false-positive somatic calls, especially in genes with low probability of loss intolerance (pLI)14 scores such as TET2,15 and false-negative calls, often resulting from the presence of clonal hematopoiesis of indeterminate potential (CHIP) variants (ie, DNMT3A p.R882) coded as polymorphisms in "normal" population databases.16,17 The limit of detection for standard NGS assays is generally determined by the sequencing coverage depth and typically ranges from 2% to 5% VAF (Figure 2B); reliable detection of variants below this level typically requires error-correction methods (described in further sections).

Key concepts in sequencing-based diagnostics. (A) VAF represents the ratio of sequencing reads that contain a variant divided by the total number of reads at that position. Because most somatic mutations are heterozygous, doubling the VAF generally indicates the fraction of cells with the mutation (except when the mutation occurs in a region of copy number alteration). (B) Coverage represents the number of sequencing reads (red and blue indicating forward and reverse reads, respectively) that span a particular region. Approximate coverage levels for different sequencing approaches are compared. Higher coverage (or more independent observations) generally yields more sensitive sequencing. Shown on the right is the coverage depth required to detect mutations at various VAFs. Binomial sampling probability for detection of variants with VAFs of 50% (typical inherited variants; black), 2% (general sensitivity for targeted panels; red), and 0.1% (MRD assays; blue) assuming each variant must be seen at least twice. (C) DNA-sequencing methods. In WGS, libraries are created by ligating sequencing adapters (gray and orange) to the 3′ and 5′ ends of short genomic DNA fragments called “inserts.” Gene panels or exome sequencing enriches DNA of interest form a library using antisense capture probes (green) labeled with biotin, which are then hybridized to DNA inserts from the sequencing and then physically enriched using streptavidin-coated magnetic beads. (D) High-sensitivity sequencing for MRD detection requires error correction to reliably identify mutations below the intrinsic error rate of the sequencer and to account for PCR errors. Error-corrected deep sequencing reduces false-positive calls for low VAF variants by tagging individual DNA molecules with unique molecular identifiers (UMIs). In this example a “true” mutation “T” is present in a single DNA molecule that labeled with a UMI (green). Library amplification and sequencing will result in duplicate DNA molecules each labeled with the same UMI. Randomly accumulated sequencing and PCR errors (orange) will be present in only a subset of reads with a particular UMI (green, purple, red). During sequencing analysis, variants present on only a subset of reads from a particular “read family” with the same UMI will be discarded as errors; true mutations present in the original DNA molecule will be detected in all reads within a read family with the same UMI. UMI methods can be further improved by tracking both DNA strands using “duplex sequencing,” which can yield sensitivities of 10−6.31 Professional illustration by Patrick Lane, ScEYEnce Studios.

In addition to interlaboratory differences in VAFs and assignment of somatic status to variants, variant classification and annotation, including assigning variants to different "tiers" based on pathogenicity or clinical significance, may differ between laboratories. In general, it is advised that interpretations follow professional guidelines such as the Association for Molecular Pathology, the American Society for Clinical Oncology, and the College of American Pathologists guidelines,13 or disease-specific National Comprehensive Cancer Network (NCCN) guidelines, or World Health Organization guidelines, where possible. Variant annotations should also be considered in the context of the patient’s disease, and genomic data should be interpreted in conjunction with blood and bone marrow (BM) morphology, flow cytometry, and relevant clinical data.

Targeted gene panels Driver mutations (recurrent somatic mutations known to be involved in disease pathogenesis) in specific myeloid neoplasms and acute leukemias tend to occur in a core group of 20 to 50 genes and are ideally suited to detection by small gene panels.18,19 Targeted panels, used most frequently in clinical laboratories, direct sequencing to specific genes or genetic regions that have defined clinical relevance and dictate clinical management.20,21 Key genes that should be included in sequencing panels for different diagnostic entities are summarized in Table 2. Enrichment strategies (Figure 2C) include the capture of genetic regions of interest though hybridization (hybrid capture using DNA or RNA probes) or PCR amplification (amplicon enrichment). This provides critical benefits over broad exome (WES) and genome (WGS) sequencing by increasing sensitivity in clinically relevant regions and by decreasing sequencing cost. Depending on the design, targeted panels may be able to detect CNAs and chromosomal translocations in addition to SNVs and indels. It should be noted that the detection of larger insertions including FLT3 internal tandem duplications (ITDs) and KMT2A (MLL) partial tandem duplications (PTDs) generally require specialized informatics approaches and may not be detected by all panels.22,23 Targeted sequencing methods may also be applied to RNA via capture or PCR amplification of cDNA, techniques used primarily to detect recurrent chromosomal translocations in hematologic malignancies. Turnaround times for targeted gene panels are generally 5 to 14 days.

Genomic sequencing (WES and WGS) and transcriptomic sequencing (WTS or RNA-seq) Broad genomic sequencing assays allow for the detection of genomic alterations anywhere in the coding genome (WES, interrogating 1%-2% of the whole genome) or entire genome (WGS).24,25 WGS and WES entail higher sequencing costs and more extensive data analysis pipelines, and do not typically achieve the same level of coverage depth as targeted gene panels, resulting in lower analytic sensitivity. Most WES applications are primarily limited to the research setting. In comparison, WGS, which can detect a full range of genomic alterations including CNAs and chromosomal rearrangements, has shown promise as a clinical application, especially in cases with unsuccessful conventional cytogenetics.26 Transcriptome-wide sequencing (WTS or RNA-seq) detects both chromosomal rearrangements and changes in messenger RNA (mRNA) and microRNA (miRNA) expression and is primarily limited to research and discovery.27 In ALL, WTS has led to the identification of unique B-cell ALL (B-ALL) subtypes and development of targeted panels for clinical use.28 Decreasing cost, increasing wider availability, and evidence for clinical utility will likely foster the integration of genomic sequencing technologies into routine clinical testing.
Molecular MRD methods
qPCR and FISH The oldest and most established method for monitoring MRD in hematopoietic neoplasms rely on the detection of previously identified translocations (PML::RARA, BCR::ABL1, RUNX1::RUNX1T1) or recurrent insertions/deletion (NPM1 exon 11 mutation [ENST00000296930]; the same mutation may also be annotated in exon 12 depending on the transcript used by the laboratory). Recurrent translocations may be detected either by FISH or more sensitive qPCR from RNA, with sensitivities of 10−2 and 10−6, respectively.29 In AML, high sensitivity monitoring for highly recurrent NPM1 exon 11 gene mutation can be accomplished by qPCR with a sensitivity of ∼10−3 or lower.30
High-sensitivity sequencing for somatic variants The sensitivity and specificity of NGS can be improved with UMIs to tag individual DNA templates on single or dual strands of the target and can increase the analytic detection sensitivity to up to 10−6.31, 32, 33 For detecting low levels of molecular disease after treatment in AML, a sensitivity of at least 10−3 is recommended.34 UMIs are used to computationally “collapse” DNA sequence information into “consensus” reads, allowing removal of PCR or sequencing errors absent in identically tagged templates (Figure 2D).35, 36, 37, 38 UMI-based sequencing can be coupled with DNA enrichment to create generalized MRD panels or to monitor previously detected mutations in a patient-specific manner.
T-cell receptor (TR) and immunoglobulin NGS-based MRD approaches NGS of the hypervariable regions of immunoglobulin (IGH, IGK, or IGL) and/or TR (TRB, TRG) can be used to measure MRD in B- or T-cell lymphoblastic leukemias. Although IGH/TR rearrangements are reasonably specific for identifying a patient’s neoplastic clone, importantly, these sequences may rarely occur as part of the normal immune repertoire at <10−4, and conversely, clonal sequences may continue to change through ongoing variable diversity joining recombination, potentially resulting in false-negative calls. Thus, caution should be exercised in interpreting very low levels of an IG/TR clone that is identical to the patient’s ALL clone.39, 40, 41, 42 It is also recommended that laboratories follow more than 1 IG/TR clone (when possible) to reduce the chance of false-negative MRD errors.
Appropriate genomic testing depends on the clinical scenario or diagnostic disease category. The sections that follow provide recommendations for genomic testing in specific clinical contexts and myeloid neoplasm subgroups, for diagnosis, classification, prognosis, and disease monitoring after therapy. Suggested genes to be tested in specific diagnostic entities are summarized in Table 2.

Myeloid neoplasms and inherited/germline disorders
With the advent of NGS, individuals are increasingly recognized as having potentially deleterious germline variants that predispose to hematologic neoplasms, especially myeloid neoplasms (Table 3).43, 44, 45, 46, 47, 48, 49 Most of these are inherited, but some can occur de novo and are newly acquired in that individual’s germline, and as such, can be inherited by that individual’s descendants. Current indications for germline genetic testing include patients with ≥2 cancers, 1 of which is a hematologic neoplasm,50 and those with a hematologic neoplasm and a positive family history. Although, historically, germline genetic testing has been performed mainly on patients with myeloid neoplasms who received the diagnosis under the age of 40 to -50 years, it is now recognized that young age at diagnosis or positive family history are not required to justify genetic testing.51,52 Thus, germline predisposition risk should be considered for all patients diagnosed with a myeloid neoplasm regardless of age because some germline predisposition alleles, such as those in DDX41, present at older ages.43,48,53, 54, 55 In patients with mutations detected on sequencing panels that could represent pathogenic germline variants (eg, CEBPA, DDX41, GATA2, RUNX1, or TP53 mutations, among others) and occurring at ∼50% VAF, germline predisposition testing should be considered particularly if mutations persist in remission.56 Genetic counselors and health care providers should be familiar with testing options, including optimal sample types (eg, cultured skin fibroblasts to ensure exclusion of somatic mutations present in hematopoietic cells) and other types of tissues accepted by some laboratories (including hair follicles or skin biopsies washed to remove blood), as well as available testing platforms.57 Challenges to clinical testing for these disorders include the lack of training for most clinicians regarding these conditions, the rapid increase in genes under consideration, the high proportion of variants of uncertain significance (VUSs) in less well-studied genes, the need to distinguish germline from somatic mutations, and a lack of standardization in the field regarding which patients and which genes should be tested.57


Clinical considerations for germline predisposition testing

Clinical considerations regarding germline predisposition testing
WHO?	Individual with ≥2 cancers, 1 of which is an HM
OR
Individual with a history of an HN AND
 A relative within 2 generations diagnosed with an HN, OR
 A relative within 2 generations diagnosed with a solid tumor at age ≤50, OR
 A relative within 2 generations diagnosed with another hematopoietic abnormality
OR
Individual whose tumor-based molecular profiling identified a deleterious variant with a VAF consistent with germline status∗
OR
HM diagnosis at a much younger age than is typical
IDEAL AGE for testing?	Individuals of all ages should be considered for germline predisposition testing because some gene variants drive myeloid malignancies even at advanced ages (e.g., DDX41)
WHAT SAMPLE?	Ideal:	Gold standard cultured skin fibroblasts (some clinical laboratories also accept BM-derived mesenchymal stromal cells)
Possible:	Skin biopsy, with washout of PB
Hair follicles (may not yield sufficient DNA for comprehensive testing)
Buccal swab (may have low-level PB contamination)
Not recommended:	Saliva (highly contaminated with PB)
Fingernails (may be contaminated with monocytes)
WHAT TEST?†	WES augmented with spike-in probes for noncoding regions known to contain predisposition loci followed by analysis of gene groups
WGS (if available), with a virtual panel of appropriate genes, including noncoding regions and copy number variation studies
Panel-based NGS
COMPLEMENTARY testing	Telomere flow–FISH can identify individuals with short-telomere syndromes, although interpretation can be confounded by active disease and/or treatment
Diepoxybutane and mitomycin C analyses identify excessive chromosome breakage and assist in the diagnosis of FA
HOW can you tell if a variant is germline?	Variant is present in DNA derived from a preferred tissue source (see above) at a VAF consistent with germline status∗ OR
Variant is present in the index patient plus one other relative at a VAF consistent with germline∗
WHEN?	At HN diagnosis
At recognition of a potential germline allele from tumor or other screening, including somatic variants suggestive of an underlying germline variant (eg, R525H-encoding variant in DDX41)
before HSCT using a relative as a donor
WHY?	Plan surveillance for other cancers or organ dysfunction
Plan HSCT using a related donor
Allow pre-implantation genetic testing
Cascade testing throughout the family
Open in a separate window
HN, hematopoietic neoplasm.

Certain germline disorders may be associated with additional clinical features, such as those associated with quantitative and qualitative platelet defects: ANKRD26, ETV6, and RUNX1; and those variably associated with additional organ dysfunction, for example: GATA2 with immunodeficiency; Shwachman-Diamond syndrome with exocrine pancreatic insufficiency and skeletal dysplasia; Fanconi anemia (FA) with congenital anomalies, squamous cell carcinomas, and liver tumors; and dyskeratosis congenita with pulmonary fibrosis, liver cirrhosis, and vascular anomalies; among others. Some genes, such as CEBPA, confer germline risk only to myeloid neoplasms, whereas other genes may confer risk to a variety of hematologic neoplasms and solid tumors. Additional testing can be a helpful complement to germline genetic testing. For example, telomere flow FISH can identify patients with short telomere syndromes, ∼30% of whom will not have a gene variant identified on panel testing.64 FA chromosomal breakage studies by diepoxybutane/mitomycin-C analysis are useful because of the challenges of FA genetic testing, including common VUSs in FA genes, deletions, distinguishing cis vs trans arrangements of FA gene mutations, treatment effects, and somatic mosaicism, which may mask the diagnosis. Of note, the tumor spectrum associated with each disorder may expand over time as longer follow-up of additional individuals and families becomes available. In addition, germline predisposition to lymphoid malignancies is emerging in importance and often overlaps with myeloid neoplasm risk genes. Future work will reveal a more comprehensive list of hematologic neoplasm predisposition genes and will influence how broadly to offer predisposition testing among patients with established myeloid neoplasms as well as those with sustained cytopenias.

With more than a dozen genes incorporated within current AML classification and risk stratification systems, the use of gene-panel testing provides the most cost-effective testing approach. Due to limitations with most NGS-based assays, FLT3-ITD and FLT3-TKD determination is often performed separately by PCR and capillary electrophoresis. Rapid annotation of additional mutations of therapeutic relevance, such as IDH1, IDH2, FLT3-ITD, and FLT3-TKD, is necessary to determine best treatment approaches given the availability of targeted mutant-specific inhibitors. Immunohistochemistry can rapidly detect abnormal cytoplasmic expression of mutant NPM1 in formalin-fixed paraffin-embedded samples or cell blocks, providing utility in situations of myeloid sarcomas, NPM1 mutations outside exon 11, or in resource-limited settings where molecular techniques are not available.148,149 A subset of hotspot mutations in IDH1 and IDH2 can also be rapidly evaluated by immunohistochemistry, and p53 protein accumulation or null-pattern expression correlates with the presence of TP53 mutations in most cases of AML.150, 151, 152, 153, 154, 155 TP53 mutation present at a VAF > 10% now defines the new category of AML with mutated TP53. For other class-defining or risk-defining mutations, the VAF cutoff has not been established.

Conventional karyotyping at diagnosis can be aided by rapid testing for gene fusions (either by qPCR, FISH, or NGS-based fusion panels), for example, PML::RARA, RUNX1::RUNX1T1, and CBFB::MYH11. “Myeloid FISH panels” to test for common MDS-associated chromosomal aberrations associated with adverse risk can be useful, particularly in settings where metaphase cytogenetics are not available.144 Any clonal karyotype or FISH positivity present above the validated laboratory threshold should be considered a positive result. In the setting of nonevaluable cytogenetics, CMA can also be a useful adjunct to identify unbalanced abnormalities as well as cryptic CNAs. More recently, WGS has been proposed as a single comprehensive assay for the evaluation of AML.26

Once in remission, monitoring of MRD by molecular methods (qPCR, dPCR, NGS) and multiparameter flow cytometry (MFC) allows ongoing refinement of relapse risk estimations, providing the opportunity to identify impending relapse and possibly allow for early intervention or modified treatment approaches, such as consideration of allo-HSCT in patients with favorable risk who retain detectable MRD by qPCR after completion of planned consolidation therapy. The importance of MRD in AML was confirmed in a meta-analysis of >80 publications with >10 000 patients; the estimated 5-year overall survival was 68% vs 34% in patients in AML remission with MRD− vs MRD+ status.156 Although proven interventions to eradicate MRD are currently lacking, the detection of persistent MRD after completion of consolidation, or MRD “relapse,” correlates with inferior outcomes including increased risk of relapse and decreased overall survival. Current guidelines recommend MRD assessments after 2 cycles of standard therapy, at the end of treatment, and then, evaluation every 3 months (if BM) or every 4 to 6 weeks (if PB) for 24 months.34 Recommended time points for MRD assessment in patients receiving less-intensive treatment regimens are not yet established.

ALL
Genomic studies have led to the identification of new ALL entities28,162,163 of prognostic and therapeutic significance,164,165 even in the context of MRD-based risk-adapted therapy. These optimally require sequencing-based approaches to identify all genomic features of clinical importance. However, the choice of diagnostic approach depends in part on how genomic information will be used to guide management and on the availability of genomic and conventional diagnostic assays in individual laboratories.

Routine diagnostic approaches Chromosome banding analysis and FISH are widely used for identification of aneuploidy (hyperdiploidy and hypodiploidy) and subtype-defining chromosomal alterations (eg, BCR::ABL1, ETV6::RUNX1, KMT2A::AFF1, TCF3::PBX1, iAMP21, etc), many of which are used for risk assignment and treatment stratification. FISH assays may be used for rapid identification of translocations and gene fusions (Figure 4), including those in BCR::ABL1-like B-ALL for which targeted therapies are currently available (eg, ABL-family kinase genes, JAK2, CRLF2, and NTRK3). However, these assays do not detect all clinically relevant alterations, for example, focal insertions of EPOR into immunoglobulin loci166 and sequence mutations (eg, JAK1/JAK2/JAK3) and deletions (eg, SH2B3) that also drive kinase signaling.167 PCR assays can identify subtypes defined by gene fusions and point mutations (eg, PAX5 p.P80R and IKZF1 p.N159Y). Quantitative RT-PCR may be used to identify the gene expression profile of BCR::ABL1-like ALL,168 but subsequent testing (eg, FISH, targeted or transcriptome sequencing) is required to identify the driver kinase-activating alterations. Quantitative RT-PCR can also be used to identify deregulated gene expression characteristic of recently identified entities (DUX4, EPOR, NUTM1, and CDX2/UBTF),163,169 but alteration-specific confirmatory diagnostic approaches are desirable.
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Figure 4.
Identification of distinct subtypes of ALL through gene expression profiling. (A) Representative break-apart FISH for KMT2A::AFF1 fusion. The upper panel shows a cell with DNA FISH for KMT2A 5′ and 3′ showing 1 intact allele and 1 disrupted allele. The lower panel shows a second hybridization added on top of the first with AFF1 3′, which confirms disruption of KMT2A and fusion to AFF1 3′. (B) Illustration showing overexpression of CRLF2 and detection by flow cytometry. The image was created in Biorender (https://biorender.com/). (C) Schematic representation of NUTM1 rearrangements with multiple fusion partners and multiple breakpoints detected by WTS and visualized in ProteinPaint (https://proteinpaint.stjude.org/). Ex, exon. The approach in parenthesis (WGS) is alternative to WTS. (D) Integrative Genomics Viewer visualization of BCL11B Enhancer Tandem Amplification (BETA), observed in 20% of BCL11B-activated lineage ambiguous leukemia.162 (E) t-distributed stochastic neighbor embedding (t-SNE) representation from WTS data of B-ALL subtypes highlighted in different colors. Each dot represents a sample (N = 2004). Image is from Kimura et al.191

Several entities may benefit from flow cytometry analysis of subtype-defining antigen expression patterns, such as CD371 in DUX4-rearranged ALL and surface expression of TSLPR (encoded by CRLF2), a sensitive and specific indicator of CRLF2-rearrangement (Figure 4). A major advantage of flow cytometry–based evaluation is rapid turnaround time within 1 to 2 days.

Capture-based sequencing approaches The diverse genomic landscape of some subtypes, in particular BCR::ABL1-like B-ALL, can make diagnosis challenging. Capture-based approaches170 or amplicon-based sequencing can detect most common chimeric fusions in B-ALL simultaneously and are well suited to identify the wide spectrum of rearrangements in BCR::ABL1-like B-ALL; however they may fail to detect complex rearrangements, like EPOR in BCR::ABL1-like B-ALL, and some fusions are difficult to capture if breakpoints involve regions in the introns, which may not be feasible to comprehensively tile. DNA-based NGS panels are commonly used to detect common secondary mutations, such as mutations in Jak and Ras pathway signaling genes and mutations associated with relapsed ALL (eg, TP53, CREBBP, and NT5C2).
MLPA assays are widely used to identify focal DNA CNAs in single genes and the “IKZF1plus” composite genotype171 (defined as IKZF1 deletions cooccurring with deletions in CDKN2A/B, PAX5, or in the pseudoautosomal region 1, PAR1, in the absence of ERG deletion), which has been associated with high-risk features. However, MLPA cannot be used to identify all relevant alterations: PAR1 deletions accompany P2RY8::CRLF2 but not IGH::CRLF2 rearrangements, and ERG is deleted in only ∼50% of DUX4-rearranged ALL. Thus, PAR1 and ERG deletions identify only a subset of CRLF2- and DUX4-rearranged ALL, respectively.

Transcriptomic and genomic sequencing 
In contrast to targeted approaches, genome-wide sequencing can identify the full spectrum of alterations in a single approach, virtually diagnosing all different entities.
Transcriptome sequencing provides comprehensive characterization of fusion transcript chimeras (Figure 4), mutant allele expression (Figure 4), gene expression profiling, and ploidy169,172 to identify subgroups and several phenocopies (eg, BCR::ABL1-like ALL, ETV6-RUNX1-like, KMT2A-like, and ZNF384/362-like).169,173 The availability of a reference dataset of leukemia transcriptomes, such as the St Jude Cloud (https://www.stjude.cloud),174 allows analysis and classification of individual samples against a reference data set, without the need for an extensive local cohort. Limitations of WTS include poor sensitivity to detect rearrangements that involve complex/repetitive regions (eg, involving antigen receptor loci and DUX4)175,176 or those that do not generate a chimeric transcript, and limited sensitivity for sequence variants that are not expressed or result in nonsense-mediated decay; however, these alterations can be detected by WGS.

T-ALL subgroups are mostly defined by deregulation of T-lineage transcription factors. These are highly diverse in terms of the genes involved and the genomic drivers of deregulation, challenging to identify comprehensively, and are not consistently associated with outcome, thus are not typically identified in current diagnostic workflows.28,162 WGS can detect the diverse genomic alterations that, more commonly in T-ALL than B-ALL, involve intergenic regions (eg, TLX3, T-cell receptor gene loci) that deregulate oncogenes, and noncoding sequence mutations that generate neo-enhancers (eg, TAL1 and LMO1/2).28,162 One entity of clinical relevance is early T-cell precursor ALL (ETP ALL), a high-risk subset of early T-lineage and stem cell leukemias most commonly identified by immunophenotyping (CD7+ and typically cytoplasmic CD3+; CD2+; CD1a−; CD8−; myeloperoxidase negative but positive for at least 1 stem cell/myeloid marker).177 ETP ALL is genetically diverse, but one-third of ETP ALL and T/myeloid mixed phenotype acute leukemia cases have structural variants deregulating BCL11B, which may be detected by WGS (Figure 4) or, for the majority, by FISH to detect disruption of the BCL11B locus.178

Molecular quantitation of MRD
Early MRD monitoring at the end of induction and consolidation phases of therapy has important prognostic and, subsequently, therapeutic implications.179, 180, 181 Conventional approaches include MFC and allele-specific PCR for IG/TR gene rearrangements.180,182 To be clinically relevant, MRD analysis needs to be accurate and sensitive (at least ≤10−4). Recently, high-throughput NGS of IG/TR rearrangements,182 which can reach a sensitivity of 10−6, is becoming more widely used.


General conclusions and future directions
Myeloid neoplasms and acute leukemias are characterized by a complex coexistence of multiple clones that evolve over time.183 Historically, these have been studied in bulk samples, precluding a more direct understanding of the clinicopathologic effect of such clonal complexity. Recent studies of clonal architecture at a single-cell level offer unique insights into the interaction of clones, suggesting that the presence of distinct clones may potentially affect the growth and fitness of the others.184, 185, 186 At present the use of single-cell sequencing remains confined to a research setting; however, in the future such assays could help predict disease progression and may guide therapeutic strategies to intercept clonal evolution and allow for individual targeting of clones in multiclonal disease. The field will continue to be shaped by advanced methods such as proteomics and cellular indexing of transcriptomes and epitopes sequencing, which can identify potential therapeutic targets and characterize simultaneous gene and protein expression at the single-cell level,187,188 and computation artificial intelligence approaches that can identify new relationships in complex data sets.189,190 In the nearer term, it is expected that the continued decline in sequencing costs will drive further adoption of more frequent panel-based and MRD testing for disease monitoring and broader genomic methods such as WGS for comprehensive genomic evaluation.

It is important to note that these recommendations reflect current practice, based on current treatments and disease classification. As the classification of myeloid neoplasms and their treatments evolve and as genomic testing methods continue to advance, the recommendations for testing will inevitably change and require updating.