A clinical transcriptome approach to patient stratification and therapy selection in acute myeloid leukemia

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.

Nucleic acid therapeutics as differentiation agents for myeloid leukemias

Differentiation therapy has proven to be a success story for patients with acute promyelocytic leukemia. However, the remaining subtypes of acute myeloid leukemia (AML) are treated with cytotoxic chemotherapies that have limited efficacy and a high likelihood of resistance. As differentiation arrest is a hallmark of AML, there is increased interest in developing differentiation-inducing agents to enhance disease-free survival. Here, we provide a comprehensive review of current reports and future avenues of nucleic acid therapeutics for AML, focusing on the use of targeted nucleic acid drugs to promote differentiation. Specifically, we compare and discuss the precision of small interfering RNA, small activating RNA, antisense oligonucleotides, and aptamers to modulate gene expression patterns that drive leukemic cell differentiation. We delve into preclinical and clinical studies that demonstrate the efficacy of nucleic acid-based differentiation therapies to induce leukemic cell maturation and reduce disease burden. By directly influencing the expression of key genes involved in myeloid maturation, nucleic acid therapeutics hold the potential to induce the differentiation of leukemic cells towards a more mature and less aggressive phenotype. Furthermore, we discuss the most critical challenges associated with developing nucleic acid therapeutics for myeloid malignancies. By introducing the progress in the field and identifying future opportunities, we aim to highlight the power of nucleic acid therapeutics in reshaping the landscape of myeloid leukemia treatment.

A 4-gene prognostic index for enhancing acute myeloid leukaemia survival prediction

Despite advancements in utilizing genetic markers to enhance acute myeloid leukaemia (AML) outcome prediction, significant disease heterogeneity persists, hindering clinical management. To refine survival predictions, we assessed the transcriptome of non-acute promyelocytic leukaemia chemotherapy-treated AML patients from five cohorts (n = 975). This led to the identification of a 4-gene prognostic index (4-PI) comprising CYP2E1, DHCR7, IL2RA and SQLE. The 4-PI effectively stratified patients into risk categories, with the high 4-PI group exhibiting TP53 mutations and cholesterol biosynthesis signatures. Single-cell RNA sequencing revealed enrichment for leukaemia stem cell signatures in high 4-PI cells. Validation across three cohorts (n = 671), including one with childhood AML, demonstrated the reproducibility and clinical utility of the 4-PI, even using cost-effective techniques like real-time quantitative polymerase chain reaction. Comparative analysis with 56 established prognostic indexes revealed the superior performance of the 4-PI, highlighting its potential to enhance AML risk stratification. Finally, the 4-PI demonstrated to be potential marker to reclassified patients from the intermediate ELN2017 category to the adverse category. In conclusion, the 4-PI emerges as a robust and straightforward prognostic tool to improve survival prediction in AML patients.

Multi-OMICS approaches in cancer biology: New era in cancer therapy

Innovative multi-omics frameworks integrate diverse datasets from the same patients to enhance our understanding of the molecular and clinical aspects of cancers. Advanced omics and multi-view clustering algorithms present unprecedented opportunities for classifying cancers into subtypes, refining survival predictions and treatment outcomes, and unravelling key pathophysiological processes across various molecular layers. However, with the increasing availability of cost-effective high-throughput technologies (HTT) that generate vast amounts of data, analyzing single layers often falls short of establishing causal relations. Integrating multi-omics data spanning genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offers unique prospects to comprehend the underlying biology of complex diseases like cancer. This discussion explores algorithmic frameworks designed to uncover cancer subtypes, disease mechanisms, and methods for identifying pivotal genomic alterations. It also underscores the significance of multi-omics in tumor classifications, diagnostics, and prognostications. Despite its unparalleled advantages, the integration of multi-omics data has been slow to find its way into everyday clinics. A major hurdle is the uneven maturity of different omics approaches and the widening gap between the generation of large datasets and the capacity to process this data. Initiatives promoting the standardization of sample processing and analytical pipelines, as well as multidisciplinary training for experts in data analysis and interpretation, are crucial for translating theoretical findings into practical applications.

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.

Stellae-123 gene expression signature improved risk stratification in taiwanese acute myeloid leukemia patients

The European Leukemia Net recommendations provide valuable guidance in treatment decisions of patients with acute myeloid leukemia (AML). However, the genetic complexity and heterogeneity of AML are not fully covered, notwithstanding that gene expression analysis is crucial in the risk stratification of AML. The Stellae-123 score, an AI-based model that captures gene expression patterns, has demonstrated robust survival predictions in AML patients across four western-population cohorts. This study aims to evaluate the applicability of Stellae-123 in a Taiwanese cohort. The Stellae-123 model was applied to 304 de novo AML patients diagnosed and treated at the National Taiwan University Hospital. We find that the pretrained (BeatAML-based) model achieved c-indexes of 0.631 and 0.632 for the prediction of overall survival (OS) and relapse-free survival (RFS), respectively. Model retraining within our cohort further improve the cross-validated c-indexes to 0.667 and 0.667 for OS and RFS prediction, respectively. Multivariable analysis identify both pretrained and retrained models as independent prognostic biomarkers. We further show that incorporating age, Stellae-123, and ELN classification remarkably improves risk stratification, revealing c-indices of 0.73 and 0.728 for OS and RFS, respectively. In summary, the Stellae-123 gene expression signature is a valuable prognostic tool for AML patients and model retraining can improve the accuracy and applicability of the model in different populations.

circRNAs as prognostic markers in pediatric acute myeloid leukemia

Circular RNAs (circRNAs) arise from precursor mRNA processing through back-splicing and have been increasingly recognized for their functions in various cancers including acute myeloid leukemia (AML). However, the prognostic implications of circRNA in AML remain unclear. We conducted a comprehensive genome-wide analysis of circRNAs using RNA-seq data in pediatric AML. We revealed a group of circRNAs associated with inferior outcomes, exerting effects on cancer-related pathways. Several of these circRNAs were transcribed directly from genes with established functions in AML, such as circRUNX1, circWHSC1, and circFLT3. Further investigations indicated the increased number of circRNAs and linear RNAs splicing were significantly correlated with inferior clinical outcomes, highlighting the pivotal role of splicing dysregulation. Subsequent analysis identified a group of upregulated RNA binding proteins in AMLs associated with high number of circRNAs, with TROVE2 being a prominent candidate, suggesting their involvement in circRNA associated prognosis. Through the integration of drug sensitivity data, we pinpointed 25 drugs that could target high-risk AMLs characterized by aberrant circRNA transcription. These findings underscore prognostic significance of circRNAs in pediatric AML and offer an alternative perspective for treating high-risk cases in this malignancy.

T cell-mediated tumor killing sensitivity gene signature-based prognostic score for acute myeloid leukemia

BACKGROUND AND OBJECTIVE

Acute myeloid leukemia (AML) is an aggressive, heterogenous hematopoetic malignancies with poor long-term prognosis. T-cell mediated tumor killing plays a key role in tumor immunity. Here, we explored the prognostic performance and functional significance of a T-cell mediated tumor killing sensitivity gene (GSTTK)-based prognostic score (TTKPI).

METHODS

Publicly available transcriptomic data for AML were obtained from TCGA and NCBI-GEO. GSTTK were identified from the TISIDB database. Signature GSTTK for AML were identified by differential expression analysis, COX proportional hazards and LASSO regression analysis and a comprehensive TTKPI score was constructed. Prognostic performance of the TTKPI was examined using Kaplan-Meier survival analysis, Receiver operating curves, and nomogram analysis. Association of TTKPI with clinical phenotypes, tumor immune cell infiltration patterns, checkpoint expression patterns were analysed. Drug docking was used to identify important candidate drugs based on the TTKPI-component genes.

RESULTS

From 401 differentially expressed GSTTK in AML, 24 genes were identified as signature genes and used to construct the TTKPI score. High-TTKPI risk score predicted worse survival and good prognostic accuracy with AUC values ranging from 75 to 96%. Higher TTKPI scores were associated with older age and cancer stage, which showed improved prognostic performance when combined with TTKPI. High TTKPI was associated with lower naïve CD4 T cell and follicular helper T cell infiltrates and higher M2 macrophages/monocyte infiltration. Distinct patterns of immune checkpoint expression corresponded with TTKPI score groups. Three agents; DB11791 (Capmatinib), DB12886 (GSK-1521498) and DB14773 (Lifirafenib) were identified as candidates for AML.

CONCLUSION

A T-cell mediated killing sensitivity gene-based prognostic score TTKPI showed good accuracy in predicting survival in AML. TTKPI corresponded to functional and immunological features of the tumor microenvironment including checkpoint expression patterns and should be investigated for precision medicine approaches.

Cellular abundance-based prognostic model associated with deregulated gene expression of leukemic stem cells in acute myeloid leukemia

Background: Previous studies have reported that genes highly expressed in leukemic stem cells (LSC) may dictate the survival probability of patients and expression-based cellular deconvolution may be informative in forecasting prognosis. However, whether the prognosis of acute myeloid leukemia (AML) can be predicted using gene expression and deconvoluted cellular abundances is debatable. Methods: Nine different cell-type abundances of a training set composed of the AML samples of 422 patients, were used to build a model for predicting prognosis by least absolute shrinkage and selection operator Cox regression. This model was validated in two different validation sets, TCGA-LAML and Beat AML (n = 179 and 451, respectively). Results: We introduce a new prognosis predicting model for AML called the LSC activity (LSCA) score, which incorporates the abundance of 5 cell types, granulocyte-monocyte progenitors, common myeloid progenitors, CD45RA + cells, megakaryocyte-erythrocyte progenitors, and multipotent progenitors. Overall survival probabilities between the high and low LSCA score groups were significantly different in TCGA-LAML and Beat AML cohorts (log-rank p-value = 3.3 × 10 - 4 and 4.3 × 10 - 3 , respectively). Also, multivariate Cox regression analysis on these two validation sets shows that LSCA score is independent prognostic factor when considering age, sex, and cytogenetic risk (hazard ratio, HR = 2.17; 95% CI 1.40-3.34; p < 0.001 and HR = 1.20; 95% CI 1.02-1.43; p < 0.03, respectively). The performance of the LSCA score was comparable to other prognostic models, LSC17, APS, and CTC scores, as indicated by the area under the curve. Gene set variation analysis with six LSC-related functional gene sets indicated that high and low LSCA scores are associated with upregulated and downregulated genes in LSCs. Conclusion: We have developed a new prognosis prediction scoring system for AML patients, the LSCA score, which uses deconvoluted cell-type abundance only.

Aging and comprehensive molecular profiling in acute myeloid leukemia

Acute myeloid leukemia (AML) is an aging-related and heterogeneous hematopoietic malignancy. In this study, a total of 1,474 newly diagnosed AML patients with RNA sequencing data were enrolled, and targeted or whole exome sequencing data were obtained in 94% cases. The correlation of aging-related factors including age and clonal hematopoiesis (CH), gender, and genomic/transcriptomic profiles (gene fusions, genetic mutations, and gene expression networks or pathways) was systematically analyzed. Overall, AML patients aged 60 y and older showed an apparently dismal prognosis. Alongside age, the frequency of gene fusions defined in the World Health Organization classification decreased, while the positive rate of gene mutations, especially CH-related ones, increased. Additionally, the number of genetic mutations was higher in gene fusion-negative (GF-) patients than those with GF. Based on the status of CH- and myelodysplastic syndromes (MDS)-related mutations, three mutant subgroups were identified among the GF- AML cohort, namely, CH-AML, CH-MDS-AML, and other GF- AML. Notably, CH-MDS-AML demonstrated a predominance of elderly and male cases, cytopenia, and significantly adverse clinical outcomes. Besides, gene expression networks including HOXA/B, platelet factors, and inflammatory responses were most striking features associated with aging and poor prognosis in AML. Our work has thus unraveled the intricate regulatory circuitry of interactions among different age, gender, and molecular groups of AML.

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.

Identification and validation of a prognostic risk-scoring model for AML based on m7G-associated gene clustering

Background

Acute myeloid leukemia (AML) patients still suffer from poor 5-year survival and relapse after remission. A better prognostic assessment tool is urgently needed. New evidence demonstrates that 7-methylguanosine (m7G) methylation modifications play an important role in AML, however, the exact role of m7G-related genes in the prognosis of AML remains unclear.

Methods

The study obtained AML expression profiles and clinical information from TCGA, GEO, and TARGET databases. Using the patient data from the TCGA cohort as the training set. Consensus clustering was performed based on 29 m7G-related genes. Survival analysis was performed by KM curves. The subgroup characteristic gene sets were screened using WGCNA. And tumor immune infiltration correlation analysis was performed by ssGSEA.

Results

The patients were classified into 3 groups based on m7G-related genebased cluster analysis, and the differential genes were screened by differential analysis and WGCNA. After LASSO regression analysis, 6 characteristic genes (including CBR1, CCDC102A, LGALS1, RD3L, SLC29A2, and TWIST1) were screened, and a prognostic risk-score model was constructed. The survival rate of low-risk patients was significantly higher than that of high-risk patients (p < 0.0001). The area under the curve values at 1, 3, and 5 years in the training set were 0.871, 0.874, and 0.951, respectively, indicating that this predictive model has an excellent predictive effect. In addition, after univariate and multivariate Cox regression screening, histograms were constructed with clinical characteristics and prognostic risk score models to better predict individual survival. Further analysis showed that the prognostic risk score model was associated with immune cell infiltration.

Conclusion

These findings suggest that the scoring model and essential risk genes could provide potential prognostic biomarkers for patients with acute myeloid leukemia.

Development and validation of a promising 5-gene prognostic model for pediatric acute myeloid leukemia

Risk classification in pediatric acute myeloid leukemia (P-AML) is crucial for personalizing treatments. Thus, we aimed to establish a risk-stratification tool for P-AML patients and eventually guide individual treatment. A total of 256 P-AML patients with accredited mRNA-seq data from the TARGET database were divided into training and internal validation datasets. A gene-expression-based prognostic score was constructed for overall survival (OS), by using univariate Cox analysis, LASSO regression analysis, Kaplan-Meier (K-M) survival, and multivariate Cox analysis. A P-AML-5G prognostic score bioinformatically derived from expression levels of 5 genes (ZNF775, RNFT1, CRNDE, COL23A1, and TTC38), clustered P-AML patients in training dataset into high-risk group (above optimal cut-off) with shorter OS, and low-risk group (below optimal cut-off) with longer OS (p < 0.0001). Meanwhile, similar results were obtained in internal validation dataset (p = 0.005), combination dataset (p < 0.001), two treatment sub-groups (p < 0.05), intermediate-risk group defined with the Children's Oncology Group (COG) (p < 0.05) and an external Japanese P-AML dataset (p = 0.005). The model was further validated in the COG study AAML1031(p = 0.001), and based on transcriptomic analysis of 943 pediatric patients and 70 normal bone marrow samples from this dataset, two genes in the model demonstrated significant differential expression between the groups [all log2(foldchange) > 3, p < 0.001]. Independent of other prognostic factors, the P-AML-5G groups presented the highest concordance-index values in training dataset, chemo-therapy only treatment subgroups of the training and internal validation datasets, and whole genome-sequencing subgroup of the combined dataset, outperforming two Children's Oncology Group (COG) risk stratification systems, 2022 European LeukemiaNet (ELN) risk classification tool and two leukemic stem cell expression-based models. The 5-gene prognostic model generated by a single assay can further refine the current COG risk stratification system that relies on numerous tests and may have the potential for the risk judgment and identification of the high-risk pediatric AML patients receiving chemo-therapy only treatment.

Evaluation of noninvasive biospecimens for transcriptome studies

Transcriptome studies disentangle functional mechanisms of gene expression regulation and may elucidate the underlying biology of disease processes. However, the types of tissues currently collected typically assay a single post-mortem timepoint or are limited to investigating cell types found in blood. Noninvasive tissues may improve disease-relevant discovery by enabling more complex longitudinal study designs, by capturing different and potentially more applicable cell types, and by increasing sample sizes due to reduced collection costs and possible higher enrollment from vulnerable populations. Here, we develop methods for sampling noninvasive biospecimens, investigate their performance across commercial and in-house library preparations, characterize their biology, and assess the feasibility of using noninvasive tissues in a multitude of transcriptomic applications. We collected buccal swabs, hair follicles, saliva, and urine cell pellets from 19 individuals over three to four timepoints, for a total of 300 unique biological samples, which we then prepared with replicates across three library preparations, for a final tally of 472 transcriptomes. Of the four tissues we studied, we found hair follicles and urine cell pellets to be most promising due to the consistency of sample quality, the cell types and expression profiles we observed, and their performance in disease-relevant applications. This is the first study to thoroughly delineate biological and technical features of noninvasive samples and demonstrate their use in a wide array of transcriptomic and clinical analyses. We anticipate future use of these biospecimens will facilitate discovery and development of clinical applications.

Integration of Transcriptomic Features to Improve Prognosis Prediction of Pediatric Acute Myeloid Leukemia With KMT2A Rearrangement

Lysine methyltransferase 2A-rearranged acute myeloid leukemia (KMT2A-r AML) is a special entity in the 2022 World Health Organization classification of myeloid neoplasms, characterized by high relapse rate and adverse outcomes. Current risk stratification was established on the treatment response and translocation partner of KMT2A. To study the transcriptomic feature and refine the current stratification of pediatric KMT2A-r AML, we analyzed clinical and RNA sequencing data of 351 patients. By implementing least absolute shrinkage and selection operator algorithm, we identified 7 genes (KIAA1522, SKAP2, EGFL7, GAB2, HEBP1, FAM174B, and STARD8) of which the expression levels were strongly associated with outcomes. We then developed a transcriptome-based score, dividing patients into 2 groups with distinct gene expression patterns and prognosis, which was further validated in an independent cohort and outperformed the LSC17 score. We also found cell cycle, oxidative phosphorylation, and metabolism pathways were upregulated in patients with inferior outcomes. By integrating clinical characteristics, we proposed a simple-to-use prognostic scoring system with excellent discriminability, which allowed us to distinguish allogeneic hematopoietic stem cell transplantation candidates more precisely. In conclusion, pediatric KMT2A-r AML is heterogenous on transcriptomic level and the newly proposed scoring system combining clinical characteristics and transcriptomic features can be instructive in clinical routines.

Identification of a Complex Karyotype Signature with Clinical Implications in AML and MDS-EB Using Gene Expression Profiling

Complex karyotype (CK) is associated with a poor prognosis in both acute myeloid leukemia (AML) and myelodysplastic syndrome with excess blasts (MDS-EB). Transcriptomic analyses have improved our understanding of the disease and risk stratification of myeloid neoplasms; however, CK-specific gene expression signatures have been rarely investigated. In this study, we developed and validated a CK-specific gene expression signature. Differential gene expression analysis between the CK and non-CK groups using data from 348 patients with AML and MDS-EB from four cohorts revealed enrichment of the downregulated genes localized on chromosome 5q or 7q, suggesting that haploinsufficiency due to the deletion of these chromosomes possibly underlies CK pathogenesis. We built a robust transcriptional model for CK prediction using LASSO regression for gene subset selection and validated it using the leave-one-out cross-validation method for fitting the logistic regression model. We established a 10-gene CK signature (CKS) predictive of CK with high predictive accuracy (accuracy 94.22%; AUC 0.977). CKS was significantly associated with shorter overall survival in three independent cohorts, and was comparable to that of previously established risk stratification models for AML. Furthermore, we explored of therapeutic targets among the genes comprising CKS and identified the dysregulated expression of superoxide dismutase 1 (SOD1) gene, which is potentially amenable to SOD1 inhibitors.

Deciphering Potential Molecular Signatures to Differentiate Acute Myeloid Leukemia (AML) with BCR::ABL1 from Chronic Myeloid Leukemia (CML) in Blast Crisis

Acute myeloid leukemia (AML) with BCR::ABL1 has recently been recognized as a distinct subtype in international classifications. Distinguishing it from myeloid blast crisis chronic myeloid leukemia (BC-CML) without evidence of a chronic phase (CP), remains challenging. We aimed to better characterize this entity by integrating clonal architecture analysis, mutational landscape assessment, and gene expression profiling. We analyzed a large retrospective cohort study including CML and AML patients. Two AML patients harboring a BCR::ABL1 fusion were included in the study. We identified BCR::ABL1 fusion as a primary event in one patient and a secondary one in the other. AML-specific variants were identified in both. Real-time RT-PCR experiments demonstrated that CD25 mRNA is overexpressed in advanced-phase CML compared to AML. Unsupervised principal component analysis showed that AML harboring a BCR::ABL1 fusion was clustered within AML. An AML vs. myeloid BC-CML differential expression signature was highlighted, and while ID4 (inhibitor of DNA binding 4) mRNA appears undetectable in most myeloid BC-CML samples, low levels are detected in AML samples. Therefore, CD25 and ID4 mRNA expression might differentiate AML with BCR::ABL1 from BC-CML and assign it to the AML group. A method for identifying this new WHO entity is then proposed. Finally, the hypothesis of AML with BCR::ABL1 arising from driver mutations on a BCR::ABL1 background behaving as a clonal hematopoiesis mutation is discussed. Validation of our data in larger cohorts and basic research are needed to better understand the molecular and cellular aspects of AML with a BCR::ABL1 entity.

m6A genotypes and prognostic signature for assessing the prognosis of patients with acute myeloid leukemia

BACKGROUND

N6-methyladenosine (m6A) has been confirmed to function critically in acute myeloid leukemia (AML) progression. Hitherto, the subtyping and prognostic predictive significance of m6A-correlated genes in AML is unclear.

METHOD

From The Cancer Genome Atlas (TCGA-LAML), Therapeutically Applicable Research to Generate Effective Treatments (TARGET-AML) and Gene Expression Omnibus (GEO, GSE71014) databases, we collected the sequencing data of AML patients. The batch effect was removed via limma package for TCGA-LAML and TARGET-AML, and the aggregated samples were AML cohorts. Samples in the AML cohort identified m6A models in AML by consensus clustering based on 23-m6A-related modulators. M6A-related differentially expressed genes (m6ARDEGs) influencing the overall survival (OS) of AML were determined by performing differential expression analysis and univariate COX analysis, and consensus-based clustering was utilized to access AML molecular subtypes. LASSO and multivariate COX analyses were performed to obtain the optimized m6ARDEGs to construct the m6A Prognostic Risk Score (m6APR_Score). Whether the model was robust was evaluated according to Kaplan-Meier (K-M) and receiver operator characteristic (ROC) curves. Further, the abundance of immune cell infiltration was explored in different m6A modification patterns and molecular subtypes and m6APR_Score groupings. Finally, nomogram was constructed to predict OS in AML. Quantitative real-time polymerase chain reaction (RT-qPCR) and cell counting kit-8 (CCK-8) assay were used to validate the genes in m6APR_Score in AML cells.

RESULTS

The m6A models (m6AM1, m6AM2, m6AM3) and molecular subtypes (C1, C2, C3) were identified in the AML cohort, exhibiting different prognosis and immunoreactivity. We recognized novel prognostic biomarkers of AML such as CD83, NRIP1, ACSL1, METTL7B, OGT, and C4orf48. AML patients were grouped into high-m6APR_Score and low-m6APR_Score groups, with the later group showing a better prognosis than former one. Both the AML cohort and the validation cohort GSE71014 demonstrated excellent prediction. Finally, the nomogram accurately predicted the survival of patients suffering from AML. Further, the decision curves showed that both nomogram and m6APR_Score showed excellent prediction. It was confirmed in vitro experiments that mRNA expressions of NRIP1, ACSL1, METTL7B and OGT were elevated, while CD83 and C4orf48 mRNA expressions downregulated in AML cells. A significant increase in the viability of U937 and THP-1 cell lines after inhibition of CD83, while siMETTL7B had contrast results.

CONCLUSION

Our study demonstrated that m6APR_Score and CD83, NRIP1, ACSL1, METTL7B, OGT, and C4orf48 potentially provided novel and promising prognostic support for AML patients.

Monoclonal antibodies binding to different epitopes of CD20 differentially sensitize DLBCL to different classes of chemotherapy

Introduction

Rituximab (R), an anti-CD20 monoclonal antibody (mAb) and the world's first approved antibody for oncology patients, was combined with the CHOP chemotherapy regimen and markedly improved the prognosis of all B- cell-derived lymphomas, the most common hematological malignancy worldwide. However, there is a 35% disease recurrence with no advancement in the first-line treatment since R was combined with the archetypal CHOP chemotherapy regimen nearly 30 years ago. There is evidence that R synergizes with chemotherapy, but the pharmacological interactions between R and CHOP or between newer anti-CD20 mAbs and CHOP remain largely unexplored.

Methods

We used in vitro models to score pharmacological interactions between R and CHOP across various lymphoma cell lines. We compared these pharmacological interactions to ofatumumab, a second-generation anti-CD20 mAb, and CHOP. Lastly, we used RNA-sequencing to characterize the transcriptional profiles induced by these two antibodies and potential molecular pathways that mediate their different effects.

Results

We discovered vast heterogeneity in the pharmacological interactions between R and CHOP in a way not predicted by the current clinical classification. We then discovered that R and ofatumumab differentially synergize with the cytotoxic and cytostatic capabilities of CHOP in separate distinct subsets of B-cell lymphoma cell lines, thereby expanding favorable immunochemotherapy interactions across a greater range of cell lines beyond those induced by R-CHOP. Lastly, we discovered these two mAbs differentially modulate genes enriched in the JNK and p38 MAPK family, which regulates apoptosis and proliferation.

Discussion

Our findings were completely unexpected because these mAbs were long considered to be biological and clinical equivalents but, in practice, may perform better than the other in a patient-specific manner. This finding may have immediate clinical significance because both immunochemotherapy combinations are already FDA-approved with no difference in toxicity across phase I, II, and III clinical trials. Therefore, this finding could inform a new precision medicine strategy to provide additional therapeutic benefit to patients with B-cell lymphoma using immunochemotherapy combinations that already meet the clinical standard of care.

Cytogenetics analysis as the central point of genetic testing in acute myeloid leukemia (AML): a laboratory perspective for clinical applications

Chromosomal abnormalities in acute myeloid leukemia (AML) have significantly contributed to scientific understanding of its molecular pathogenesis, which has aided in the development of therapeutic strategies and enhanced management of AML patients. The diagnosis, prognosis and treatment of AML have also rapidly transformed in recent years, improving initial response to treatment, remission rates, risk stratification and overall survival. Hundreds of rare chromosomal abnormalities in AML have been discovered thus far using chromosomal analysis and next-generation sequencing. As a result, the World Health Organization (WHO) has categorized AML into subgroups based on genetic, genomic and molecular characteristics, to complement the existing French-American classification which is solely based on morphology. In this review, we aim to highlight the most clinically relevant chromosomal aberrations in AML together with the technologies employed to detect these aberrations in laboratory settings.

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.

Verification of prognostic expression biomarkers is improved by examining enriched leukemic blasts rather than mononuclear cells from acute myeloid leukemia patients

BACKGROUND

Studies have not systematically compared the ability to verify performance of prognostic transcripts in paired bulk mononuclear cells versus viable CD34-expressing leukemic blasts from patients with acute myeloid leukemia. We hypothesized that examining the homogenous leukemic blasts will yield different biological information and may improve prognostic performance of expression biomarkers.

METHODS

To assess the impact of cellular heterogeneity on expression biomarkers in acute myeloid leukemia, we systematically examined paired mononuclear cells and viable CD34-expressing leukemic blasts from SWOG diagnostic specimens. After enrichment, patients were assigned into discovery and validation cohorts based on availability of extracted RNA. Analyses of RNA sequencing data examined how enrichment impacted differentially expressed genes associated with pre-analytic variables, patient characteristics, and clinical outcomes.

RESULTS

Blast enrichment yielded significantly different expression profiles and biological pathways associated with clinical characteristics (e.g., cytogenetics). Although numerous differentially expressed genes were associated with clinical outcomes, most lost their prognostic significance in the mononuclear cells and blasts after adjusting for age and ELN risk, with only 11 genes remaining significant for overall survival in both cell populations (CEP70, COMMD7, DNMT3B, ECE1, LNX2, NEGR1, PIK3C2B, SEMA4D, SMAD2, TAF8, ZNF444). To examine the impact of enrichment on biomarker verification, these 11 candidate biomarkers were examined by quantitative RT/PCR in the validation cohort. After adjusting for ELN risk and age, expression of 4 genes (CEP70, DNMT3B, ECE1, and PIK3CB) remained significantly associated with overall survival in the blasts, while none met statistical significance in mononuclear cells.

CONCLUSIONS

This study provides insights into biological information gained/lost by examining viable CD34-expressing leukemic blasts versus mononuclear cells from the same patient and shows an improved verification rate for expression biomarkers in blasts.

Control of focal adhesion kinase activation by RUNX1-regulated miRNAs in high-risk AML

We recently described a 16-gene expression signature for improved risk stratification of acute myeloid leukemia (AML) patients called the AML Prognostic Score (APS). A subset of APS-high-risk AML patients showed increased levels of focal adhesion kinase (FAK), encoded by the Protein Tyrosine Kinase 2 (PTK2) gene, which was correlated with RUNX1 mutations. RUNX1 mutant cells are more sensitive to PTK2 inhibitors. As we were not able to detect RUNX1-binding sites in the PTK2 promoter, we hypothesized that RUNX1 might regulate micro(mi)RNAs that repress PTK2, such that loss-of-function RUNX1 mutations would result in reduced miRNA expression and derepression of PTK2. Examination of paired RNA-seq and miRNA-seq data from 301 AML cases revealed two miRNAs that positively correlated with RUNX1 expression, contained RUNX1-binding sites in their promoters and were predicted to target PTK2. We show that the hsa-let7a-2-3p and hsa-miR-135a-5p promoters are regulated by RUNX1, and that PTK2 is a direct target of both miRNAs. Even in the absence of RUNX1 mutations, hsa-let7a-2-3p and hsa-miR-135a-5p regulate PTK2 expression, and reduced expression of these two miRNAs sensitizes AML cells to PTK2 inhibition. These data explain how RUNX1 regulates PTK2, and identify potential miRNA biomarkers for targeting AML with PTK2 inhibitors.

Leukemic stem cells and therapy resistance in acute myeloid leukemia

A major obstacle in the treatment of acute myeloid leukemia (AML) is refractory disease or relapse after achieving remission. The latter arises from a few therapy-resistant cells within minimal residual disease (MRD). Resistant cells with long-term self-renewal capacity that drive clonal outgrowth are referred to as leukemic stem cells (LSC). The cancer stem cell concept considers LSC as relapse-initiating cells residing at the top of each genetically defined AML subclone forming epigenetically controlled downstream hierarchies. LSC display significant phenotypic and epigenetic plasticity, particularly in response to therapy stress, which results in various mechanisms mediating treatment resistance. Given the inherent chemotherapy resistance of LSC, targeted strategies must be incorporated into first-line regimens to prevent LSC-mediated AML relapse. The combination of venetoclax and azacitidine is a promising current strategy for the treatment of AML LSC. Nevertheless, the selection of patients who would benefit either from standard chemotherapy or venetoclax + azacitidine treatment in first-line therapy has yet to be established and the mechanisms of resistance still need to be discovered and overcome. Clinical trials are currently underway that investigate LSC susceptibility to first-line therapies. The era of single-cell multi-omics has begun to uncover the complex clonal and cellular architectures and associated biological networks. This should lead to a better understanding of the highly heterogeneous AML at the inter- and intra-patient level and identify resistance mechanisms by longitudinal analysis of patients' samples. This review discusses LSC biology and associated resistance mechanisms, potential therapeutic LSC vulnerabilities and current clinical trial activities.

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.

Implementation of Whole-Genome and Transcriptome Sequencing Into Clinical Cancer Care

PURPOSE

The combination of whole-genome and transcriptome sequencing (WGTS) is expected to transform diagnosis and treatment for patients with cancer. WGTS is a comprehensive precision diagnostic test that is starting to replace the standard of care for oncology molecular testing in health care systems around the world; however, the implementation and widescale adoption of this best-in-class testing is lacking.

METHODS

Here, we address the barriers in integrating WGTS for cancer diagnostics and treatment selection and answer questions regarding utility in different cancer types, cost-effectiveness and affordability, and other practical considerations for WGTS implementation.

RESULTS

We review the current studies implementing WGTS in health care systems and provide a synopsis of the clinical evidence and insights into practical considerations for WGTS implementation. We reflect on regulatory, costs, reimbursement, and incidental findings aspects of this test.

CONCLUSION

WGTS is an appropriate comprehensive clinical test for many tumor types and can replace multiple, cascade testing approaches currently performed. Decreasing sequencing cost, increasing number of clinically relevant aberrations and discovery of more complex biomarkers of treatment response, should pave the way for health care systems and laboratories in implementing WGTS into clinical practice, to transform diagnosis and treatment for patients with cancer.

Transcriptomic and Proteomic Profiles for Elucidating Cisplatin Resistance in Head-and-Neck Squamous Cell Carcinoma

To identify the novel genes involved in chemoresistance in head and neck squamous cell carcinoma (HNSCC), we explored the expression profiles of the following cisplatin (CDDP) resistant (R) versus parental (sensitive) cell lines by RNA-sequencing (RNA-seq): JHU029, HTB-43 and CCL-138. Using the parental condition as a control, 30 upregulated and 85 downregulated genes were identified for JHU029-R cells; 263 upregulated and 392 downregulated genes for HTB-43-R cells, and 154 upregulated and 68 downregulated genes for CCL-138-R cells. Moreover, we crossed-checked the RNA-seq results with the proteomic profiles of HTB-43-R (versus HTB-43) and CCL-138-R (versus CCL-138) cell lines. For the HTB-43-R cells, 21 upregulated and 72 downregulated targets overlapped between the proteomic and transcriptomic data; whereas in CCL-138-R cells, four upregulated and three downregulated targets matched. Following an extensive literature search, six genes from the RNA-seq (CLDN1, MAGEB2, CD24, CEACAM6, IL1B and ISG15) and six genes from the RNA-seq and proteomics crossover (AKR1C3, TNFAIP2, RAB7A, LGALS3BP, PSCA and SSRP1) were selected to be studied by qRT-PCR in 11 HNSCC patients: six resistant and five sensitive to conventional therapy. Interestingly, the high MAGEB2 expression was associated with resistant tumours and is revealed as a novel target to sensitise resistant cells to therapy in HNSCC patients.

The role of next-generation sequencing in acute myeloid leukemia

PURPOSE OF REVIEW

The development of high-throughput techniques like next-generation sequencing (NGS) has unraveled the genetic profile of cancer. In this review, we discuss the role of NGS on the diagnostic, risk stratification, and follow-up of patients with acute myeloid leukemia (AML).

RECENT FINDINGS

NGS has become an essential tool in clinical practice for AML management. Therefore, efforts are being made to improve its applications, automation, and turnaround time. Other high-throughput techniques, such as whole genome sequencing or RNA-sequencing, can be also used to this end. However, not all institutions may be able to implement these approaches. NGS is being investigated for measurable residual disease (MRD) assessment, especially with the development of error-correction NGS. New data analysis approaches like machine learning are being investigated in order to integrate genomic and clinical data and develop comprehensive classifications and risk scores.

SUMMARY

NGS has proven to be a useful approach for the analysis of genomic alterations in patients with AML, which aids patient management. Current research is being directed at reducing turnaround time and simplifying processes so that these techniques can be universally integrated into clinical practice.

Identification of the Thyrotropin-Releasing Hormone (TRH) as a Novel Biomarker in the Prognosis for Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a biologically and genetically heterogeneous hematological malignance with an unsatisfactory risk stratification system. Recently, through the novel single-cell RNA sequencing technology, we revealed heterogeneous leukemia myeloblasts in RUNX1-RUNX1T1 AML. Thyrotropin-releasing hormone (TRH), as biomarkers of CD34⁺CD117^bri myeloblasts, were found to be prognostic in RUNX1-RUNX1T1 AML. However, the clinical and genetic features of TRH in AML patients are poorly understood. Here, with data from TCGA AML, TRH was found to be downregulated in patients older than 60 years old, with DNMT3A and NPM1 mutations, while overexpressed in patients with KIT mutations. This was further validated in three other cohorts of primary AML including Beat AML (n = 223), GSE6891 (n = 461), and GSE17855 (n = 237). Furthermore, we demonstrated that the expression of TRH in AML could be used to improve the ELN 2017 risk stratification system. In conclusion, our preliminary analysis revealed that TRH, a novel biomarker for AML patients, could be used to evaluate the survival of AML.

Large-Scale In Vitro and In Vivo CRISPR-Cas9 Knockout Screens Identify a 16-Gene Fitness Score for Improved Risk Assessment in Acute Myeloid Leukemia

PURPOSE

The molecular complexity of acute myeloid leukemia (AML) presents a considerable challenge to implementation of clinical genetic testing for accurate risk stratification. Identification of better biomarkers therefore remains a high priority to enable improving established stratification and guiding risk-adapted therapy decisions.

EXPERIMENTAL DESIGN

We systematically integrated and analyzed the genome-wide CRISPR-Cas9 data from more than 1,000 in vitro and in vivo knockout screens to identify the AML-specific fitness genes. A prognostic fitness score was developed using the sparse regression analysis in a training cohort of 618 cases and validated in five publicly available independent cohorts (n = 1,570) and our RJAML cohort (n = 157) with matched RNA sequencing and targeted gene sequencing performed.

RESULTS

A total of 280 genes were identified as AML fitness genes and a 16-gene AML fitness (AFG16) score was further generated and displayed highly prognostic power in more than 2,300 patients with AML. The AFG16 score was able to distill downstream consequences of several genetic abnormalities and can substantially improve the European LeukemiaNet classification. The multi-omics data from the RJAML cohort further demonstrated its clinical applicability. Patients with high AFG16 scores had significantly poor response to induction chemotherapy. Ex vivo drug screening indicated that patients with high AFG16 scores were more sensitive to the cell-cycle inhibitors flavopiridol and SNS-032, and exhibited strongly activated cell-cycle signaling.

CONCLUSIONS

Our findings demonstrated the utility of the AFG16 score as a powerful tool for better risk stratification and selecting patients most likely to benefit from chemotherapy and alternative experimental therapies.

A six-gene prognostic signature for both adult and pediatric acute myeloid leukemia identified with machine learning

BACKGROUND

Although it is well-known that adult and pediatric acute myeloid leukemias (AMLs) are genetically distinct diseases, they still share certain gene expression profiles. The age-related genetic heterogeneities of AMLs have been well-studied, but the common prognostic signatures and molecular mechanisms of adult and pediatric AMLs are less investigated.

AIM

To identify genes and pathways that are associated with both pediatric and adult AMLs and discover a gene signature for overall survival (OS) prediction.

METHODS

Through mining the transcriptome profiles of The Cancer Genome Atlas (TCGA) data sets of adult cancers and The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) data of pediatric cancers, we identified genes that are commonly dysregulated in both pediatric and adult AMLs, further discovered a common gene signature, and built two risk score models for TCGA and TARGET cohorts, respectively with L ₀ regularized global AUC (area under the receiver operating characteristic curve) summary maximization.

RESULTS

We identified 57 genes that are differentially expressed and prognostically significant in both adult and childhood AMLs. The top 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched with those 57 genes include transcriptional misregulation, focal adhesion, PI3K-Akt signaling pathway, and signaling pathways regulating pluripotency of stem cells. We further identified a 6-gene signature including genes of ADAMTS3, DNMT3B, NYNRIN, SORT1, ZFHX3, and ZG16B for risk prediction. We constructed a risk score model with one dataset (either TCGA or TARGET) and evaluated its performance with the other. The test AUCs for the risk prediction of TCGA data with a 2-year and 5-year OS cutoffs are 0.762 (P = 2.33e-13, 95% CI: 0.69-0.83) and 0.759 (P = 7.26e-08, 95% CI: 0.66-0.85), respectively, while the test AUCs of TARGET data with the same cutoffs are 0.71 (P = 3.3e-07, 95% CI: 0.62-0.79) and 0.72 (P= 5.25e-09, 95% CI: 0.65-0.80), respectively. We further stratified patients into 3 equal sized prognostic subtypes with the 6-gene risk scores. The P-values of the tertile partitions are 1.74e-07 and 3.28e-08 for the TARGET and TCGA cohorts, respectively, which are significantly better than the standard cytogenetic risk stratification of both cohorts (TARGET: P = 1.64e-06; TCGA: P = 1.79e-05). When validated with two other independent cohorts, the 6-gene risk score models remain a significant predictor for OS. Investigating the common gene expression program is significant in that we may extrapolate the findings from adults to children and avoid unnecessary pediatric clinical trials.

Clinical interpretation of whole-genome and whole-transcriptome sequencing for precision oncology

Whole-genome sequencing either alone or in combination with whole-transcriptome sequencing has started to be used to analyze clinical tumor samples to improve diagnosis, provide risk stratification, and select patient-specific therapies. Compared with current genomic testing strategies, largely focused on small number of genes tested individually or targeted panels, whole-genome and transcriptome sequencing (WGTS) provides novel opportunities to identify and report a potentially much larger number of actionable alterations with diagnostic, prognostic, and/or predictive impact. Such alterations include point mutations, indels, copy- number aberrations and structural variants, but also germline variants, fusion genes, noncoding alterations and mutational signatures. Nevertheless, these comprehensive tests are accompanied by many challenges ranging from the extent and diversity of sequence alterations detected by these methods to the complexity and limited existing standardization in interpreting them. We describe the challenges of WGTS interpretation and the opportunities with comprehensive genomic testing.

Evaluation of the Stellae-123 prognostic gene expression signature in acute myeloid leukemia

Risk stratification in acute myeloid leukemia (AML) has been extensively improved thanks to the incorporation of recurrent cytogenomic alterations into risk stratification guidelines. However, mortality rates among fit patients assigned to low or intermediate risk groups are still high. Therefore, significant room exists for the improvement of AML prognostication. In a previous work, we presented the Stellae-123 gene expression signature, which achieved a high accuracy in the prognostication of adult patients with AML. Stellae-123 was particularly accurate to restratify patients bearing high-risk mutations, such as ASXL1, RUNX1 and TP53. The intention of the present work was to evaluate the prognostic performance of Stellae-123 in external cohorts using RNAseq technology. For this, we evaluated the signature in 3 different AML cohorts (2 adult and 1 pediatric). Our results indicate that the prognostic performance of the Stellae-123 signature is reproducible in the 3 cohorts of patients. Additionally, we evidenced that the signature was superior to the European LeukemiaNet 2017 and the pediatric clinical risk scores in the prediction of survival at most of the evaluated time points. Furthermore, integration with age substantially enhanced the accuracy of the model. In conclusion, Stellae-123 is a reproducible machine learning algorithm based on a gene expression signature with promising utility in the field of AML.

Ferroptosis-related gene signature predicts the clinical outcome in pediatric acute myeloid leukemia patients and refines the 2017 ELN classification system

Background: The prognostic roles of ferroptosis-related mRNAs (FG) and lncRNAs (FL) in pediatric acute myeloid leukemia (P-AML) patients remain unclear.

Methods: RNA-seq and clinical data of P-AML patients were downloaded from the TARGET project. Cox and LASSO regression analyses were performed to identify FG, FL, and FGL (combination of FG and FL) prognostic models, and their performances were compared. Tumor microenvironment, functional enrichment, mutation landscape, and anticancer drug sensitivity were analyzed.

Results: An FGL model of 22 ferroptosis-related signatures was identified as an independent parameter, and it showed performance better than FG, FL, and four additional public prognostic models. The FGL model divided patients in the discovery cohort (N = 145), validation cohort (N = 111), combination cohort (N = 256), and intermediate-risk group (N = 103) defined by the 2017 European LeukemiaNet (ELN) classification system into two groups with distinct survival. The high-risk group was enriched in apoptosis, hypoxia, TNFA signaling via NFKB, reactive oxygen species pathway, oxidative phosphorylation, and p53 pathway and associated with low immunity, while patients in the low-risk group may benefit from anti-TIM3 antibodies. In addition, patients within the FGL high-risk group might benefit from treatment using SB505124_1194 and JAK_8517_1739.

Conclusion: Our established FGL model may refine and provide a reference for clinical prognosis judgment and immunotherapies for P-AML patients.

In vivo genome-wide CRISPR screening in murine acute myeloid leukemia uncovers microenvironmental dependencies

In vivo CRISPR screens in AML define key interactors of the microenvironment, including integrins, immune modulators, and glycosylation.

Eight in vivo–specific hits are recurrently associated with adverse prognosis: BTBD6, FERMT3, ILK, SLC19A1, TAP2, TLN1, TPST2, and TRMT12.

Genome-wide CRISPR screens have been extremely useful in identifying therapeutic targets in diverse cancers by defining genes that are essential for malignant growth. However, most CRISPR screens were performed in vitro and thus cannot identify genes that are essential for interactions with the microenvironment in vivo. Here, we report genome-wide CRISPR screens in 2 in vivo murine models of acute myeloid leukemia (AML) driven by the KMT2A/MLLT3 fusion or by the constitutive coexpression of Hoxa9 and Meis1. Secondary validation using a focused library identified 72 genes specifically essential for leukemic growth in vivo, including components of the major histocompatibility complex class I complex, Cd47, complement receptor Cr1l, and the β-4-galactosylation pathway. Importantly, several of these in vivo–specific hits have a prognostic effect or are inferred to be master regulators of protein activity in human AML cases. For instance, we identified Fermt3, a master regulator of integrin signaling, as having in vivo–specific dependency with high prognostic relevance. Overall, we show an experimental and computational pipeline for genome-wide functional screens in vivo in AML and provide a genome-wide resource of essential drivers of leukemic growth in vivo.

A cellular hierarchy framework for understanding heterogeneity and predicting drug response in acute myeloid leukemia

The treatment landscape of acute myeloid leukemia (AML) is evolving, with promising therapies entering clinical translation, yet patient responses remain heterogeneous, and biomarkers for tailoring treatment are lacking. To understand how disease heterogeneity links with therapy response, we determined the leukemia cell hierarchy makeup from bulk transcriptomes of more than 1,000 patients through deconvolution using single-cell reference profiles of leukemia stem, progenitor and mature cell types. Leukemia hierarchy composition was associated with functional, genomic and clinical properties and converged into four overall classes, spanning Primitive, Mature, GMP and Intermediate. Critically, variation in hierarchy composition along the Primitive versus GMP or Primitive versus Mature axes were associated with response to chemotherapy or drug sensitivity profiles of targeted therapies, respectively. A seven-gene biomarker derived from the Primitive versus Mature axis was associated with response to 105 investigational drugs. Cellular hierarchy composition constitutes a novel framework for understanding disease biology and advancing precision medicine in AML.

Chimeric RNAs Discovered by RNA Sequencing and Their Roles in Cancer and Rare Genetic Diseases

Chimeric RNAs are transcripts that are generated by gene fusion and intergenic splicing events, thus comprising nucleotide sequences from different parental genes. In the past, Northern blot analysis and RT-PCR were used to detect chimeric RNAs. However, they are low-throughput and can be time-consuming, labor-intensive, and cost-prohibitive. With the development of RNA-seq and transcriptome analyses over the past decade, the number of chimeric RNAs in cancer as well as in rare inherited diseases has dramatically increased. Chimeric RNAs may be potential diagnostic biomarkers when they are specifically expressed in cancerous cells and/or tissues. Some chimeric RNAs can also play a role in cell proliferation and cancer development, acting as tools for cancer prognosis, and revealing new insights into the cell origin of tumors. Due to their abilities to characterize a whole transcriptome with a high sequencing depth and intergenically identify spliced chimeric RNAs produced with the absence of chromosomal rearrangement, RNA sequencing has not only enhanced our ability to diagnose genetic diseases, but also provided us with a deeper understanding of these diseases. Here, we reviewed the mechanisms of chimeric RNA formation and the utility of RNA sequencing for discovering chimeric RNAs in several types of cancer and rare inherited diseases. We also discussed the diagnostic, prognostic, and therapeutic values of chimeric RNAs.

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.

An overview of technologies for MS-based proteomics-centric multi-omics

INTRODUCTION

Mass spectrometry-based proteomics reveals dynamic molecular signatures underlying phenotypes reflecting normal and perturbed conditions in living systems. Although valuable on its own, the proteome has only one level of moleclar information, with the genome, epigenome, transcriptome, and metabolome, all providing complementary information. Multi-omic analysis integrating information from one or more of these other domains with proteomic information provides a more complete picture of molecular contributors to dynamic biological systems.

AREAS COVERED

Here, we discuss the improvements to mass spectrometry-based technologies, focused on peptide-based, bottom-up approaches that have enabled deep, quantitative characterization of complex proteomes. These advances are facilitating the integration of proteomics data with other 'omic information, providing a more complete picture of living systems. We also describe the current state of bioinformatics software and approaches for integrating proteomics and other 'omics data, critical for enabling new discoveries driven by multi-omics.

EXPERT COMMENTARY

Multi-omics, centered on the integration of proteomics information with other 'omic information, has tremendous promise for biological and biomedical studies. Continued advances in approaches for generating deep, reliable proteomic data and bioinformatics tools aimed at integrating data across 'omic domains will ensure the discoveries offered by these multi-omic studies continue to increase.

Identification and Validation of a Prognostic Risk-Scoring Model Based on Ferroptosis-Associated Cluster in Acute Myeloid Leukemia

Background: Emerging evidence has proven that ferroptosis plays an important role in the development of acute myeloid leukemia (AML), whereas the exact role of ferroptosis-associated genes in AML patients’ prognosis remained unclear.

Materials and Methods: Gene expression profiles and corresponding clinical information of AML cases were obtained from the TCGA (TCGA-LAML), GEO (GSE71014), and TARGET databases (TARGET-AML). Patients in the TCGA cohort were well-grouped into two clusters based on ferroptosis-related genes, and differentially expressed genes were screened between the two clusters. Univariate Cox and LASSO regression analyses were applied to select prognosis-related genes for the construction of a prognostic risk-scoring model. Survival analysis was analyzed by Kaplan–Meier and receiver operator characteristic curves. Furthermore, we explored the correlation of the prognostic risk-scoring model with immune infiltration and chemotherapy response. Risk gene expression level was detected by quantitative reverse transcription polymerase chain reaction.

Results: Eighteen signature genes, including ZSCAN4, ASTN1, CCL23, DLL3, EFNB3, FAM155B, FOXL1, HMX2, HRASLS, LGALS1, LHX6, MXRA5, PCDHB12, PRINS, TMEM56, TWIST1, ZFPM2, and ZNF560, were developed to construct a prognostic risk-scoring model. AML patients could be grouped into high- and low-risk groups, and low-risk patients showed better survival than high-risk patients. Area under the curve values of 1, 3, and 5 years were 0.81, 0.827, and 0.786 in the training set, respectively, indicating a good predictive efficacy. In addition, age and risk score were the independent prognostic factors after univariate and multivariate Cox regression analyses. A nomogram containing clinical factors and prognostic risk-scoring model was constructed to better estimate individual survival. Further analyses demonstrated that risk score was associated with the immune infiltration and response to chemotherapy. Our experiment data revealed that LGALS1 and TMEM56 showed notably decreased expression in AML samples than that of the normal samples.

Conclusion: Our study shows that the prognostic risk-scoring model and key risk gene may provide potential prognostic biomarkers and therapeutic option for AML patients.

Nrf2 overexpression increases the resistance of acute myeloid leukemia to cytarabine by inhibiting replication factor C4

Drug resistance is a key factor in the treatment failure of acute myeloid leukemia (AML). Nuclear factor E2-related factor 2 (Nrf2) plays a crucial role in tumor chemotherapy resistance. However, the potential mechanism of Nrf2 regulating DNA mismatch repair (MMR) pathway to mediate gene-instability drug resistance in AML is still unclear. Here, it was found that Nrf2 expression was closely related to the disease progression of AML as well as highly expressed in AML patients with poor prognostic gene mutations. Meanwhile, it was also found that the expression of Nrf2 was significantly negatively correlated with DNA MMR gene replication factor C4 (RFC4) in AML. CHIP analysis combined with luciferase reporter gene results further showed that Nrf2 may inhibit the expression of RFC4 by its interaction with the RFC4 promoter. In vitro and vivo experiments showed that the overexpression of Nrf2 decreased the killing effect of chemotherapy drug cytarabine (Ara-C) on leukemia cells and inhibited the expression of RFC4. Mechanistically, The result that Nrf2-RFC4 axis mediated AML genetic instability drug resistance might be received by activating the JNK/NF-κB signaling pathway. Taken together, these findings may provide a new idea for improving AML drug resistance.

Deconvolution of Bulk Gene Expression Profiles with Single-Cell Transcriptomics to Develop a Cell Type Composition-Based Prognostic Model for Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is one of the malignant hematologic cancers with rapid progress and poor prognosis. Most AML prognostic stratifications focused on genetic abnormalities. However, none of them was established based on the cell type compositions (CTCs) of peripheral blood or bone marrow aspirates from patients at diagnosis. Here we sought to develop a novel prognostic model for AML in adults based on the CTCs. First, we applied the CIBERSORT algorithm to estimate the CTCs for patients from two public datasets (GSE6891 and TCGA-LAML) using a custom gene expression signature reference constructed by an AML single-cell RNA sequencing dataset (GSE116256). Then, a CTC-based prognostic model was established using least absolute shrinkage and selection operator Cox regression, termed CTC score. The constructed prognostic model CTC score comprised 3 cell types, GMP-like, HSC-like, and T. Compared with the low-CTC-score group, the high-CTC-score group showed a 1.57-fold [95% confidence interval (CI), 1.23 to 2.00; p = 0.0002] and a 2.32-fold (95% CI, 1.53 to 3.51; p < 0.0001) higher overall mortality risk in the training set (GSE6891) and validation set (TCGA-LAML), respectively. When adjusting for age at diagnosis, cytogenetic risk, and karyotype, the CTC score remained statistically significant in both the training set [hazard ratio (HR) = 2.25; 95% CI, 1.20 to 4.24; p = 0.0119] and the validation set (HR = 7.97; 95% CI, 2.95 to 21.56; p < 0.0001]. We further compared the performance of the CTC score with two gene expression-based prognostic scores: the 17-gene leukemic stem cell score (LSC17 score) and the AML prognostic score (APS). It turned out that the CTC score achieved comparable performance at 1-, 2-, 3-, and 5-years timepoints and provided independent and additional prognostic information different from the LSC17 score and APS. In conclusion, the CTC score could serve as a powerful prognostic marker for AML and has great potential to assist clinicians to formulate individualized treatment plans.

Leukemic Stem Cells: From Leukemic Niche Biology to Treatment Opportunities

Acute myeloid leukemia (AML) is one of the most common types of leukemia in adults. While complete remission can be obtained with intensive chemotherapy in young and fit patients, relapse is frequent and prognosis remains poor. Leukemic cells are thought to arise from a pool of leukemic stem cells (LSCs) which sit at the top of the hierarchy. Since their discovery, more than 30 years ago, LSCs have been a topic of intense research and their identification paved the way for cancer stem cell research. LSCs are defined by their ability to self-renew, to engraft into recipient mice and to give rise to leukemia. Compared to healthy hematopoietic stem cells (HSCs), LSCs display specific mutations, epigenetic modifications, and a specific metabolic profile. LSCs are usually considered resistant to chemotherapy and are therefore the drivers of relapse. Similar to their HSC counterpart, LSCs reside in a highly specialized microenvironment referred to as the “niche”. Bidirectional interactions between leukemic cells and the microenvironment favor leukemic progression at the expense of healthy hematopoiesis. Within the niche, LSCs are thought to be protected from genotoxic insults. Improvement in our understanding of LSC gene expression profile and phenotype has led to the development of prognosis signatures and the identification of potential therapeutic targets. In this review, we will discuss LSC biology in the context of their specific microenvironment and how a better understanding of LSC niche biology could pave the way for new therapies that target AML.

Transcriptomic analysis reveals pro-inflammatory signatures associated with acute myeloid leukemia progression

Progression of AML is associated with pro-inflammatory mediators through altered expression levels of CR1, DPEP1, IL1R1, and ST18.

Upregulated CD6 and downregulated INSR are nodes in gene expression networks linked to AML relapse, according to machine learning analysis.

Numerous studies have been performed over the last decade to exploit the complexity of genomic and transcriptomic lesions driving the initiation of acute myeloid leukemia (AML). These studies have helped improve risk classification and treatment options. Detailed molecular characterization of longitudinal AML samples is sparse, however; meanwhile, relapse and therapy resistance represent the main challenges in AML care. To this end, we performed transcriptome-wide RNA sequencing of longitudinal diagnosis, relapse, and/or primary resistant samples from 47 adult and 23 pediatric AML patients with known mutational background. Gene expression analysis revealed the association of short event-free survival with overexpression of GLI2 and IL1R1, as well as downregulation of ST18. Moreover, CR1 downregulation and DPEP1 upregulation were associated with AML relapse both in adults and children. Finally, machine learning–based and network-based analysis identified overexpressed CD6 and downregulated INSR as highly copredictive genes depicting important relapse-associated characteristics among adult patients with AML. Our findings highlight the importance of a tumor-promoting inflammatory environment in leukemia progression, as indicated by several of the herein identified differentially expressed genes. Together, this knowledge provides the foundation for novel personalized drug targets and has the potential to maximize the benefit of current treatments to improve cure rates in AML.

Transcriptional Regulation by the NFAT Family in Acute Myeloid Leukaemia

Acute myeloid leukaemia (AML) is a haematological cancer with poor outcomes due to a lack of efficacious targeted therapies. The Nuclear Factor of Activated T Cells (NFAT) family of transcription factors is well characterised as a regulator of the cell cycle and differentiation in the myeloid lineage. Recent evidence has demonstrated that NFAT family members may have roles in regulating AML leukemogenesis and resistance to targeted therapy in myeloid leukaemia. Furthermore, gene expression data from patient samples show that some NFATs are more highly expressed in poorly differentiated AML and after disease relapse, implying that the NFAT family may have roles in specific types of AML. This review outlines the evidence for the role of NFAT in healthy myeloid tissue and explores how NFAT might regulate AML pathogenesis, highlighting the potential to target specific NFAT proteins therapeutically in AML.