A 2-gene classifier for predicting response to the farnesyltransferase inhibitor tipifarnib in acute myeloid leukemia

Differential co-expression network analysis elucidated genes associated with sensitivity to farnesyltransferase inhibitor and prognosis of acute myeloid leukemia

Acute myeloid leukemia (AML) is a heterogeneous disease and the most common form of acute leukemia with a poor prognosis. Due to its complexity, the disease requires the identification of biomarkers for reliable prognosis. To identify potential disease genes that regulate patient prognosis, we used differential co-expression network analysis and transcriptomics data from relapsed, refractory, and previously untreated AML patients based on their response to treatment in the present study. In addition, we combined functional genomics and transcriptomics data to identify novel and therapeutically potential systems biomarkers for patients who do or do not respond to treatment. As a result, we constructed co-expression networks for response and non-response cases and identified a highly interconnected group of genes consisting of SECISBP2L, MAN1A2, PRPF31, VASP, and SNAPC1 in the response network and a group consisting of PHTF2, SLC11A2, PDLIM5, OTUB1, and KLRD1 in the non-response network, both of which showed high prognostic performance with hazard ratios of 4.12 and 3.66, respectively. Remarkably, ETS1, GATA2, AR, YBX1, and FOXP3 were found to be important transcription factors in both networks. The prognostic indicators reported here could be considered as a resource for identifying tumorigenesis and chemoresistance to farnesyltransferase inhibitor. They could help identify important research directions for the development of new prognostic and therapeutic techniques for AML.

Serum circRNA_100199 is a Prognostic Biomarker in Acute Myeloid Leukemia

Background

An aberrant level of serum microRNA expression has been demonstrated to be a prognostic marker for acute myeloid leukemia (AML). The therapeutic relevance of serum circRNA 100199 remained unknown, however. This research aimed to investigate the probable prognostic significance of serum circRNA_100199 for AML.

Methods

This study included a total of 200 participants consisting of 114 AML-diagnosed patients and 86 healthy people. Blood samples were taken, and the level of circRNA_100199 in the serum was measured using quantitative reverse transcription polymerase chain reaction (qRT-PCR) to explore its potential clinical significance.

Results

Our study demonstrated that circRNA_100199 expression in the serum was substantially higher in AML subjects than in healthy persons. This increase in serum circRNA_100199 levels was particularly noticeable in M4/M5 subtype AML patients, and those with poor cytogenetic risk or higher white blood cell counts. Using receiver operating characteristic (ROC) analysis, AML cases were effectively differentiated from healthy persons based on the level of serum circRNA_100199. Furthermore, it was found that high serum circRNA_100199 expression was strongly linked with shorter survival times and more severe clinical features. Our study also confirmed that high serum circRNA_100199 expression was an independent predictor of relapse-free survival (RFS) and overall survival (OS) in AML patients. Interestingly, the serum expression level of circRNA_100199 was significantly reduced following treatment, and its levels were substantially lower in AML patients who achieved complete remission (CR) than those who did not.

Conclusion

Overall, these findings suggest that serum circRNA_100199 has the potential to be a favorable prognostic biomarker for AML.

Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome

BACKGROUND

Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers.

METHODS

We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient's response to a variety of therapies in multiple cancer types without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: personalized oncology (PO), aimed at prioritizing treatments for a single patient, and clinical trial design (CTD), selecting the most likely responders in a patient cohort.

FINDINGS

Evaluating ENLIGHT's performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patient's treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable with that of supervised predictors developed for specific indications and drugs. In combination with the interferon-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy.

CONCLUSIONS

ENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome.

FUNDING

This research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority.

Acute Myeloid Leukemia: New Multiomics Molecular Signatures and Implications for Systems Medicine Diagnostics and Therapeutics Innovation

Acute myeloid leukemia (AML) is a common, complex, and multifactorial malignancy of the hematopoietic system. AML diagnosis and treatment outcomes display marked heterogeneity and patient-to-patient variations. To date, AML-related biomarker discovery research has employed single omics inquiries. Multiomics analyses that reconcile and integrate the data streams from multiple levels of the cellular hierarchy, from genes to proteins to metabolites, offer much promise for innovation in AML diagnostics and therapeutics. We report, in this study, a systems medicine and multiomics approach to integrate the AML transcriptome data and reporter biomolecules at the RNA, protein, and metabolite levels using genome-scale biological networks. We utilized two independent transcriptome datasets (GSE5122, GSE8970) in the Gene Expression Omnibus database. We identified new multiomics molecular signatures of relevance to AML: miRNAs (e.g., mir-484 and miR-519d-3p), receptors (ACVR1 and PTPRG), transcription factors (PRDM14 and GATA3), and metabolites (in particular, amino acid derivatives). The differential expression profiles of all reporter biomolecules were crossvalidated in independent RNA-Seq and miRNA-Seq datasets. Notably, we found that PTPRG holds important prognostication potential as evaluated by Kaplan-Meier survival analyses. The multiomics relationships unraveled in this analysis point toward the genomic pathogenesis of AML. These multiomics molecular leads warrant further research and development as potential diagnostic and therapeutic targets.

FNTB Promoter Polymorphisms Are Independent Predictors of Survival in Patients with Triple Negative Breast Cancer

In breast cancer, the promising efficacy of farnesyltransferase inhibitors (FTIs) in preclinical studies is in contrast to only limited effects in clinical Phase II–III trials. The objective of this study was to explore the clinical relevance of farnesyltransferase β-subunit (FNTB) single nucleotide promoter polymorphisms (FNTB-173 6G > 5G (rs3215788), -609 G > C (rs11623866) and -179 T > A (rs192403314)) in early breast cancer. FNTB genotyping was performed by pyrosequencing in 797 patients from a prospective multicentre observational PiA trial (NCT 01592825). In the total cohort, the FNTB-173 6G > 5G polymorphism was an independent predictor of RFI (HR = 0.568; 95% CI = 0.339–0.949, p = 0.031), OS (HR = 0.629; 95% CI = 0.403–0.980, p = 0.040) and BCSS (HR = 0.433; 95% CI = 0.213–0.882; p = 0.021), whereas the FNTB-609 G > C polymorphism was an independent predictor of RFI (HR = 0.453; 95% CI = 0.226–0.910, p = 0.026) and BCSS (HR = 0.227; 95% CI = 0.075–0.687, p = 0.009). Subtype analysis revealed the independent prognostic relevance of FNTB promoter polymorphisms, particularly in TNBC but not in luminal or HER2-positive intrinsic subtypes. Finally, we used electrophoretic mobility shift assays (EMSAs) to confirm in vitro that the polymorphism FNTB-173 6G > 5G resulted in the differential binding of nuclear proteins from five different breast cancer cell lines. This is the first study on breast cancer suggesting that FNTB promoter polymorphisms (i) are independent prognostic biomarkers, particularly in patients with early TNBC, and (ii) could modulate FNTB’s transcriptional activity.

Identification of biomarkers for acute leukemia via machine learning-based stemness index

Traditional methods to understand leukemia stem cell (LSC)'s biological characteristics include constructing LSC-like cells and mouse models by transgenic or knock-in methods. However, there are some potential pitfalls in using this method, such as retroviral insertion mutagenesis, non-physiological level gene expression, non-physiological expansion, and difficulty to construct. The mRNAsi index for each sample of the Cancer Genome Atlas (TCGA) could avoid these potential pitfalls by machine learning. In this work, we aimed to construct a network of LSC genes utilizing the mRNAsi. First, mRNAsi value was analyzed with expressions distributions, survival analysis, age, and gender in acute myeloid leukemia (AML) samples. Then, we used the weighted gene co-expression network analysis (WGCNA) to construct modules of stemness genes. The correlation of the LSC genes transcription and interplay among LSC proteins was analyzed. We performed functional and pathway enrichment analysis to annotate stemness genes. Survival analysis further identified prognostic biomarkers by clinical data of TCGA and the Gene Expression Omnibus (GEO) database. We found that the result of mRNAsi overall survival is not significant, which may be due to the heterogeneity of AML in the stage of myeloid differentiation, French-American-British (FAB) classification systems. Enrichment analysis indicated that the stemness genes were biologically clustered as a group and mainly associated with cell cycle and mitosis. Moreover, 10 key genes (SNRNP40, RFC4, RFC5, CDC6, HSPE1, PA2G4, SNAP23P, DARS2, MIS18A, and HPRT1) were screened by survival analysis with the data from TCGA and GEO. Among them, RFC4 and RFC5 were the distinguished biomarkers for their double-validated prognostic value in both databases. Additionally, the expression of RFC4 and RFC5 had the same trend as mRNAsi score in FAB subtypes. In conclusion, our result demonstrated that mRNAsi based LSC-related genes were found to have strong interactions as a cluster. These genes, especially RFC4 and RFC5, could be the therapeutic targets for inhibiting the stemness characteristics of AML. This work is also a comprehensive pipeline for future cancer stem cell studies.

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.

High HSPA8 expression predicts adverse outcomes of acute myeloid leukemia

Background

Acute myeloid leukemia (AML) remains one of the most common hematological malignancies, posing a serious challenge to human health. HSPA8 is a chaperone protein that facilitates proper protein folding. It contributes to various activities of cell function and also is associated with various types of cancers. To date, the role of HSPA8 in AML is still undetermined.

Methods

In this study, public datasets available from the TCGA (Cancer Genome Atlas) and GEO (Gene Expression Omnibus) were mined to discover the association between the expression of HSPA8 and clinical phenotypes of CN-AML. A series of bioinformatics analysis methods, including functional annotation and miRNA-mRNA regulation network analysis, were employed to investigate the role of HSPA8 in CN-AML.

Results

HSPA8 was highly expressed in the AML patients compared to the healthy controls. The high HSPA8 expression had lower overall survival (OS) rate than those with low HSPA8 expression. High expression of HSPA8 was also an independent prognostic factor for overall survival (OS) of CN-AML patients by multivariate analysis. The differential expressed genes (DEGs) associated with HSPA8 high expression were identified, and they were enriched PI3k-Akt signaling, cAMP signaling, calcium signaling pathway. HSPA8 high expression was also positively associated with micro-RNAs (hsa-mir-1269a, hsa-mir-508-3p, hsa-mir-203a), the micro-RNAs targeted genes (VSTM4, RHOB, HOBX7) and key known oncogenes (KLF5, RAN, and IDH1), and negatively associated with tumor suppressors (KLF12, PRKG1, TRPS1, NOTCH1, RORA).

Conclusions

Our research revealed HSPA8 as a novel potential prognostic factor to predict the survival of CN-AML patients. Our data also revealed the possible carcinogenic mechanism and the complicated microRNA-mRNA network associated with the HSPA8 high expression in AML.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08193-w.

Function of Deptor and its roles in hematological malignancies

Deptor is a protein that interacts with mTOR and that belongs to the mTORC1 and mTORC2 complexes. Deptor is capable of inhibiting the kinase activity of mTOR. It is well known that the mTOR pathway is involved in various signaling pathways that are involved with various biological processes such as cell growth, apoptosis, autophagy, and the ER stress response. Therefore, Deptor, being a natural inhibitor of mTOR, has become very important in its study. Because of this, it is important to research its role regarding the development and progression of human malignancies, especially in hematologic malignancies. Due to its variation in expression in cancer, it has been suggested that Deptor can act as an oncogene or tumor suppressor depending on the cellular or tissue context. This review discusses recent advances in its transcriptional and post-transcriptional regulation of Deptor. As well as the advances regarding the activities of Deptor in hematological malignancies, its possible role as a biomarker, and its possible clinical relevance in these malignancies.

High expression of HOXA5 is associated with poor prognosis in acute myeloid leukemia

BACKGROUND

HOXA5 is considered as an oncogene in many tumors. This study in- vestigated the HOXA5 expression in Chinese acute myeloid leukemia (AML) patients and evaluated the predictive significance of HOXA5 with a single-center retrospective study.

METHODS

We investigated the expression pattern and prognostic value of HOXA5 in patients with AML through by using a series of databases and various datasets, including the ONCOMINE, TCGA, and STRING datasets. The bone marrow samples of 53 newly diagnosed AML patients (non-M3 subtype) and 19 benign individuals were collected in our center. HOXA5 mRNA expression levels were detected by real-time qPCR, HOXA5 protein expression levels were detected by Western Blot. Clinical data was obtained from inpatient medical records.

RESULTS

Two microarrays in Oncomine showed that the expression level of HOXA5 was significantly upregulated in AML. Our data revealed that AML patients had higher HOXA5 mRNA and protein expression levels than the controls (P < 0.001). The blast percentage in bone marrow of HOXA5 high-expression group was higher that of HOXA5 low-expression group (P < 0.05). Higher expression level of HOXA5 revealed a worse OS in AML (P < 0.05).

CONCLUSION

Our findings suggested that HOXA5 might have the potential ability to act as a diagnostic biomarker and potential therapeutic target for AML.

Distinguishing Kawasaki Disease from Febrile Infectious Disease Using Gene Pair Signatures

Kawasaki disease (KD) is an acute systemic vasculitis of childhood with prolonged fever, and the diagnosis of KD is mainly based on clinical criteria, which is prone to misdiagnosis with other febrile infectious (FI) diseases. Currently, there remain no effective molecular markers for KD diagnosis. In this study, we aimed to use a relative-expression-based method k-TSP and resampling framework to identify robust gene pair signatures to distinguish KD from bacterial and virus febrile infectious diseases. Our study pool consisted of 808 childhood patients from several studies and assigned to three groups, namely, the discovery set (n = 224), validation set-1 (n = 197), and validation set-2 (n = 387). We had identified 60 biologically relevant gene pairs and developed a top-ranked gene pair classifier (TRGP) using the first seven signatures, with the area under the receiver-operating characteristic curves (AUROC) of 0.947 (95% CI, 0.918-0.976), a sensitivity of 0.936 (95% CI, 0.872-0.987), and a specificity of 0.774 (95% CI, 0.705-0.836) in the discovery set. In the validation set-1, the TRGP classifier distinguished KD from FI with AUROC of 0.955 (95% CI, 0.919-0.991), a sensitivity of 0.959 (95% CI, 0.925-0.986), and a specificity of 0.863 (95% CI, 0.764-0.961). In the validation set-2, the predictive performance of classification was with an AUROC of 0.796 (95% CI, 0.747-0.845), a sensitivity of 0.797 (95% CI, 0.720-0.864), and a specificity of 0.661 (95% CI, 0.606-0.717). Our study reveals that gene pair signatures are robust across diverse studies and can be utilized as objective biomarkers to distinguish KD from FI, helping to develop a fast, simple, and effective molecular approach to improve the diagnosis of KD.

Normal myeloid progenitor cell subset-associated gene signatures for acute myeloid leukaemia subtyping with prognostic impact

Acute myeloid leukaemia (AML) is characterised by phenotypic heterogeneity, which we hypothesise is a consequence of deregulated differentiation with transcriptional reminiscence of the normal compartment or cell-of-origin. Here, we propose a classification system based on normal myeloid progenitor cell subset-associated gene signatures (MAGS) for individual assignments of AML subtypes. We generated a MAGS classifier including the progenitor compartments CD34⁺/CD38^- for haematopoietic stem cells (HSCs), CD34⁺/CD38⁺/CD45RA^- for megakaryocyte-erythroid progenitors (MEPs), and CD34⁺/CD38⁺/CD45RA⁺ for granulocytic-monocytic progenitors (GMPs) using regularised multinomial regression with three discrete outcomes and an elastic net penalty. The regularisation parameters were chosen by cross-validation, and MAGS assignment accuracy was validated in an independent data set (N = 38; accuracy = 0.79) of sorted normal myeloid subpopulations. The prognostic value of MAGS assignment was studied in two clinical cohorts (TCGA: N = 171; GSE6891: N = 520) and had a significant prognostic impact. Furthermore, multivariate Cox regression analysis using the MAGS subtype, FAB subtype, cytogenetics, molecular genetics, and age as explanatory variables showed independent prognostic value. Molecular characterisation of subtypes by differential gene expression analysis, gene set enrichment analysis, and mutation patterns indicated reduced proliferation and overrepresentation of RUNX1 and IDH2 mutations in the HSC subtype; increased proliferation and overrepresentation of CEBPA mutations in the MEP subtype; and innate immune activation and overrepresentation of WT1 mutations in the GMP subtype. We present a differentiation-dependent classification system for AML subtypes with distinct pathogenetic and prognostic importance that can help identify candidates poorly responding to combination chemotherapy and potentially guide alternative treatments.

The power and potential of integrated diagnostics in acute myeloid leukaemia

The field of acute myeloid leukaemia (AML) diagnostics, initially based solely on morphological assessment, has integrated more and more disciplines. Today, state-of-the-art AML diagnostics relies on cytomorphology, cytochemistry, immunophenotyping, cytogenetics and molecular genetics. Only the integration of all of these methods allows for a comprehensive and complementary characterisation of each case, which is prerequisite for optimal AML diagnosis and management. Here, we will review why multidisciplinary diagnostics is mandatory today and will gain even more importance in the future, especially in the context of precision medicine. We will discuss ideas and strategies that are likely to shape and improve multidisciplinary diagnostics in AML and may even overcome some of today's gold standards. This includes recent technical advances that provide genome-wide molecular insights. The enormous amount of data obtained by these latter techniques represents a great challenge, but also a unique chance. We will reflect on how this increase in knowledge can be incorporated into the routine to pave the way for personalised medicine in AML.

The mitochondrial transcription machinery genes are upregulated in acute myeloid leukemia and associated with poor clinical outcome

BACKGROUND

Acute myeloid leukemia (AML) is characterized by rapid growth of abnormal blasts that overcrowd normal hematopoiesis. Defective mitochondrial biogenesis has been implicated in AML, which we believe is partly due to the deregulation of the mitochondrial transcription machinery (MTM) genes influencing the expression of mitochondrial genes. Here, we aim to characterize MTM gene upregulation in AML.

METHODS

Molecular and clinical patient data were retrieved from several public AML datasets. Kaplan-Meier survival curves were used to compare overall survival between patients, while Mann-Whitney U's non-parametric and Fisher's exact test were used for comparing continuous and categorical variables, respectively.

RESULTS

The MTM genes TFB1M, TFB2M, TFAM, and POLRMT were upregulated in patients with AML compared with healthy donors. Upregulation of one or more of these genes was associated with higher percentage of peripheral blood blasts (P = 0.002), normal cytogenetic status (P = 0.027) and NPM1 mutations (P = 0.009). Additionally, patients with high expression of MTM genes (Z ≥ 1) had shorter median overall survival compared with low MTM gene expression (Z < 1) (months: 11.8 vs 24.1, P = 0.027; multivariate survival analysis Cox Proportional Hazards model, HR: 1.82 (1.22-2.70); p-value: 0.003).

CONCLUSION

The mitochondrial transcriptional machinery is upregulated and associated with worse clinical outcome in patients with AML and may present a viable therapeutic target.

MicroRNAs as possible indicators of drug sensitivity in breast cancer cell lines

MicroRNAs (miRNAs) regulate gene expression post-transcriptionally. In this way they might influence whether a cell is sensitive or resistant to a certain drug. So far, only a limited number of relatively small scale studies comprising few cell lines and/or drugs have been performed. To obtain a broader view on miRNAs and their association with drug response, we investigated the expression levels of 411 miRNAs in relation to drug sensitivity in 36 breast cancer cell lines. For this purpose IC₅₀ values of a drug screen involving 34 drugs were associated with miRNA expression data of the same breast cancer cell lines. Since molecular subtype of the breast cancer cell lines is considered a confounding factor in drug association studies, multivariate analysis taking subtype into account was performed on significant miRNA-drug associations which retained 13 associations. These associations consisted of 11 different miRNAs and eight different drugs (among which Paclitaxel, Docetaxel and Veliparib). The taxanes, Paclitaxel and Docetaxel, were the only drugs having miRNAs in common: hsa-miR-187-5p and hsa-miR-106a-3p indicative of drug resistance while Paclitaxel sensitivity alone associated with hsa-miR-556-5p. Tivantinib was associated with hsa-let-7d-5p and hsa-miR-18a-5p for sensitivity and hsa-miR-637 for resistance. Drug sensitivity was associated with hsa-let-7a-5p for Bortezomib, hsa-miR-135a-3p for JNJ-707 and hsa-miR-185-3p for Panobinostat. Drug resistance was associated with hsa-miR-182-5p for Veliparib and hsa-miR-629-5p for Tipifarnib. Pathway analysis for significant miRNAs was performed to reveal biological roles, aiding to find a potential mechanistic link for the observed associations with drug response. By doing so hsa-miR-187-5p was linked to the cell cycle G2-M checkpoint in line with this checkpoint being the target of taxanes. In conclusion, our study shows that miRNAs could potentially serve as biomarkers for intrinsic drug resistance and that pathway analyses can provide additional information in this context.

Genetic profiling in acute myeloid leukemia: a path to predicting treatment outcome

Despite substantial progresses in acute myeloid leukemia (AML) diagnosis and treatment, at least half of patient will eventually die for the disease. In the last decades, the use of genetic and genomic approaches allowed the identification of patients with higher risk of recurrence after and/or resistance to CHT. However, though many novel drugs have been proposed and tested, only little clinical improvements have been made concerning the treatment of the so called 'high risk' patients. Areas covered: In this article, the authors, based on their own experience and the most updated literature, review the basic knowledge of AML prognostication and treatment prediction developed throughout genetic and genomic profiling, and focus on the use of gene expression profiling as a promising predictive tool. The role of next generation sequencing, run on qPCR/digital PCR platforms or polyvalent ones such as the Nanostring NCounter™ and RNA-sequencing techniques in the near future will also be briefly discussed. Expert commentary: The authors believe that a combination of genetic (including both germline and somatic data), epigenetic and transcriptional data will represent, in the future, the molecular basis for treatment decision with the highest predictive potential.

A 4-gene expression prognostic signature might guide post-remission therapy in patients with intermediate-risk cytogenetic acute myeloid leukemia

In intermediate-risk cytogenetic acute myeloid leukemia (IRC-AML) patients, novel biomarkers to guide post-remission therapy are needed. We analyzed with high-density arrays 40 IRC-AML patients who received a non-allogeneic hematopoietic stem-cell transplantation-based post-remission therapy, and identified a signature that correlated with early relapse. Subsequently, we analyzed selected 187 genes in 49 additional IRC-AML patients by RT-PCR. BAALC, MN1, SPARC and HOPX overexpression correlated to refractoriness. BAALC or ALDH2 overexpression correlated to shorter overall survival (OS) (5-year OS: 33 ± 8.6% vs. 73.7 ± 10.1%, p = .006; 32 ± 9.3% vs. 66.4 ± 9.7%, p = .016), whereas GPR44 or TP53INP1 overexpression correlated to longer survival (5-year OS: 66.7 ± 10.3% vs. 35.4 ± 9.1%, p = .04; 58.3 ± 8.2% vs. 23.1 ± 11.7%, p = .029). A risk-score combining these four genes expression distinguished low-risk and high-risk patients (5-year OS: 79 ± 9% vs. 30 ± 8%, respectively; p = .001) in our cohort and in an independent set of patients from a public repository. Our 4-gene signature may add prognostic information and guide post-remission treatment in IRC-AML patients.

Genes and pathways associated with the occurrence of malignancy in benign lymphoepithelial lesions

There is increasing evidence concerning the occurrence of malignant lymphoma among people suffering from Mikulicz disease, also termed benign lymphoepithelial lesion (BLEL) and immunoglobulin G4-associated disease. However, the underlying molecular mechanism of the malignant transformation remains unclear. The present study aimed to investigate the gene expression profile between BLEL and malignant lymphoepithelial lesion (MLEL) conditions using tissue microarray analysis, to identify genes and pathways which may be associated with the risk of malignant transformation. Comparing gene expression profiles between BLEL tissues (n=13) and MLEL (n=14), a total of 1,002 differentially expressed genes (DEGs) were identified including 364 downregulated and 638 upregulated DEGs in BLEL. The downregulated DEGs in BLEL were frequently associated with immune-based functions, immune cell differentiation, proliferation and survival, and metabolic functions, whereas the upregulated DEGs were primarily associated with organ, gland and tissue developmental processes. The B cell receptor signaling pathway, the transcription factor p65 signaling pathway, low affinity immunoglobulin γ Fc region receptor II-mediated phagocytosis, the high affinity immunoglobulin ε receptor subunit γ signaling pathway and Epstein-Barr virus infection, and pathways in cancer, were the pathways associated with the downregulated DEGs. The upregulated DEGs were associated with three pathways, including glutathione metabolism, salivary secretion and mineral absorption pathways. These results suggested that the identified signaling pathways and their associated genes may be crucial for understanding the molecular mechanisms underlying malignant transformation from BLEL, and they may be considered to be markers for predicting malignancy among the BLEL group.

Identification of gene pairs through penalized regression subject to constraints

Background

This article concerns the identification of gene pairs or combinations of gene pairs associated with biological phenotype or clinical outcome, allowing for building predictive models that are not only robust to normalization but also easily validated and measured by qPCR techniques. However, given a small number of biological samples yet a large number of genes, this problem suffers from the difficulty of high computational complexity and imposes challenges to the accuracy of identification statistically.

Results

In this paper, we propose a parsimonious model representation and develop efficient algorithms for identification. Particularly, we derive an equivalent model subject to a sum-to-zero constraint in penalized linear regression, where the correspondence between nonzero coefficients in these models is established. Most importantly, it reduces the model complexity of the traditional approach from the quadratic order to the linear order in the number of candidate genes, while overcoming the difficulty of model nonidentifiablity. Computationally, we develop an algorithm using the alternating direction method of multipliers (ADMM) to deal with the constraint. Numerically, we demonstrate that the proposed method outperforms the traditional method in terms of the statistical accuracy. Moreover, we demonstrate that our ADMM algorithm is more computationally efficient than a coordinate descent algorithm with a local search. Finally, we illustrate the proposed method on a prostate cancer dataset to identify gene pairs that are associated with pre-operative prostate-specific antigen.

Conclusion

Our findings demonstrate the feasibility and utility of using gene pairs as biomarkers.

Transcriptional memory of cells of origin overrides β-catenin requirement of MLL cancer stem cells

While β-catenin has been demonstrated as an essential molecule and therapeutic target for various cancer stem cells (CSCs) including those driven by MLL fusions, here we show that transcriptional memory from cells of origin predicts AML patient survival and allows β-catenin-independent transformation in MLL-CSCs derived from hematopoietic stem cell (HSC)-enriched LSK population but not myeloid-granulocyte progenitors. Mechanistically, β-catenin regulates expression of downstream targets of a key transcriptional memory gene, Hoxa9 that is highly enriched in LSK-derived MLL-CSCs and helps sustain leukemic self-renewal. Suppression of Hoxa9 sensitizes LSK-derived MLL-CSCs to β-catenin inhibition resulting in abolishment of CSC transcriptional program and transformation ability. In addition, further molecular and functional analyses identified Prmt1 as a key common downstream mediator for β-catenin/Hoxa9 functions in LSK-derived MLL-CSCs. Together, these findings not only uncover an unexpectedly important role of cells of origin transcriptional memory in regulating CSC self-renewal, but also reveal a novel molecular network mediated by β-catenin/Hoxa9/Prmt1 in governing leukemic self-renewal.

Exploring the putative self-binding property of the human farnesyltransferase alpha-subunit

Farnesylation is an important post-translational protein modification in eukaryotes. Farnesylation is performed by protein farnesyltransferase, a heterodimer composed of an α- (FTα) and a β-subunit. Recently, homodimerization of truncated rat and yeast FTα has been detected, suggesting a new role for FTα homodimers in signal transduction. We investigated the putative dimerization behaviour of human and rat FTα. Different in vitro and in vivo approaches revealed no self-dimerization and a presumably artificial formation of homotrimers and higher homo-oligomers in vitro. Our study contributes to the clarification of the physiological features of FTase in different species and may be important for the ongoing development of FTase inhibitors.

Protein Lipidation As a Regulator of Apoptotic Calcium Release: Relevance to Cancer

Calcium is a critical regulator of cell death pathways. One of the most proximal events leading to cell death is activation of plasma membrane and endoplasmic reticulum-resident calcium channels. A large body of evidence indicates that defects in this pathway contribute to cancer development. Although we have a thorough understanding of how downstream elevations in cytosolic and mitochondrial calcium contribute to cell death, it is much less clear how calcium channels are activated upstream of the apoptotic stimulus. Recently, it has been shown that protein lipidation is a potent regulator of apoptotic signaling. Although classically thought of as a static modification, rapid and reversible protein acylation has emerged as a new signaling paradigm relevant to many pathways, including calcium release and cell death. In this review, we will discuss the role of protein lipidation in regulating apoptotic calcium signaling with direct therapeutic relevance to cancer.

PBX3 is essential for leukemia stem cell maintenance in MLL-rearranged leukemia

Interaction of HOXA9/MEIS1/PBX3 is responsible for hematopoietic system transformation in MLL-rearranged (MLL-r) leukemia. Of these genes, HOXA9 has been shown to be critical for leukemia cell survival, while MEIS1 has been identified as an essential regulator for leukemia stem cell (LSC) maintenance. Although significantly high expression of PBX3 was observed in clinical acute myeloid leukemia (AML) samples, the individual role of PBX3 in leukemia development is still largely unknown. In this study, we explored the specific role of PBX3 and its associated regulatory network in leukemia progression. By analyzing the clinical database, we found that the high expression of PBX3 is significantly correlated with a poor prognosis in AML patients. ChIP-Seq/qPCR analysis in MLL-r mouse models revealed aberrant epigenetic modifications with increased H3K79me2, and decreased H3K9me3 and H3K27me3 levels in LSCs, which may account for the high expression levels of Pbx3. To further examine the role of Pbx3 in AML maintenance and progression, we used the CRISPR/Cas9 system to delete Pbx3 in leukemic cells in the MLL-AF9 induced AML mouse model. We found that Pbx3 deletion significantly prolonged the survival of leukemic mice and decreased the leukemia burden by decreasing the capacity of LSCs and promoting LSC apoptosis. In conclusion, we found that PBX3 is epigenetically aberrant in the LSCs of MLL-r AML and is essential for leukemia development. Significantly, the differential expression of PBX3 in normal and malignant hematopoietic cells suggests PBX3 as a potential prognostic marker and therapeutic target for MLL-r leukemia.

Apoptosis-Related Gene Expression Profiling in Hematopoietic Cell Fractions of MDS Patients

Although the vast majority of patients with a myelodysplastic syndrome (MDS) suffer from cytopenias, the bone marrow is usually normocellular or hypercellular. Apoptosis of hematopoietic cells in the bone marrow has been implicated in this phenomenon. However, in MDS it remains only partially elucidated which genes are involved in this process and which hematopoietic cells are mainly affected. We employed sensitive real-time PCR technology to study 93 apoptosis-related genes and gene families in sorted immature CD34+ and the differentiating erythroid (CD71+) and monomyeloid (CD13/33+) bone marrow cells. Unsupervised cluster analysis of the expression signature readily distinguished the different cellular bone marrow fractions (CD34+, CD71+ and CD13/33+) from each other, but did not discriminate patients from healthy controls. When individual genes were regarded, several were found to be differentially expressed between patients and controls. Particularly, strong over-expression of BIK (BCL2-interacting killer) was observed in erythroid progenitor cells of low- and high-risk MDS patients (both p = 0.001) and TNFRSF4 (tumor necrosis factor receptor superfamily 4) was down-regulated in immature hematopoietic cells (p = 0.0023) of low-risk MDS patients compared to healthy bone marrow.

Microarray-based analysis and clinical validation identify ubiquitin-conjugating enzyme E2E1 (UBE2E1) as a prognostic factor in acute myeloid leukemia

Background

Previous research suggested that single gene expression might be correlated with acute myeloid leukemia (AML) survival. Therefore, we conducted a systematical analysis for AML prognostic gene expressions.

Methods

We performed a microarray-based analysis for correlations between gene expression and adult AML overall survival (OS) using datasets GSE12417 and GSE8970. Positive findings were validated in an independent cohort of 50 newly diagnosed, non-acute promyelocytic leukemia (APL) AML patients by quantitative RT-PCR and survival analysis.

Results

Microarray-based analysis suggested that expression of eight genes was each associated with 1-year and 3-year AML OS in both GSE12417 and GSE8970 datasets (p < 0.05). Next, we validated our findings in an independent cohort of AML samples collected in our hospital. We found that ubiquitin-conjugating enzyme E2E1 (UBE2E1) expression was adversely correlated with AML survival (p = 0.04). Multivariable analysis showed that UBE2E1^high patients had a significant shorter OS and shorter progression-free survival after adjusting other known prognostic factors (p = 0.03). At last, we found that UBE2E1 expression was negatively correlated with patients’ response to induction chemotherapy (p < 0.05).

Conclusions

In summary, we demonstrated that UBE2E1 expression was a novel prognostic factor in adult, non-APL AML patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s13045-016-0356-0) contains supplementary material, which is available to authorized users.

MetaKTSP: a meta-analytic top scoring pair method for robust cross-study validation of omics prediction analysis

MOTIVATION

Supervised machine learning is widely applied to transcriptomic data to predict disease diagnosis, prognosis or survival. Robust and interpretable classifiers with high accuracy are usually favored for their clinical and translational potential. The top scoring pair (TSP) algorithm is an example that applies a simple rank-based algorithm to identify rank-altered gene pairs for classifier construction. Although many classification methods perform well in cross-validation of single expression profile, the performance usually greatly reduces in cross-study validation (i.e. the prediction model is established in the training study and applied to an independent test study) for all machine learning methods, including TSP. The failure of cross-study validation has largely diminished the potential translational and clinical values of the models. The purpose of this article is to develop a meta-analytic top scoring pair (MetaKTSP) framework that combines multiple transcriptomic studies and generates a robust prediction model applicable to independent test studies.

RESULTS

We proposed two frameworks, by averaging TSP scores or by combining P-values from individual studies, to select the top gene pairs for model construction. We applied the proposed methods in simulated data sets and three large-scale real applications in breast cancer, idiopathic pulmonary fibrosis and pan-cancer methylation. The result showed superior performance of cross-study validation accuracy and biomarker selection for the new meta-analytic framework. In conclusion, combining multiple omics data sets in the public domain increases robustness and accuracy of the classification model that will ultimately improve disease understanding and clinical treatment decisions to benefit patients.

AVAILABILITY AND IMPLEMENTATION

An R package MetaKTSP is available online. (http://tsenglab.biostat.pitt.edu/software.htm).

CONTACT

ctseng@pitt.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A novel role of farnesylation in targeting a mitotic checkpoint protein, human Spindly, to kinetochores

The mitotic checkpoint protein Spindly is farnesylated in vivo and this modification is required for its interaction with the RZZ complex and its localization to kinetochores.

Kinetochore (KT) localization of mitotic checkpoint proteins is essential for their function during mitosis. hSpindly KT localization is dependent on the RZZ complex and hSpindly recruits the dynein–dynactin complex to KTs during mitosis, but the mechanism of hSpindly KT recruitment is unknown. Through domain-mapping studies we characterized the KT localization domain of hSpindly and discovered it undergoes farnesylation at the C-terminal cysteine residue. The N-terminal 293 residues of hSpindly are dispensable for its KT localization. Inhibition of farnesylation using a farnesyl transferase inhibitor (FTI) abrogated hSpindly KT localization without affecting RZZ complex, CENP-E, and CENP-F KT localization. We showed that hSpindly is farnesylated in vivo and farnesylation is essential for its interaction with the RZZ complex and hence KT localization. FTI treatment and hSpindly knockdown displayed the same mitotic phenotypes, indicating that hSpindly is a key FTI target in mitosis. Our data show a novel role of lipidation in targeting a checkpoint protein to KTs through protein–protein interaction.

An argument for mechanism-based statistical inference in cancer

Cancer is perhaps the prototypical systems disease, and as such has been the focus of extensive study in quantitative systems biology. However, translating these programs into personalized clinical care remains elusive and incomplete. In this perspective, we argue that realizing this agenda—in particular, predicting disease phenotypes, progression and treatment response for individuals—requires going well beyond standard computational and bioinformatics tools and algorithms. It entails designing global mathematical models over network-scale configurations of genomic states and molecular concentrations, and learning the model parameters from limited available samples of high-dimensional and integrative omics data. As such, any plausible design should accommodate: biological mechanism, necessary for both feasible learning and interpretable decision making; stochasticity, to deal with uncertainty and observed variation at many scales; and a capacity for statistical inference at the patient level. This program, which requires a close, sustained collaboration between mathematicians and biologists, is illustrated in several contexts, including learning biomarkers, metabolism, cell signaling, network inference and tumorigenesis.

Phase II/III trial of a pre-transplant farnesyl transferase inhibitor in juvenile myelomonocytic leukemia: a report from the Children's Oncology Group

BACKGROUND

Juvenile myelomonocytic leukemia (JMML) is not durably responsive to chemotherapy, and approximately 50% of patients relapse after hematopoietic stem cell transplant (HSCT). Here we report the activity and acute toxicity of the farnesyl transferase inhibitor tipifarnib, the response rate to 13-cis retinoic acid (CRA) in combination with cytoreductive chemotherapy, and survival following HSCT in children with JMML.

PROCEDURE

Eighty-five patients with newly diagnosed JMML were enrolled on AAML0122 between 2001 and 2006. Forty-seven consented to receive tipifarnib in a phase II window before proceeding to a phase III trial of CRA in combination with fludarabine and cytarabine followed by HSCT and maintenance CRA. Thirty-eight patients enrolled only in the phase III trial.

RESULTS

Overall response rate was 51% after tipifarnib and 68% after fludarabine/cytarabine/CRA. Tipifarnib did not increase pre-transplant toxicities. Forty-six percent of the 44 patients who received protocol compliant HSCT relapsed. Five-year overall survival was 55 ± 11% and event-free survival was 41 ± 11%, with no significant difference between patients who did or did not receive tipifarnib.

CONCLUSIONS

Administration of tipifarnib in the window setting followed by HSCT in patients with newly diagnosed JMML was safe and yielded a 51% initial response rate as a single agent, but failed to reduce relapse rates or improve long-term overall survival.

Protein prenylation: enzymes, therapeutics, and biotechnology applications

Protein prenylation is a ubiquitous covalent post-translational modification found in all eukaryotic cells, comprising attachment of either a farnesyl or a geranylgeranyl isoprenoid. It is essential for the proper cellular activity of numerous proteins, including Ras family GTPases and heterotrimeric G-proteins. Inhibition of prenylation has been extensively investigated to suppress the activity of oncogenic Ras proteins to achieve antitumor activity. Here, we review the biochemistry of the prenyltransferase enzymes and numerous isoprenoid analogs synthesized to investigate various aspects of prenylation and prenyltransferases. We also give an account of the current status of prenyltransferase inhibitors as potential therapeutics against several diseases including cancers, progeria, aging, parasitic diseases, and bacterial and viral infections. Finally, we discuss recent progress in utilizing protein prenylation for site-specific protein labeling for various biotechnology applications.

Four different regimens of farnesyltransferase inhibitor tipifarnib in older, untreated acute myeloid leukemia patients: North American Intergroup Phase II study SWOG S0432

We report on 348 patients ≥ 70 years (median age 78 years) with acute myeloid leukemia (>50% with secondary AML) randomized to receive either 600 mg or 300 mg of tipifarnib orally twice daily on days 1-21 or days 1-7 and 15-21, repeated every 28 days (4 treatment regimens). Responses were seen in all regimens, with overall response rate (CR + CRi + PR) highest (20%) among patients receiving tipifarnib 300 mg twice daily on days 1-21. Toxicities were acceptable. Unless predictors of response to tipifarnib are identified, further study as a single agent in this population is unwarranted.

Comparing cancer vs normal gene expression profiles identifies new disease entities and common transcriptional programs in AML patients

This study describes a method for the comparison of gene expression data of any type of cancer cells with their corresponding normal cells. Our analyses reveal novel disease entities, identify common deregulated transcriptional networks, and predict survival.

New agents: great expectations not realized

A number of new agents in acute myeloid leukemia (AML) have held much promise in recent years, but most have failed to change the therapeutic landscape. Indeed, with the exception of gemtuzumab ozogamicin (which was subsequently voluntarily withdrawn from the commercial market), no new agent has been approved for acute myeloid leukemia (AML) beyond the 7 + 3 regimen, which was has been in use for over 40 years. This review touches upon the potential reasons for these failures and explores the newer therapeutic approaches being pursued in AML.

Farnesyltransferase inhibitor tipifarnib inhibits Rheb prenylation and stabilizes Bax in acute myelogenous leukemia cells

Although farnesyltransferase inhibitors have shown promising activity in relapsed lymphoma and sporadic activity in acute myelogenous leukemia, their mechanism of cytotoxicity is incompletely understood, making development of predictive biomarkers difficult. In the present study, we examined the action of tipifarnib in human acute myelogenous leukemia cell lines and clinical samples. In contrast to the Ras/MEK/ERK pathway-mediated Bim upregulation that is responsible for tipifarnib-induced killing of malignant lymphoid cells, inhibition of Rheb-induced mTOR signaling followed by dose-dependent upregulation of Bax and Puma occurred in acute myelogenous leukemia cell lines undergoing tipifarnib-induced apoptosis. Similar Bax and Puma upregulation occurred in serial bone marrow samples harvested from a subset of acute myelogenous leukemia patients during tipifarnib treatment. Expression of FTI-resistant Rheb M184L, like knockdown of Bax or Puma, diminished tipifarnib-induced killing. Further analysis demonstrated that increased Bax and Puma levels reflect protein stabilization rather than increased gene expression. In U937 cells selected for tipifarnib resistance, neither inhibition of signaling downstream of Rheb nor Bax and Puma stabilization occurred. Collectively, these results not only identify a pathway downstream from Rheb that contributes to tipifarnib cytotoxicity in human acute myelogenous leukemia cells, but also demonstrate that FTI-induced killing of lymphoid versus myeloid cells reflects distinct biochemical mechanisms downstream of different farnesylated substrates. (ClinicalTrials.gov identifier NCT00602771).

Combined p21-activated kinase and farnesyltransferase inhibitor treatment exhibits enhanced anti-proliferative activity on melanoma, colon and lung cancer cell lines

Background

Farnesyltransferase inhibitors (FTIs) are anticancer agents with a spectrum of activity in Ras-dependent and independent tumor cellular and xenograph models. How inhibition of protein farnesylation by FTIs results in reduced cancer cell proliferation is poorly understood due to the multiplicity of potential FTase targets. The low toxicity and oral availability of FTIs led to their introduction into clinical trials for the treatment of breast cancer, hematopoietic malignancy, advanced solid tumor and pancreatic cancer treatment, and Hutchinson-Gilford Progeria Syndrome. Although their efficacy in combinatorial therapies with conventional anticancer treatment for myeloid malignancy and solid tumors is promising, the overall results of clinical tests are far below expectations. Further exploitation of FTIs in the clinic will strongly rely on understanding how these drugs affect global cellular activity.

Methods

Using FTase inhibitor I and genome-wide chemical profiling of the yeast barcoded deletion strain collection, we identified genes whose inactivation increases the antiproliferative action of this FTI peptidomimetic. The main findings were validated in a panel of cancer cell lines using FTI-277 in proliferation and biochemical assays paralleled by multiparametric image-based analyses.

Results

ABC transporter Pdr10 or p-21 activated kinase (PAK) gene deletion increases the antiproliferative action of FTase inhibitor I in yeast cells. Consistent with this, enhanced inhibition of cell proliferation by combining group I PAK inhibition, using IPA3, with FTI-277 was observed in melanoma (A375MM), lung (A549) and colon (HT29), but not in epithelial (HeLa) or breast (MCF7), cancer cell lines. Both HeLa and A375MM cells show changes in the nuclear localization of group 1 PAKs in response to FTI-277, but up-regulation of PAK protein levels is observed only in HeLa cells.

Conclusions

Our data support the view that group I PAKs are part of a pro-survival pathway activated by FTI treatment, and group I PAK inactivation potentiates the anti-proliferative action of FTIs in yeast as well as in cancer cells. These findings open new perspectives for the use of FTIs in combinatorial strategies with PAK inhibitors in melanoma, lung and colon malignancy.

A simple and reproducible breast cancer prognostic test

Background

A small number of prognostic and predictive tests based on gene expression are currently offered as reference laboratory tests. In contrast to such success stories, a number of flaws and errors have recently been identified in other genomic-based predictors and the success rate for developing clinically useful genomic signatures is low. These errors have led to widespread concerns about the protocols for conducting and reporting of computational research. As a result, a need has emerged for a template for reproducible development of genomic signatures that incorporates full transparency, data sharing and statistical robustness.

Results

Here we present the first fully reproducible analysis of the data used to train and test MammaPrint, an FDA-cleared prognostic test for breast cancer based on a 70-gene expression signature. We provide all the software and documentation necessary for researchers to build and evaluate genomic classifiers based on these data. As an example of the utility of this reproducible research resource, we develop a simple prognostic classifier that uses only 16 genes from the MammaPrint signature and is equally accurate in predicting 5-year disease free survival.

Conclusions

Our study provides a prototypic example for reproducible development of computational algorithms for learning prognostic biomarkers in the era of personalized medicine.

Impact of genetic targets on cancer therapy in acute myelogenous leukemia

Acute myelogenous leukemia (AML) is characterized by uncontrolled proliferation of the cells of myeloid origin. It can present at all ages, but is more common in adults. It is one of the most common leukemias in adults and continues to pose significant challenge in diagnosis and long-term management.AML is a disease at the forefront of genetic and genomic approaches to medicine. It is a disease that has witnessed rapid advances in terms of diagnosis, classification, prognosis and ultimately individualized therapy. Newly diagnosed AML patients are now routinely stratified according to cytogenetics and molecular markers which guides long-term prognosis and treatment. On the other hand, with few exceptions, the initial treatment (also known as induction treatment) of AML has been 'one-size-fits-all'. It remains a great challenge for patients and physicians to consolidate and translate these advances into eventual success in clinic [1, 2].

The clinically relevant pharmacogenomic changes in acute myelogenous leukemia

Acute myelogenous leukemia (AML) is an extremely heterogeneous neoplasm with several clinical, pathological, genetic and molecular subtypes. Combinations of various doses and schedules of cytarabine and different anthracyclines have been the mainstay of treatment for all forms of AMLs in adult patients. Although this combination, with the addition of an occasional third agent, remains effective for treatment of some young-adult patients with de novo AML, the prognosis of AML secondary to myelodysplastic syndromes or myeloproliferative neoplasms, treatment-related AML, relapsed or refractory AML, and AML that occurs in older populations remains grim. Taken into account the heterogeneity of AML, one size does not and should not be tried to fit all. In this article, the authors review currently understood, applicable and relevant findings related to cytarabine and anthracycline drug-metabolizing enzymes and drug transporters in adult patients with AML. To provide a prime-time example of clinical applicability of pharmacogenomics in distinguishing a subset of patients with AML who might be better responders to farnesyltransferase inhibitors, the authors also reviewed findings related to a two-gene transcript signature consisting of high RASGRP1 and low APTX, the ratio of which appears to positively predict clinical response in AML patients treated with farnesyltransferase inhibitors.

Current treatment of acute myeloid leukemia

PURPOSE OF REVIEW

The objectives of this review are to discuss standard and investigational nontransplant treatment strategies for acute myeloid leukemia (AML), excluding acute promyelocytic leukemia.

RECENT FINDINGS

Most adults with AML die from their disease. The standard treatment paradigm for AML is remission induction chemotherapy with an anthracycline/cytarabine combination, followed by either consolidation chemotherapy or allogeneic stem cell transplantation, depending on the patient's ability to tolerate intensive treatment and the likelihood of cure with chemotherapy alone. Although this approach has changed little in the last three decades, increased understanding of the pathogenesis of AML and improvements in molecular genomic technologies are leading to novel drug targets and the development of personalized, risk-adapted treatment strategies. Recent findings related to prognostically relevant and potentially 'druggable' molecular targets are reviewed.

SUMMARY

At the present time, AML remains a devastating and mostly incurable disease, but the combination of optimized chemotherapeutics and molecularly targeted agents holds significant promise for the future.

New therapeutic approaches to acute myeloid leukemia

BACKGROUND

The heterogeneity of acute myeloid leukemia (AML) has been established by many new insights into the pathogenesis and treatment of patients with AML. Understanding the basic cellular and molecular pathogenesis of leukemic cells is vital to the development of new treatment approaches.

OBJECTIVE/METHODS

To review progress until now with agents that are showing promise in the treatment of AML, we summarize the published preclinical and clinical trials that have been completed.

RESULTS

Based on recent progress of investigations, more specifically targeted agents have been developed for the treatment of AML such as tyrosine kinase inhibitors, monoclonal antibodies, epigenetic agents, antiangiogenic agents, and farnesyl transferase inhibitors.

CONCLUSION

In the future, in addition to performing therapeutic trials of these agents, it will be important to identify other highly specific therapeutic agents based on our evolving understanding of the biology of AML.

AUREA: an open-source software system for accurate and user-friendly identification of relative expression molecular signatures

Background

Public databases such as the NCBI Gene Expression Omnibus contain extensive and exponentially increasing amounts of high-throughput data that can be applied to molecular phenotype characterization. Collectively, these data can be analyzed for such purposes as disease diagnosis or phenotype classification. One family of algorithms that has proven useful for disease classification is based on relative expression analysis and includes the Top-Scoring Pair (TSP), k-Top-Scoring Pairs (k-TSP), Top-Scoring Triplet (TST) and Differential Rank Conservation (DIRAC) algorithms. These relative expression analysis algorithms hold significant advantages for identifying interpretable molecular signatures for disease classification, and have been implemented previously on a variety of computational platforms with varying degrees of usability. To increase the user-base and maximize the utility of these methods, we developed the program AUREA (Adaptive Unified Relative Expression Analyzer)—a cross-platform tool that has a consistent application programming interface (API), an easy-to-use graphical user interface (GUI), fast running times and automated parameter discovery.

Results

Herein, we describe AUREA, an efficient, cohesive, and user-friendly open-source software system that comprises a suite of methods for relative expression analysis. AUREA incorporates existing methods, while extending their capabilities and bringing uniformity to their interfaces. We demonstrate that combining these algorithms and adaptively tuning parameters on the training sets makes these algorithms more consistent in their performance and demonstrate the effectiveness of our adaptive parameter tuner by comparing accuracy across diverse datasets.

Conclusions

We have integrated several relative expression analysis algorithms and provided a unified interface for their implementation while making data acquisition, parameter fixing, data merging, and results analysis ‘point-and-click’ simple. The unified interface and the adaptive parameter tuning of AUREA provide an effective framework in which to investigate the massive amounts of publically available data by both ‘in silico’ and ‘bench’ scientists. AUREA can be found at http://price.systemsbiology.net/AUREA/.

Recent advances in protein prenyltransferases: substrate identification, regulation, and disease interventions

Protein post-translational modifications increase the functional diversity of the proteome by covalently adding chemical moieties onto proteins thereby changing their activation state, cellular localization, interacting partners, and life cycle. Lipidation is one such modification that enables membrane association of naturally cytosolic proteins. Protein prenyltransferases irreversibly install isoprenoid units of varying length via a thioether linkage onto proteins that exert their cellular activity at membranes. Substrates of prenyltransferases are involved in countless signaling pathways and processes within the cell. Identification of new prenylation substrates, prenylation pathway regulators, and dynamic trafficking of prenylated proteins are all avenues of intense, ongoing research that are challenging, exciting, and have the potential to significantly advance the field in the near future.

Targeting of CD34+CD38- cells using Gemtuzumab ozogamicin (Mylotarg) in combination with tipifarnib (Zarnestra) in Acute Myeloid Leukaemia

BACKGROUND

The CD34+CD38- subset of AML cells is enriched for resistance to current chemotherapeutic agents and considered to contribute to disease progression and relapse in Acute Myeloid Leukaemia (AML) patients following initial treatment.

METHODS

Chemosensitivity in phenotypically defined subsets from 34 primary AML samples was measured by flow cytometry following 48 hr in vitro treatment with gemtuzumab ozogamicin (GO, Mylotarg) and the farnesyltransferase inhibitor tipifarnib/zarnestra. The DNA damage response was measured using flow cytometry, immunofluorescence and immunohistochemistry.

RESULTS

Using a previously validated in vitro minimal residual disease model, we now show that the combination of GO (10 ng/ml) and tipifarnib (5 μM) targets the CD34+CD38- subset resulting in 65% median cell loss compared to 28% and 13% CD34+CD38- cell loss in GO-treated and tipifarnib-treated cells, respectively. Using phosphokinome profiling and immunofluorescence in the TF-1a cell line, we demonstrate that the drug combination is characterised by the activation of a DNA damage response (induction of γH2A.X and thr68 phosphorylation of chk2). Higher induction of γH2AX was found in CD34+CD38- than in CD34+CD38+ patient cells. In a model system, we show that dormancy impairs damage resolution, allowing accumulation of γH2AX foci.

CONCLUSIONS

The chemosensitivity of the CD34+CD38- subset, combined with enhanced damage indicators, suggest that this subset is primed to favour programmed cell death as opposed to repairing damage. This interaction between tipifarnib and GO suggests a potential role in the treatment of AML.

Is there a future for prenyltransferase inhibitors in cancer therapy?

It has been over 20 years since it was first recognized that the function of both normal and oncogenic Ras is dependent on the post-translational modification termed farnesylation. Since that time, intense effort has been expended on the development of farnesyltransferase inhibitors as novel anticancer agents. Over 70 clinical trials have now been conducted, with limited efficacy demonstrated. Here we provide an update of the most recently published clinical trials, discuss the use of the RASGRP1/APTX two-gene expression screen to select patients with acute myeloid leukemia for therapy, and report on the latest discoveries related to the targets of prenyltransferase inhibitors.

Lonafarnib for cancer and progeria

INTRODUCTION

Lonafarnib is a non-peptidomimetic inhibitor of farnesyl transferase, an enzyme responsible for the post-translational lipid modification of a wide variety of cellular proteins that are involved in the pathogenic pathways of various diseases including cancer and progeria. Although extensive clinical research indicates limited activity of lonafarnib in solid tumors, there is recent interest in combinations of farnesyl transferase inhibitors with imatinib or bortezomib in hematological malignancies and to investigate the role of lonafarnib in progeria.

AREAS COVERED

This review examines the in vitro and in vivo pharmacology of lonafarnib and the available clinical data for lonafarnib monotherapy and combination therapy in the treatment of solid and hematological malignancies as well as progeria, using studies identified from the PubMed database supplemented by computerized search of relevant abstracts from major cancer and hematology conferences.

EXPERT OPINION

There is no evidence to support the use of lonafarnib in solid tumors. There is ongoing interest to explore lonafarnib for progeria and to investigate other farnesyl transferase inhibitors for chronic and acute leukemias.