Comprehensive diagnostics of acute myeloid leukemia by whole transcriptome RNA sequencing

Genome wide-scale CRISPR-Cas9 knockout screens identify a fitness score for optimized risk stratification in colorectal cancer

BACKGROUND

The molecular complexity of colorectal cancer poses a significant challenge to the clinical implementation of accurate risk stratification. There is still an urgent need to find better biomarkers to enhance established risk stratification and guide risk-adapted treatment decisions.

METHODS

we systematically analyzed cancer dependencies of 17 colorectal cancer cells and 513 other cancer cells based on genome-scale CRISPR-Cas9 knockout screens to identify colorectal cancer-specific fitness genes. A regression model was built using colorectal cancer-specific fitness genes, which was validated in other three independent cohorts. 30 published gene expression signatures were also retrieved.

FINDINGS

We defined a total of 1828 genes that were colorectal cancer-specific fitness genes and identified a 22 colorectal cancer-specific fitness gene (CFG22) score. A high CFG22 score represented unfavorable recurrence and mortality rates, which was validated in three independent cohorts. Combined with age, and TNM stage, the CFG22 model can provide guidance for the prognosis of colorectal cancer patients. Analysis of genomic abnormalities and infiltrating immune cells in the CFG22 risk stratification revealed molecular pathological difference between the subgroups. Besides, drug analysis found that CFG22 high patients were more sensitive to clofibrate.

INTERPRETATION

The CFG22 model provided a powerful auxiliary prediction tool for identifying colorectal cancer patients with high recurrence risk and poor prognosis, optimizing precise treatment and improving clinical efficacy.

Application of omics in the diagnosis, prognosis, and treatment of acute myeloid leukemia

Acute myeloid leukemia (AML) is the most frequent leukemia in adults with a high mortality rate. Current diagnostic criteria and selections of therapeutic strategies are generally based on gene mutations and cytogenetic abnormalities. Chemotherapy, targeted therapies, and hematopoietic stem cell transplantation (HSCT) are the major therapeutic strategies for AML. Two dilemmas in the clinical management of AML are related to its poor prognosis. One is the inaccurate risk stratification at diagnosis, leading to incorrect treatment selections. The other is the frequent resistance to chemotherapy and/or targeted therapies. Genomic features have been the focus of AML studies. However, the DNA-level aberrations do not always predict the expression levels of genes and proteins and the latter is more closely linked to disease phenotypes. With the development of high-throughput sequencing and mass spectrometry technologies, studying downstream effectors including RNA, proteins, and metabolites becomes possible. Transcriptomics can reveal gene expression and regulatory networks, proteomics can discover protein expression and signaling pathways intimately associated with the disease, and metabolomics can reflect precise changes in metabolites during disease progression. Moreover, omics profiling at the single-cell level enables studying cellular components and hierarchies of the AML microenvironment. The abundance of data from different omics layers enables the better risk stratification of AML by identifying prognosis-related biomarkers, and has the prospective application in identifying drug targets, therefore potentially discovering solutions to the two dilemmas. In this review, we summarize the existing AML studies using omics methods, both separately and combined, covering research fields of disease diagnosis, risk stratification, prognosis prediction, chemotherapy, as well as targeted therapy. Finally, we discuss the directions and challenges in the application of multi-omics in precision medicine of AML. Our review may inspire both omics researchers and clinical physicians to study AML from a different angle.

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, which achieved accuracies of 88.47% for males and 88.94% for females, respectively. These models outperformed a single general model despite the smaller sample sizes, 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 .

Raman spectroscopy can recognize the KMT2A rearrangement as a distinct subtype of leukemia

Acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) are the two most common hematologic malignancies, challenging to treat and associated with high recurrence and mortality rates. This work aims to identify specific Raman biomarkers of ALL cells with the KMT2A gene rearrangement (KMT2A-r), representing a highly aggressive subtype of childhood leukemia with a poor prognosis. The proposed approach combines the sensitivity and specificity of Raman spectroscopy with machine learning and allows us to distinguish not only myelo- and lymphoblasts but also discriminate B-cell precursor (BCP) ALL with KMT2A-r from other blasts of BCP-ALL. We have found that KMT2A-r ALL cells fixed with 0.5% glutaraldehyde exhibit a unique spectroscopic profile that enables us to identify this subtype from other leukemias and normal cells. Therefore, a rapid and label-free method was developed to identify ALL blasts with KMT2A-r based on the ratio of the two Raman bands assigned to phenylalanine - 1040 and 1008 cm-1. This is the first time that a particular group of leukemic cells has been identified in a label-free way. The identified biomarker can be used as a screening method in diagnostic laboratories or non-reference medical centers.

ADCY3: the pivotal gene in classical ketogenic diet for the treatment of epilepsy

Objective

Epilepsy is a common neurological disorder characterized by recurrent epilepsy episodes. As a non-pharmacological treatment, the ketogenic diet has been widely applied in treating epilepsy. However, the exact therapeutic mechanism of the ketogenic diet for epilepsy remains unclear. This study investigates the molecular mechanisms of the ketogenic diet in regulating fatty acid metabolism and activating the ADCY3-initiated cAMP signaling pathway to enhance neuronal inhibition and thereby treat epilepsy.

Methods and results

Meta-analysis reveals that the ketogenic diet is superior to the conventional diet in treating epilepsy. Animal experiments demonstrate that the ketogenic diet is more effective than the conventional diet in treating epilepsy, with the best results achieved using the classic ketogenic diet. Transcriptome sequencing analysis identifies six essential genes, among which ADCY3 shows increased expression in the ketogenic diet. In vivo experiments confirm that the activation of the cAMP-PKA signaling pathway by ADCY3 enhances neuronal inhibition and improves epilepsy control.

Conclusion

Clinical observations indicate that the ketogenic diet improves patient epilepsy episodes by regulating the ADCY3-initiated cAMP signaling pathway.

A transcriptomic based deconvolution framework for assessing differentiation stages and drug responses of AML

The diagnostic spectrum for AML patients is increasingly based on genetic abnormalities due to their prognostic and predictive value. However, information on the AML blast phenotype regarding their maturational arrest has started to regain importance due to its predictive power for drug responses. Here, we deconvolute 1350 bulk RNA-seq samples from five independent AML cohorts on a single-cell healthy BM reference and demonstrate that the morphological differentiation stages (FAB) could be faithfully reconstituted using estimated cell compositions (ECCs). Moreover, we show that the ECCs reliably predict ex-vivo drug resistances as demonstrated for Venetoclax, a BCL-2 inhibitor, resistance specifically in AML with CD14+ monocyte phenotype. We validate these predictions using LUMC proteomics data by showing that BCL-2 protein abundance is split into two distinct clusters for NPM1-mutated AML at the extremes of CD14+ monocyte percentages, which could be crucial for the Venetoclax dosing patients. Our results suggest that Venetoclax resistance predictions can also be extended to AML without recurrent genetic abnormalities and possibly to MDS-related and secondary AML. Lastly, we show that CD14+ monocytic dominated Ven/Aza treated patients have significantly lower overall survival. Collectively, we propose a framework for allowing a joint mutation and maturation stage modeling that could be used as a blueprint for testing sensitivity for new agents across the various subtypes of AML.

Characteristics of phosgene aspiration lung injury analyzed based on transcriptomics and proteomics

Background

Phosgene is a chemical material widely used worldwide. No effective method has been developed to reverse its pathological injuries. Some studies have shown that neuronal inflammation in lung tissue is involved, but the specific mechanism has not been reported.

Objective

To analyze the expression alterations of whole transcriptome gene sequencing bioinformatics and protein expression profile in lung tissue after phosgene aspiration lung injury (P-ALI) and find the main factors and pathways affecting the prognosis of P-ALI.

Methods

Rat models of P-ALI were made by phosgene. Rats were divided into a P-ALI group and a blank group. Hematoxylin-eosin (HE) staining and lung wet/dry ratio measurement were used to evaluate the lung injury. The levels of inflammatory factors were measured by ELISA. High-throughput sequencing was used to measure the expression profile of each gene. Protein expression profiles were determined by label-free relative quantification of the differential proteome.

Results

Lung injury such as the disordered structure of alveolar wall and inflammatory factors (IL-1β, IL-18, and IL-33) were significantly increased in the P-ALI group (p < 0.05). There were 225 differentially expressed lncRNAs, including 85 upregulated and 140 downregulated genes. They were also the genomes with the most significant changes in transcriptome gene expression, mainly constituting cytoplasmic, synaptic structures and transporters, and involved in amino acid and carbon metabolism. There were 42 differentially expressed circRNAs, including 25 upregulated genes and 17 downregulated genes, mainly involved in cell composition, growth, differentiation, and division. There were only 10 differentially expressed miRNAs genes, all upregulated and mainly involved in the inflammatory response pathway. Proteome identification showed 79 differentially expressed proteins. KEGG enrichment analysis showed that it was mainly involved in the N-glycan biosynthesis pathway.

Conclusion

We discovered that differentially regulated genes (lncRNAs, circRNAs, and miRNAs) were primarily associated with neuronal reflexes and synaptic signaling, including neurotransmitter transmission, ion signaling pathway conduction, neuronal projection, and synaptic vesicle circulation. They affected inflammatory factors and other metabolic pathways. This finding could be explored in future studies.

Hotspot DNA Methyltransferase 3A (DNMT3A) and Isocitrate Dehydrogenase 1 and 2 (IDH1/2) Mutations in Acute Myeloid Leukemia and Their Relevance as Targets for Immunotherapy

DNA methyltransferase 3A (DNMT3A) and isocitrate dehydrogenase 1 and 2 (IDH1/2) are genes involved in epigenetic regulation, each mutated in 7-23% of patients with acute myeloid leukemia. Here, we investigated whether hotspot mutations in these genes encode neoantigens that can be targeted by immunotherapy. Five human B-lymphoblastoid cell lines expressing common HLA class I alleles were transduced with a minigene construct containing mutations that often occur in DNMT3A or IDH1/2. From these minigene-transduced cell lines, peptides were eluted from HLA class I alleles and analyzed using tandem mass spectrometry. The resulting data are available via ProteomeXchange under the identifier PXD050560. Mass spectrometry revealed an HLA-A*01:01-binding DNMT3AR882H peptide and an HLA-B*07:02-binding IDH2R140Q peptide as potential neoantigens. For these neopeptides, peptide-HLA tetramers were produced to search for specific T-cells in healthy individuals. Various T-cell clones were isolated showing specific reactivity against cell lines transduced with full-length DNMT3AR882H or IDH2R140Q genes, while cell lines transduced with wildtype genes were not recognized. One T-cell clone for DNMT3AR882H also reacted against patient-derived acute myeloid leukemia cells with the mutation, while patient samples without the mutation were not recognized, thereby validating the surface presentation of a DNMT3AR882H neoantigen that can potentially be targeted in acute myeloid leukemia via immunotherapy.

Mapping AML heterogeneity - multi-cohort transcriptomic analysis identifies novel clusters and divergent ex-vivo drug responses

Subtyping of acute myeloid leukaemia (AML) is predominantly based on recurrent genetic abnormalities, but recent literature indicates that transcriptomic phenotyping holds immense potential to further refine AML classification. Here we integrated five AML transcriptomic datasets with corresponding genetic information to provide an overview (n = 1224) of the transcriptomic AML landscape. Consensus clustering identified 17 robust patient clusters which improved identification of CEBPA-mutated patients with favourable outcomes, and uncovered transcriptomic subtypes for KMT2A rearrangements (2), NPM1 mutations (5), and AML with myelodysplasia-related changes (AML-MRC) (5). Transcriptomic subtypes of KMT2A, NPM1 and AML-MRC showed distinct mutational profiles, cell type differentiation arrests and immune properties, suggesting differences in underlying disease biology. Moreover, our transcriptomic clusters show differences in ex-vivo drug responses, even when corrected for differentiation arrest and superiorly capture differences in drug response compared to genetic classification. In conclusion, our findings underscore the importance of transcriptomics in AML subtyping and offer a basis for future research and personalised treatment strategies. Our transcriptomic compendium is publicly available and we supply an R package to project clusters to new transcriptomic studies.

Comprehensive Molecular Profiling of NPM1-Mutated Acute Myeloid Leukemia Using RNAseq Approach

Acute myeloid leukemia (AML) is a complex hematologic malignancy with high morbidity and mortality. Nucleophosmin 1 (NPM1) mutations occur in approximately 30% of AML cases, and NPM1-mutated AML is classified as a distinct entity. NPM1-mutated AML patients without additional genetic abnormalities have a favorable prognosis. Despite this, 30-50% of them experience relapse. This study aimed to investigate the potential of total RNAseq in improving the characterization of NPM1-mutated AML patients. We explored genetic variations independently of myeloid stratification, revealing a complex molecular scenario. We showed that total RNAseq enables the uncovering of different genetic alterations and clonal subtypes, allowing for a comprehensive evaluation of the real expression of exome transcripts in leukemic clones and the identification of aberrant fusion transcripts. This characterization may enhance understanding and guide improved treatment strategies for NPM1mut AML patients, contributing to better outcomes. Our findings underscore the complexity of NPM1-mutated AML, supporting the incorporation of advanced technologies for precise risk stratification and personalized therapeutic strategies. The study provides a foundation for future investigations into the clinical implications of identified genetic variations and highlights the importance of evolving diagnostic approaches in leukemia management.

The landscape of cytogenetic and molecular genetic methods in diagnostics for hematologic neoplasia

Improvements made during the last decades in the management of patients with hematologic neoplasia have resulted in increase of overall survival. These advancements have become possible through progress in our understanding of genetic basis of different hematologic malignancies and their role in the current risk-adapted treatment protocols. In this review, we provide an overview of current cytogenetic and molecular genetic methods, commonly used in the genetic characterization of hematologic malignancies, describe the current developments in the cytogenetic and molecular diagnostics, and give an outlook into their future development. Furthermore, we give a brief overview of the most important public databases and guidelines for sequence variant interpretation.

Depicting the molecular features of suicidal behavior: a review from an "omics" perspective

Background Suicide is one of the leading global causes of death. Behavior patterns from suicide ideation to completion are complex, involving multiple risk factors. Advances in technologies and large-scale bioinformatic tools are changing how we approach biomedical problems. The "omics" field may provide new knowledge about suicidal behavior to improve identification of relevant biological pathways associated with suicidal behavior. Methods We reviewed transcriptomic, proteomic, and metabolomic studies conducted in blood and post-mortem brains from individuals who experienced suicide or suicidal behavior. Omics data were combined using systems biology in silico, aiming at identifying major biological mechanisms and key molecules associated with suicide. Results Post-mortem samples of suicide completers indicate major dysregulations in pathways associated with glial cells (astrocytes and microglia), neurotransmission (GABAergic and glutamatergic systems), neuroplasticity and cell survivor, immune responses and energy homeostasis. In the periphery, studies found alterations in molecules involved in immune responses, polyamines, lipid transport, energy homeostasis, and amino and nucleic acid metabolism. Limitations We included only exploratory, non-hypothesis-driven studies; most studies only included one brain region and whole tissue analysis, and focused on suicide completers who were white males with almost none confounding factors. Conclusions We can highlight the importance of synaptic function, especially the balance between the inhibitory and excitatory synapses, and mechanisms associated with neuroplasticity, common pathways associated with psychiatric disorders. However, some of the pathways highlighted in this review, such as transcriptional factors associated with RNA splicing, formation of cortical connections, and gliogenesis, point to mechanisms that still need to be explored.

Screening and identification of differential-expressed RNAs in thrombin-induced in vitro model of intracerebral hemorrhage

Survival of olfactory mucosal mesenchymal stem cells (OM-MSCs) remains the low level in the cerebral microenvironment during intracerebral hemorrhage (ICH). This article aims to reveal the differential expression profile of ICH-stimulated OM-MSCs based on whole transcriptome sequence analysis. OM-MSCs were isolated from 6-week C57BL/6 mice. Morphology and surface markers of OM-MSCs were investigated by light microscope and flow cytometry, respectively. OM-MSCs were incubated with 20 U/mL thrombin for 24 h to mimic ICH-induced injury in vitro. Total RNA was extracted for whole transcriptome sequencing and qPCR. OM-MSCs were characterized by negative for CD45 and CD34, and positive for CD44, CD90 and CD29. Thrombin led to decrease in cell viability and increase in senescence and apoptosis in OM-MSCs. In total, 736 lncRNAs (upregulated: 393; downregulated: 343), 21 miRNAs (upregulated: 7; downregulated: 14) and 807 mRNAs (upregulated: 422; downregulated: 385) were identified. GO and KEGG pathways were enriched in protein heterodimerization activity, trans-synaptic signaling, membrane pathway, alcohol metabolic process, organic hydroxy compound biosynthesis process, secondary alcohol metabolic process, alcoholism, neutrophil extracellular trap formation, systemic lupus erythematosus, metabolic process, steroid biosynthesis and drug metabolism-cytochrome P450. 200 lncRNA-miRNA-mRNA were predicted in thrombin-induced OM-MSCs. Based on qPCR, we validated COMMD1B, MOAP1, lncRNA CAPN15, lncRNA ALDH1L2, miR-3473b and miR-1964-3p were upregulated in thrombin-stimulated OM-MSCs, and GM20431, lncRNA GAPDH and miR-122b-3p were downregulated. Our findings provide novel understanding for thrombin-induced injury in OM-MSCs. Differently-expressed RNAs can be the targets of improving therapeutic application of OM-MSCs.

Identification of Cell Type-Specific Effects of DNMT3A Mutations on Relapse in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a heterogeneous disease caused by distinctive mutations in individual patients; therefore, each patient may display different cell-type compositions. Although most patients with AML achieve complete remission (CR) through intensive chemotherapy, the likelihood of relapse remains high. Several studies have attempted to characterize the genetic and cellular heterogeneity of AML; however, our understanding of the cellular heterogeneity of AML remains limited. In this study, we performed single-cell RNA sequencing (scRNAseq) of bone marrow-derived mononuclear cells obtained from same patients at different AML stages (diagnosis, CR, and relapse). We found that hematopoietic stem cells (HSCs) at diagnosis were abnormal compared to normal HSCs. By improving the detection of the DNMT3A R882 mutation with targeted scRNAseq, we identified that DNMT3A-mutant cells that mainly remained were granulocyte-monocyte progenitors (GMPs) or lymphoid-primed multipotential progenitors (LMPPs) from CR to relapse and that DNMT3A-mutant cells have gene signatures related to AML and leukemic cells. Copy number variation analysis at the single-cell level indicated that the cell type that possesses DNMT3A mutations is an important factor in AML relapse and that GMP and LMPP cells can affect relapse in patients with AML. This study advances our understanding of the role of DNMT3A in AML relapse and our approach can be applied to predict treatment outcomes.

Novel precision medicine approaches and treatment strategies in hematological malignancies

Genetic testing has been applied for decades in clinical routine diagnostics of hematological malignancies to improve disease (sub)classification, prognostication, patient management, and survival. In recent classifications of hematological malignancies, disease subtypes are defined by key recurrent genetic alterations detected by conventional methods (i.e., cytogenetics, fluorescence in situ hybridization, and targeted sequencing). Hematological malignancies were also one of the first disease areas in which targeted therapies were introduced, the prime example being BCR::ABL1 inhibitors, followed by an increasing number of targeted inhibitors hitting the Achilles' heel of each disease, resulting in a clear patient benefit. Owing to the technical advances in high-throughput sequencing, we can now apply broad genomic tests, including comprehensive gene panels or whole-genome and whole-transcriptome sequencing, to identify clinically important diagnostic, prognostic, and predictive markers. In this review, we give examples of how precision diagnostics has been implemented to guide treatment selection and improve survival in myeloid (myelodysplastic syndromes and acute myeloid leukemia) and lymphoid malignancies (acute lymphoblastic leukemia, diffuse large B-cell lymphoma, and chronic lymphocytic leukemia). We discuss the relevance and potential of monitoring measurable residual disease using ultra-sensitive techniques to assess therapy response and detect early relapses. Finally, we bring up the promising avenue of functional precision medicine, combining ex vivo drug screening with various omics technologies, to provide novel treatment options for patients with advanced disease. Although we are only in the beginning of the field of precision hematology, we foresee rapid development with new types of diagnostics and treatment strategies becoming available to the benefit of our patients.

Comprehensive transcriptional analysis of pig facial skin development

Background

Skin development is a complex process that is influenced by many factors. Pig skin is used as an ideal material for xenografts because it is more anatomically and physiologically similar to human skin. It has been shown that the skin development of different pig breeds is different, and some Chinese pig breeds have the characteristics of skin thickness and facial skin folds, but the specific regulatory mechanism of this skin development is not yet clear.

Methods

In this study, the facial skin of Chenghua sows in the four developmental stages of postnatal Day 3 (D3) , Day 90 (D90) , Day 180 (D180), and Year 3 (Y3) were used as experimental materials, and RNA sequencing (RNA-seq) analysis was used to explore the changes in RNA expression in skin development at the four developmental stages, determine the differentially expressed messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs), and perform functional analysis of related genes by Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses.

Results

A pairwise comparison of the four developmental stages identified several differentially expressed genes (DEGs) and found that the number of differentially expressed RNAs (DE RNAs) increased with increasing developmental time intervals. Elastin (ELN) is an important component of the skin. Its content affects the relaxation of the epidermis and dermal connection, and its expression is continuously downregulated during the four developmental stages. The functions of DEGs at different developmental stages were examined by performing GO and KEGG analyses, and the GO terms and enrichment pathways of mRNAs, lncRNAs, miRNAs, and circRNAs highly overlapped, among which the PPAR signaling pathway, a classical pathway for skin development, was enriched by DEGs of D3 vs. D180, D90 vs. D180 and D180 vs. Y3. In addition, we constructed lncRNA-miRNA-mRNA and circRNA-miRNA interaction networks and found genes that may be associated with skin development, but their interactions need further study.

Conclusions

We identified a number of genes associated with skin development, performed functional analyses on some important DEGs and constructed interaction networks that facilitate further studies of skin development.

TP53-Mutated Myelodysplasia and Acute Myeloid Leukemia

TP53-mutated myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) form a distinct and heterogeneous group of myeloid malignancies associated with poor outcomes. Studies carried out in the last years have in part elucidated the complex role played by TP53 mutations in the pathogenesis of these myeloid disorders and in the mechanisms of drug resistance. A consistent number of studies has shown that some molecular parameters, such as the presence of a single or multiple TP53 mutations, the presence of concomitant TP53 deletions, the association with co-occurring mutations, the clonal size of TP53 mutations, the involvement of a single (monoallelic) or of both TP53 alleles (biallelic) and the cytogenetic architecture of concomitant chromosome abnormalities are major determinants of outcomes of patients. The limited response of these patients to standard treatments, including induction chemotherapy, hypomethylating agents and venetoclax-based therapies and the discovery of an immune dysregulation have induced a shift to new emerging therapies, some of which being associated with promising efficacy. The main aim of these novel immune and nonimmune strategies consists in improving survival and in increasing the number of TP53-mutated MDS/AML patients in remission amenable to allogeneic stem cell transplantation.

Long Noncoding RNA Expression Independently Predicts Outcome in Pediatric Acute Myeloid Leukemia

PURPOSE

Optimized strategies for risk classification are essential to tailor therapy for patients with biologically distinctive disease. Risk classification in pediatric acute myeloid leukemia (pAML) relies on detection of translocations and gene mutations. Long noncoding RNA (lncRNA) transcripts have been shown to associate with and mediate malignant phenotypes in acute myeloid leukemia (AML) but have not been comprehensively evaluated in pAML.

METHODS

To identify lncRNA transcripts associated with outcomes, we evaluated the annotated lncRNA landscape by transcript sequencing of 1,298 pediatric and 96 adult AML specimens. Upregulated lncRNAs identified in the pAML training set were used to establish a regularized Cox regression model of event-free survival (EFS), yielding a 37 lncRNA signature (lncScore). Discretized lncScores were correlated with initial and postinduction treatment outcomes using Cox proportional hazards models in validation sets. Predictive model performance was compared with standard stratification methods by concordance analysis.

RESULTS

Training set cases with positive lncScores had 5-year EFS and overall survival rates of 26.7% and 42.7%, respectively, compared with 56.9% and 76.3% with negative lncScores (hazard ratio, 2.48 and 3.16; P < .001). Pediatric validation cohorts and an adult AML group yielded comparable results in magnitude and significance. lncScore remained independently prognostic in multivariable models, including key factors used in preinduction and postinduction risk stratification. Subgroup analysis suggested that lncScores provide additional outcome information in heterogeneous subgroups currently classified as indeterminate risk. Concordance analysis showed that lncScore adds to overall classification accuracy with at least comparable predictive performance to current stratification methods that rely on multiple assays.

CONCLUSION

Inclusion of the lncScore enhances predictive power of traditional cytogenetic and mutation-defined stratification in pAML with potential, as a single assay, to replace these complex stratification schemes with comparable predictive accuracy.

Genomic Alterations, Gene Expression Profiles and Functional Enrichment of Normal-Karyotype Acute Myeloid Leukaemia Based on Targeted Next-Generation Sequencing

Characterising genomic variants is paramount in understanding the pathogenesis and heterogeneity of normal-karyotype acute myeloid leukaemia (AML-NK). In this study, clinically significant genomic biomarkers were ascertained using targeted DNA sequencing and RNA sequencing on eight AML-NK patients' samples collected at disease presentation and after complete remission. In silico and Sanger sequencing validations were performed to validate variants of interest, and they were followed by the performance of functional and pathway enrichment analyses for overrepresentation analysis of genes with somatic variants. Somatic variants involving 26 genes were identified and classified as follows: 18/42 (42.9%) as pathogenic, 4/42 (9.5%) as likely pathogenic, 4/42 (9.5%) as variants of unknown significance, 7/42 (16.7%) as likely benign and 9/42 (21.4%) as benign. Nine novel somatic variants were discovered, of which three were likely pathogenic, in the CEBPA gene with significant association with its upregulation. Transcription misregulation in cancer tops the affected pathways involving upstream genes (CEBPA and RUNX1) that were deregulated in most patients during disease presentation and were closely related to the most enriched molecular function gene ontology category, DNA-binding transcription activator activity RNA polymerase II-specific (GO:0001228). In summary, this study elucidated putative variants and their gene expression profiles along with functional and pathway enrichment in AML-NK patients.

Quantitative multiple fragment monitoring with enhanced in-source fragmentation/annotation mass spectrometry

Analytical techniques with high sensitivity and selectivity are essential to the quantitative analysis of clinical samples. Liquid chromatography coupled to tandem mass spectrometry is the gold standard in clinical chemistry. However, tandem mass spectrometers come at high capital expenditure and maintenance costs. We recently showed that it is possible to generate very similar results using a much simpler single mass spectrometry detector by performing enhanced in-source fragmentation/annotation (EISA) combined with correlated ion monitoring. Here we provide a step-by-step protocol for optimizing the analytical conditions for EISA, so anyone properly trained in liquid chromatography-mass spectrometry can follow and apply this technique for any given analyte. We exemplify the approach by using 2-hydroxyglutarate (2-HG) which is a clinically relevant metabolite whose D-enantiomer is considered an 'oncometabolite', characteristic of cancers associated with mutated isocitrate dehydrogenases 1 or 2 (IDH1/2). We include procedures for determining quantitative robustness, and show results of these relating to the analysis of DL-2-hydroxyglutarate in cells, as well as in serum samples from patients with acute myeloid leukemia that contain the IDH1/2 mutation. This EISA-mass spectrometry protocol is a broadly applicable and low-cost approach for the quantification of small molecules that has been developed to work well for both single-quadrupole and time-of-flight mass analyzers.

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.

Application of precision medicine in clinical routine in haematology-Challenges and opportunities

Precision medicine is revolutionising patient care in cancer. As more knowledge is gained about the impact of specific genetic lesions on diagnosis, prognosis and treatment response, diagnostic precision and the possibility for optimal individual treatment choice have improved. Identification of hallmark genetic aberrations such as the BCR::ABL1 gene fusion in chronic myeloid leukaemia (CML) led to the rapid development of efficient targeted therapy and molecular follow-up, vastly improving survival for patients with CML during recent decades. The assessment of translocations, copy number changes and point mutations are crucial for the diagnosis and risk stratification of acute myeloid leukaemia and myelodysplastic syndromes. Still, the often heterogeneous and complex genetic landscape of haematological malignancies presents several challenges for the implementation of precision medicine to guide diagnosis, prognosis and treatment choice. This review provides an introduction and overview of the important molecular characteristics and methods currently applied in clinical practice to guide clinical decision making in haematological malignancies of myeloid and lymphoid origin. Further, experimental ways to guide the choice of targeted therapy for refractory patients are reviewed, such as functional precision medicine using drug profiling. An example of the use of pipeline studies where the treatment is chosen according to the molecular characteristics in rare solid malignancies is also provided. Finally, the future opportunities and remaining challenges of precision medicine in the real world are discussed.

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.

Clinical whole-genome sequencing in cancer diagnosis

Characterizing the genomic landscape of cancers is a routine part of clinical care that began with the discovery of the Philadelphia chromosome and has since coevolved with genomic technologies. Genomic analysis of tumors at the nucleotide level using DNA sequencing has revolutionized the understanding of cancer biology and identified new molecular drivers of disease that have led to therapeutic advances and improved patient outcomes. However, the application of next-generation sequencing in the clinical laboratory has generally been limited until very recently to targeted analysis of selected genes. Recent technological innovations and reductions in sequencing costs are now able to deliver the long-promised goal of tumor whole-genome sequencing as a practical clinical assay.

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.

Whole-transcriptome bioinformatics revealed HTRA3, KRT8, KRT17, and RHEX as novel targets in acute myeloid leukaemia

Acute myeloid leukaemia (AML) is characterised by heterogeneous genomic signatures that vary among different patient groups. Hence, the current study aims to conduct a whole transcriptome analysis of a female patient with AML and a family history of the disease at the time of diagnosis. Genetic profiling has a useful impact on clinical management and treatment success of the disease as the complex genetic landscape of AML and differential responses to treatment might indicate inadequate therapeutic targeting. A 37 year old female patient with AML was admitted to the hospital complaining of general fatigue arthralgia and chest pain. AML diagnosis was confirmed by complete blood count and blood smears before being confirmed by cytogenetic analysis. Herein, we conducted whole-transcriptome sequencing analysis to assess differential gene expression profiles in patients and healthy control subjects. In addition, single nucleotide polymorphism/insertion-deletion analyses (SNP/INDEL) were performed to investigate gene variants in the present case. The results revealed a remarkable differential gene expression profile in AML compared to the corresponding control at the time of diagnosis, indicating that HTRA3, KRT8, KRT17, and RHEX are potential novel therapeutic targets. Additionally, a number of novel gene variants were also reported in the current study, as concluded from the SNP/INDEL analysis, which might be associated with disease risk assessment and probably affect prognosis. These genes and their new variants might be worth reporting to the scientific community for further exploration of AML.

Fusion gene detection by RNA-sequencing complements diagnostics of acute myeloid leukemia and identifies recurring NRIP1-MIR99AHG rearrangements

Identification of fusion genes in clinical routine is mostly based on cytogenetics and targeted molecular genetics, such as metaphase karyotyping, fluorescence in situ hybridization and reverse-transcriptase polymerase chain reaction. However, sequencing technologies are becoming more important in clinical routine as processing time and costs per sample decrease. To evaluate the performance of fusion gene detection by RNAsequencing compared to standard diagnostic techniques, we analyzed 806 RNA-sequencing samples from patients with acute myeloid leukemia using two state-of-the-art software tools, namely Arriba and FusionCatcher. RNA-sequencing detected 90% of fusion events that were reported by routine with high evidence, while samples in which RNA-sequencing failed to detect fusion genes had overall lower and inhomogeneous sequence coverage. Based on properties of known and unknown fusion events, we developed a workflow with integrated filtering strategies for the identification of robust fusion gene candidates by RNA-sequencing. Thereby, we detected known recurrent fusion events in 26 cases that were not reported by routine and found discrepancies in evidence for known fusion events between routine and RNA-sequencing in three cases. Moreover, we identified 157 fusion genes as novel robust candidates and comparison to entries from ChimerDB or Mitelman Database showed novel recurrence of fusion genes in 14 cases. Finally, we detected the novel recurrent fusion gene NRIP1- MIR99AHG resulting from inv(21)(q11.2;q21.1) in nine patients (1.1%) and LTN1-MX1 resulting from inv(21)(q21.3;q22.3) in two patients (0.25%). We demonstrated that NRIP1-MIR99AHG results in overexpression of the 3' region of MIR99AHG and the disruption of the tricistronic miRNA cluster miR-99a/let-7c/miR-125b-2. Interestingly, upregulation of MIR99AHG and deregulation of the miRNA cluster, residing in the MIR99AHG locus, are known mechanisms of leukemogenesis in acute megakaryoblastic leukemia. Our findings demonstrate that RNA-sequencing has a strong potential to improve the systematic detection of fusion genes in clinical applications and provides a valuable tool for fusion discovery.

CBFB Break-Apart FISH Testing: An Analysis of 1629 AML Cases with a Focus on Atypical Findings and Their Implications in Clinical Diagnosis and Management

Fluorescence in situ hybridization (FISH) is a confirmatory test to establish a diagnosis of inv(16)/t(16;16) AML. However, incidental findings and their clinical diagnostic implication have not been systemically studied. We studied 1629 CBFB FISH cases performed in our institution, 262 (16.1%), 1234 (75.7%), and 133 (8.2%) were reported as positive, normal, and abnormal, respectively. The last included CBFB copy number changes (n = 120) and atypical findings such as 3'CBFB deletion (n = 11), 5'CBFB deletion (n = 1), and 5'CBFB gain (n = 1). Correlating with CBFB-MYH11 RT-PCR results, totally 271 CBFB rearrangement cases were identified, including five with discrepancies between FISH and RT-PCR due to new partner genes (n = 3), insertion (n = 1), or rare CBFB-MYH11 variant (n = 1) and eight with 3'CBFB deletion. All cases with atypical findings and/or discrepancies presented clinical diagnostic challenges. Correlating FISH signal patterns and karyotypes, additional chromosome 16 aberrations (AC16As) show impacts on the re-definition of a complex karyotype and prognostic prediction. The CBFB rearrangement but not all AC16As will be detected by NGS-based methods. Therefore, FISH testing is currently still needed to provide a quick and straightforward confirmatory inv(16)/t(16;16) AML diagnosis and additional information related to clinical management.

A novel upregulated LncRNA-AC026150.8 promotes chemo-resistance and predicts poor prognosis in acute myeloid leukemia

BACKGROUND

AML is a common hematological malignancy with poor prognosis, the pathogenesis is still unclear. lncRNA takes part in occurrence and development of AML. This research aims to explore new differentially expressed lncRNAs and their effects on AML.

METHODS

Database-based bioinformatics analysis was performed to screen differentially expressed lncRNA in AML, real-time PCR was used to analyze gene expression. Kaplan-Meier survival analysis was performed to determine prognostic effect of AC026150.8 in AML. The cell drug resistance experiment was performed to test effect of AC026150.8 on chemo-resistance of AML cells. Catrapid online software and RNA pull-down, mass spectrometry, western-blot were used to predict and verify the combination of AC026150.8 and RNA splicing factors.

RESULTS

AC026150.8 was upregulated in AML patients and related to poor prognosis. High leukocyte counts, FAB classification, MLL-AF9 expression and NPM1 mutations were associated with high AC026150.8 expression. Upregulated of AC026150.8 increased the drug resistance of AML cells. AC026150.8 could be combined with splicing factor PCBP1.

CONCLUSIONS

For the first time, our study found that the upregulated AC026150.8 in AML is related to poor prognosis, overexpression of AC026150.8 could increase drug resistance of AML cells, and confirmed its scaffolding effect in combination with splicing factors. It is necessary to further study AC026150.8 and its downstream target genes to clarify the mechanism of AC026150.8 in AML.

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.

From Descriptive to Functional Genomics of Leukemias Focusing on Genome Engineering Techniques

Genome engineering makes the precise manipulation of DNA sequences possible in a cell. Therefore, it is essential for understanding gene function. Meganucleases were the start of genome engineering, and it continued with the discovery of Zinc finger nucleases (ZFNs), followed by Transcription activator-like effector nucleases (TALENs). They can generate double-strand breaks at a desired target site in the genome, and therefore can be used to knock in mutations or knock out genes in the same way. Years later, genome engineering was transformed by the discovery of clustered regularly interspaced short palindromic repeats (CRISPR). Implementation of CRISPR systems involves recognition guided by RNA and the precise cleaving of DNA molecules. This property proves its utility in epigenetics and genome engineering. CRISPR has been and is being continuously successfully used to model mutations in leukemic cell lines and control gene expression. Furthermore, it is used to identify targets and discover drugs for immune therapies. The descriptive and functional genomics of leukemias is discussed in this study, with an emphasis on genome engineering methods. The CRISPR/Cas9 system's challenges, viewpoints, limits, and solutions are also explored.

Integrative Analysis of Gene Expression Data by RNA Sequencing for Differential Diagnosis of Acute Leukemia: Potential Application of Machine Learning

BCR-ABL1–positive acute leukemia can be classified into three disease categories: B-lymphoblastic leukemia (B-ALL), acute myeloid leukemia (AML), and mixed-phenotype acute leukemia (MPAL). We conducted an integrative analysis of RNA sequencing (RNA-seq) data obtained from 12 BCR-ABL1–positive B-ALL, AML, and MPAL samples to evaluate its diagnostic utility. RNA-seq facilitated the identification of all p190 BCR-ABL1 with accurate splicing sites and a new gene fusion involving MAP2K2. Most of the clinically significant mutations were also identified including single-nucleotide variations, insertions, and deletions. In addition, RNA-seq yielded differential gene expression profile according to the disease category. Therefore, we selected 368 genes differentially expressed between AML and B-ALL and developed two differential diagnosis models based on the gene expression data using 1) scoring algorithm and 2) machine learning. Both models showed an excellent diagnostic accuracy not only for our 12 BCR-ABL1–positive cases but also for 427 public gene expression datasets from acute leukemias regardless of specific genetic aberration. This is the first trial to develop models of differential diagnosis using RNA-seq, especially to evaluate the potential role of machine learning in identifying the disease category of acute leukemia. The integrative analysis of gene expression data by RNA-seq facilitates the accurate differential diagnosis of acute leukemia with successful detection of significant gene fusion and/or mutations, which warrants further investigation.

The Application of Targeted RNA Sequencing for KMT2A-Partial Tandem Duplication Identification and Integrated Analysis of Molecular Characterization in Acute Myeloid Leukemia

The partial tandem duplication of histone-lysine N-methyltransferase 2A (KMT2A-PTD) is an important genetic alteration in acute myeloid leukemia (AML) and is associated with poor clinical outcome. Accurate and rapid detection of KMT2A-PTD is important for outcome prediction and clinical management, but next-generation sequencing-based quantitative research is still lacking. In this study, we developed a targeted RNA-based next-generation sequencing panel, together with single primer enrichment and unique molecular identifiers, to identify KMT2A-PTD as well as AML-related gene fusions and other driver mutations. Our panel showed high sensitivity, accuracy, and reproducibility in detecting the fusion ratio of KMT2A-PTD. We characterized the mutation profile of KMT2A-PTD-positive patients with AML and found different distribution patterns of driver mutations according to KMT2A-PTD fusion ratio level. Survival analyses revealed that the fusion ratio of KMT2A-PTD did not affect clinical outcome, but a novel molecular combination, namely, KMT2A-PTD/DNMT3A/FMS-like tyrosine kinase 3-internal tandem duplication, was associated with poor prognosis. Finally, we proved that the dynamic changes in the KMT2A-PTD fusion ratio were consistent with the overall process of disease progression. In summary, we applied the unique molecular identifier-based RNA panel to quantitatively detect KMT2A-PTD and elucidate its clinical relevance, which complemented the integrative network of various genetic alterations in AML.

Clinical application of whole transcriptome sequencing for the classification of patients with acute lymphoblastic leukemia

BACKGROUND

Considering the clinical and genetic characteristics, acute lymphoblastic leukemia (ALL) is a rather heterogeneous hematological neoplasm for which current standard diagnostics require various analyses encompassing morphology, immunophenotyping, cytogenetics, and molecular analysis of gene fusions and mutations. Hence, it would be desirable to rely on a technique and an analytical workflow that allows the simultaneous analysis and identification of all the genetic alterations in a single approach. Moreover, based on the results with standard methods, a significant amount of patients have no established abnormalities and hence, cannot further be stratified.

METHODS

We performed WTS and WGS in 279 acute lymphoblastic leukemia (ALL) patients (B-cell: n = 211; T-cell: n = 68) to assess the accuracy of WTS, to detect relevant genetic markers, and to classify ALL patients.

RESULTS

DNA and RNA-based genotyping was used to ensure correct WTS-WGS pairing. Gene expression analysis reliably assigned samples to the B Cell Precursor (BCP)-ALL or the T-ALL group. Subclassification of BCP-ALL samples was done progressively, assessing first the presence of chromosomal rearrangements by the means of fusion detection. Compared to the standard methods, 97% of the recurrent risk-stratifying fusions could be identified by WTS, assigning 76 samples to their respective entities. Additionally, read-through fusions (indicative of CDKN2A and RB1 gene deletions) were recurrently detected in the cohort along with 57 putative novel fusions, with yet untouched diagnostic potentials. Next, copy number variations were inferred from WTS data to identify relevant ploidy groups, classifying an additional of 31 samples. Lastly, gene expression profiling detected a BCR-ABL1-like signature in 27% of the remaining samples.

CONCLUSION

As a single assay, WTS allowed a precise genetic classification for the majority of BCP-ALL patients, and is superior to conventional methods in the cases which lack entity defining genetic abnormalities.

Fusion gene map of acute leukemia revealed by transcriptome sequencing of a consecutive cohort of 1000 cases in a single center

Fusion genes (FGs) are important genetic abnormalities in acute leukemias, but their variety and occurrence in acute leukemias remain to be systematically described. Whole transcriptome sequencing (WTS) provides a powerful tool for analyzing FGs. Here we report the FG map revealed by WTS in a consecutive cohort of 1000 acute leukemia cases in a single center, including 539 acute myeloid leukemia (AML), 437 acute lymphoblastic leukemia (ALL), and 24 mixed-phenotype acute leukemia (MPAL) patients. Bioinformatic analysis identified 792 high-confidence in-frame fusion events (296 distinct fusions) which were classified into four tiers. Tier A (pathogenic), B (likely pathogenic), and C (uncertain significance) FGs were identified in 61.8% cases of the total cohort (59.7% in AML, 64.5% in ALL, and 63.6% in MPAL). FGs involving protein kinase, transcription factor, and epigenetic genes were detected in 10.7%, 48.5%, and 15.1% cases, respectively. A considerable amount of novel FGs (82 in AML, 88 in B-ALL, 13 in T-ALL, and 9 in MPAL) was identified. This comprehensively described real map of FGs in acute leukemia revealed multiple FGs with clinical relevance that have not been previously recognized. WTS is a valuable tool and should be widely used in the routine diagnostic workup of acute leukemia.

Emerging molecular subtypes and therapeutic targets in B-cell precursor acute lymphoblastic leukemia

B-cell precursor acute lymphoblastic leukemia (BCP-ALL) is characterized by genetic alterations with high heterogeneity. Precise subtypes with distinct genomic and/or gene expression patterns have been recently revealed using high-throughput sequencing technology. Most of these profiles are associated with recurrent non-overlapping rearrangements or hotspot point mutations that are analogous to the established subtypes, such as DUX4 rearrangements, MEF2D rearrangements, ZNF384/ZNF362 rearrangements, NUTM1 rearrangements, BCL2/MYC and/or BCL6 rearrangements, ETV6-RUNX1-like gene expression, PAX5alt (diverse PAX5 alterations, including rearrangements, intragenic amplifications, or mutations), and hotspot mutations PAX5 (p.Pro80Arg) with biallelic PAX5 alterations, IKZF1 (p.Asn159Tyr), and ZEB2 (p.His1038Arg). These molecular subtypes could be classified by gene expression patterns with RNA-seq technology. Refined molecular classification greatly improved the treatment strategy. Multiagent therapy regimens, including target inhibitors (e.g., imatinib), immunomodulators, monoclonal antibodies, and chimeric antigen receptor T-cell (CAR-T) therapy, are transforming the clinical practice from chemotherapy drugs to personalized medicine in the field of risk-directed disease management. We provide an update on our knowledge of emerging molecular subtypes and therapeutic targets in BCP-ALL.

Targeted RNA-Sequencing Enables Detection of Relevant Translocations and Single Nucleotide Variants and Provides a Method for Classification of Hematological Malignancies-RANKING

BACKGROUND

Patients with hematological malignancies (HMs) carry a wide range of chromosomal and molecular abnormalities that impact their prognosis and treatment. Since no current technique can detect all relevant abnormalities, technique(s) are chosen depending on the reason for referral, and abnormalities can be missed. We tested targeted transcriptome sequencing as a single platform to detect all relevant abnormalities and compared it to current techniques.

MATERIAL AND METHODS

We performed RNA-sequencing of 1385 genes (TruSight RNA Pan-Cancer, Illumina) in bone marrow from 136 patients with a primary diagnosis of HM. We then applied machine learning to expression profile data to perform leukemia classification, a method we named RANKING. Gene fusions for all the genes in the panel were detected, and overexpression of the genes EVI1, CCND1, and BCL2 was quantified. Single nucleotide variants/indels were analyzed in acute myeloid leukemia (AML), myelodysplastic syndrome and patients with acute lymphoblastic leukemia (ALL) using a virtual myeloid (54 genes) or lymphoid panel (72 genes).

RESULTS

RANKING correctly predicted the leukemia classification of all AML and ALL samples and improved classification in 3 patients. Compared to current methods, only one variant was missed, c.2447A>T in KIT (RT-PCR at 10-4), and BCL2 overexpression was not seen due to a t(14; 18)(q32; q21) in 2% of the cells. Our RNA-sequencing method also identified 6 additional fusion genes and overexpression of CCND1 due to a t(11; 14)(q13; q32) in 2 samples.

CONCLUSIONS

Our combination of targeted RNA-sequencing and data analysis workflow can improve the detection of relevant variants, and expression patterns can assist in establishing HM classification.

Pharmacological impact of FLT3 mutations on receptor activity and responsiveness to tyrosine kinase inhibitors

Acute myelogenous leukaemia (AML) is an aggressive blood cancer characterized by the rapid proliferation of immature myeloid blast cells, resulting in a high mortality rate. The 5-year overall survival rate for AML patients is approximately 25%. Circa 35% of all patients carry a mutation in the FLT3 gene which have a poor prognosis. Targeting FLT3 receptor tyrosine kinase has become a treatment strategy in AML patients possessing FLT3 mutations. The most common mutations are internal tandem duplications (ITD) within exon 14 and a single nucleotide polymorphism (SNP) that leads to a point mutation in the D835 of the tyrosine kinase domain (TKD). Variations in the ITD sequence and the occurrence of other point mutations that lead to ligand-independent FLT3 receptor activation create difficulties in developing personalized therapeutic strategies to overcome observed mutation-driven drug resistance. Midostaurin and quizartinib are tyrosine kinase inhibitors (TKIs) with inhibitory efficacy against FLT3-ITD, but exhibit limited clinical impact. In this review, we focus on the structural aspects of the FLT3 receptor and correlate those mutations with receptor activation and the consequences for molecular and clinical responsiveness towards therapies targeting FLT3-ITD positive AML.

Targeted Inhibition of the NUP98-NSD1 Fusion Oncogene in Acute Myeloid Leukemia

Simple Summary

NUP98-NSD1-positive acute myeloid leukemia (AML) frequently shows an additional mutation in Neuroblastoma rat sarcoma (NRAS). However, the synergistic effect of NUP98-NSD1 and NRASG12D in leukemic transformation remained unclear. In addition, NUP98-NSD1 positive AML patients respond poorly to chemotherapy and lack a targeted therapeutic option. Our study aimed to identify the cooperation of NUP98-NSD1 fusion and NRASG12D mutation and to develop a novel therapeutic approach for this AML. We found that NUP98-NSD1 alone can cause leukemia with long latency, and NRASG12D contributes to the aggressiveness of this AML. Additionally, we validated a novel NUP98-NSD1-targeting siRNA/lipid nanoparticle formulation that significantly prolonged the survival of patient-derived xenograft (PDX) mice with NUP98-NSD1-positive AML.

Abstract

NUP98-NSD1-positive acute myeloid leukemia (AML) is a poor prognostic subgroup that is frequently diagnosed in pediatric cytogenetically normal AML. NUP98-NSD1-positive AML often carries additional mutations in genes including FLT3, NRAS, WT1, and MYC. The purpose of our study was to characterize the cooperative potential of the fusion and its associated Neuroblastoma rat sarcoma (NRAS) mutation. By constitutively expressing NUP98-NSD1 and NRASG12D in a syngeneic mouse model and using a patient-derived xenograft (PDX) model from a NUP98-NSD1-positive AML patient, we evaluated the functional role of these genes and tested a novel siRNA formulation that inhibits the oncogenic driver NUP98-NSD1. NUP98-NSD1 transformed murine bone marrow (BM) cells in vitro and induced AML in vivo. While NRASG12D expression was insufficient to transform cells alone, co-expression of NUP98-NSD1 and NRASG12D enhanced the leukemogenicity of NUP98-NSD1. We developed a NUP98-NSD1-targeting siRNA/lipid nanoparticle formulation that significantly prolonged the survival of the PDX mice. Our study demonstrates that mutated NRAS cooperates with NUP98-NSD1 and shows that direct targeting of the fusion can be exploited as a novel treatment strategy in NUP98-NSD1-positive AML patients.

Aberrant Chloride Intracellular Channel 4 Expression Is Associated With Adverse Outcome in Cytogenetically Normal Acute Myeloid Leukemia

Background and Methods: Acute myeloid leukemia (AML), which starts in the bone marrow, is a group of hematopoietic stem cell disorders. Chloride intracellular channel 4 (CLIC4) is regulated by p53, c-Myc, and TGF-β. It induces the NF-κB-dependent activation of HIF (hypoxia-inducible factor) and participates in tumor growth through its microenvironmental function. However, its prognostic value in AML remains unclear, as well as its co-expression biomarkers. In this study, we evaluated the prognostic significance of CLIC4 expression using two independent large cohorts of cytogenetically normal AML (CN-AML) patients. Multivariable analysis and multi-omics analysis with weighted correlation network analysis (WGCNA) in the CN-AML group were also presented. Based on CLIC4 and its related genes, microRNA-target gene interaction network analysis and downstream gene ontology analysis were performed to unveil the complex functions behind CLIC4. Results: We demonstrated that the overexpression of CLIC4 was notably associated with unfavorable outcome in the two independent cohorts of CN-AML patients [overall survival (OS) and event-free survival (EFS): P < 0.0001, n = 185; OS: P = 0.016, n = 232], as well as in the European LeukemiaNet (ELN) Intermediate-I group (OS: P = 0.015, EFS: P = 0.012, n = 115), the National Comprehensive Cancer Network Intermediate Risk AML group (OS and EFS: P < 0.0001, n = 225), and the non-M3 AML group (OS and EFS: P < 0.0001, n = 435). Multivariable analysis further validated CLIC4 as a high-risk factor in the CN-AML group. Multi-omics analysis presented the overexpression of CLIC4 as associated with the co-expression of the different gene sets in leukemia, up/downregulation of the immune-related pathways, dysregulation of microRNAs, and hypermethylation around the CpG islands, in open sea regions, and in different gene structural fragments including TSS1500, gene body, 5'UTR region, 3'UTR region, and the first exon. By further performing WGCNA on multi-omics data, certain biomarkers that are co-expressed with CLIC4 were also unveiled. Conclusion: We demonstrated that CLIC4 is a novel, potential unfavorable prognosticator and therapeutic target for CN-AML. As having a key role in CN-AML, the interactions between CLIC4 and other genomics and transcriptomics data were confirmed by performing microRNA-target gene interaction network analysis and gene ontology enrichment analysis. The experimental result provides evidence for the clinical strategy selection of CN-AML patients.

Integrated transcriptomic and genomic analysis improves prediction of complete remission and survival in elderly patients with acute myeloid leukemia

Relevant molecular tools for treatment stratification of patients ≥65 years with acute myeloid leukemia (AML) are lacking. We combined clinical data with targeted DNA- and full RNA-sequencing of 182 intensively and palliatively treated patients to predict complete remission (CR) and survival in AML patients ≥65 years. Intensively treated patients with NPM1 and IDH2^R172 mutations had longer overall survival (OS), whereas mutated TP53 conferred lower CR rates and shorter OS. FLT3-ITD and TP53 mutations predicted worse OS in palliatively treated patients. Gene expression levels most predictive of CR were combined with somatic mutations for an integrated risk stratification that we externally validated using the beatAML cohort. We defined a high-risk group with a CR rate of 20% in patients with mutated TP53, compared to 97% CR in low-risk patients defined by high expression of ZBTB7A and EEPD1 without TP53 mutations. Patients without these criteria had a CR rate of 54% (intermediate risk). The difference in CR rates translated into significant OS differences that outperformed ELN stratification for OS prediction. The results suggest that an integrated molecular risk stratification can improve prediction of CR and OS and could be used to guide treatment in elderly AML patients.