A three-gene expression-based risk score can refine the European LeukemiaNet AML classification

Unlocking reproducible transcriptomic signatures for acute myeloid leukaemia: Integration, classification and drug repurposing

Acute myeloid leukaemia (AML) is a highly heterogeneous disease, which lead to various findings in transcriptomic research. This study addresses these challenges by integrating 34 datasets, including 26 control groups, 6 prognostic datasets and 2 single-cell RNA sequencing (scRNA-seq) datasets to identify 10,000 AML-related genes (ARGs). We focused on genes with low variability and high consistency and successfully discovered 191 AML signatures (ASs). Leveraging machine learning techniques, specifically the XGBoost model and our custom framework, we classified AML subtypes with both scRNA-seq and bulk RNA-seq data, complementing the ELN2022 classification approach. Our research also identified promising treatments for AML through drug repurposing, with solasonine showing potential efficacy for high-risk AML patients, supported by molecular docking and transcriptomic analyses. To enhance reproducibility and customizability, we developed CSAMLdb, a user-friendly database platform. It facilitates the reuse and personalized analysis of nearly all results obtained in this research, including single-gene prognostics, multi-gene scoring, enrichment analysis, machine learning risk assessment, drug repositioning analysis and literature abstract named entity recognition. CSAMLdb is available at http://www.csamldb.com.

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.

Gene expression profiling unveils the temporal dynamics of CIGB-300-regulated transcriptome in AML cell lines

BACKGROUND

Protein kinase CK2 activity is implicated in the pathogenesis of various hematological malignancies like Acute Myeloid Leukemia (AML) that remains challenging concerning treatment. This kinase has emerged as an attractive molecular target in therapeutic. Antitumoral peptide CIGB-300 blocks CK2 phospho-acceptor sites on their substrates but it also binds to CK2α catalytic subunit. Previous proteomic and phosphoproteomic experiments showed molecular and cellular processes with relevance for the peptide action in diverse AML backgrounds but earlier transcriptional level events might also support the CIGB-300 anti-leukemic effect. Here we used a Clariom S HT assay for gene expression profiling to study the molecular events supporting the anti-leukemic effect of CIGB-300 peptide on HL-60 and OCI-AML3 cell lines.

RESULTS

We found 183 and 802 genes appeared significantly modulated in HL-60 cells at 30 min and 3 h of incubation with CIGB-300 for p < 0.01 and FC >  = │1.5│, respectively; while 221 and 332 genes appeared modulated in OCI-AML3 cells. Importantly, functional enrichment analysis evidenced that genes and transcription factors related to apoptosis, cell cycle, leukocyte differentiation, signaling by cytokines/interleukins, and NF-kB, TNF signaling pathways were significantly represented in AML cells transcriptomic profiles. The influence of CIGB-300 on these biological processes and pathways is dependent on the cellular background, in the first place, and treatment duration. Of note, the impact of the peptide on NF-kB signaling was corroborated by the quantification of selected NF-kB target genes, as well as the measurement of p50 binding activity and soluble TNF-α induction. Quantification of CSF1/M-CSF and CDKN1A/P21 by qPCR supports peptide effects on differentiation and cell cycle.

CONCLUSIONS

We explored for the first time the temporal dynamics of the gene expression profile regulated by CIGB-300 which, along with the antiproliferative mechanism, can stimulate immune responses by increasing immunomodulatory cytokines. We provided fresh molecular clues concerning the antiproliferative effect of CIGB-300 in two relevant AML backgrounds.

A novel scoring system for the quantitative prediction of prognosis in acute myeloid leukemia

Background

Acute myeloid leukemia (AML) is a heterogeneous hematopoietic malignancy. Patient prognosis cannot be accurately assessed in National Comprehensive Cancer Network (NCCN) risk stratification subgroups based on the current criteria. This study aimed to develop a novel prognostic score model for the quantitative prediction of prognosis in AML.

Results

We developed a prognostic risk scoring model of AML using differentially expressed genes to predict prognosis in patients with AML. Furthermore, we evaluated the effectiveness and clinical significance of this prognostic model in 4 AML cohorts and 905 patients with AML. A prognostic risk scoring model of AML containing eight prognosis-related genes was constructed using a multivariate Cox regression model. The model had a higher predictive value for the prognosis of AML in the training and validation sets. In addition, patients with lower scores had significantly better overall survival (OS) and even-free survival (EFS) than those with higher scores among patients with intermediate-risk AML according to the NCCN guidelines, indicating that the model could be used to further predict the prognosis of the intermediate-risk AML populations. Similarly, patients with high scores had remarkably poor OS and EFS in the normal-karyotype populations, indicating that the scoring model had an excellent predictive performance for patients with AML having normal karyotype.

Conclusions

Our study provided an individualized prognostic risk score model that could predict the prognosis of patients with AML.

A Novel Fatty Acid Metabolism-Associated Risk Model for Prognosis Prediction in Acute Myeloid Leukaemia

Acute myeloid leukaemia (AML) is the most common acute leukaemia in adults, with an unfavourable outcome and a high rate of recurrence due to its heterogeneity. Dysregulation of fatty acid metabolism plays a crucial role in the development of several tumours. However, the value of fatty acid metabolism (FAM) in the progression of AML remains unclear. In this study, we obtained RNA sequencing and corresponding clinicopathological information from the TCGA and GEO databases. Univariate Cox regression analysis and subsequent LASSO Cox regression analysis were utilized to identify prognostic FAM-related genes and develop a potential prognostic risk model. Kaplan-Meier analysis was used for prognostic significances. We also performed ROC curve to illustrate that the risk model in prognostic prediction has good performance. Moreover, significant differences in immune infiltration landscape were found between high-risk and low-risk groups using ESTIMATE and CIBERSOT algorithms. In the end, differential expressed genes (DEGs) were analyzed by gene set enrichment analysis (GSEA) to preliminarily explore the possible signaling pathways related to the prognosis of FAM and AML. The results of our study may provide potential prognostic biomarkers and therapeutic targets for AML patients, which is conducive to individualized precision therapy.

A novel immune prognostic model of non-M3 acute myeloid leukemia

Acute myeloid leukemia (AML) is a common hematological malignancy in adults. AML patients exhibit clinical heterogeneity with complications of molecular basis. The leukemogenesis of AML involves immune escape, and the immunosuppression status of the patient might have great impact on AML treatment outcome. In this study, we established an immune prognostic model of AML using bioinformatics tools. With the data in the TCGA and GTEx datasets, we analyzed differentially expressed genes (DEGs) in non-M3 AML and identified 420 immune-related DEGs. Among which, 49 genes' expression was found to be related to AML prognosis based on univariate Cox regression analysis. Next, we established a prognostic model with these 49 genes in AML by LASSO regression and multivariate Cox regression analyses. In our model, the expressions of 5 immune genes, MIF, DEF6, OSM, MPO, AVPR1B, were used to stratify non-M3 AML patients' treatment outcome. A patient's risk score could be calculated as Risk Score=0.40081 × MIF (MIF expression) - 0.15201 × MPO + 0.78073 × DEF6 - 0.45192 × AVPR1B + 0.25912 × OSM. The area under the curve of the risk score signature was 0.8, 0.8, and 0.96 at 1 year, 3 years, and 5 years, respectively. The prognostic model was then validated internally by TCGA data and externally by GEO data. At last, the result of single-sample gene-set enrichment analysis demonstrated that compared with healthy samples, the abundance of non-turmeric immune cells was significantly repressed in AML. To summarize, we presented an immune-related 5-gene signature prognostic model in AML.

Comprehensive Analysis of a Ferroptosis Pattern and Associated Prognostic Signature in Acute Myeloid Leukemia

Ferroptosis is a widespread form of programmed cell death. The environment of cancer cells makes them vulnerable to ferroptosis, including AML cells, yet the specific association between ferroptosis and AML outcome is little known. In this study, we utilized ferroptosis-related genes to distinguish two subtypes in TCGA cohort, which were subsequently validated in independent AML cohorts. The subtypes were linked with tumor-related immunological abnormalities, mutation landscape and pathway dysregulation, and clinical outcome. Further, we developed a 13-gene prognostic model for AML from DEG analysis in the two subtypes. A risk score was calculated for each patient, and then the overall group was stratified into high- and low-risk groups; the higher risk score correlated with short survival. The model was validated in both independent AML cohorts and pan-cancer cohorts, which demonstrated robustness and extended the usage of the model. A nomogram was constructed that integrated risk score, FLT3-ITD, TP53, and RUNX1 mutations, and age. This model had the additional value of discriminating the sensitivity of several chemotherapeutic drugs and ferroptosis inducers in the two risk groups, which increased the translational value of this model as a potential tool in clinical management. Through integrated analysis of ferroptosis pattern and its related model, our work shed new light on the relationship between ferroptosis and AML, which may facilitate clinical application and therapeutics.

Machine learning analysis of TCGA cancer data

In recent years, machine learning (ML) researchers have changed their focus towards biological problems that are difficult to analyse with standard approaches. Large initiatives such as The Cancer Genome Atlas (TCGA) have allowed the use of omic data for the training of these algorithms. In order to study the state of the art, this review is provided to cover the main works that have used ML with TCGA data. Firstly, the principal discoveries made by the TCGA consortium are presented. Once these bases have been established, we begin with the main objective of this study, the identification and discussion of those works that have used the TCGA data for the training of different ML approaches. After a review of more than 100 different papers, it has been possible to make a classification according to following three pillars: the type of tumour, the type of algorithm and the predicted biological problem. One of the conclusions drawn in this work shows a high density of studies based on two major algorithms: Random Forest and Support Vector Machines. We also observe the rise in the use of deep artificial neural networks. It is worth emphasizing, the increase of integrative models of multi-omic data analysis. The different biological conditions are a consequence of molecular homeostasis, driven by both protein coding regions, regulatory elements and the surrounding environment. It is notable that a large number of works make use of genetic expression data, which has been found to be the preferred method by researchers when training the different models. The biological problems addressed have been classified into five types: prognosis prediction, tumour subtypes, microsatellite instability (MSI), immunological aspects and certain pathways of interest. A clear trend was detected in the prediction of these conditions according to the type of tumour. That is the reason for which a greater number of works have focused on the BRCA cohort, while specific works for survival, for example, were centred on the GBM cohort, due to its large number of events. Throughout this review, it will be possible to go in depth into the works and the methodologies used to study TCGA cancer data. Finally, it is intended that this work will serve as a basis for future research in this field of study.

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.

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.

Distinct clinical and biological characteristics of acute myeloid leukemia with higher expression of long noncoding RNA KIAA0125

Expression of long non-coding RNA KIAA0125 has been incorporated in various gene expression signatures for prognostic prediction in acute myeloid leukemia (AML) patients, yet its functions and clinical significance remain unclear. This study aimed to investigate the clinical and biological characteristics of AML bearing different levels of KIAA0125. We profiled KIAA0125 expression levels in bone marrow cells from 347 de novo AML patients and found higher KIAA0125 expression was closely associated with RUNX1 mutation, but inversely correlated with t(8;21) and t(15;17) karyotypes. Among the 227 patients who received standard chemotherapy, those with higher KIAA0125 expression had a lower complete remission rate, shorter overall survival (OS) and disease-free survival (DFS) than those with lower expression. The prognostic significance was validated in both TCGA and GSE12417 cohorts. Subgroup analyses showed that higher KIAA0125 expression also predicted shorter DFS and OS in patients with normal karyotype or non-M3 AML. In multivariable analysis, higher KIAA0125 expression remained an adverse risk factor independent of age, WBC counts, karyotypes, and mutation patterns. Bioinformatics analyses revealed that higher KIAA0125 expression was associated with hematopoietic and leukemic stem cell signatures and ATP-binding cassette transporters, two predisposing factors for chemoresistance.

PLA2G4A Is a Potential Biomarker Predicting Shorter Overall Survival in Patients with Non-M3/NPM1 Wildtype Acute Myeloid Leukemia

In this study, we aimed at exploring and validating the prognostic value of PLA2G4A expression in patients with non-M3/nucleophosmin (NPM1) wildtype (WT) acute myeloid leukemia (AML) by using two independent datasets. Data from the Cancer Genome Atlas-acute myeloid leukemia (TCGA-LAML) and the therapeutically applicable research to generate effective treatments (TARGET)-AML were used to assess the prognostic value of PLA2G4A in NPM1-WT AML cases. Results showed that non-M3 AML cases had significantly increased PLA2G4A expression compared with normal peripheral blood samples. Patients with high PLA2G4A expression (separated by median gene expression) had a significantly shorter overall survival (OS) compared with the group with low PLA2G4A expression, in both TCGA-LAML and TARGET-AML. Multivariate analysis showed that high PLA2G4A expression was independently associated with shorter OS in 97 non-M3/NPM1-WT AML cases in TCGA-LAML (hazard ratio [HR]: 1.946, 95% confidence interval [CI]: 1.094-3.462, q = 0.036). The prognostic value was validated based on 120 primary non-M3/NPM1-WT AML cases in TARGET-AML (HR: 1.518, 95% CI: 1.037-2.223, q = 0.048). Therefore, PLA2G4A expression might serve as an independent prognostic marker in OS in patients with non-M3/NPM1 WT AML. Bioinformatic analysis identified that several proteins physically interacted with PLA2G4A, some of which have well-characterized oncogenic properties in AML, such as RUVBL2, cytoskeleton regulatory protein 1 (CAP1), signal transducer and activator of transcription 3 (STAT3), and MYCBP. Therefore, we hypothesized that PLA2G4A upregulation has multiple effects on the malignant phenotype of AML cells together with its partners. Future molecular studies are required to explore the detailed regulatory network involved.

Genome-wide identification of FHL1 as a powerful prognostic candidate and potential therapeutic target in acute myeloid leukaemia

Background

Acute myeloid leukaemia (AML) is a malignant haematological tumour with high heterogeneity and mortality. A reliable prognostic assessment is critical for treatment strategies. However, the current prognostic evaluation system of AML is insufficient.

Methods

Genome-wide univariate Cox regression analysis was performed on three independent AML datasets to screen for the prognostic-related genes. Kaplan–Meier survival analysis was employed to verify the efficacy of FHL1 in evaluating overall survival in 1298 de novo AML patients, 648 non-acute promyelocytic leukaemia AML patients and 407 cytogenetically normal AML patients; the data for some of these patients were also used for EFS and RFS validation. Multivariate Cox regression was performed to validate FHL1 as an independent prognostic indicator. WGCNA, GSEA, and gene correlation analysis were applied to explore the mechanism of FHL1 in AML. The synergistic cytocidal effect of FHL1 knockdown was verified in in vitro experiments.

Findings

Comprehensive genome-wide analyses and large-sample validation showed that FHL1 is a powerful prognostic candidate for overall survival, event-free survival, and relapse-free survival in AML and is independent of prognosis-related clinical factors and genetic abnormalities. The molecular mechanism may occur through regulation of FHL1 in leukaemia stem cells, tumour-associated signalling pathways, and transmembrane transport of chemotherapeutic drugs. FHL1-targeted intervention enhances the sensitivity of AML cells to cytarabine.

Interpretation

FHL1 may serve as an evaluation factor for clinical strategy selection, and its targeted intervention may be beneficial for chemotherapy in AML patients.

SOCS2 is part of a highly prognostic 4-gene signature in AML and promotes disease aggressiveness

Acute myeloid leukemia (AML) is a heterogeneous disease with respect to its genetic and molecular basis and to patients´ outcome. Clinical, cytogenetic, and mutational data are used to classify patients into risk groups with different survival, however, within-group heterogeneity is still an issue. Here, we used a robust likelihood-based survival modeling approach and publicly available gene expression data to identify a minimal number of genes whose combined expression values were prognostic of overall survival. The resulting gene expression signature (4-GES) consisted of 4 genes (SOCS2, IL2RA, NPDC1, PHGDH), predicted patient survival as an independent prognostic parameter in several cohorts of AML patients (total, 1272 patients), and further refined prognostication based on the European Leukemia Net classification. An oncogenic role of the top scoring gene in this signature, SOCS2, was investigated using MLL-AF9 and Flt3-ITD/NPM1c driven mouse models of AML. SOCS2 promoted leukemogenesis as well as the abundance, quiescence, and activity of AML stem cells. Overall, the 4-GES represents a highly discriminating prognostic parameter in AML, whose clinical applicability is greatly enhanced by its small number of genes. The newly established role of SOCS2 in leukemia aggressiveness and stemness raises the possibility that the signature might even be exploitable therapeutically.

Compound kushen injection suppresses human acute myeloid leukaemia by regulating the Prdxs/ROS/Trx1 signalling pathway

BACKGROUND

The increase in the levels of reactive oxygen species (ROS) in acute myeloid leukemia (AML) patients has been previously described; thus, it is important to regulate ROS levels in AML.

METHODS

Flow cytometry were used to assess the in vitro effect of compound kushen injection (CKI). Quantitative proteomics were used to analyse the mechanism. The AML patient-derived xenograft (PDX) model were used to evaluate the in vivo effect of CKI.

RESULTS

We found that intracellular ROS levels in AML cells were decreased, the antioxidant capacity were increased when treated with CKI. CKI inhibited the proliferation of AML cells and enhanced the cytotoxicity of AML cells, which has few toxic effects on haematopoietic stem cells (HSCs) and T cells. At the single-cell level, individual AML cells died gradually by CKI treatment on optofluidic chips. CKI promoted apoptosis and arrested cell cycle at G1/G0 phase in U937 cells. Furthermore, higher peroxiredoxin-3 (Prdx3) expression levels were identified in CKI-treated U937 cells through quantitative proteomics detection. Mechanically, the expression of Prdx3 and peroxiredoxin-2 (Prdx2) was up-regulated in CKI-treated AML cells, while thioredoxin 1 (Trx1) was reduced. Laser confocal microscopy showed that the proteins Prdx2 could be Interacted with Trx1 by CKI treatment. In vivo, the survival was longer and the disease was partially alleviated by decreased CD45+ immunophenotyping in peripheral blood in the CKI-treated group in the AML PDX model.

CONCLUSIONS

Antioxidant CKI possess better clinical application against AML through the Prdxs/ROS/Trx1 signalling pathway.

A hospital based retrospective study of factors influencing therapeutic leukapheresis in patients presenting with hyperleukocytic leukaemia

Therapeutic leukapheresis is a rapid and effective method to reduce early mortality of patients with hyperleukocytic leukaemia (HLL). However, few studies on factors influencing the efficiency have been reported. In this study, 67 cases who underwent leukapheresis were retrospectively analysed and factors related to the collection efficiency of leukapheresis (CE_WBC) were also evaluated. Paired t test showed that there was a significant decrease in statistics of white blood cell (WBC) counts after apheresis. The results of two independent samples nonparametric test suggested that WBC counts, platelet (PLT) counts, haematocrit (HCT), hemoglobin (HGB), serum chlorine (Cl) and globulin (GLB) before leukapheresis correlated with the CE_WBC. Multiple linear regression analysis with background stepwise variable selection indicated that only WBC and HCT before leukapheresis had an influence on CE_WBC significantly. Kaplan-Meier analysis and Cox regression model indicated that lymphocyte (LY) and mean corpuscular hemoglobin (MCH) pre-apheresis as independent factors significantly affected the prognostic survival of patients with HLL. Moreover, platelets and red blood cell were contaminated in the product of leukapheresis. It is an urgent problem to be solved in order to realise higher efficacy and higher purity of WBC collection to improve the survival of patients with HLL through optimising instruments.

[Efficacy and safety of IA regimen containing different doses of idarubicin in de-novo acute myeloid leukemia for adult patients]

Objective: To investigate the efficacy and safety of IA regimen which contains idarubicin (IDA) 8 mg/m(2), 10 mg/m(2) or 12 mg/m(2) as induction chemotherapy for adult patients with de-novo acute myeloid leukemia (AML) . Methods: A total of 1 215 newly diagnosed adult AML patients, ranging from May 2011 to March 2015 in the First Affiliated Hospital of Soochow University and other 36 clinical blood centers in China were enrolled in the multicenter, single-blind, non-randomized, clinical controlled study. To compare the response rate of complete remission (CR) , adverse events between different dose idarubicin combined with cytarabine (100 mg/m(2)) as induction chemotherapy in newly diagnosed patients of adult AML. Results: Of 1 207 evaluable AML patients were assigned to this analysis of CR rate. The CR rates of IDA 8 mg/m(2) group, IDA 10 mg/m(2) group and IDA 12 mg/m(2) group were 73.6% (215/292) , 84.1% (662/787) and 86.7% (111/128) , respectively (P<0.001) . After adjusted for age, blast ratio of bone marrow, FAB classification and risk stratification, the odds ratios (95% CI) of IDA 10 mg/m(2) group and IDA 12 mg/m(2) group were 0.49 (0.34-0.70) and 0.36 (0.18-0.71) , as compared with the IDA 8 mg/m(2) group (P<0.001, P=0.003) . In the intermediate and favorable groups, CR rates was 76.5% (163/213) , 86.9% (506/582) and 86.1% (68/79) in different doses of IDA (P=0.007) . Interestingly, IA regimen with IDA 10 mg/m(2) was the only beneficial factor affecting CR in this group after adjusted for age, blast ratio of bone marrow and FAB classification[OR=0.47 (95% CI 0.31-0.71) , P<0.001]. CR rates in adverse group was 50.0% (18/36) , 60.6% (43/71) and 81.8% (18/22) respectively (P=0.089) . However, the odds ratios (95% CI) of IDA 12 mg/m(2) when compared with the IDA 8 mg/m(2) was 0.22 (0.06-0.80) , after adjusted for age, blast ratio of bone marrow and FAB classification. The median time (days) of neutrophil count less than 0.5×10(9)/L in IDA 8 mg/m(2) group, IDA 10 mg/m(2) group and IDA 12 mg/m(2) group were 14 (11-18) , 15 (11-20) and 18 (14-22) , respectively (P=0.012) and of platelet count lower than 20×10(9)/L were 14 (7-17) , 15 (11-20) and 17 (15-21) , respectively (P=0.001) . The incidences of lung infection in the three groups were 9.8%, 13.5% and 25.2%, respectively (P<0.001) . Conclusions: For young adult patients (aged 18-60 years) with AML in China, intensifying induction therapy with idarubicin 10 mg/m(2) is clinically superior to IDA 8 mg/m(2) and IDA 12 mg/m(2) in favorable intermediate AML subgroup. However, idarubicin 12 mg/m(2) is more suitable to adverse AML subgroup.

Expression of a specific extracellular matrix signature is a favorable prognostic factor in acute myeloid leukemia

Relapse of acute myeloid leukemia (AML) is still dramatically frequent, imposing the need for early markers to quantify such risk. Recent evidence point to a prominent role for extracellular matrix (ECM) in AML, but its prognostic value has not yet been investigated. Here we have investigated whether the expression of a 15-ECM gene signature could be applied to clinical AML research evaluating a retrospective cohort of 61 AML patients and 12 healthy donors. Results show that patients whose ECM signature expression is at least twice as that of healthy donors have considerably longer relapse-free survival, with further stage-specific therapy outcomes.

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Extracellular matrix (ECM) expression in acute myeloid leukemia predicts relapse.

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The ECM-signature is small, with only 15 genes.

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High ECM-signature expression indicates an overall favorable outcome.

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High ECM-signature expression predicts therapeutic stage-specific outcome.

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ECM-signature expression works in RT-qPCR and microarrays.

High expression of dedicator of cytokinesis 1 (DOCK1) confers poor prognosis in acute myeloid leukemia

DOCK family genes encode evolutionarily conserved guanine nucleotide exchange factors for Rho GTPase involving multiple biological functions. Yet the patterns and prognostic significance of their expression in acute myeloid leukemia (AML) remain unexplored. Here we analyzed the expression patterns of 11 DOCK family genes in AML cells based on the array data of 347 patients from our cohort and several other published datasets. We further focused on the implications of the expression of DOCK1 since it was the only one in DOCK family to be associated with survival. Physiological functions and biological pathways associated with DOCK1 were identified using bioinformatics approaches. With a median follow up of 57 months, higher DOCK1 expression was associated with shorter disease free and overall survival. The finding could be validated by two independent cohorts. Multivariate analysis showed higher DOCK1 expression as a strong independent unfavorable prognostic factor. Higher DOCK1 expression was closely associated with older age, higher platelet and peripheral blast counts, intermediate-risk cytogenetics, FLT3-ITD, MLL-PTD and mutations in PTPN11, NPM1, RUNX1, ASXL1 and DNMT3A. Functional enrichment analysis suggested the association of DOCK1 overexpression with several key physiological pathways including cell proliferation, motility, and chemotaxis. Therefore, we suggested that AML with higher DOCK1 expression showed characteristic clinical and biological features. DOCK1 expression is an important prognostic marker and a potential therapeutic target for the treatment of AML. Studies in large prospective cohorts are necessary to confirm our findings. Further mechanistic studies to delineate the role of DOCK1 in the leukemogenesis are warranted.

Higher HOPX expression is associated with distinct clinical and biological features and predicts poor prognosis in de novo acute myeloid leukemia

Homeodomain-only protein homeobox (HOPX) is the smallest homeodomain protein. It was regarded as a stem cell marker in several non-hematopoietic systems. While the prototypic homeobox genes such as the HOX family have been well characterized in acute myeloid leukemia (AML), the clinical and biological implications of HOPX in the disease remain unknown. Thus we analyzed HOPX and global gene expression patterns in 347 newly diagnosed de novo AML patients in our institute. We found that higher HOPX expression was closely associated with older age, higher platelet counts, lower white blood cell counts, lower lactate dehydrogenase levels, and mutations in RUNX1, IDH2, ASXL1, and DNMT3A, but negatively associated with acute promyelocytic leukemia, favorable karyotypes, CEBPA double mutations and NPM1 mutation. Patients with higher HOPX expression had a lower complete remission rate and shorter survival. The finding was validated in two independent cohorts. Multivariate analysis revealed that higher HOPX expression was an independent unfavorable prognostic factor irrespective of other known prognostic parameters and gene signatures derived from multiple cohorts. Gene set enrichment analysis showed higher HOPX expression was associated with both hematopoietic and leukemia stem cell signatures. While HOPX and HOX family genes showed concordant expression patterns in normal hematopoietic stem/progenitor cells, their expression patterns and associated clinical and biological features were distinctive in AML settings, demonstrating HOPX to be a unique homeobox gene. Therefore, HOPX is a distinctive homeobox gene with characteristic clinical and biological implications and its expression is a powerful predictor of prognosis in AML patients.