The application of RNA sequencing for the diagnosis and genomic classification of pediatric acute lymphoblastic leukemia

Gap-App: A sex-distinct AI-based predictor for pancreatic ductal adenocarcinoma survival as a web application open to patients and physicians

In this study, using RNA-Seq gene expression data and advanced machine learning techniques, we identified distinct gene expression profiles between male and female pancreatic ductal adenocarcinoma (PDAC) patients. Building on this insight, we developed sex-specific 3-year survival predictive models alongside a single comprehensive model. Despite smaller sample sizes, the sex-specific models outperformed the general model. We further refined our models by selecting the most important features from the initial models. The refined sex-specific predictive models achieved higher accuracy and consistently outperformed the refined comprehensive model, highlighting the value of sex-specific analysis. To ensure robustness, all refined sex-specific models were calibrated and then evaluated using an independent dataset. Random Forest models emerged as the most effective predictors, achieving accuracies of 90.33 % for males and 90.40 % for females on the training dataset, and 81.25 % for males and 89.47 % for females on the independent test dataset. These top-performing models were integrated into Gap-App, a web application that leverages individual gene expression profiles and sex information for personalized survival predictions. As the first online tool bridging complex genomic data with clinical application, Gap-App facilitates more precise, individualized cancer care, marking a significant step in personalized prognosis prediction. This study underscores the importance of incorporating sex differences in predictive modeling and sets the stage for the shift from traditional one-size-fits-all to more personalized and targeted medicine. The Gap-App service is freely available for patients and clinicians at www.gap-app.org.

Genomics and multiomics in the age of precision medicine

Precision medicine is a transformative healthcare model that utilizes an understanding of a person's genome, environment, lifestyle, and interplay to deliver customized healthcare. Precision medicine has the potential to improve the health and productivity of the population, enhance patient trust and satisfaction in healthcare, and accrue health cost-benefits both at an individual and population level. Through faster and cost-effective genomics data, next-generation sequencing has provided us the impetus to understand the nuances of complex interactions between genes, diet, and lifestyle that are heterogeneous across the population. The emergence of multiomics technologies, including transcriptomics, proteomics, epigenomics, metabolomics, and microbiomics, has enhanced the knowledge necessary for maximizing the applicability of genomics data for better health outcomes. Integrative multiomics, the combination of multiple 'omics' data layered over each other, including the interconnections and interactions between them, helps us understand human health and disease better than any of them separately. Integration of these multiomics data is possible today with the phenomenal advancements in bioinformatics, data sciences, and artificial intelligence. Our review presents a broad perspective on the utility and feasibility of a genomics-first approach layered with other omics data, offering a practical model for adopting an integrated multiomics approach in pediatric health care and research. IMPACT: Precision medicine provides a paradigm shift from a conventional, reactive disease control approach to proactive disease prevention and health preservation. Phenomenal advancements in bioinformatics, data sciences, and artificial intelligence have made integrative multiomics feasible and help us understand human health and disease better than any of them separately. The genotype-first approach or reverse phenotyping has the potential to overcome the limitations of the phenotype-first approach by identifying new genotype-phenotype associations, enhancing the subclassification of diseases by widening the phenotypic spectrum of genetic variants, and understanding functional mechanisms of genetic variations.

Next-generation sequencing RNA fusion panel for the diagnosis of haematological malignancies

Haematological malignancies are being increasingly defined by gene rearrangements, which have traditionally been detected by karyotype, fluorescent in situ hybridisation (FISH) or reverse-transcriptase polymerase chain reaction (RT-PCR). However, these traditional methods may miss cryptic gene rearrangements and are limited by the number of gene rearrangements screened at any one time. A next-generation sequencing (NGS) RNA fusion panel is an evolving technology that can identify multiple fusion transcripts in a single molecular assay, even without prior knowledge of breakpoints or fusion partners. We explored the utility of the Illumina TruSight RNA Fusion Panel for use in haematological malignancies by sequencing 30 peripheral blood or bone marrow aspirate samples. Secondary and tertiary analyses were performed using the Illumina DRAGEN RNA pipeline and PierianDx Clinical Genomics Workspace platform. Our RNA fusion panel was able to reliably detect known fusion transcripts, such as BCR::ABL1, ETV6::RUNX1 and KMT2A::AFF1, in acute lymphoblastic leukaemia (ALL), KMT2A::MLLT3, KMT2A::MLLT6, PML::RARA and CBFB::MYH11 in acute myeloid leukaemia (AML), and FIP1L1::PDGFRA in myeloid/lymphoid neoplasm with eosinophilia (MLN-Eo). In addition, it was able to detect rare KAT6A::CREBBP and CHIC2::ETV6 fusions, which could not be confirmed by traditional methods. The assay had a transcript limit of detection of approximately 5-10% of positive controls. These findings confirm the unique utility of the NGS-based RNA fusion panel as a diagnostic tool to identify gene rearrangements that drive haematological malignancies. It can identify novel and rare gene rearrangements to assist with diagnosis, prognostication and treatment decisions.

False positive NUP98 fluorescence in situ hybridization rearrangements in B-acute lymphoblastic leukemia

Gene fusions involving NUP98 have been reported in several hematologic malignancies yet have been very rarely reported in B-acute lymphoblastic leukemia (B-ALL). Two cases of B-ALL for which chromosome banding analysis (CBA) and fluorescence in situ hybridization (FISH) suggested apparent NUP98 rearrangements were further investigated with next-generation sequencing-based methodologies to verify the findings obtained with traditional cytogenetic methodologies. In the first case, CBA revealed a hyperdiploid karyotype with multiple structural abnormalities including additional material of unknown origin at 11p15; subsequent break-apart probe (BAP) FISH for NUP98 demonstrated 2 intact fusion signals and a single separate 5'NUP98 signal. However, whole-genome sequencing found no evidence of a NUP98 gene fusion. The results obtained with conventional cytogenetic methodologies were in fact attributable to structural variants (SV) with breakpoints not within NUP98 but within the 5'NUP98 BAP probe-binding sequence. In the second case, CBA revealed several structural and numeric abnormalities including a complex translocation between chromosomes 11 (at 11p15.4) and 19 (at 19p13.3) and an insertion of unknown material at 11p15.4. BAP FISH demonstrated a typical FISH signal pattern consistent with an apparent NUP98 rearrangement. However, no evidence of a NUP98 fusion was found on RNA sequencing. In conclusion, the two cases thus presented with clinical false positive NUP98 rearrangements by FISH. In the clinical laboratory, SVs in the vicinity of genes involved in recurrent rearrangements in hematologic malignancies may result in misleading results with conventional chromosome methodologies. This may preclude an accurate definition of the genetic attributes of malignancies with ensuing impacts on risk stratification and management. Higher-resolution testing methodologies such as whole-genome sequencing and RNA sequencing may be helpful in resolving unexpected results with conventional chromosome methodologies and enhancing the accuracy of genetic characterization of hematological malignancies in the clinical laboratory.

Molecular Profiling Reveals Novel Gene Fusions and Genetic Markers for Refined Patient Stratification in Pediatric Acute Lymphoblastic Leukemia

Risk-adapted treatment protocols conferred remarkable improvement in the survival rates of pediatric acute lymphoblastic leukemia/lymphoma (ALL/LBL). Nevertheless, clinical management is still challenging in certain molecular subgroups and in the presence of alterations associated with an increased rate of relapse. In this study, disease-relevant genomic and transcriptomic profiles were established in a prospective, multicenter, real-world cohort involving 192 children diagnosed with ALL/LBL. Gene fusions were detected in 34.9% of B-ALL and 46.4% of T-ALL patients, with novel chimeric genes involving JAK2, KMT2A, PAX5, RUNX1, and NOTCH1, and with KMT2A-rearranged patients displaying the worst 3-year event-free survival (P = .019). Nonsynonymous mutations were uncovered in 74.9% of the analyzed patients, and pairwise scrutiny of genetic lesions revealed recurrent clonal selection mechanisms commonly converging on the same pathway (eg, Ras, JAK/STAT, and Notch) in individual patients. Investigation of matched diagnostic and relapse samples unraveled complex subclonal variegation, and mutations affecting the NT5C2, TP53, CDKN2A, and PIK3R1 genes, emerging at the time of relapse. TP53 and CREBBP mutations, even as subclonal aberrations, were associated with shorter 3-year event-free survival among all patients with B-ALL (TP53 mutant vs wild-type: P = .008, CREBBP mutant vs wild-type: P = .010), and notably, B-ALL patients showing no measurable residual disease on day 33 could be further stratified based on TP53 mutational status (P < .001). Our in-depth molecular characterization performed across all risk groups identified novel opportunities for molecularly targeted therapy in 55.9% of high-risk and 31.6% of standard/intermediate-risk patients.

Application of Omics Analyses in Pediatric B-Cell Acute Lymphoblastic Leukemia

Acute lymphoblastic leukemia (ALL) is the most common pediatric cancer, comprising almost 25% of all malignancies diagnosed in children younger than 20 years, and its incidence is still increasing. ALL is a blood cancer arising from the unregulated proliferation of clonal lymphoid progenitor cells. To make a diagnosis of B-cell ALL, bone marrow morphology and immunophenotyping are needed; cerebrospinal fluid examination, and chromosomal analysis are currently used as stratification exams. Currently, almost 70% of children affected by B-cell ALL are characterized by well-known cytogenetic abnormalities. However, the integration of results with "omic" techniques (genomics, transcriptomics, proteomics, and metabolomics, both individually and integrated) able to analyze simultaneously thousands of molecules, has enabled a deeper definition of the molecular scenario of B-cell ALL and the identification of new genetic alterations. Studies based on omics have greatly deepened our knowledge of ALL, expanding the horizon from the traditional morphologic and cytogenetic point of view. In this review, we focus our attention on the "omic" approaches mainly used to improve the understanding and management of B-cell ALL, crucial for the diagnosis, prognosis, and treatment of the disease, offering a pathway toward more precise and personalized therapeutic interventions.

RanBALL: An Ensemble Random Projection Model for Identifying Subtypes of B-Cell Acute Lymphoblastic Leukemia

As the most common pediatric malignancy, B-cell acute lymphoblastic leukemia (B-ALL) has multiple distinct subtypes characterized by recurrent and sporadic somatic and germline genetic alterations. Identifying B-ALL subtypes can facilitate risk stratification and enable tailored therapeutic design. Existing methods for B-ALL subtyping primarily depend on immunophenotyping, cytogenetic tests and genomic profiling, which would be costly, complicated, and laborious. To overcome these challenges, we present RanBALL (an ensemble Random projection-based model for identifying B-ALL subtypes), an accurate and cost-effective model for B-ALL subtype identification. By leveraging random projection (RP) and ensemble learning, RanBALL can preserve patient-to-patient distances after dimension reduction and yield robustly accurate classification performance for B-ALL subtyping. Benchmarking results based on > 1700 B-ALL patients demonstrated that RanBALL achieved remarkable performance (accuracy: 0.93, F1-score: 0.93, and Matthews correlation coefficient: 0.93), significantly outperforming state-of-the-art methods like ALLSorts in terms of all performance metrics. In addition, RanBALL performs better than tSNE in terms of visualizing B-ALL subtype information. We believe RanBALL will facilitate the discovery of B-ALL subtype-specific marker genes and therapeutic targets to have consequential positive impacts on downstream risk stratification and tailored treatment design. To extend its applicability and impacts, a Python-based RanBALL package is available at https://github.com/wan-mlab/RanBALL.

B Lineage Acute Lymphoblastic Leukemia with concurrent IGH and EPOR rearrangements - An Entity with Dismal Outcome

The EPOR rearrangement, an uncommon cytogenetic abnormality linked to BCR::ABL1-like B-ALL, is often underdiagnosed due to the absence of a robust testing strategy, especially in resource-constrained settings. We report six cases of B-ALL with concurrent IGH and EPOR rearrangement from India, representing 1.3% of the tested cases, and reviewed the existing literature. The age ranged from 13 to 37 years (median 17 years), with a 2:1 male dominance. Leukocytosis was observed in 67% of patients (median total leukocyte count-105.9 × 10^9/L), and CD20 expression was seen in 67%. One patient experienced induction failure, while three relapsed within a year of diagnosis and treatment. All six patients died within 6 to 21 months of follow-up. These findings align with previous reports of treatment resistance, frequent relapses, and the need for novel therapeutic agents like JAK inhibitors and CART therapy. In summary, these six B-ALL cases with IGH and EPOR rearrangements highlight the diagnostic challenges and poor outcomes associated with this condition.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12288-024-01818-7.

Plasma-based transcriptomic non-coding signature for predicting relapse in pediatric acute lymphoblastic leukemia

Pediatric Acute Lymphoblastic Leukemia (ALL) is the most common malignant tumor of the hematological system in children, and its relapse after treatment has consistently been a significant factor hindering prognosis. This study aimed to develop a blood-based non-invasive method for predicting relapse in children with ALL. Two cohorts of pediatric ALL patients were analyzed. Through high-throughput profiling, three miRNAs and three circRNAs were identified as potential biological markers, exhibiting a gradient increase in expression from healthy controls to the relapsed group. Logistic regression analysis revealed the superior predictive ability of the combined non-coding RNA panel compared to individual groups. A nomogram incorporating the non-coding RNA panel and other clinical risk features was developed. Combining the non-coding RNA panel with relevant risk features could enhance predictive accuracy. The non-coding RNA panel remained an independent predictor of relapse in the validation cohort, and its combination with clinical features formed a superior risk stratification model. In conclusion, this blood-based non-invasive method holds promise for predicting relapse in pediatric ALL patients at the time of initial diagnosis. The non-coding RNA panel, along with clinical risk features, may significantly impact patient care and outcomes.

Advances in DNA/RNA Sequencing and Their Applications in Acute Myeloid Leukemia (AML)

Acute myeloid leukemia (AML) is an aggressive malignancy that poses significant challenges due to high rates of relapse and resistance to treatment, particularly in older populations. While therapeutic advances have been made, survival outcomes remain suboptimal. The evolution of DNA and RNA sequencing technologies, including whole-genome sequencing (WGS), whole-exome sequencing (WES), and RNA sequencing (RNA-Seq), has significantly enhanced our understanding of AML at the molecular level. These technologies have led to the discovery of driver mutations and transcriptomic alterations critical for improving diagnosis, prognosis, and personalized therapy development. Furthermore, single-cell RNA sequencing (scRNA-Seq) has uncovered rare subpopulations of leukemia stem cells (LSCs) contributing to disease progression and relapse. However, widespread clinical integration of these tools remains limited by costs, data complexity, and ethical challenges. This review explores recent advancements in DNA/RNA sequencing in AML and highlights both the potential and limitations of these techniques in clinical practice.

A review on trends in development and translation of omics signatures in cancer

The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.

Tailored Digital PCR Follow-Up of Rare Fusion Transcripts after Initial Detection through RNA Sequencing in Hematological Malignancies

Minimal residual disease (MRD) monitoring plays a pivotal role in the management of hematologic malignancies. Well-established molecular targets, such as PML::RARA, CBFB::MYH11, or RUNX1::RUNX1T1, are conventionally tracked by quantitative RT-PCR. Recently, a broader landscape of fusion transcripts has been unveiled through transcriptomic analysis. These newly discovered fusion transcripts may emerge as novel molecular markers for MRD quantification. In this study, we compared a targeted RNA-sequencing (RNA-seq) approach (FusionPlex) with a whole-transcriptomic strategy (Advanta RNA-Seq XT) for fusion detection in a training set of 21 samples. We evidenced a concordance of 100% for the detection of known fusions, and showed a good correlation for gene expression quantification between the two techniques (Spearman r = 0.77). Additionally, we prospectively evaluated the identification of fusions by targeted RNA-seq in a real-life series of 126 patients with hematological malignancy. At least one fusion transcript was detected for 60 patients (48%). We designed tailored digital PCR assays for 11 rare fusions, and validated this technique for MRD quantification with a limit of detection of <0.01%. The combination of RNA-seq and tailored digital PCR may become a new standard for MRD evaluation in patients lacking conventional molecular targets.

Gap-App: A sex-distinct AI-based predictor for pancreatic ductal adenocarcinoma survival as a web application open to patients and physicians

In this study, using RNA-Seq gene expression data and advanced machine learning techniques, we identified distinct gene expression profiles between male and female pancreatic ductal adenocarcinoma (PDAC) patients. Building upon this insight, we developed sex-specific 3-year survival predictive models along with a single comprehensive model. These sex-specific models outperformed the single general model despite the smaller sample sizes. We further refined our models by using the most important features extracted from these initial models. The refined sex-specific predictive models achieved improved accuracies of 92.62% for males and 91.96% for females, respectively, versus an accuracy of 87.84% from the refined comprehensive model, further highlighting the value of sex-specific analysis. Based on these findings, we created Gap-App, a web application that enables the use of individual gene expression profiles combined with sex information for personalized survival predictions. Gap-App, the first online tool aiming to bridge the gap between complex genomic data and clinical application and facilitating more precise and individualized cancer care, marks a significant advancement in personalized prognosis. The study not only underscores the importance of acknowledging sex differences in personalized prognosis, but also sets the stage for the shift from traditional one-size-fits-all to more personalized and targeted medicine. The GAP-App service is freely available at www.gap-app.org.

Ikaros Deletions among Bulgarian Patients with Acute Lymphoblastic Leukemia/Lymphoma

The Ikaros zinc finger factor 1 is a transcription factor with a well-known role in B- and T-cell development. The deletions of IKZF1 have an established significance in acute lymphoblastic leukemia, while reports on its prevalence and prognostic significance among ALL subtypes and regions vary. Breakpoint-specific qPCR is a practical method for testing of the most frequent types of IKZF1 deletions, considering there is clustering of the deletion events. The most commonly reported deletions are Δ4-7, Δ4-8, Δ2-7, and Δ2-8, with deletion Δ4-7 being the most common one. We retrospectively administered a breakpoint-specific qPCR design for screening for the most frequent types of IKZF1 deletions to 78 ALL patients that were diagnosed and treated between 2010 and 2022. We observed the products through gel electrophoresis, and we conducted descriptive statistics, EFS, and OS analyses. Our study found 19 patients with IKZF1 deletions, with two subjects manifesting more than one deletion. The prevalence in the different subgroups was as follows: Ph/+/ B-ALL 46%, Ph/-/ B-ALL 30%, T-ALL/LBL 4%. There was a statistically significant difference in EFS of 39 vs. 0% in favor of patients without deletions (p = 0.000), which translated to a difference in OS of 49 vs. 0% (p = 0.001). This difference was preserved in the subgroup of Ph/-/ B-ALL, while there was no significant difference in the Ph/+/ B-ALL. The most frequently observed type of deletion (15 out of 19) was the Δ4-7. There is a strong negative prognostic impact of the IKZF1 deletions at diagnosis in the observed population. IKZF1 deletion testing through breakpoint-specific qPCR is a practical approach in diagnostic testing for this risk factor. IKZF1 deletions may warrant treatment decisions and intensified treatment strategies to overcome the negative prognostic impact.

Cytogenetics in the management of hematological malignancies: An overview of alternative technologies for cytogenetic characterization

Genomic characterization is an essential part of the clinical management of hematological malignancies for diagnostic, prognostic and therapeutic purposes. Although CBA and FISH are still the gold standard in hematology for the detection of CNA and SV, some alternative technologies are intended to complement their deficiencies or even replace them in the more or less near future. In this article, we provide a technological overview of these alternatives. CMA is the historical and well established technique for the high-resolution detection of CNA. For SV detection, there are emerging techniques based on the study of chromatin conformation and more established ones such as RTMLPA for the detection of fusion transcripts and RNA-seq to reveal the molecular consequences of SV. Comprehensive techniques that detect both CNA and SV are the most interesting because they provide all the information in a single examination. Among these, OGM is a promising emerging higher-solution technique that offers a complete solution at a contained cost, at the expense of a relatively low throughput per machine. WGS remains the most adaptable solution, with long-read approaches enabling very high-resolution detection of CAs, but requiring a heavy bioinformatics installation and at a still high cost. However, the development of high-resolution genome-wide detection techniques for CAs allows for a much better description of chromoanagenesis. Therefore, we have included in this review an update on the various existing mechanisms and their consequences and implications, especially prognostic, in hematological malignancies.

Low-Level BCR::ABL1 Transcript at Diagnosis in Childhood Leukemia: A 10-Year Single Institution Study

INTRODUCTION

Philadelphia chromosome-positive (Ph+) acute lymphoblastic leukemia (ALL) is a high risk form of ALL associated with dismal outcomes in the pre-tyrosine kinase inhibitor (TKI) era. Addition of a TKI to chemotherapy improves outcomes. Therefore, testing for the presence of the Philadelphia chromosome by at least two methods at the time of diagnosis is critical. Diagnostic testing may include karyotype, fluorescent in situ hybridisation (FISH), and RT-PCR for the BCR::ABL1 transcript. The significance of low-level BCR::ABL1 transcript by RT-PCR in the absence of the Philadelphia chromosome on karyotype or by FISH is unknown.

METHODS

This is a retrospective review of children diagnosed with acute leukemia at our institution from 2010 to 2020. Those positive for the BCR::ABL1 transcript by qualitative RT-PCR, and negative for t(9;22) by karyotype or FISH were analyzed for demographics, cytogenetic and molecular features at diagnosis and relapse, treatment and outcomes. The Kaplan-Meier method was used to estimate event-free and overall survival.

RESULTS

Forty-seven of 306 (15%) patients with Ph- ALL had low-level BCR::ABL1 detected by RT-PCR. Most (77%) had B-cell ALL. The e1a2 transcript was detected most frequently, in 43 (91%) patients. BCR::ABL1 was quantifiable in 12/43 (28%) patients, with a median of 0.0008% (range 0.0003-0.095%). Seven patients (15%) relapsed. No patient with low-level BCR::ABL1 at diagnosis developed Ph + ALL at relapse. There was no difference in 5-year event-free (77% versus 81%, p = 0.407) or overall survival (86% versus 91%, p = 0.3) between children with low-level BCR::ABL1 (n = 47) and those without (n = 259).

CONCLUSION

BCR::ABL1 low-level positivity in children with newly diagnosed Ph- ALL is a relatively common finding and did not adversely affect outcome for patients treated using a contemporary risk-adapted approach.

The application of targeted RNA sequencing for the analysis of fusion genes, gene mutations, IKZF1 intragenic deletion, and CRLF2 overexpression in acute lymphoblastic leukemia

INTRODUCTION

Acute lymphoblastic leukemia (ALL) is characterized by highly genetic heterogeneity, owing to recurrent fusion genes, gene mutations, intragenic deletion, and gene overexpression, which poses significant challenges in clinical detection. RNA sequencing (RNA-seq) is a powerful tool for detecting multiple genetic abnormalities, especially cryptic gene rearrangements, in a single test.

METHODS

Sixty samples (B-ALL, n = 49; T-ALL, n = 9; mixed phenotype acute leukemia (MPAL), n = 2) and 20 controls were analyzed by targeted RNA-seq panel of 507 genes developed by our lab. Of these, 16 patients were simultaneously analyzed for gene mutations at the DNA level using a next-generation sequencing panel of 51 genes. Fusion genes, CRLF2 expression, and IKZF1 intragenic deletion were also detected by reverse transcription-polymerase chain reaction (RT-PCR). Karyotype analysis was performed using the R-banding and G-banding technique on bone marrow cells after 24 hours of culture. Partial fusion genes were analyzed using fluorescence in situ hybridization (FISH).

RESULTS

Compared with the results of Karyotype analysis, FISH, and RT-PCR, the detection rate of fusion genes by targeted RNA-seq increased from 48.3% to 58.3%, and six unexpected fusion genes were discovered, along with one rare isoform of IKZF1 intragenic deletion (IK10). The DNA sequencing analysis of 16 ALL patients revealed that 96.2% (25/26) of gene mutations identified at the DNA level were also detectable at the RNA level, except for one mutation with a low variant allele fraction. The detection of CRLF2 overexpression exhibited complete concordance between RT-PCR and RNA-seq.

CONCLUSION

The utilization of RNA-seq enables the identification of clinically significant genetic abnormalities that may go undetected through conventional detection methods. Its robust analytical performance might bring great application value for clinical diagnosis, prognosis, and therapy in ALL.

Clinical Characteristics and Cytogenetics of Childhood Acute Lymphoblastic Leukemia in a Single Center in Pakistan

Objectives. This study aimed to find the association between clinical characteristics, cytogenetics, and post-induction outcomes of childhood acute lymphoblastic leukemia. Methods. The study was conducted at the Indus Hospital in Karachi. Initial total leukocyte count (TLC), cytogenetics, CNS status, and post-induction remission status were recorded. Results. Out of 108 children diagnosed with ALL, 66 (61.1%) were male and 42 (38.9%) were female. The majority 90 (83.3%) had B-ALL. CNS1 status was observed in 76 (84.4%) B-ALL and 18 (88.9%) T-ALL. All T-ALL and 89 (98.8%) B-ALL achieved remission post-induction. In B-ALL, 50 (55.5%) had a normal diploid karyotype, and 22 (24.4%) had numerical abnormalities. No typical gene rearrangement was observed in 66 (73.3%), 11 (12.2%) had BCR::ABL1, 10 (11.1%) had ETV6::RUNX1 and 3 (3.3%) KMT2A on FISH. No significant difference was observed between cytogenetics and clinical characteristics (P > .05). Conclusion. The study provides valuable data on childhood acute lymphoblastic leukemia in the Pakistani population.

The potential role of RNA sequencing in diagnosing unexplained insensitivity to conventional chemotherapy in pediatric patients with B-cell acute lymphoblastic leukemia

Pediatric B-cell acute lymphoblastic leukemia (B-ALL) is a highly heterogeneous disease. According to large-scale RNA sequencing (RNA-seq) data, B-ALL patients can be divided into more than 10 subgroups. However, many genomic defects associated with resistance mechanisms have not yet been identified. As an individual clinical tool for molecular diagnostic risk classification, RNA-seq and gene expression pattern-based therapy could be potential upcoming strategies. In this study, we retrospectively analyzed the RNA-seq gene expression profiles of 45 children whose molecular diagnostic classifications were inconsistent with the response to chemotherapy. The relationship between the transcriptome and chemotherapy response was analyzed. Fusion gene identification was conducted for the included patients who did not have known high-risk associated fusion genes or gene mutations. The most frequently detected fusion gene pair in the high-risk group was the DHRSX duplication, which is a novel finding. Fusions involving ABL1, LMNB2, NFATC1, PAX5, and TTYH3 at onset were more frequently detected in the high-risk group, while fusions involving LFNG, TTYH3, and NFATC1 were frequently detected in the relapse group. According to the pathways involved, the underlying drug resistance mechanism is related to DNA methylation, autophagy, and protein metabolism. Overall, the implementation of an RNA-seq diagnostic system will identify activated markers associated with chemotherapy response, and guide future treatment adjustments.

Transcriptome analysis of primary adult B-cell lineage acute lymphoblastic leukemia identifies pathogenic variants and gene fusions, and predicts subtypes for in depth molecular diagnosis

BACKGROUND

B-cell acute lymphoblastic leukemia (B-ALL) is classified into subgroups based on known driver oncogenes and molecular lesions, including translocations and recurrent mutations. However, the current diagnostic tests do not identify subtypes or oncogenic lesions for all B-ALL samples, creating a heterogeneous B-ALL group of unknown subtypes.

METHODS

We sorted primary adult B-ALL cells and performed transcriptome analysis by bulk RNA sequencing (RNA-seq).

RESULTS

Transcriptomic analysis of an adult B-ALL cohort allowed the classification of four patient samples with subtypes that were not previously revealed by standard gene panels. The leukemia of two patients were of the DUX4 subtype and two were CRLF2+ Ph-like B-ALL. Furthermore, single nucleotide variant analysis detected the oncogenic NRAS-G12D, KRAS-G12D, and KRAS-G13D mutations in three of the patient samples, presenting targetable mutations. Additional oncogenic variants and gene fusions were uncovered, as well as multiple variants in the PDE4DIP gene across five of the patient samples.

CONCLUSION

We demonstrate that RNA-seq is an effective tool for precision medicine in B-ALL by providing comprehensive molecular profiling of leukemia cells, identifying subtype and oncogenic lesions, and stratifying patients for appropriate therapy.

Genomic technologies for detecting structural variations in hematologic malignancies

Genomic structural variations in myeloid, lymphoid, and plasma cell neoplasms can provide key diagnostic, prognostic, and therapeutic information while elucidating the underlying disease biology. Several molecular diagnostic approaches play a central role in evaluating hematological malignancies. Traditional cytogenetic diagnostic assays, such as chromosome banding and fluorescence in situ hybridization, are essential components of the current diagnostic workup that guide clinical care for most hematologic malignancies. However, each assay has inherent limitations, including limited resolution for detecting small structural variations and low coverage, and can only detect alterations in the target regions. Recently, the rapid expansion and increasing availability of novel and comprehensive genomic technologies have led to their use in clinical laboratories for clinical management and translational research. This review aims to describe the clinical relevance of structural variations in hematologic malignancies and introduce genomic technologies that may facilitate personalized tumor characterization and treatment.

IKZF1 Alterations and Therapeutic Targeting in B-Cell Acute Lymphoblastic Leukemia

IKZF1 encodes the transcription factor IKAROS, a zinc finger DNA-binding protein with a key role in lymphoid lineage development. IKAROS plays a critical role in the development of lineage-restricted mature lymphocytes. Deletions within IKZF1 in B-cell acute lymphoblastic leukemia (B-ALL) lead to a loss of normal IKAROS function, conferring leukemic stem cell properties, including self-renewal and subsequent uncontrolled growth. IKZF1 deletions are associated with treatment resistance and inferior outcomes. Early identification of IKZF1 deletions in B-ALL may inform the intensification of therapy and other potential treatment strategies to improve outcomes in this high-risk leukemia.

Transcriptome Sequencing Allows Comprehensive Genomic Characterization of Pediatric B-Acute Lymphoblastic Leukemia in an Academic Clinical Laboratory

Studies have shown the power of transcriptome sequencing [RNA sequencing (RNA-Seq)] in identifying known and novel oncogenic drivers and molecular subtypes of B-acute lymphoblastic leukemia (B-ALL). The current study investigated whether the clinically validated RNA-Seq assay, coupled with a custom analysis pipeline, could be used for a comprehensive B-ALL classification. Following comprehensive clinical testing, RNA-Seq was performed on 76 retrospective B-ALL cases, 28 of which had known and 48 had undetermined subtype. Subtypes were accurately identified in all 28 known cases, and in 38 of 48 unknown cases (79%). The subtypes of the unknown cases included the following: PAX5alt (n = 12), DUX4-rearranged (n = 6), Philadelphia chromosome-like (n = 5), low hyperdiploid (n = 4), ETV6::RUNX1-like (n = 3), MEF2D-rearranged (n = 2), PAX5 P80R (n = 2), ZEB2/CEBP (n = 1), NUTM1-rearranged (n = 1), ZNF384-rearranged (n = 1), and TCF3::PBX1 (n = 1). In 15 of 38 cases (39%), classification based on expression profile was corroborated by detection of subtype-defining oncogenic drivers missed by clinical testing. RNA-Seq analysis also detected large copy number abnormalities, oncogenic hot-spot sequence variants, and intragenic IKZF1 deletions. This pilot study confirms the feasibility of implementing an RNA-Seq workflow for clinical diagnosis of molecular subtypes in pediatric B-ALL, reinforcing that RNA-Seq represents a promising global genomic assay for this heterogeneous leukemia.

Updates on lymphoblastic leukemia/lymphoma classification and minimal/measurable residual disease analysis

Lymphoblastic leukemia/lymphoma (ALL/LBL), especially certain subtypes, continues to confer morbidity and mortality despite significant therapeutic advances. The pathologic classification of ALL/LBL, especially that of B-ALL, has recently substantially expanded with the identification of several distinct and prognostically important genetic drivers. These discoveries are reflected in both current classification systems, the World Health Organization (WHO) 5th edition and the new International Consensus Classification (ICC). In this article, novel subtypes of B-ALL are reviewed, including DUX4, MEF2D and ZNF384-rearranged B-ALL; the rare pediatric entity B-ALL with TLF3::HLF, now added to the classifications, is discussed; updates to the category of B-ALL with BCR::ABL1-like features (Ph-like B-ALL) are summarized; and emerging genetic subtypes of T-ALL are presented. The second half of the article details current approaches to minimal/measurable residual disease (MRD) detection in B-ALL and T-ALL and presents anticipated challenges to current approaches in the burgeoning era of antigen-directed immunotherapy.

Cytogenetics in the management of T-cell acute lymphoblastic leukemia (T-ALL): Guidelines from the Groupe Francophone de Cytogénétique Hématologique (GFCH)

Molecular analysis is the hallmark of T-cell acute lymphoblastic leukemia (T-ALL) categorization. Several T-ALL sub-groups are well recognized based on the aberrant expression of specific transcription factors. This recently resulted in the implementation of eight provisional T-ALL entities into the novel 2022 International Consensus Classification, albeit not into the updated World Health Organization classification system. Despite this extensive molecular characterization, cytogenetic analysis remains the backbone of T-ALL diagnosis in many countries as chromosome banding analysis and fluorescence in situ hybridization are relatively inexpensive techniques to obtain results of diagnostic, prognostic and therapeutic interest. Here, we provide an overview of recurrent chromosomal abnormalities detectable in T-ALL patients and propose guidelines regarding their detection. By referring in parallel to the more general molecular classification approach, we hope to offer a diagnostic framework useful in a broad clinical genetic setting.

Cytogenetics in the management of B-cell acute lymphoblastic leukemia: Guidelines from the Groupe Francophone de Cytogénétique Hématologique (GFCH)

Cytogenetic analysis is mandatory at initial assessment of B-cell acute lymphoblastic leukemia (B-ALL) due to its diagnostic and prognostic value. Results from chromosome banding analysis and complementary FISH are taken into account in therapeutic protocols and further completed by other techniques (RT-PCR, SNP-array, MLPA, NGS, OGM). Indeed, new genomic entities have been identified by NGS, mostly RNA sequencing, such as Ph-like ALL that can benefit from targeted therapy. Here, we have attempted to establish cytogenetic guidelines by reviewing the most recent published data including the novel 5th World Health Organization and International Consensus Classifications. We also focused on newly described cytogenomic entities and indicate alternative diagnostic tools such as NGS technology, as its importance is vastly increasing in the diagnostic setting.

Outlier Expression of Isoforms by Targeted or Total RNA Sequencing Identifies Clinically Significant Genomic Variants in Hematolymphoid Tumors

Recognition of aberrant gene isoforms due to DNA events can impact risk stratification and molecular classification of hematolymphoid tumors. In myelodysplastic syndromes, KMT2A partial tandem duplication (PTD) was one of the top adverse predictors in the International Prognostic Scoring System-Molecular study. In B-cell acute lymphoblastic leukemia (B-ALL), ERG isoforms have been proposed as markers of favorable-risk DUX4 rearrangements, whereas deletion-mediated IKZF1 isoforms are associated with adverse prognosis and have been extended to the high-risk IKZF1plus signature defined by codeletions, including PAX5. In this limited study, outlier expression of isoforms as markers of IKZF1 intragenic or 3' deletions, DUX4 rearrangements, or PAX5 intragenic deletions were 92.3% (48/52), 90% (9/10), or 100% (9/9) sensitive, respectively, and 98.7% (368/373), 100% (35/35), or 97.1% (102/105) specific, respectively, by targeted RNA sequencing, and 84.0% (21/25), 85.7% (6/7), or 81.8% (9/11) sensitive, respectively, and 98.2% (109/111), 98.4% (127/129), or 98.7% (78/79) specific, respectively, by total RNA sequencing. Comprehensive split-read analysis identified expressed DNA breakpoints, cryptic splice sites associated with IKZF1 3' deletions, PTD of IKZF1 exon 5 spanning N159Y in B-ALL with mutated IKZF1 N159Y, and truncated KMT2A-PTD isoforms. Outlier isoforms were also effective targeted RNA markers for PAX5 intragenic amplifications (B-ALL), KMT2A-PTD (myeloid malignant cancers), and rare NOTCH1 intragenic deletions (T-cell acute lymphoblastic leukemia). These findings support the use of outlier isoform analysis as a robust strategy for detecting clinically significant DNA events.

Modern Classification and Management of Pediatric B-cell Leukemia and Lymphoma

Although pediatric hematopathology overlaps with that of adults, certain forms of leukemia and lymphoma, and many types of reactive conditions affecting the bone marrow and lymph nodes, are unique to children. As part of this series focused on lymphomas, this article (1) details the novel subtypes of lymphoblastic leukemia seen primarily in children and described since the 2017 World Health Organization classification and (2) discusses unique concepts in pediatric hematopathology, including nomenclature changes and evaluation of surgical margins in selected lymphomas.

Toblerone: detecting exon deletion events in cancer using RNA-seq

Cancer is driven by mutations of the genome that can result in the activation of oncogenes or repression of tumour suppressor genes. In acute lymphoblastic leukemia (ALL) focal deletions in IKAROS family zinc finger 1 (IKZF1) result in the loss of zinc-finger DNA-binding domains and a dominant negative isoform that is associated with higher rates of relapse and  poorer patient outcomes. Clinically, the presence of IKZF1 deletions informs prognosis and treatment options. In this work we developed a method for detecting exon deletions in genes using RNA-seq with application to IKZF1. We developed a pipeline that first uses a custom transcriptome reference consisting of transcripts with exon deletions.  Next, RNA-seq reads are mapped using a pseudoalignment algorithm to identify reads that uniquely support deletions. These are then evaluated for evidence of the deletion with respect to gene expression and other samples. We applied the algorithm, named Toblerone, to a cohort of 99 B-ALL paediatric samples including validated IKZF1 deletions. Furthermore, we developed a graphical desktop app for non-bioinformatics users that can quickly and easily identify and report deletions in IKZF1 from RNA-seq data with informative graphical outputs.

Clinical screening for Ph-like ALL and the developing role of TKIs

Philadelphia chromosome-like acute lymphoblastic leukemia (Ph-like ALL) is a common subtype of B-lineage acute lymphoblastic leukemia (B-ALL) with increasing frequency across the age spectrum. Characterized by a kinase-activated gene expression profile and driven by a variety of genetic alterations involving cytokine receptors and kinases, Ph-like ALL is associated with high rates of residual disease and relapse in patients treated with conventional chemotherapy. In this case-based review, we describe the biology of the 2 major ABL-class and JAK pathway genetic subtypes of Ph-like ALL, discuss current diagnostic testing methodologies, and highlight targeted inhibitor and chemo/immunotherapy approaches under clinical investigation in children, adolescents, and adults with these high-risk leukemias.

Transcriptome-based molecular subtypes and differentiation hierarchies improve the classification framework of acute myeloid leukemia

The current classification of acute myeloid leukemia (AML) relies largely on genomic alterations. Robust identification of clinically and biologically relevant molecular subtypes from nongenomic high-throughput sequencing data remains challenging. We established the largest multicenter AML cohort (n = 655) in China, with all patients subjected to RNA sequencing (RNA-Seq) and 619 (94.5%) to targeted or whole-exome sequencing (TES/WES). Based on an enhanced consensus clustering, eight stable gene expression subgroups (G1-G8) with unique clinical and biological significance were identified, including two unreported (G5 and G8) and three redefined ones (G4, G6, and G7). Apart from four well-known low-risk subgroups including PML::RARA (G1), CBFB::MYH11 (G2), RUNX1::RUNX1T1 (G3), biallelic CEBPA mutations or -like (G4), four meta-subgroups with poor outcomes were recognized. The G5 (myelodysplasia-related/-like) subgroup enriched clinical, cytogenetic and genetic features mimicking secondary AML, and hotspot mutations of IKZF1 (p.N159S) (n = 7). In contrast, most NPM1 mutations and KMT2A and NUP98 fusions clustered into G6-G8, showing high expression of HOXA/B genes and diverse differentiation stages, from hematopoietic stem/progenitor cell down to monocyte, namely HOX-primitive (G7), HOX-mixed (G8), and HOX-committed (G6). Through constructing prediction models, the eight gene expression subgroups could be reproduced in the Cancer Genome Atlas (TCGA) and Beat AML cohorts. Each subgroup was associated with distinct prognosis and drug sensitivities, supporting the clinical applicability of this transcriptome-based classification of AML. These molecular subgroups illuminate the complex molecular network of AML, which may promote systematic studies of disease pathogenesis and foster the screening of targeted agents based on omics.

RaScALL: Rapid (Ra) screening (Sc) of RNA-seq data for prognostically significant genomic alterations in acute lymphoblastic leukaemia (ALL)

RNA-sequencing (RNA-seq) efforts in acute lymphoblastic leukaemia (ALL) have identified numerous prognostically significant genomic alterations which can guide diagnostic risk stratification and treatment choices when detected early. However, integrating RNA-seq in a clinical setting requires rapid detection and accurate reporting of clinically relevant alterations. Here we present RaScALL, an implementation of the k-mer based variant detection tool km, capable of identifying more than 100 prognostically significant lesions observed in ALL, including gene fusions, single nucleotide variants and focal gene deletions. We compared genomic alterations detected by RaScALL and those reported by alignment-based de novo variant detection tools in a study cohort of 180 Australian patient samples. Results were validated using 100 patient samples from a published North American cohort. RaScALL demonstrated a high degree of accuracy for reporting subtype defining genomic alterations. Gene fusions, including difficult to detect fusions involving EPOR and DUX4, were accurately identified in 98% of reported cases in the study cohort (n = 164) and 95% of samples (n = 63) in the validation cohort. Pathogenic sequence variants were correctly identified in 75% of tested samples, including all cases involving subtype defining variants PAX5 p.P80R (n = 12) and IKZF1 p.N159Y (n = 4). Intragenic IKZF1 deletions resulting in aberrant transcript isoforms were also detectable with 98% accuracy. Importantly, the median analysis time for detection of all targeted alterations averaged 22 minutes per sample, significantly shorter than standard alignment-based approaches. The application of RaScALL enables rapid identification and reporting of previously identified genomic alterations of known clinical relevance.

Molecular Diagnostic Testing for Hematopoietic Neoplasms: Linking Pathogenic Drivers to Personalized Diagnosis

Molecular diagnostics inhabit an increasingly central role in characterizing hematopoietic malignancies. This brief review summarizes the genomic targets important for many major categories of hematopoietic neoplasia by focusing on disease pathogenesis. In myeloid disease, recurrent mutations in key functional classes drive clonal hematopoiesis, on which additional variants can specify clinical presentation and accelerate progression. Lymphoblastic leukemias are frequently initiated by oncogenic fusions that block lymphoid maturation while, in concert with additional mutations, driving proliferation. The links between genetic aberrations and lymphoma patient outcomes have been clarified substantially through the clustering of genomic profiles. Finally, the addition of next-generation sequencing strategies to cytogenetics is refining risk stratification for plasma cell myeloma. In all categories, molecular diagnostics shed light on the unique mechanistic underpinnings of each individual malignancy, thereby empowering more rational, personalized care for these patients.

Novel Diagnostic and Therapeutic Options for KMT2A-Rearranged Acute Leukemias

The KMT2A (MLL) gene rearrangements (KMT2A-r) are associated with a diverse spectrum of acute leukemias. Although most KMT2A-r are restricted to nine partner genes, we have recently revealed that KMT2A-USP2 fusions are often missed during FISH screening of these genetic alterations. Therefore, complementary methods are important for appropriate detection of any KMT2A-r. Here we use a machine learning model to unravel the most appropriate markers for prediction of KMT2A-r in various types of acute leukemia. A Random Forest and LightGBM classifier was trained to predict KMT2A-r in patients with acute leukemia. Our results revealed a set of 20 genes capable of accurately estimating KMT2A-r. The SKIDA1 (AUC: 0.839; CI: 0.799-0.879) and LAMP5 (AUC: 0.746; CI: 0.685-0.806) overexpression were the better markers associated with KMT2A-r compared to CSPG4 (also named NG2; AUC: 0.722; CI: 0.659-0.784), regardless of the type of acute leukemia. Of importance, high expression levels of LAMP5 estimated the occurrence of all KMT2A-USP2 fusions. Also, we performed drug sensitivity analysis using IC50 data from 345 drugs available in the GDSC database to identify which ones could be used to treat KMT2A-r leukemia. We observed that KMT2A-r cell lines were more sensitive to 5-Fluorouracil (5FU), Gemcitabine (both antimetabolite chemotherapy drugs), WHI-P97 (JAK-3 inhibitor), Foretinib (MET/VEGFR inhibitor), SNX-2112 (Hsp90 inhibitor), AZD6482 (PI3Kβ inhibitor), KU-60019 (ATM kinase inhibitor), and Pevonedistat (NEDD8-activating enzyme (NAE) inhibitor). Moreover, IC50 data from analyses of ex-vivo drug sensitivity to small-molecule inhibitors reveals that Foretinib is a promising drug option for AML patients carrying FLT3 activating mutations. Thus, we provide novel and accurate options for the diagnostic screening and therapy of KMT2A-r leukemia, regardless of leukemia subtype.

Optimizing the diagnostic workflow for acute lymphoblastic leukemia by optical genome mapping

Acute lymphoblastic leukemia (ALL) is a malignancy that can be subdivided into distinct entities based on clinical, immunophenotypic and genomic features, including mutations, structural variants (SVs), and copy number alterations (CNA). Chromosome banding analysis (CBA) and Fluorescent In‐Situ Hybridization (FISH) together with Multiple Ligation‐dependent Probe Amplification (MLPA), array and PCR‐based methods form the backbone of routine diagnostics. This approach is labor‐intensive, time‐consuming and costly. New molecular technologies now exist that can detect SVs and CNAs in one test. Here we apply one such technology, optical genome mapping (OGM), to the diagnostic work‐up of 41 ALL cases. Compared to our standard testing pathway, OGM identified all recurrent CNAs and SVs as well as additional recurrent SVs and the resulting fusion genes. Based on the genomic profile obtained by OGM, 32 patients could be assigned to one of the major cytogenetic risk groups compared to 23 with the standard approach. The latter identified 24/34 recurrent chromosomal abnormalities, while OGM identified 33/34, misinterpreting only 1 case with low hypodiploidy. The results of MLPA were concordant in 100% of cases. Overall, there was excellent concordance between the results. OGM increased the detection rate and cytogenetic resolution, and abrogated the need for cascade testing, resulting in reduced turnaround times. OGM also provided opportunities for better patient stratification and accurate treatment options. However, for comprehensive cytogenomic testing, OGM still needs to be complemented with CBA or SNP‐array to detect ploidy changes and with BCR::ABL1 FISH to assign patients as soon as possible to targeted therapy.

Multi-Cohort Transcriptomic Subtyping of B-Cell Acute Lymphoblastic Leukemia

RNA sequencing provides a snapshot of the functional consequences of genomic lesions that drive acute lymphoblastic leukemia (ALL). The aims of this study were to elucidate diagnostic associations (via machine learning) between mRNA-seq profiles, independently verify ALL lesions and develop easy-to-interpret transcriptome-wide biomarkers for ALL subtyping in the clinical setting. A training dataset of 1279 ALL patients from six North American cohorts was used for developing machine learning models. Results were validated in 767 patients from Australia with a quality control dataset across 31 tissues from 1160 non-ALL donors. A novel batch correction method was introduced and applied to adjust for cohort differences. Out of 18,503 genes with usable expression, 11,830 (64%) were confounded by cohort effects and excluded. Six ALL subtypes (ETV6::RUNX1, KMT2A, DUX4, PAX5 P80R, TCF3::PBX1, ZNF384) that covered 32% of patients were robustly detected by mRNA-seq (positive predictive value ≥ 87%). Five other frequent subtypes (CRLF2, hypodiploid, hyperdiploid, PAX5 alterations and Ph-positive) were distinguishable in 40% of patients at lower accuracy (52% ≤ positive predictive value ≤ 73%). Based on these findings, we introduce the Allspice R package to predict ALL subtypes and driver genes from unadjusted mRNA-seq read counts as encountered in real-world settings. Two examples of Allspice applied to previously unseen ALL patient samples with atypical lesions are included.

Guiding the global evolution of cytogenetic testing for hematologic malignancies

Cytogenetics has long represented a critical component in the clinical evaluation of hematologic malignancies. Chromosome banding studies provide a simultaneous snapshot of genome-wide copy number and structural variation, which have been shown to drive tumorigenesis, define diseases, and guide treatment. Technological innovations in sequencing have ushered in our present-day clinical genomics era. With recent publications highlighting novel sequencing technologies as alternatives to conventional cytogenetic approaches, we, an international consortium of laboratory geneticists, pathologists, and oncologists, describe herein the advantages and limitations of both conventional chromosome banding and novel sequencing technologies and share our considerations on crucial next steps to implement these novel technologies in the global clinical setting for a more accurate cytogenetic evaluation, which may provide improved diagnosis and treatment management. Considering the clinical, logistic, technical, and financial implications, we provide points to consider for the global evolution of cytogenetic testing.

Acute Leukemia Classification Using Transcriptional Profiles From Low-Cost Nanopore mRNA Sequencing

PURPOSE

Most cases of pediatric acute leukemia occur in low- and middle-income countries, where health centers lack the tools required for accurate diagnosis and disease classification. Recent research shows the robustness of using unbiased short-read RNA sequencing to classify genomic subtypes of acute leukemia. Compared with short-read sequencing, nanopore sequencing has low capital and consumable costs, making it suitable for use in locations with limited health infrastructure.

MATERIALS AND METHODS

We show the feasibility of nanopore mRNA sequencing on 134 cryopreserved acute leukemia specimens (26 acute myeloid leukemia [AML], 73 B-lineage acute lymphoblastic leukemia [B-ALL], 34 T-lineage acute lymphoblastic leukemia, and one acute undifferentiated leukemia). Using multiple library preparation approaches, we generated long-read transcripts for each sample. We developed a novel composite classification approach to predict acute leukemia lineage and major B-ALL and AML molecular subtypes directly from gene expression profiles.

RESULTS

We demonstrate accurate classification of acute leukemia samples into AML, B-ALL, or T-lineage acute lymphoblastic leukemia (96.2% of cases are classifiable with a probability of > 0.8, with 100% accuracy) and further classification into clinically actionable genomic subtypes using shallow RNA nanopore sequencing, with 96.2% accuracy for major AML subtypes and 94.1% accuracy for major B-lineage acute lymphoblastic leukemia subtypes.

CONCLUSION

Transcriptional profiling of acute leukemia samples using nanopore technology for diagnostic classification is feasible and accurate, which has the potential to improve the accuracy of cancer diagnosis in low-resource settings.

Monitoring of Leukemia Clones in B-cell Acute Lymphoblastic Leukemia at Diagnosis and During Treatment by Single-cell DNA Amplicon Sequencing

Acute lymphoblastic leukemia (ALL) is characterized by the presence of chromosomal changes, including numerical changes, translocations, and deletions, which are often associated with additional single-nucleotide mutations. In this study, we used single cell–targeted DNA sequencing to evaluate the clonal heterogeneity of B-ALL at diagnosis and during chemotherapy treatment. We designed a custom DNA amplicon library targeting mutational hotspot regions (in 110 genes) present in ALL, and we measured the presence of mutations and small insertions/deletions (indels) in bone marrow or blood samples from 12 B-ALL patients, with a median of 7973 cells per sample. Nine of the 12 cases showed at least 1 subclonal mutation, of which cases with PAX5 alterations or high hyperdiploidy (with intermediate to good prognosis) showed a high number of subclones (1 to 7) at diagnosis, defined by a variety of mutations in the JAK/STAT, RAS, or FLT3 signaling pathways. Cases with RAS pathway mutations had multiple mutations in FLT3, NRAS, KRAS, or BRAF in various clones. For those cases where we detected multiple mutational clones at diagnosis, we also studied blood samples during the first weeks of chemotherapy treatment. The leukemia clones disappeared during treatment with various kinetics, and few cells with mutations were easily detectable, even at low frequency (<0.1%). Our data illustrate that about half of the B-ALL cases show >2 subclones at diagnosis and that even very rare mutant cells can be detected at diagnosis or during treatment by single cell–targeted DNA sequencing.

SFPQ-ABL1 and BCR-ABL1 utilize different signalling networks to drive B-cell acute lymphoblastic leukaemia

SFPQ-ABL1 is localized to the nuclear compartment and is a relatively weaker driver of cellular proliferation compared with BCR-ABL1.

SFPQ-ABL1 and BCR-ABL1 activate distinct signaling networks, both of which converge on inhibiting apoptosis and driving proliferation.

Philadelphia-like (Ph-like) acute lymphoblastic leukemia (ALL) is a high-risk subtype of B-cell ALL characterized by a gene expression profile resembling Philadelphia chromosome–positive ALL (Ph⁺ ALL) in the absence of BCR-ABL1. Tyrosine kinase–activating fusions, some involving ABL1, are recurrent drivers of Ph-like ALL and are targetable with tyrosine kinase inhibitors (TKIs). We identified a rare instance of SFPQ-ABL1 in a child with Ph-like ALL. SFPQ-ABL1 expressed in cytokine-dependent cell lines was sufficient to transform cells and these cells were sensitive to ABL1-targeting TKIs. In contrast to BCR-ABL1, SFPQ-ABL1 localized to the nuclear compartment and was a weaker driver of cellular proliferation. Phosphoproteomics analysis showed upregulation of cell cycle, DNA replication, and spliceosome pathways, and downregulation of signal transduction pathways, including ErbB, NF-κB, vascular endothelial growth factor (VEGF), and MAPK signaling in SFPQ-ABL1–expressing cells compared with BCR-ABL1–expressing cells. SFPQ-ABL1 expression did not activate phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT) signaling and was associated with phosphorylation of G2/M cell cycle proteins. SFPQ-ABL1 was sensitive to navitoclax and S-63845 and promotes cell survival by maintaining expression of Mcl-1 and Bcl-xL. SFPQ-ABL1 has functionally distinct mechanisms by which it drives ALL, including subcellular localization, proliferative capacity, and activation of cellular pathways. These findings highlight the role that fusion partners have in mediating the function of ABL1 fusions.

Whole-transcriptome analysis in acute lymphoblastic leukemia: a report from the DFCI ALL Consortium Protocol 16-001

RNA-seq is feasible in the context of a prospective clinical trial for de novo ALL within a clinically sensitive turnaround time.

RNA-seq identified several genetic alterations not detected by conventional methods that confer potential prognostic and therapeutic impact.

The molecular hallmark of childhood acute lymphoblastic leukemia (ALL) is characterized by recurrent, prognostic genetic alterations, many of which are cryptic by conventional cytogenetics. RNA sequencing (RNA-seq) is a powerful next-generation sequencing technology that can simultaneously identify cryptic gene rearrangements, sequence mutations and gene expression profiles in a single assay. We examined the feasibility and utility of incorporating RNA-seq into a prospective multicenter phase 3 clinical trial for children with newly diagnosed ALL. The Dana-Farber Cancer Institute ALL Consortium Protocol 16-001 enrolled 173 patients with ALL who consented to optional studies and had samples available for RNA-seq. RNA-seq identified at least 1 alteration in 157 patients (91%). Fusion detection was 100% concordant with results obtained from conventional cytogenetic analyses. An additional 56 gene fusions were identified by RNA-seq, many of which confer prognostic or therapeutic significance. Gene expression profiling enabled further molecular classification into the following B-cell ALL (B-ALL) subgroups: high hyperdiploid (n = 36), ETV6-RUNX1/-like (n = 31), TCF3-PBX1 (n = 7), KMT2A-rearranged (KMT2A-R; n = 5), intrachromosomal amplification of chromosome 21 (iAMP21) (n = 1), hypodiploid (n = 1), Philadelphia chromosome (Ph)-positive/Ph-like (n = 16), DUX4-R (n = 11), PAX5 alterations (PAX5 alt; n = 11), PAX5 P80R (n = 1), ZNF384-R (n = 4), NUTM1-R (n = 1), MEF2D-R (n = 1), and others (n = 10). RNA-seq identified 141 nonsynonymous mutations in 93 patients (54%); the most frequent were RAS-MAPK pathway mutations. Among 79 patients with both low-density array and RNA-seq data for the Philadelphia chromosome-like gene signature prediction, results were concordant in 74 patients (94%). In conclusion, RNA-seq identified several clinically relevant genetic alterations not detected by conventional methods, which supports the integration of this technology into front-line pediatric ALL trials. This trial was registered at www.clinicaltrials.gov as #NCT03020030.

Slinker: Visualising novel splicing events in RNA-Seq data

Visualisation of the transcriptome relative to a reference genome is fraught with sparsity. This is due to RNA sequencing (RNA-Seq) reads being predominantly mapped to exons that account for just under 3% of the human genome. Recently, we have used exon-only references, superTranscripts, to improve visualisation of aligned RNA-Seq data through the omission of supposedly unexpressed regions such as introns. However, variation within these regions can lead to novel splicing events that may drive a pathogenic phenotype. In these cases, the loss of information in only retaining annotated exons presents significant drawbacks. Here we present Slinker, a bioinformatics pipeline written in Python and Bpipe that uses a data-driven approach to assemble sample-specific superTranscripts. At its core, Slinker uses Stringtie2 to assemble transcripts with any sequence across any gene. This assembly is merged with reference transcripts, converted to a superTranscript, of which rich visualisations are made through Plotly with associated annotation and coverage information. Slinker was validated on five novel splicing events of rare disease samples from a cohort of primary muscular disorders. In addition, Slinker was shown to be effective in visualising deletion events within transcriptomes of tumour samples in the important leukemia gene, IKZF1. Slinker offers a succinct visualisation of RNA-Seq alignments across typically sparse regions and is freely available on Github.

Transcriptional and Mutational Profiling of B-Other Acute Lymphoblastic Leukemia for Improved Diagnostics

B-cell precursor acute lymphoblastic leukemia (BCP-ALL) is the most common cancer in children, and significant progress has been made in diagnostics and the treatment of this disease based on the subtypes of BCP-ALL. However, in a large proportion of cases (B-other), recurrent BCP-ALL-associated genomic alterations remain unidentifiable by current diagnostic procedures. In this study, we performed RNA sequencing and analyzed gene fusions, expression profiles, and mutations in diagnostic samples of 185 children with BCP-ALL. Gene expression clustering showed that a subset of B-other samples partially clusters with some of the known subgroups, particularly DUX4-positive. Mutation analysis coupled with gene expression profiling revealed the presence of distinctive BCP-ALL subgroups, characterized by the presence of mutations in known ALL driver genes, e.g., PAX5 and IKZF1. Moreover, we identified novel fusion partners of lymphoid lineage transcriptional factors ETV6, IKZF1 and PAX5. In addition, we report on low blast count detection thresholds and show that the use of EDTA tubes for sample collection does not have adverse effects on sequencing and downstream analysis. Taken together, our findings demonstrate the applicability of whole-transcriptome sequencing for personalized diagnostics in pediatric ALL, including tentative classification of the B-other cases that are difficult to diagnose using conventional methods.

MINTIE: identifying novel structural and splice variants in transcriptomes using RNA-seq data

Calling fusion genes from RNA-seq data is well established, but other transcriptional variants are difficult to detect using existing approaches. To identify all types of variants in transcriptomes we developed MINTIE, an integrated pipeline for RNA-seq data. We take a reference-free approach, combining de novo assembly of transcripts with differential expression analysis to identify up-regulated novel variants in a case sample. We compare MINTIE with eight other approaches, detecting > 85% of variants while no other method is able to achieve this. We posit that MINTIE will be able to identify new disease variants across a range of disease types.

Genetic characterisation of childhood B-other-acute lymphoblastic leukaemia in UK patients by fluorescence in situ hybridisation and Multiplex Ligation-dependent Probe Amplification

While next-generation sequencing technologies provide excellent strategies to screen for newly defined genetic abnormalities of prognostic or therapeutic significance in patients with B-other-acute lymphoblastic leukaemia (ALL), they are not widely available. We used a dual screening approach, incorporating fluorescence in situ hybridisation (FISH) and Multiplex Ligation-dependent Probe Amplification (MLPA), to establish the frequency and long-term outcome of a representative cohort of specific subgroups of B-other-ALL recruited to the childhood ALL trial, UKALL2003. We focussed on abnormalities of known prognostic significance, including ABL-class fusions and ERG deletions, as a surrogate marker for DUX4-rearranged ALL. ABL-class fusions accounted for ~4% of B-other-ALL and were associated with high levels of minimal residual disease (MRD; 14/23 with MRD >5%) and a high relapse rate (55·7%) following treatment without tyrosine kinase inhibitor (TKI), confirming the importance of prospective screening with a view to incorporating TKI into therapy. Patients with deletions of ERG (~10% of B-other-ALL) had a 10-year event-free-survival of 97·2%, validating previous reports of their excellent outcome. Rearrangements of ZNF384, MEF2D and NUTM1 were observed at low frequencies. Here, we estimate that approximately one third of B-other-ALL patients can be reliably classified into one of the known genetic subgroups using our dual screening method. This approach is rapid, accurate and readily incorporated into routine testing.

Transcriptomics paving the way for improved diagnostics and precision medicine of acute leukemia

Transcriptional profiling of acute leukemia, specifically by RNA-sequencing or whole transcriptome sequencing (WTS), has provided fundamental insights into its underlying disease biology and allows unbiased detection of oncogenic gene fusions, as well as of gene expression signatures that can be used for improved disease classification. While used as a research tool for many years, RNA-sequencing is becoming increasingly used in clinical diagnostics. Here, we highlight key transcriptomic studies of acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) that have improved our biological understanding of these heterogeneous malignant disorders and have paved the way for translation into clinical diagnostics. Recent single-cell transcriptomic studies of ALL and AML, which provide new insights into the cellular ecosystem of acute leukemia and point to future clinical utility, are also reviewed. Finally, we discuss current challenges that need to be overcome for a more wide-spread adoption of RNA-sequencing in clinical diagnostics and how this technology significantly can aid the identification of genetic alterations in current guidelines and of newly emerging disease entities, some of which are critical to identify because of the availability of targeted therapies, thereby paving the way for improved precision medicine of acute leukemia.

Practical Considerations for Using RNA Sequencing in Management of B-Cell Acute Lymphoblastic Leukemia: Malaysia-Singapore Acute Lymphoblastic Leukemia-Sequencing 2020 Implementation Strategy

Despite the immense genetic heterogeneity of B-cell acute lymphoblastic leukemia (ALL), RNA sequencing (RNA-Seq) could comprehensively interrogate its genetic drivers, assigning a specific molecular subtype in >90% of patients. However, study groups have only started to use RNA-Seq. For broader clinical use, technical, quality control, and appropriate performance validation are needed. We describe the development and validation of an RNA-Seq workflow for subtype classification, TPMT/NUDT15/TP53 variant discovery, and IGH disease clone identification for Malaysia-Singapore ALL sequencing (ALL-Seq) 2020. We validated this workflow in 377 patients in our preceding Malaysia-Singapore ALL-Seq 2003/Malaysia-Singapore ALL-Seq 2010 studies and proposed the quality control measures for RNA quality, library size, sequencing, and data analysis using the International Organization for Standardization 15189 quality and competence standard for medical laboratories. Compared with conventional methods, we achieved >95% accuracy in oncogene fusion identification, digital karyotyping, and TPMT and NUDT15 variant discovery. We found seven pathogenic TP53 mutations, confirmed with Sanger sequencing, which conferred a poorer outcome. Applying this workflow prospectively to the first 21 patients in Malaysia-Singapore ALL-Seq 2020, we identified the genetic drivers and IGH disease clones in >90% of patients with concordant TPMT, NUDT15, and TP53 variants using PCR-based methods. The median turnaround time was 12 days, which was clinically actionable. In conclusion, RNA-Seq workflow could be used clinically in management of B-cell ALL patients.