Identification of a 24-gene prognostic signature that improves the European LeukemiaNet risk classification of acute myeloid leukemia: an international collaborative study

SMARCA5 reprograms AKR1B1-mediated fructose metabolism to control leukemogenesis

A significant variation in chromatin accessibility is an epigenetic feature of leukemia. The cause of this variation in leukemia, however, remains elusive. Here, we identify SMARCA5, a core ATPase of the imitation switch (ISWI) chromatin remodeling complex, as being responsible for aberrant chromatin accessibility in leukemia cells. We find that SMARCA5 is required to maintain aberrant chromatin accessibility for leukemogenesis and then promotes transcriptional activation of AKR1B1, an aldo/keto reductase, by recruiting transcription co-activator DDX5 and transcription factor SP1. Higher levels of AKR1B1 are associated with a poor prognosis in leukemia patients and promote leukemogenesis by reprogramming fructose metabolism. Moreover, pharmacological inhibition of AKR1B1 has been shown to have significant therapeutic effects in leukemia mice and leukemia patient cells. Thus, our findings link the aberrant chromatin state mediated by SMARCA5 to AKR1B1-mediated endogenous fructose metabolism reprogramming and shed light on the essential role of AKR1B1 in leukemogenesis, which may provide therapeutic strategies for leukemia.

Single-cell analysis defines highly specific leukemia-induced neutrophils and links MMP8 expression to recruitment of tumor associated neutrophils during FGFR1 driven leukemogenesis

BACKGROUND

Leukemias driven by activated, chimeric FGFR1 kinases typically progress to AML which have poor prognosis. Mouse models of this syndrome allow detailed analysis of cellular and molecular changes occurring during leukemogenesis. We have used these models to determine the effects of leukemia development on the immune cell composition in the leukemia microenvironment during leukemia development and progression.

METHODS

Single cell RNA sequencing (scRNA-Seq) was used to characterize leukemia associated neutrophils and define gene expression changes in these cells during leukemia progression.

RESULTS

scRNA-Seq revealed six distinct subgroups of neutrophils based on their specific differential gene expression. In response to leukemia development, there is a dramatic increase in only two of the neutrophil subgroups. These two subgroups show specific gene expression signatures consistent with neutrophil precursors which give rise to immature polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs). Analysis of gene expression in these precursor cells identified pathways that were specifically upregulated, the most pronounced of which involved matrix metalloproteinases Mmp8 and Mmp9, during leukemia progression. Pharmacological inhibition of MMPs using Ilomastat preferentially restricted in vitro migration of neutrophils from leukemic mice and led to a significantly improved survival in vivo, accompanied by impaired PMN-MDSC recruitment. As a result, levels of T-cells were proportionally increased. In clinically annotated TCGA databases, MMP8 was shown to act as an independent indicator for poor prognosis and correlated with higher neutrophil infiltration and poor pan-cancer prognosis.

CONCLUSION

We have defined specific leukemia responsive neutrophil subgroups based on their unique gene expression profile, which appear to be the precursors of neutrophils specifically associated with leukemia progression. An important event during development of these neutrophils is upregulation MMP genes which facilitated mobilization of these precursors from the BM in response to cancer progression, suggesting a possible therapeutic approach to suppress the development of immune tolerance.

Lysosome-related genes predict acute myeloid leukemia prognosis and response to immunotherapy

Background

Acute myeloid leukemia (AML) is a highly aggressive and pathogenic hematologic malignancy with consistently high mortality. Lysosomes are organelles involved in cell growth and metabolism that fuse to form specialized Auer rods in AML, and their role in AML has not been elucidated. This study aimed to identify AML subtypes centered on lysosome-related genes and to construct a prognostic model to guide individualized treatment of AML.

Methods

Gene expression data and clinical data from AML patients were downloaded from two high-throughput sequencing platforms. The 191 lysosomal signature genes were obtained from the database MsigDB. Lysosomal clusters were identified by unsupervised consensus clustering. The differences in molecular expression, biological processes, and the immune microenvironment among lysosomal clusters were subsequently analyzed. Based on the molecular expression differences between lysosomal clusters, lysosomal-related genes affecting AML prognosis were screened by univariate cox regression and multivariate cox regression analyses. Algorithms for LASSO regression analyses were employed to construct prognostic models. The risk factor distribution, KM survival curve, was applied to evaluate the survival distribution of the model. Time-dependent ROC curves, nomograms and calibration curves were used to evaluate the predictive performance of the prognostic models. TIDE scores and drug sensitivity analyses were used to explore the implication of the model for AML treatment.

Results

Our study identified two lysosomal clusters, cluster1 has longer survival time and stronger immune infiltration compared to cluster2. The differences in biological processes between the two lysosomal clusters are mainly manifested in the lysosomes, vesicles, immune cell function, and apoptosis. The prognostic model consisting of six prognosis-related genes was constructed. The prognostic model showed good predictive performance in all three data sets. Patients in the low-risk group survived significantly longer than those in the high-risk group and had higher immune infiltration and stronger response to immunotherapy. Patients in the high-risk group showed greater sensitivity to cytarabine, imatinib, and bortezomib, but lower sensitivity to ATRA compared to low -risk patients.

Conclusion

Our prognostic model based on lysosome-related genes can effectively predict the prognosis of AML patients and provide reference evidence for individualized immunotherapy and pharmacological chemotherapy for AML.

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.

Comprehensive pan-cancer analysis identifies the RNA-binding protein LRPPRC as a novel prognostic and immune biomarker

AIMS

RNA-binding proteins (RBPs) play pivotal roles in carcinogenesis and immunotherapy. Leucine-rich pentapeptide repeat-containing protein (LRPPRC) is crucial for RNA polyadenylation, transport, and stability. Although recent studies have suggested LRPPRC's potential role in tumor progression, its significance in tumor prognosis, diagnosis, and immunology remains unclear.

MAIN METHODS

We comprehensively analyzed LRPPRC expression in tumors using various databases, including Human Transcriptome Cell Atlas (HTCA), University of California Santa Cruz (UCSC), Human Protein Atlas (HPA), Sangerbox, TISIDB, GeneMANIA, GSCALite, and CellMiner. We examined the correlation between LRPPRC expression level and prognosis, immune infiltration, immunotherapy, methylation, biological function, and drug sensitivity. Single-cell analysis was performed using Tumor Immune Single Cell Hub (TISCH) and CancerSEA software. Patients with acute myeloid leukemia (AML) were categorized based on LRPPRC levels for functional and immune infiltration analyses. The role of LRPPRC in cancer was validated using in vitro experiments.

KEY FINDINGS

Our findings revealed that LRPPRC was highly expressed in almost all cancer types, indicating its significant prognostic and diagnostic potential. Notably, LRPPRC was associated with diverse immune features, such as immune cell infiltration, immune checkpoint genes, tumor mutational burden, and microsatellite instability, suggesting its value in guiding immunotherapy strategies. Within AML, the high-expression group had lower levels of immune cells, including CD8+ T cells. In vitro experiments confirmed the inhibitory effects of LRPPRC knockdown on AML cell proliferation.

SIGNIFICANCE

This study highlights LRPPRC as a reliable pan-cancer prognostic and immune biomarker, particularly in AML. It lays the groundwork for future research on LRPPRC-targeted cancer therapies.

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.

Identification of hub genes and potential molecular mechanisms related to drug sensitivity in acute myeloid leukemia based on machine learning

Background: Acute myeloid leukemia (AML) is the most common form of leukemia among adults and is characterized by uncontrolled proliferation and clonal expansion of hematopoietic cells. There has been a significant improvement in the treatment of younger patients, however, prognosis in the elderly AML patients remains poor. Methods: We used computational methods and machine learning (ML) techniques to identify and explore the differential high-risk genes (DHRGs) in AML. The DHRGs were explored through multiple in silico approaches including genomic and functional analysis, survival analysis, immune infiltration, miRNA co-expression and stemness features analyses to reveal their prognostic importance in AML. Furthermore, using different ML algorithms, prognostic models were constructed and validated using the DHRGs. At the end molecular docking studies were performed to identify potential drug candidates targeting the selected DHRGs. Results: We identified a total of 80 DHRGs by comparing the differentially expressed genes derived between AML patients and normal controls and high-risk AML genes identified by Cox regression. Genetic and epigenetic alteration analyses of the DHRGs revealed a significant association of their copy number variations and methylation status with overall survival (OS) of AML patients. Out of the 137 models constructed using different ML algorithms, the combination of Ridge and plsRcox maintained the highest mean C-index and was used to build the final model. When AML patients were classified into low- and high-risk groups based on DHRGs, the low-risk group had significantly longer OS in the AML training and validation cohorts. Furthermore, immune infiltration, miRNA coexpression, stemness feature and hallmark pathway analyses revealed significant differences in the prognosis of the low- and high-risk AML groups. Drug sensitivity and molecular docking studies revealed top 5 drugs, including carboplatin and austocystin-D that may significantly affect the DHRGs in AML. Conclusion: The findings from the current study identified a set of high-risk genes that may be used as prognostic and therapeutic markers for AML patients. In addition, significant use of the ML algorithms in constructing and validating the prognostic models in AML was demonstrated. Although our study used extensive bioinformatics and machine learning methods to identify the hub genes in AML, their experimental validations using knock-out/-in methods would strengthen our findings.

SLC25A51 decouples the mitochondrial NAD+/NADH ratio to control proliferation of AML cells

SLC25A51 selectively imports oxidized NAD+ into the mitochondrial matrix and is required for sustaining cell respiration. We observed elevated expression of SLC25A51 that correlated with poorer outcomes in patients with acute myeloid leukemia (AML), and we sought to determine the role SLC25A51 may serve in this disease. We found that lowering SLC25A51 levels led to increased apoptosis and prolonged survival in orthotopic xenograft models. Metabolic flux analyses indicated that depletion of SLC25A51 shunted flux away from mitochondrial oxidative pathways, notably without increased glycolytic flux. Depletion of SLC25A51 combined with 5-azacytidine treatment limits expansion of AML cells in vivo. Together, the data indicate that AML cells upregulate SLC25A51 to decouple mitochondrial NAD+/NADH for a proliferative advantage by supporting oxidative reactions from a variety of fuels. Thus, SLC25A51 represents a critical regulator that can be exploited by cancer cells and may be a vulnerability for refractory AML.

Penalized Bayesian forward continuation ratio model with application to high-dimensional data with discrete survival outcomes

While time-to-event data are often continuous, there are several instances where discrete survival data, which are inherently ordinal, may be available or are more appropriate or useful. Several discrete survival models exist, but the forward continuation ratio model with a complementary log-log link has a survival interpretation and is closely related to the Cox proportional hazards model, despite being an ordinal model. This model has previously been implemented in the high-dimensional setting using the ordinal generalized monotone incremental forward stagewise algorithm. Here, we propose a Bayesian penalized forward continuation ratio model with a complementary log-log link and explore different priors to perform variable selection and regularization. Through simulations, we show that our Bayesian model outperformed the existing frequentist method in terms of variable selection performance, and that a 10% prior inclusion probability performed better than 1% or 50%. We also illustrate our model on a publicly available acute myeloid leukemia dataset to identify genomic features associated with discrete survival. We identified nine features that map to ten unique genes, five of which have been previously associated with leukemia in the literature. In conclusion, our proposed Bayesian model is flexible, allows simultaneous variable selection and uncertainty quantification, and performed well in simulation studies and application to real data.

Development and validation of a cuproptosis-related prognostic model for acute myeloid leukemia patients using machine learning with stacking

Our objective is to develop a prognostic model focused on cuproptosis, aimed at predicting overall survival (OS) outcomes among Acute myeloid leukemia (AML) patients. The model utilized machine learning algorithms incorporating stacking. The GSE37642 dataset was used as the training data, and the GSE12417 and TCGA-LAML cohorts were used as the validation data. Stacking was used to merge the three prediction models, subsequently using a random survival forests algorithm to refit the final model using the stacking linear predictor and clinical factors. The prediction model, featuring stacking linear predictor and clinical factors, achieved AUC values of 0.840, 0.876 and 0.892 at 1, 2 and 3 years within the GSE37642 dataset. In external validation dataset, the corresponding AUCs were 0.741, 0.754 and 0.783. The predictive performance of the model in the external dataset surpasses that of the model simply incorporates all predictors. Additionally, the final model exhibited good calibration accuracy. In conclusion, our findings indicate that the novel prediction model refines the prognostic prediction for AML patients, while the stacking strategy displays potential for model integration.

ATP1A1/BCL2L1 predicts the response of myelomonocytic and monocytic acute myeloid leukemia to cardiac glycosides

Myelomonocytic and monocytic acute myeloid leukemia (AML) subtypes are intrinsically resistant to venetoclax-based regimens. Identifying targetable vulnerabilities would limit resistance and relapse. We previously documented the synergism of venetoclax and cardiac glycoside (CG) combination in AML. Despite preclinical evidence, the repurposing of cardiac glycosides (CGs) in cancer therapy remained unsuccessful due to a lack of predictive biomarkers. We report that the ex vivo response of AML patient blasts and the in vitro sensitivity of established cell lines to the hemi-synthetic CG UNBS1450 correlates with the ATPase Na+/K+ transporting subunit alpha 1 (ATP1A1)/BCL2 like 1 (BCL2L1) expression ratio. Publicly available AML datasets identify myelomonocytic/monocytic differentiation as the most robust prognostic feature, along with core-binding factor subunit beta (CBFB), lysine methyltransferase 2A (KMT2A) rearrangements, and missense Fms-related receptor tyrosine kinase 3 (FLT3) mutations. Mechanistically, BCL2L1 protects from cell death commitment induced by the CG-mediated stepwise triggering of ionic perturbation, protein synthesis inhibition, and MCL1 downregulation. In vivo, CGs showed an overall tolerable profile while impacting tumor growth with an effect ranging from tumor growth inhibition to regression. These findings suggest a predictive marker for CG repurposing in specific AML subtypes.

Preleukemic single-cell landscapes reveal mutation-specific mechanisms and gene programs predictive of AML patient outcomes

Acute myeloid leukemia (AML) and myeloid neoplasms develop through acquisition of somatic mutations that confer mutation-specific fitness advantages to hematopoietic stem and progenitor cells. However, our understanding of mutational effects remains limited to the resolution attainable within immunophenotypically and clinically accessible bulk cell populations. To decipher heterogeneous cellular fitness to preleukemic mutational perturbations, we performed single-cell RNA sequencing of eight different mouse models with driver mutations of myeloid malignancies, generating 269,048 single-cell profiles. Our analysis infers mutation-driven perturbations in cell abundance, cellular lineage fate, cellular metabolism, and gene expression at the continuous resolution, pinpointing cell populations with transcriptional alterations associated with differentiation bias. We further develop an 11-gene scoring system (Stem11) on the basis of preleukemic transcriptional signatures that predicts AML patient outcomes. Our results demonstrate that a single-cell-resolution deep characterization of preleukemic biology has the potential to enhance our understanding of AML heterogeneity and inform more effective risk stratification strategies.

Endoplasmic reticulum stress-related signature predicts prognosis and immune infiltration analysis in acute myeloid leukemia

OBJECTIVES

To construct an endoplasmic reticulum stress-related prognostic risk score (RS) model to predict prognosis and perform a preliminary analysis of immune infiltration in patients with acute myeloid leukemia (AML).

METHODS

The whole-genome expression data for AML and endoplasmic reticulum stress (ER stress)-related genes were downloaded from the GEO and GSEA databases, respectively. The samples were divided into death and survival groups, combined with clinical prognosis information. LASSO regression was used to construct a prognostic RS model. The Kaplan-Meier curve method was used to evaluate the association between different risk groups and actual survival prognosis information. A cox regression analysis was used to screen for independent survival prognostic clinical factors and construct a nomogram. CIBERSORT and ssGSEA was used for immune-related analysis.

RESULTS

Eighteen ER-stress related genes were identified and a comprehensive network was constructed. Further, 5 CC, 8 MF, 17 BP, and 2 KEGG pathways were enriched. Ten optimal DEGs were obtained and a prognostic risk model was constructed. Compared to the low RS group, the OS values of the high RS group were significantly lower. A significant correlation between the different risk groups and the actual prognosis was demonstrated. Ten immune cells with significantly different distributions in different risk groups were screened. KEGG enrichment analysis showed that there were 5 signaling pathways in the high-risk group.

CONCLUSIONS

The RS model can effectively predict the prognosis and has clinical implications for the prognosis of AML, combined with the correlation between different RS groups and the immune microenvironment.

Comprehensive analysis of the RSK gene family in acute myeloid leukemia determines a prognostic signature for the prediction of clinical prognosis and treatment responses

OBJECTIVE

The prognosis of acute myeloid leukemia (AML) remains poor although the basic and translational research has been highly productive in understanding the genetics and pathopoiesis of AML and a plethora of targeted therapies have been developed. Consequently, it is crucial to deepen the knowledge of molecular pathogenesis underlying AML for the advancement of new treatment options.

METHOD

A RSK gene family-related signature was constructed to investigate whether RSK gene family members were useful in predicting the prognosis of AML patients. The relationship between the RSK gene family-related signature and the infiltration of immune cells was further assessed using the CIBERSORT algorithm. The 'oncoPredict' package was used to analyze relationships between the RSK gene family-related signature and the sensitivity to drugs or small molecules.

RESULTS

Patients were classified into two groups using the RSK gene family-related signature following the median risk score. Overall survival (OS) was significantly longer in patients with low-risk scores than that in patients with high-risk scores as showed by both training and validation datasets. Moreover, the signature was helpful in predicting 1-year, 3-year, and 5-year OS in training and validation datasets. In addition, it was identified that low-risk patients exhibited greater sensitivity to 20 drugs or small molecules and that high-risk patients had higher sensitivity to 38 drugs or small molecules.

CONCLUSION

RSK gene family members, particularly RPS6KA1 and RPS6KA4, may help to predict prognosis for AML patients. Furthermore, RPS6KA1 may serve as a novel drug target for AML.

Comprehensive characterization of TGFB1 across hematological malignancies

TGFB1, which encodes TGF-β1, a potent cytokine regulating varies cellular processes including immune responses. TGF-β1 plays context-dependent roles in cancers and is increasingly recognized as a therapeutic target to enhance immunotherapy responses. We comprehensively evaluated expression of TGFB1 and its clinical and biological effects across hematological malignancies. TGFB1 expression was first explored using data from the GTEx, CCLE, and TCGA databases. The expression and clinical significances of TGFB1 in hematological malignancies were analyzed using Hemap and our In Silico curated datasets. We also analyzed the relationship between TGFB1 with immune scores and immune cell infiltrations in Hemap. We further assessed the value of TGFB1 in predicting immunotherapy response using TIDE and real-world immunotherapy datasets. TGFB1 showed a hematologic-tissue-specific expression pattern both across normal tissues and cancer types. TGFB1 expression were broadly dysregulated in blood cancers and generally associated with adverse prognosis. TGFB1 expression were associated with distinct TME properties among different blood cancer types. In addition, TGFB1 expression was found to be a useful marker in predicting immunotherapy responses. Our results suggest that TGFB1 is broadly dysregulated in hematological malignancies. TGFB1 might regulate the immune microenvironment in a cancer-type-specific manner, which could be applied in the development of new targeted drugs for immunotherapy.

Pan-cancer analysis revealing that PTPN2 is an indicator of risk stratification for acute myeloid leukemia

The non-receptor protein tyrosine phosphatases gene family (PTPNs) is involved in the tumorigenesis and development of many cancers, but the role of PTPNs in acute myeloid leukemia (AML) remains unclear. After a comprehensive evaluation on the expression patterns and immunological effects of PTPNs using a pan-cancer analysis based on RNA sequencing data obtained from The Cancer Genome Atlas, the most valuable gene PTPN2 was discovered. Further investigation of the expression patterns of PTPN2 in different tissues and cells showed a robust correlation with AML. PTPN2 was then systematically correlated with immunological signatures in the AML tumor microenvironment and its differential expression was verified using clinical samples. In addition, a prediction model, being validated and compared with other models, was developed in our research. The systematic analysis of PTPN family reveals that the effect of PTPNs on cancer may be correlated to mediating cell cycle-related pathways. It was then found that PTPN2 was highly expressed in hematologic diseases and bone marrow tissues, and its differential expression in AML patients and normal humans was verified by clinical samples. Based on its correlation with immune infiltrates, immunomodulators, and immune checkpoint, PTPN2 was found to be a reliable biomarker in the immunotherapy cohort and a prognostic predictor of AML. And PTPN2'riskscore can accurately predict the prognosis and response of cancer immunotherapy. These findings revealed the correlation between PTPNs and immunophenotype, which may be related to cell cycle. PTPN2 was differentially expressed between clinical AML patients and normal people. It is a diagnostic biomarker and potentially therapeutic target, providing targeted guidance for clinical treatment.

Diagnosis of acute myeloid leukaemia on microarray gene expression data using categorical gradient boosted trees

We define an iterative method for dimensionality reduction using categorical gradient boosted trees and Shapley values and created four machine learning models which potentially could be used as diagnostic tests for acute myeloid leukaemia (AML). For the final Catboost model we use a dataset of 2177 individuals using as features 16 probe sets and the age in order to classify if someone has AML or is healthy. The dataset is multicentric and consists of data from 27 organizations, 25 cities, 15 countries and 4 continents. The performance of our last model is specificity: 0.9909, sensitivity: 0.9985, F1-score: 0.9976 and its ROC-AUC: 0.9962 using ten fold cross validation. On an inference dataset the perormance is: specificity: 0.9909, sensitivity: 0.9969, F1-score: 0.9969 and its ROC-AUC: 0.9939. To the best of our knowledge the performance of our model is the best one in the literature, as regards the diagnosis of AML using similar or not data. Moreover, there has not been any bibliographic reference which associates AML or any other type of cancer with the 16 probe sets we used as features in our final model.

A novel 5-gene prognostic signature to improve risk stratification of cytogenetically normal acute myeloid leukemia

PURPOSE

Prognostic prediction is a challenging task in cytogenetically normal acute myeloid leukemia (CN-AML) patients. In this study, we aimed at developing a novel prognostic signature to predict and stratify the survival of CN-AML patients.

METHODS

Using a training dataset (GSE12417), 5-gene prognostic signature was established to predict survival of CN-AML patients. The prognostic performance of this prognostic signature was further validated in testing dataset (TCGA CN-AML cohort) and validation dataset (GSE6891 CN-AML cohort).

RESULTS

In training, testing and validation datasets, the increased 5-gene risk score was significantly related with inferior overall survival (OS) of patients, and the area under the receiver operating characteristic curve (AUC) demonstrated that our prognostic signature had overall prediction accuracy. The excellent prognostic value of the 5-gene prognostic signature was also supported by the comparison with three previously proposed prognostic models. For the intermediate-risk CN-AML patients and the CN-AML patients with FLT3 or NPM1 mutation, our model could also well dichotomize them into two subgroups with distinct prognosis. Multivariate analysis demonstrated that 5-gene risk score was the only independent risk factor in TCGA CN-AML cohort. Nomogram including the 5-gene risk score performed well in predicting 1-year, 2-year and 3-year OS.

CONCLUSION

In summary, our novel 5-gene prognostic signature facilitated the improvement in risk stratification of CN-AML patients.

High Expression of Microtubule-associated Protein TBCB Predicts Adverse Outcome and Immunosuppression in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a devastating blood cancer with high heterogeneity and ill-fated outcome. Despite numerous advances in AML treatment, the prognosis remains poor for a significant proportion of patients. Consequently, it is necessary to accurately and comprehensively identify biomarkers as soon as possible to enhance the efficacy of diagnosis, prognosis and treatment of AML. In this study, we aimed to identify prognostic markers of AML by analyzing the cohorts from TCGA-LAML database and GEO microarray datasets. Interestingly, the transcriptional level of microtubule-associated protein TBCB in AML patients was noticeably increased when compared with normal individuals, and this was verified in two independent cohorts (GSE9476 and GSE13159) and with our AML patients. Furthermore, univariate and multivariate regression analysis revealed that high TBCB expression was an independent poor prognostic factor for AML. GO and GSEA enrichment analysis hinted that immune-related signaling pathways were enriched in up-regulated DEGs between two populations separated by the median expression level of TBCB. By constructing a protein-protein interaction network, we obtained six hub genes, all of which are immune-related molecules, and their expression levels were positively linked to that of TBCB. In addition, the high expression of three hub genes was significantly associated with a poor prognosis in AML. Moreover, we found that the tumor microenvironment in AML with high TBCB expression tended to be infiltrated by NK cells, especially CD56^bright NK cells. The transcriptional levels of NK cell inhibitory receptors and their ligands were positively related to that of TBCB, and their high expression levels also predicted poor prognosis in AML. Notably, we found that the down-regulation of TBCB suppressed cell proliferation in AML cell lines by enhancing the apoptosis and cell cycle arrest. Finally, drug sensitivity prediction illustrated that cells with high TBCB expression were more responsive to ATRA and midostaurin but resistant to cytarabine, dasatinib, and imatinib. In conclusion, our findings shed light on the feasibility of TBCB as a potential predictor of poor outcome and to be an alternative target of treatment in AML.

Engineering amino acid uptake or catabolism promotes CAR T-cell adaption to the tumor environment

Cancer cells take up amino acids from the extracellular space to drive cell proliferation and viability. Similar mechanisms are applied by immune cells, resulting in the competition between conventional T cells, or indeed chimeric antigen receptor (CAR) T cells and tumor cells, for the limited availability of amino acids within the environment. We demonstrate that T cells can be re-engineered to express SLC7A5 or SLC7A11 transmembrane amino acid transporters alongside CARs. Transporter modifications increase CAR T-cell proliferation under low tryptophan or cystine conditions with no loss of CAR cytotoxicity or increased exhaustion. Transcriptomic and phenotypic analysis reveals that downstream, SLC7A5/SLC7A11-modified CAR T cells upregulate intracellular arginase expression and activity. In turn, we engineer and phenotype a further generation of CAR T cells that express functional arginase 1/arginase 2 enzymes and have enhanced CAR T-cell proliferation and antitumor activity. Thus, CAR T cells can be adapted to the amino acid metabolic microenvironment of cancer, a hitherto recognized but unaddressed barrier for successful CAR T-cell therapy.

Deregulated Gene Expression Profiles and Regulatory Networks in Adult and Pediatric RUNX1/RUNX1T1-Positive AML Patients

Acute myeloid leukemia (AML) is a heterogeneous and complex disease concerning molecular aberrations and prognosis. RUNX1/RUNX1T1 is a fusion oncogene that results from the chromosomal translocation t(8;21) and plays a crucial role in AML. However, its impact on the transcriptomic profile of different age groups of AML patients is not completely understood. Here, we investigated the deregulated gene expression (DEG) profiles in adult and pediatric RUNX1/RUNX1T1-positive AML patients, and compared their functions and regulatory networks. We retrospectively analyzed gene expression data from two independent Gene Expression Omnibus (GEO) datasets (GSE37642 and GSE75461) and computed their differentially expressed genes and upstream regulators, using limma, GEO2Enrichr, and X2K. For validation purposes, we used the TCGA-LAML (adult) and TARGET-AML (pediatric) patient cohorts. We also analyzed the protein–protein interaction (PPI) networks, as well as those composed of transcription factors (TF), intermediate proteins, and kinases foreseen to regulate the top deregulated genes in each group. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analyses were further performed for the DEGs in each dataset. We found that the top upregulated genes in (both adult and pediatric) RUNX1/RUNX1T1-positive AML patients are enriched in extracellular matrix organization, the cell projection membrane, filopodium membrane, and supramolecular fiber. Our data corroborate that RUNX1/RUNX1T1 reprograms a large transcriptional network to establish and maintain leukemia via intricate PPI interactions and kinase-driven phosphorylation events.

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.

Transcriptionally imprinted glycomic signatures of acute myeloid leukemia

BACKGROUND

Acute myeloid leukemia (AML) is a genetically and phenotypically heterogeneous disease that has been suffering from stagnant survival curves for decades. In the endeavor toward improved diagnosis and treatment, cellular glycosylation has emerged as an interesting focus area in AML. While mechanistic insights are still limited, aberrant glycosylation may affect intracellular signaling pathways of AML blasts, their interactions within the microenvironment, and even promote chemoresistance. Here, we performed a meta-omics study to portray the glycomic landscape of AML, thereby screening for potential subtypes and responsible glyco-regulatory networks.

RESULTS

Initially, by integrating comprehensive N-, O-, and glycosphingolipid (GSL)-glycomics of AML cell lines with transcriptomics from public databases, we were able to pinpoint specific glycosyltransferases (GSTs) and upstream transcription factors (TFs) associated with glycan phenotypes. Intriguingly, subtypes M5 and M6, as classified by the French-American-British (FAB) system, emerged with distinct glycomic features such as high (sialyl) Lewis^x/a ((s)Le^x/a) and high sialylation, respectively. Exploration of transcriptomics datasets of primary AML cells further substantiated and expanded our findings from cell lines as we observed similar gene expression patterns and regulatory networks that were identified to be involved in shaping AML glycan signatures.

CONCLUSIONS

Taken together, our data suggest transcriptionally imprinted glycomic signatures of AML, reflecting their differentiation status and FAB classification. This study expands our insights into the emerging field of AML glycosylation and paves the way for studies of FAB class-associated glycan repertoires of AML blasts and their functional implications.

The transcriptomic landscape of elderly acute myeloid leukemia identifies B7H3 and BANP as a favorable signature in high-risk patients

Acute myeloid leukemia (AML) in the elderly remains a clinical challenge, with a five-year overall survival rate below 10%. The current ELN 2017 genetic risk classification considers cytogenetic and mutational characteristics to stratify fit AML patients into different prognostic groups. However, this classification is not validated for elderly patients treated with a non-intensive approach, and its performance may be suboptimal in this context. Indeed, the transcriptomic landscape of AML in the elderly has been less explored and it might help stratify this group of patients. In the current study, we analyzed the transcriptome of 224 AML patients > 65 years-old at diagnosis treated in the Spanish PETHEMA-FLUGAZA clinical trial in order to identify new prognostic biomarkers in this population. We identified a specific transcriptomic signature for high-risk patients with mutated TP53 or complex karyotype, revealing that low expression of B7H3 gene with high expression of BANP gene identifies a subset of high-risk AML patients surviving more than 12 months. This result was further validated in the BEAT AML cohort. This unique signature highlights the potential of transcriptomics to identify prognostic biomarkers in in elderly AML.

Targeting EZH2 Promotes Chemosensitivity of BCL-2 Inhibitor through Suppressing PI3K and c-KIT Signaling in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is one of the most common hematological malignancies with high heterogeneity, characterized by a differentiating block at the early progenitor stage. The selective BCL-2 inhibitor, Venetoclax (Ven), has shown exciting clinical results in a certain group of AML patients. However, Ven alone is insufficient to reach an enduringly complete response, which leads to the concern of Ven resistance. Alternative combined therapies with Ven are demanded in AML. Here, we reported the synergistic effect and molecular mechanism of the enhancer of zeste homolog 2 (EZH2) inhibitor DZNeP with Ven in AML cells. Results showed that the combination of DZNeP with Ven significantly induces cell proliferation arrest compared to single-drug control in AML cells and primary samples, and CalcuSyn analysis showed their significant synergy. The combination also significantly promotes apoptosis and increases the expression of pro-apoptotic proteins. The whole transcriptome analysis showed that phosphoinositide-3-kinase-interacting protein1 (PIK3IP1), the PI3K/AKT/mTOR signaling suppressor, is upregulated upon DZNeP treatment. Moreover, EZH2 is upregulated but PIK3IP1 is downregulated in 88 newly diagnosed AML cohorts compared to 70 healthy controls, and a higher expression of EZH2 is associated with poor outcomes in AML patients. Particularly, the combination of DZNeP with Ven dramatically eliminated CD117 (c-KIT) (+) AML blasts, suggesting the effect of the combination on tumor stem cells. In summary, our data indicated that DZNeP increases the sensitivity of Ven in AML by affecting PI3K and c-KIT signaling in AML. Our results also suggested that the therapeutic targeting of both EZH2 and BCL-2 provides a novel potential combined strategy against 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.

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.

Unraveling the mystery: How bad is BAG3 in hematological malignancies?

BAG3, also known as BIS and CAIR-1, interacts with Hsp70 via its BAG domain and with other molecules through its WW domain, PXXP repeats and IPV motifs. BAG3 can participate in major cellular pathways including apoptosis, autophagy, cytoskeleton structure, and motility by regulating the expression, location, and activity of its chaperone proteins. As a multifunctional protein, BAG3 is highly expressed in skeletal muscle, cardiomyocytes and multiple tumors, and its intracellular expression can be stimulated by stress. The functions and mechanisms of BAG3 in hematological malignancies have recently been a topic of interest. BAG3 has been confirmed to be involved in the development and chemoresistance of hematological malignancies and to act as a prognostic indicator. Modulation of BAG3 and its corresponding proteins has thus emerged as a promising therapeutic and experimental target. In this review, we consider the characteristics of BAG3 in hematological malignancies as a reference for further clinical and fundamental investigations.

An immunity and pyroptosis gene-pair signature predicts overall survival in acute myeloid leukemia

Treatment responses of patients with acute myeloid leukemia (AML) are known to be heterogeneous, posing challenges for risk scoring and treatment stratification. In this retrospective multi-cohort study, we investigated whether combining pyroptosis- and immune-related genes improves prognostic classification of AML patients. Using a robust gene pairing approach, which effectively eliminates batch effects across heterogeneous patient cohorts and transcriptomic data, we developed an immunity and pyroptosis-related prognostic (IPRP) signature that consists of 15 genes. Using 5 AML cohorts (n = 1327 patients total), we demonstrate that the IPRP score leads to more consistent and accurate survival prediction performance, compared with 10 existing signatures, and that IPRP scoring is widely applicable to various patient cohorts, treatment procedures and transcriptomic technologies. Compared to current standards for AML patient stratification, such as age or ELN2017 risk classification, we demonstrate an added prognostic value of the IPRP risk score for providing improved prediction of AML patients. Our web-tool implementation of the IPRP score and a simple 4-factor nomogram enables practical and robust risk scoring for AML patients. Even though developed for AML patients, our pan-cancer analyses demonstrate a wider application of the IPRP signature for prognostic prediction and analysis of tumor-immune interplay also in multiple solid tumors.

A truncated derivative of FGFR1 kinase cooperates with FLT3 and KIT to transform hematopoietic stem cells in syndromic and de novo AML

Background

Myeloid and lymphoid malignancies associated with chimeric FGFR1 kinases are the hallmark of stem cell leukemia and lymphoma syndrome (SCLL). In all cases, FGFR1 kinase is constitutively phosphoactivated as a result of chromosome translocations, which lead to acquisition of dimerization motifs in the chimeric proteins. Recently, we demonstrated that these chimeric kinases could be cleaved by granzyme B to generate a truncated derivative, tnFGFR1, which localized exclusively into the nucleus and was not phosphorylated.

Methods

Stem cell transduction and transplantation in syngeneic mice was used to assess the transforming ability of tnFGFR1 in bone marrow stem cells, and RPPA and RNA-Seq was used to examine the related signaling pathways and regulated target genes.

Results

For the first time, we show that this non-classical truncated form of FGFR1 can independently lead to oncogenic transformation of hematopoietic stem cells in an animal model in vivo. These leukemia cells show a mixed immunophenotype with a B-cell B220 + Igm- profile in the majority of cells and Kit+ in virtually all cells, suggesting a stem cell disease. tnFGFR1, however, does not activate classic FGFR1 downstream signaling pathways but induces a distinct profile of altered gene expression with significant upregulation of transmembrane signaling receptors including FLT3 and KIT. We further show that de novo human AML also express tnFGFR1 which correlates with upregulation of FLT3 and KIT as in mouse leukemia cells. ChIP analysis demonstrates tnFGFR1 occupancy at the Flt3 and Kit promoters, suggesting a direct transcriptional regulation. Cells transformed with tnFGFR1 are insensitive to FGFR1 inhibitors but treatment of these cells with the Quizartinib (AC220) FLT3 inhibitor, suppresses in vitro growth and development of leukemia in vivo. Combined treatment with FGFR1 and FLT3 inhibitors provides increased survival compared to FGFR1 inhibition alone.

Conclusions

This study demonstrates a novel model for transformation of hematopoietic stem cells by chimeric FGFR1 kinases with the combined effects of direct protein activation by the full-length kinases and transcriptional regulation by the truncated nuclear tnFGFR1 derivative, which is associated with GZMB expression levels. Genes significantly upregulated by tnFGFR1 include Flt3 and Kit which promote a leukemia stem cell phenotype. In human AML, tnFGFR1 activation leads to increased FLT3 and KIT expression, and higher FLT3 and GZMB expression levels are associated with an inferior prognosis. These observations provide insights into the relative therapeutic value of targeting FGFR1 and FLT3 in treating AML with this characteristic gene expression profile.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12943-022-01628-3.

Development and Validation of an Individualized Metabolism-Related Prognostic Model for Adult Acute Myeloid Leukemia Patients

Objectives

Acute myeloid leukemia (AML) is a highly heterogeneous hematologic malignancy with widely variable prognosis. For this reason, a more tailored-stratified approach for prognosis is urgently needed to improve the treatment success rates of AML patients.

Methods

In the investigation of metabolic pattern in AML patients, we developed a metabolism-related prognostic model, which was consisted of metabolism-related gene pairs (MRGPs) identified by pairwise comparison. Furthermore, we analyzed the predictive ability and clinical significance of the prognostic model.

Results

Given the significant differences in metabolic pathways between AML patients and healthy donors, we proposed a metabolism-related prognostic signature index (MRPSI) consisting of three MRGPs, which were remarkedly related with the overall survival of AML patients in the training set. The association of MRPSI with prognosis was also validated in two other independent cohorts, suggesting that high MRPSI score can identify patients with poor prognosis. The MRPSI and age were confirmed to be independent prognostic factors via multivariate Cox regression analysis. Furthermore, we combined MRPSI with age and constructed a composite metabolism-clinical prognostic model index (MCPMI), which demonstrated better prognostic accuracy in all cohorts. Stratification analysis and multivariate Cox regression analysis revealed that the MCPMI was an independent prognostic factor. By estimating the sensitivity of anti-cancer drugs in different AML patients, we selected five drugs that were more sensitive to patients in MCPMI-high group than those in MCPMI-low group.

Conclusion

Our study provided an individualized metabolism-related prognostic model that identified high-risk patients and revealed new potential therapeutic drugs for AML patients with poor prognosis.

Pan-cancer analysis identifies CD300 molecules as potential immune regulators and promising therapeutic targets in acute myeloid leukemia

BACKGROUND

CD300s are a group of proteins playing vital roles in immune responses. However, much is yet to be elucidated regarding the expression patterns and clinical significances of CD300s in cancers.

METHODS

In this study, we comprehensively investigated CD300s in a pan-cancer manner using multi-omic data from The Cancer Genome Atlas. We also studied the relationship between CD300s and the immune landscape of AML.

RESULTS

We found that CD300A-CD300LF were generally overexpressed in tumors (especially AML), whereas CD300LG was more often downregulated. In AML, transactivation of CD300A was not mediated by genetic alterations but by histone modification. Survival analyses revealed that high CD300A-CD300LF expression predicted poor outcome in AML patients; the prognostic value of CD300A was validated in seven independent datasets and a meta dataset including 1115 AML patients. Furthermore, we demonstrated that CD300A expression could add prognostic value in refining existing risk models in AML. Importantly, CD300A-CD300LF expression was closely associated with T-cell dysfunction score and could predict response to AML immunotherapy. Also, CD300A was found to be positively associated with HLA genes and critical immune checkpoints in AML, such as VISTA, CD86, CD200R1, Tim-3, and the LILRB family genes.

CONCLUSIONS

Our study demonstrated CD300s as potential prognostic biomarker and an ideal immunotherapy target in AML, which warrants future functional and clinical studies.

Novel Prognostic Signature for Acute Myeloid Leukemia: Bioinformatics Analysis of Combined CNV-Driven and Ferroptosis-Related Genes

Background: Acute myeloid leukemia (AML), which has a difficult prognosis, is the most common hematologic malignancy. The role of copy number variations (CNVs) and ferroptosis in the tumor process is becoming increasingly prominent. We aimed to identify specific CNV-driven ferroptosis-related genes (FRGs) and establish a prognostic model for AML.

Methods: The combined analysis of CNV differential data and differentially expressed genes (DEGs) data from The Cancer Genome Atlas (TCGA) database was performed to identify key CNV-driven FRGs for AML. A risk model was constructed based on univariate and multivariate Cox regression analysis. The Gene Expression Omnibus (GEO) dataset was used to validate the model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to clarify the functional roles of DEGs and CNV-driven FRGs.

Results: We identified a total of 6828 AML-related DEGs, which were shown to be significantly associated with cell cycle and immune response processes. After a comprehensive analysis of CNVs and corresponding DEGs and FRGs, six CNV-driven FRGs were identified, and functional enrichment analysis indicated that they were involved in oxidative stress, cell death, and inflammatory response processes. Finally, we screened 2 CNV-driven FRGs (DNAJB6 and HSPB1) to develop a prognostic risk model. The overall survival (OS) of patients in the high-risk group was significantly shorter in both the TCGA and GEO (all p < 0.05) datasets compared to the low-risk group.

Conclusion: A novel signature based on CNV-driven FRGs was established to predict the survival of AML patients and displayed good performance. Our results may provide potential targets and new research ideas for the treatment and early detection of AML.

N6-Methyladenosine-Related lncRNAs Are Novel Prognostic Markers and Predict the Immune Landscape in Acute Myeloid Leukemia

Background: Acute myelocytic leukemia (AML) is one of the hematopoietic cancers with an unfavorable prognosis. However, the prognostic value of N 6-methyladenosine-associated long non-coding RNAs (lncRNAs) in AML remains elusive.

Materials and Methods: The transcriptomic data of m6A-related lncRNAs were collected from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. AML samples were classified into various subgroups according to the expression of m6A-related lncRNAs. The differences in terms of biological function, tumor immune microenvironment, copy number variation (CNV), and drug sensitivity in AML between distinct subgroups were investigated. Moreover, an m6A-related lncRNA prognostic model was established to evaluate the prognosis of AML patients.

Results: Nine prognosis-related m6A-associated lncRNAs were selected to construct a prognosis model. The accuracy of the model was further determined by the Kaplan–Meier analysis and time-dependent receiver operating characteristic (ROC) curve. Then, AML samples were classified into high- and low-risk groups according to the median value of risk scores. Gene set enrichment analysis (GSEA) demonstrated that samples with higher risks were featured with aberrant immune-related biological processes and signaling pathways. Notably, the high-risk group was significantly correlated with an increased ImmuneScore and StromalScore, and distinct immune cell infiltration. In addition, we discovered that the high-risk group harbored higher IC50 values of multiple chemotherapeutics and small-molecule anticancer drugs, especially TW.37 and MG.132. In addition, a nomogram was depicted to assess the overall survival (OS) of AML patients. The model based on the median value of risk scores revealed reliable accuracy in predicting the prognosis and survival status.

Conclusion: The present research has originated a prognostic risk model for AML according to the expression of prognostic m6A-related lncRNAs. Notably, the signature might also serve as a novel biomarker that could guide clinical applications, for example, selecting AML patients who could benefit from immunotherapy.

eRNAs Identify Immune Microenvironment Patterns and Provide a Novel Prognostic Tool in Acute Myeloid Leukemia

Background: Enhancer RNAs (eRNAs) play an essential role in tumorigenesis as non-coding RNAs transcribed from enhancer regions. However, the landscape of eRNAs in acute myeloid leukemia (AML) and the potential roles of eRNAs in the tumor microenvironment (TME) remain unclear.

Method: Gene expression data collected from The Cancer Genome Atlas (TCGA) project were combined with Histone ChIP-seq so as to reveal the comprehensive landscape of eRNAs. Single-sample gene set enrichment analysis algorithm (ssGSEA) and ESTIMATE were employed to enumerate immune cell infiltration and tumor purity.

Results: Most prognostic eRNAs were enriched in immune-related pathways. Two distinct immune microenvironment patterns, the immune-active subtype and the immune-resistant subtype, were identified in AML. We further developed an eRNA-derived score (E-score) that could quantify immune microenvironment patterns and predict the response to immune checkpoint inhibitor (ICI) treatment. Finally, we established a prognostic nomogram combining E-score and other clinical features, which showed great discriminative power in both the training set [Harrell’s concordance index (C index): 0.714 (0.651–0.777), p < 0.0001] and validation set [C index: 0.684 (0.614–0.755), p < 0.0001]. Calibration of the nomogram was also validated independently.

Conclusion: In this study, we systematically understood the roles of eRNAs in regulating TME diversity and complexity. Moreover, our E-score model provided the first predictive model for ICI treatment in AML.

A 29-gene signature associated with NOX2 discriminates acute myeloid leukemia prognosis and survival

The molecular complexity displayed in acute myeloid leukemia (AML) hinders patient stratification and treatment decisions. Previous studies support the utility of using specific gene panels for this purpose. Focusing on two salient features of AML, the production of reactive oxygen species (ROS) by NADPH oxidases (NOX) and metabolism, we aimed to identify a gene panel that could improve patient stratification. A pairwise comparison of AML versus healthy gene expression revealed the downregulation of four members of the NOX2 complex including CYBB (coding for NOX2) in AML patients. We analyzed the expression of 941 genes related to metabolism and found 28 genes with expression correlated to CYBB. This panel of 29 genes (29G) effectively divides AML samples according to their prognostic group. The robustness of 29G was confirmed by 6 AML cohort datasets with a total of 1821 patients (overall accuracies of 85%, 78%, 80%, 75%, 59% and 83%). An expression index (EI) was developed according to the expression of the selected discriminatory genes. Overall Survival (OS) was higher for low 29G expression index patients than for the high 29G expression index group, which was confirmed in three different datasets with a total of 1069 patients. Moreover, 29G can dissect intermediate‐prognosis patients in four clusters with different OS, which could improve the current AML stratification scheme. In summary, we have found a gene signature (29G) that can be used for AML classification and for OS prediction. Our results confirm NOX and metabolism as suitable therapeutic targets in AML.

Pan-cancer analysis of necroptosis-related gene signature for the identification of prognosis and immune significance

BACKGROUND

Necroptosis is a novel programmed cell death mode independent on caspase. A number of studies have revealed that the induction of necroptosis could act as an alternative therapeutic strategy for drug-resistant tumors as well as affect tumor immune microenvironment.

METHODS

Gene expression profiles and clinical data were downloaded from XENA-UCSC (including The Cancer Genome Atlas and Genotype-Tissue Expression), Gene Expression Omnibus, International Cancer Genome Consortium and Chinese Glioma Genome Atlas. We used non-negative matrix factorization method to conduct tumor classification. The least absolute shrinkage and selection operator regression was applied to establish risk models, whose prognostic effectiveness was examined in both training and testing sets with Kaplan-Meier analysis, time-dependent receiver operating characteristic curves as well as uni- and multi-variate survival analysis. Principal Component Analysis, t-distributed Stochastic Neighbor Embedding and Uniform Manifold Approximation and Projection were conducted to check the risk group distribution. Gene Set Enrichment Analyses, immune infiltration analysis based on CIBERSORT, EPIC, MCPcounter, ssGSEA and ESTIMATE, gene mutation and drug sensitivity between the risk groups were also taken into consideration.

RESULTS

There were eight types of cancer with at least ten differentially expressed necroptosis-related genes which could influence patients' prognosis, namely, adrenocortical carcinoma (ACC), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), acute myeloid leukemia (LAML), brain lower grade glioma (LGG), pancreatic adenocarcinoma (PAAD), liver hepatocellular carcinoma (LIHC), skin cutaneous melanoma (SKCM) and thymoma (THYM). Patients could be divided into different clusters with distinct overall survival in all cancers above except for LIHC. The risk models could efficiently predict prognosis of ACC, LAML, LGG, LIHC, SKCM and THYM patients. LGG patients from high-risk group had a higher infiltration level of M2 macrophages and cancer-associated fibroblasts. There were more CD8+ T cells, Th1 cells and M1 macrophages in low-risk SKCM patients' tumor microenvironment. Gene mutation status and drug sensitivity are also different between low- and high-risk groups in the six cancers.

CONCLUSIONS

Necroptosis-related genes can predict clinical outcomes of ACC, LAML, LGG, LIHC, SKCM and THYM patients and help to distinguish immune infiltration status for LGG and SKCM.

Lysosome-mediated chemoresistance in acute myeloid leukemia

Despite the outstanding advances in understanding the biology underlying the pathophysiology of acute myeloid leukemia (AML) and the promising preclinical data published lastly, AML treatment still relies on a classic chemotherapy regimen largely unchanged for the past five decades. Recently, new drugs have been approved for AML, but the real clinical benefit is still under evaluation. Nevertheless, primary refractory and relapse AML continue to represent the main clinical challenge, as the majority of AML patients will succumb to the disease despite achieving a complete remission during the induction phase. As such, treatments for chemoresistant AML represent an unmet need in this disease. Although great efforts have been made to decipher the biological basis for leukemogenesis, the mechanism by which AML cells become resistant to chemotherapy is largely unknown. The identification of the signaling pathways involved in resistance may lead to new combinatory therapies or new therapeutic approaches suitable for this subset of patients. Several mechanisms of chemoresistance have been identified, including drug transporters, key secondary messengers, and metabolic regulators. However, no therapeutic approach targeting chemoresistance has succeeded in clinical trials, especially due to broad secondary effects in healthy cells. Recent research has highlighted the importance of lysosomes in this phenomenon. Lysosomes' key role in resistance to chemotherapy includes the potential to sequester drugs, central metabolic signaling role, and gene expression regulation. These results provide further evidence to support the development of new therapeutic approaches that target lysosomes in AML.

A Prognostic Model for Acute Myeloid Leukemia Based on IL-2/STAT5 Pathway-Related Genes

Accurate prognostic stratification of patients can provide guidance for personalized therapy. Many prognostic models for acute myeloid leukemia (AML) have been reported, but most have considerable inaccuracies due to contained variables with insufficient capacity of predicting survival and lack of adequate verification. Here, 235 genes strongly related to survival in AML were systematically identified through univariate Cox regression analysis of eight independent AML datasets. Pathway enrichment analysis of these 235 genes revealed that the IL-2/STAT5 signaling pathway was the most highly enriched. Through Cox proportional-hazards regression model and stepwise algorithm, we constructed a six-gene STAT5-associated signature based on the most robustly survival-related genes related to the IL-2/STAT5 signaling pathway. Good prognostic performance was observed in the training cohort (GSE37642-GPL96), and the signature was validated in seven other validation cohorts. As an independent prognostic factor, the STAT5-associated signature was positively correlated with patient age and ELN2017 risk levels. An integrated score based on these three prognostic factors had higher prognostic accuracy than the ELN2017 risk category. Characterization of immune cell infiltration indicated that impaired B-cell adaptive immunity, immunosuppressive effects, serious infection, and weakened anti-inflammatory function tended to accompany high-risk patients. Analysis of in-house clinical samples revealed that the STAT5-assocaited signature risk scores of AML patients were significantly higher than those of healthy people. Five chemotherapeutic drugs that were effective in these high-risk patients were screened in silico. Among the five drugs, MS.275, a known HDAC inhibitor, selectively suppressed the proliferation of cancer cells with high STAT5 phosphorylation levels in vitro. Taken together, the data indicate that the STAT5-associated signature is a reliable prognostic model that can be used to optimize prognostic stratification and guide personalized AML treatments.

Construction and Validation of a Novel Ferroptosis-Related Prognostic Model for Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a clonal malignant proliferative blood disorder with a poor prognosis. Ferroptosis, a novel form of programmed cell death, holds great promise for oncology treatment, and has been demonstrated to interfere with the development of various diseases. A range of genes are involved in regulating ferroptosis and can serve as markers of it. Nevertheless, the prognostic significance of these genes in AML remains poorly understood. Transcriptomic and clinical data for AML patients were acquired from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Univariate Cox analysis was performed to identify ferroptosis-related genes with prognostic value, and the least absolute shrinkage and selection operator (LASSO) algorithm and stepwise multivariate Cox regression analysis were utilized to optimize gene selection from the TCGA cohort (132 samples) for model construction. Tumor samples from the GEO database (136 samples and 104 samples) were used as validation groups to estimate the predictive performance of the risk model. Finally, an eight-gene prognostic signature (including CHAC1, CISD1, DPP4, GPX4, AIFM2, SQLE, PGD, and ACSF2) was identified for the prediction of survival probability and was used to stratify AML patients into high- and low-risk groups. Survival analysis illustrated significantly prolonged overall survival and lower mortality in the low-risk group. The area under the receiver operating characteristic curve demonstrated good results for the training set (1-year: 0.846, 2-years: 0.826, and 3-years: 0.837), which verified the accuracy of the model for predicting patient survival. Independent prognostic analysis indicated that the model could be used as a prognostic factor (p ≤ 0.001). Functional enrichment analyses revealed underlying mechanisms and notable differences in the immune status of the two risk groups. In brief, we conducted and validated a novel ferroptosis-related prognostic model for outcome prediction and risk stratification in AML, with great potential to guide individualized treatment strategies in the future.

IRAK1-regulated IFN-γ signaling induces MDSC to facilitate immune evasion in FGFR1-driven hematological malignancies

BACKGROUND

Stem Cell leukemia/lymphoma syndrome (SCLL) presents as a myeloproliferative disease which can progress to acute myeloid leukemia and is associated with the coincident development of B-cell and T-cell lymphomas. SCLL is driven by the constitutive activation of fibroblast growth factor receptor-1 (FGFR1) as a result of chromosome translocations with poor outcome. Mouse models have been developed which faithfully recapitulate the human disease and have been used to characterize the molecular genetic events that are associated with development and progression of the disease.

METHODS

CRISPR/Cas9 approaches were used to generate SCLL cells null for Interleukin receptor associated kinase 1 (IRAK1) and interferon gamma (IFNG) which were introduced into syngeneic hosts through tail vein injection. Development of the disease and changes in immune cell composition and activity were monitored using flow cytometry. Bead-based immunoassays were used to compare the cytokine and chemokine profiles of control and knock out (KO) cells. Antibody mediated, targeted depletion of T cell and MDSCs were performed to evaluate their role in antitumor immune responses.

RESULTS

In SCLL, FGFR1 activation silences miR-146b-5p through DNMT1-mediated promoter methylation, which derepresses the downstream target IRAK1. IRAK1 KO SCLL cells were xenografted into immunocompetent syngeneic mice where the typical rapid progression of disease was lost and the mice remained disease free. IRAK1 in this system has no effect on cell cycle progression or apoptosis and robust growth of the KO cells in immunodeficient mice suggested an effect on immune surveillance. Depletion of T-cells in immunocompetent mice restored leukemogenesis of the KO cells, and tumor killing assays confirmed the role of T cells in tumor clearance. Analysis of the immune cell profile in mice transplanted with the IRAK1 expressing mock control (MC) cells shows that there is an increase in levels of myeloid-derived suppressor cells (MDSCs) with a concomitant decrease in CD4+/CD8+ T-cell levels. MDSC suppression assays and depletion experiments showed that these MDSCs were responsible for suppression of the T cell mediated leukemia cell elimination. Immuno-profiling of a panel of secreted cytokines and chemokines showed that activation of IFN-γ is specifically impaired in the KO cells. In vitro and in vivo expression assays and engraftment with interferon gamma receptor-1 (IFNGR1) null mice and IFNG KO SCLL cells, showed the leukemia cells produced IFN-γ directly participating in the induction of MDSCs to establish immune evasion. Inhibition of IRAK1 using pacritinib suppresses leukemogenesis with impaired induction of MDSCs and attenuated suppression of CD4+/CD8+ T-cells.

CONCLUSIONS

IRAK1 orchestrates a previously unknown FGFR1-directed immune escape mechanism in SCLL, through induction of MDSCs via regulation of IFN-γ signaling from leukemia cells, and targeting IRAK1 may provide a means of suppressing tumor growth in this syndrome by restoring immune surveillance.

Cytotoxic Marine Alkaloid 3,10-Dibromofascaplysin Induces Apoptosis and Synergizes with Cytarabine Resulting in Leukemia Cell Death

Myeloid leukemia is a hematologic neoplasia characterized by a clonal proliferation of hematopoietic stem cell progenitors. Patient prognosis varies depending on the subtype of leukemia as well as eligibility for intensive treatment regimens and allogeneic stem cell transplantation. Although significant progress has been made in the therapy of patients including novel targeted treatment approaches, there is still an urgent need to optimize treatment outcome. The most common therapy is based on the use of chemotherapeutics cytarabine and anthrayclines. Here, we studied the effect of the recently synthesized marine alkaloid 3,10-dibromofascaplysin (DBF) in myeloid leukemia cells. Unsubstituted fascaplysin was early found to affect cell cycle via inhibiting CDK4/6, thus we compared the activity of DBF and other brominated derivatives with known CDK4/6 inhibitor palbociclib, which was earlier shown to be a promising candidate to treat leukemia. Unexpectedly, the effect DBF on cell cycle differs from palbociclib. In fact, DBF induced leukemic cells apoptosis and decreased the expression of genes responsible for cancer cell survival. Simultaneously, DBF was found to activate the E2F1 transcription factor. Using bioinformatical approaches we evaluated the possible molecular mechanisms, which may be associated with DBF-induced activation of E2F1. Finally, we found that DBF synergistically increase the cytotoxic effect of cytarabine in different myeloid leukemia cell lines. In conclusion, DBF is a promising drug candidate, which may be used in combinational therapeutics approaches to reduce leukemia cell growth.

The establishment of a prognostic scoring model based on the new tumor immune microenvironment classification in acute myeloid leukemia

BACKGROUND

The high degree of heterogeneity brought great challenges to the diagnosis and treatment of acute myeloid leukemia (AML). Although several different AML prognostic scoring models have been proposed to assess the prognosis of patients, the accuracy still needs to be improved. As important components of the tumor microenvironment, immune cells played important roles in the physiological functions of tumors and had certain research value. Therefore, whether the tumor immune microenvironment (TIME) can be used to assess the prognosis of AML aroused our great interest.

METHODS

The patients' gene expression profile from 7 GEO databases was normalized after removing the batch effect. TIME cell components were explored through Xcell tools and then hierarchically clustered to establish TIME classification. Subsequently, a prognostic model was established by Lasso-Cox. Multiple GEO databases and the Cancer Genome Atlas dataset were employed to validate the prognostic performance of the model. Receiver operating characteristic (ROC) and the concordance index (C-index) were utilized to assess the prognostic efficacy.

RESULTS

After analyzing the composition of TIME cells in AML, we found infiltration of ten types of cells with prognostic significance. Then using hierarchical clustering methods, we established a TIME classification system, which clustered all patients into three groups with distinct prognostic characteristics. Using the differential genes between the first and third groups in the TIME classification, we constructed a 121-gene prognostic model. The model successfully divided 1229 patients into the low and high groups which had obvious differences in prognosis. The high group with shorter overall survival had more patients older than 60 years and more poor-risk patients (both P< 0.001). Besides, the model can perform well in multiple datasets and could further stratify the cytogenetically normal AML patients and intermediate-risk AML population. Compared with the European Leukemia Net Risk Stratification System and other AML prognostic models, our model had the highest C-index and the largest AUC of the ROC curve, which demonstrated that our model had the best prognostic efficacy.

CONCLUSION

A prognostic model for AML based on the TIME classification was constructed in our study, which may provide a new strategy for precision treatment in AML.

Clinical and molecular relevance of genetic variants in the non-coding transcriptome of patients with cytogenetically normal acute myeloid leukemia

Expression levels of long non-coding RNA (lncRNA) have been shown to associate with clinical outcome of patients with cytogenetically normal acute myeloid leukemia (CN-AML). However, the frequency and clinical significance of genetic variants in the nucleotide sequences of lncRNA in AML patients is unknown. Herein, we analyzed total RNA sequencing data of 377 younger adults (aged <60 years) with CN-AML, who were comprehensively characterized with regard to clinical outcome. We used available genomic databases and stringent filters to annotate genetic variants unequivocally located in the non-coding transcriptome of AML patients. We detected 981 variants, which are recurrently present in lncRNA that are expressed in leukemic blasts. Among these variants, we identified a cytosine-to-thymidine variant in the lncRNA RP5-1074L1.4 and a cytosine-to-thymidine variant in the lncRNA SNHG15, which independently associated with longer survival of CN-AML patients. The presence of the SNHG15 cytosine-to-thymidine variant was also found to associate with better outcome in an independent dataset of CN-AML patients, despite differences in treatment protocols and RNA sequencing techniques. In order to gain biological insights, we cloned and overexpressed both wild-type and variant versions of the SNHG15 lncRNA. In keeping with its negative prognostic impact, overexpression of the wild-type SNHG15 associated with higher proliferation rate of leukemic blasts when compared with the cytosine-to-thymidine variant. We conclude that recurrent genetic variants of lncRNA that are expressed in the leukemic blasts of CN-AML patients have prognostic and potential biological significance.

Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch integration

With the tremendous increase of publicly available single-cell RNA-sequencing (scRNA-seq) datasets, bioinformatics methods based on gene co-expression network are becoming efficient tools for analyzing scRNA-seq data, improving cell type prediction accuracy and in turn facilitating biological discovery. However, the current methods are mainly based on overall co-expression correlation and overlook co-expression that exists in only a subset of cells, thus fail to discover certain rare cell types and sensitive to batch effect. Here, we developed independent component analysis-based gene co-expression network inference (ICAnet) that decomposed scRNA-seq data into a series of independent gene expression components and inferred co-expression modules, which improved cell clustering and rare cell-type discovery. ICAnet showed efficient performance for cell clustering and batch integration using scRNA-seq datasets spanning multiple cells/tissues/donors/library types. It works stably on datasets produced by different library construction strategies and with different sequencing depths and cell numbers. We demonstrated the capability of ICAnet to discover rare cell types in multiple independent scRNA-seq datasets from different sources. Importantly, the identified modules activated in acute myeloid leukemia scRNA-seq datasets have the potential to serve as new diagnostic markers. Thus, ICAnet is a competitive tool for cell clustering and biological interpretations of single-cell RNA-seq data analysis.

A clinical prediction model identifies a subgroup with inferior survival within intermediate risk acute myeloid leukemia

Intermediate risk acute myeloid leukemia (AML) comprises around 50% of AML patients and is featured with heterogeneous clinical outcomes. The study aimed to generate a prediction model to identify intermediate risk AML patients with an inferior survival. We performed targeted next generation sequencing analysis for 121 patients with 2017 European LeukemiaNet-defined intermediate risk AML, revealing 122 mutated genes, with 24 genes mutated in > 10% of patients. A prognostic nomogram characterized by white blood cell count ≥10×10⁹/L at diagnosis, mutated DNMT3A and genes involved in signaling pathways was developed for 110 patients who were with clinical outcomes. Two subgroups were identified: intermediate low risk (ILR; 43.6%, 48/110) and intermediate high risk (IHR; 56.4%, 62/110). The model was prognostic of overall survival (OS) and relapse-free survival (RFS) (OS: Concordance index [C-index]: 0.703, 95%CI: 0.643-0.763; RFS: C-index: 0.681, 95%CI 0.620-0.741), and was successfully validated with two independent cohorts. Allogeneic hematopoietic stem cell transplantation (alloHSCT) reduced the relapse risk of IHR patients (3-year RFS: alloHSCT: 40.0±12.8% vs. chemotherapy: 8.6±5.8%, P= 0.010). The prediction model can help identify patients with an unfavorable prognosis and refine risk-adapted therapy for intermediate risk AML patients.

Prognosis and Characterization of Immune Microenvironment in Acute Myeloid Leukemia Through Identification of an Autophagy-Related Signature

Acute myeloid leukemia (AML) is one of the most common hematopoietic malignancies that has an unfavorable outcome and a high rate of relapse. Autophagy plays a vital role in the development of and therapeutic responses to leukemia. This study identifies a potential autophagy-related signature to monitor the prognoses of patients of AML. Transcriptomic profiles of AML patients (GSE37642) with the relevant clinical information were downloaded from Gene Expression Omnibus (GEO) as the training set while TCGA-AML and GSE12417 were used as validation cohorts. Univariate regression analyses and multivariate stepwise Cox regression analysis were respectively applied to identify the autophagy-related signature. The univariate Cox regression analysis identified 32 autophagy-related genes (ARGs) that were significantly associated with the overall survival (OS) of the patients, and were mainly rich in signaling pathways for autophagy, p53, AMPK, and TNF. A prognostic signature that comprised eight ARGs (BAG3, CALCOCO2, CAMKK2, CANX, DAPK1, P4HB, TSC2, and ULK1) and had good predictive capacity was established by LASSO–Cox stepwise regression analysis. High-risk patients were found to have significantly shorter OS than patients in low-risk group. The signature can be used as an independent prognostic predictor after adjusting for clinicopathological parameters, and was validated on two external AML sets. Differentially expressed genes analyzed in two groups were involved in inflammatory and immune signaling pathways. An analysis of tumor-infiltrating immune cells confirmed that high-risk patients had a strong immunosuppressive microenvironment. Potential druggable OS-related ARGs were then investigated through protein–drug interactions. This study provides a systematic analysis of ARGs and develops an OS-related prognostic predictor for AML patients. Further work is needed to verify its clinical utility and identify the underlying molecular mechanisms in AML.

Gene expression changes contribute to stemness and therapy resistance of relapsed acute myeloid leukemia: roles of SOCS2, CALCRL, MTSS1, and KDM6A

Relapse is associated with therapy resistance and is a major cause of death in acute myeloid leukemia (AML). It is thought to result from the accretion of therapy-refractory leukemic stem cells. Genetic and transcriptional changes that are recurrently gained at relapse are likely to contribute to the increased stemness and decreased therapy responsiveness at this disease stage. Despite the recent approval of several targeted drugs, chemotherapy with cytosine arabinoside and anthracyclines is still the mainstay of AML therapy. Accordingly, a number of studies have investigated genetic and gene expression changes between diagnosis and relapse of patients subjected to such treatment. Genetic alterations recurrently acquired at relapse were identified, but were restricted to small proportions of patients, and their functional characterization is still largely pending. In contrast, the expression of a substantial number of genes was altered consistently between diagnosis and recurrence of AML. Recent studies corroborated the roles of the upregulation of SOCS2 and CALCRL and of the downregulation of MTSS1 and KDM6A in therapy resistance and/or stemness of AML. These findings spur the assumption that functional investigations of genes consistently altered at recurrence of AML have the potential to promote the development of novel targeted drugs that may help to improve the outcome of this currently often fatal disease.

DNA methylation analysis improves the prognostication of acute myeloid leukemia

Integration of orthogonal data could provide new opportunities to pinpoint the underlying molecular mechanisms of hematologic disorders. Using a novel gene network approach, we integrated DNA methylation data from The Cancer Genome Atlas (n = 194 cases) with the corresponding gene expression profile. Our integrated gene network analysis classified AML patients into low-, intermediate-, and high-risk groups. The identified high-risk group had significantly shorter overall survival compared to the low-risk group (p-value ≤10-11). Specifically, our approach identified a particular subgroup of nine high-risk AML cases that died within 2 years after diagnosis. These high-risk cases otherwise would be incorrectly classified as intermediate-risk solely based on cytogenetics, mutation profiles, and common molecular characteristics of AML. We confirmed the prognostic value of our integrative gene network approach using two independent datasets, as well as through comparison with European LeukemiaNet and LSC17 criteria. Our approach could be useful in the prognostication of a subset of borderline AML cases. These cases would not be classified into appropriate risk groups by other approaches that use gene expression, but not DNA methylation data. Our findings highlight the significance of epigenomic data, and they indicate integrating DNA methylation data with gene coexpression networks can have a synergistic effect.