A four-gene LincRNA expression signature predicts risk in multiple cohorts of acute myeloid leukemia patients.

Prognostic gene expression signatures have been proposed as clinical tools to clarify therapeutic options in acute myeloid leukemia (AML). However, these signatures rely on measuring large numbers of genes and often perform poorly when applied to independent cohorts or those with older patients. Long intergenic non-coding RNAs (lincRNAs) are emerging as important regulators of cell identity and oncogenesis, but knowledge of their utility as prognostic markers in AML is limited. Here we analyze transcriptomic data from multiple cohorts of clinically annotated AML patients and report that (i) microarrays designed for coding gene expression can be repurposed to yield robust lincRNA expression data, (ii) some lincRNA genes are located in close proximity to hematopoietic coding genes and show strong expression correlations in AML, (iii) lincRNA gene expression patterns distinguish cytogenetic and molecular subtypes of AML, (iv) lincRNA signatures composed of three or four genes are independent predictors of clinical outcome and further dichotomize survival in European Leukemia Net (ELN) risk groups and (v) an analytical tool based on logistic regression analysis of quantitative PCR measurement of four lincRNA genes (LINC4) can be used to determine risk in AML

Prospective Identification of Acute Myeloid Leukemia Patients Who Benefit from Gene-Expression Based Risk Stratification

Background: Acute myeloid leukemia (AML) is a highly heterogeneous malignancy and risk stratification based on genetic and clinical variables is standard practice. However, current models incorporating these factors accurately predict clinical outcomes for only 64-80% of patients and fail to provide clear treatment guidelines for patients with intermediate genetic risk. A plethora of prognostic gene expression signatures (PGES) have been proposed to improve outcome predictions but none of these have entered routine clinical practice and their role remains uncertain. Methods: To clarify clinical utility, we performed a systematic evaluation of eight highly-cited PGES i.e. Marcucci-7, Ng-17, Li-24, Herold-29, Eppert-LSCR-48, Metzeler-86, Eppert-HSCR-105, and Bullinger-133. We investigated their constituent genes, methodological frameworks and prognostic performance in four cohorts of non-FAB M3 AML patients (n= 1175). All patients received intensive anthracycline and cytarabine based chemotherapy and were part of studies conducted in the United States of America (TCGA), the Netherlands (HOVON) and Germany (AMLCG). Results: There was a minimal overlap of individual genes and component pathways between different PGES and their performance was inconsistent when applied across different patient cohorts. Concerningly, different PGES often assigned the same patient into opposing adverse- or favorable- risk groups (Figure 1A: Rand index analysis; RI=1 if all patients were assigned to equal risk groups and RI =0 if all patients were assigned to different risk groups). Differences in the underlying methodological framework of different PGES and the molecular heterogeneity between AMLs contributed to these low-fidelity risk assignments. However, all PGES consistently assigned a significant subset of patients into the same adverse- or favorable-risk groups (40%-70%; Figure 1B: Principal component analysis of the gene components from the eight tested PGES). These patients shared intrinsic and measurable transcriptome characteristics (Figure 1C: Hierarchical cluster analysis of the differentially expressed genes) and could be prospectively identified using a high-fidelity prediction algorithm (FPA). In the training set (i.e. from the HOVON), the FPA achieved an accuracy of ~80% (10-fold cross-validation) and an AUC of 0.79 (receiver-operating characteristics). High-fidelity patients were dichotomized into adverse- or favorable- risk groups with significant differences in overall survival (OS) by all eight PGES (Figure 1D) and low-fidelity patients by two of the eight PGES (Figure 1E). In the three independent test sets (i.e. form the TCGA and AMLCG), patients with predicted high-fidelity were consistently dichotomized into the same adverse- or favorable- risk groups with significant differences in OS by all eight PGES. However, in-line with our previous analysis, patients with predicted low-fidelity were dichotomized into opposing adverse- or favorable- risk groups by the eight tested PGES. Conclusion: With appropriate patient selection, existing PGES improve outcome predictions and could guide treatment recommendations for patients without accurate genetic risk predictions (~18-25%) and for those with intermediate genetic risk (~32-35%). Figure 1 Disclosures Hiddemann: Celgene: Consultancy, Honoraria; Roche: Consultancy, Honoraria, Research Funding; Bayer: Research Funding; Vector Therapeutics: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding. Metzeler:Celgene: Honoraria, Research Funding; Otsuka: Honoraria; Daiichi Sankyo: Honoraria. Pimanda:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Beck:Gilead: Research Funding. </jats:sec

ETS1 Mediates MEK1/2-Dependent Overexpression of Cancerous Inhibitor of Protein Phosphatase 2A (CIP2A) in Human Cancer Cells

EGFR-MEK-ERK signaling pathway has an established role in promoting malignant growth and disease progression in human cancers. Therefore identification of transcriptional targets mediating the oncogenic effects of the EGFR-MEK-ERK pathway would be highly relevant. Cancerous inhibitor of protein phosphatase 2A (CIP2A) is a recently characterized human oncoprotein. CIP2A promotes malignant cell growth and is over expressed at high frequency (40–80%) in most of the human cancer types. However, the mechanisms inducing its expression in cancer still remain largely unexplored. Here we present systematic analysis of contribution of potential gene regulatory mechanisms for high CIP2A expression in cancer. Our data shows that evolutionary conserved CpG islands at the proximal CIP2A promoter are not methylated both in normal and cancer cells. Furthermore, sequencing of the active CIP2A promoter region from altogether seven normal and malignant cell types did not reveal any sequence alterations that would increase CIP2A expression specifically in cancer cells. However, treatment of cancer cells with various signaling pathway inhibitors revealed that CIP2A mRNA expression was sensitive to inhibition of EGFR activity as well as inhibition or activation of MEK-ERK pathway. Moreover, MEK1/2-specific siRNAs decreased CIP2A protein expression. Series of CIP2A promoter-luciferase constructs were created to identify proximal −27 to −107 promoter region responsible for MEK-dependent stimulation of CIP2A expression. Additional mutagenesis and chromatin immunoprecipitation experiments revealed ETS1 as the transcription factor mediating stimulation of CIP2A expression through EGFR-MEK pathway. Thus, ETS1 is probably mediating high CIP2A expression in human cancers with increased EGFR-MEK1/2-ERK pathway activity. These results also suggest that in addition to its established role in invasion and angiogenesis, ETS1 may support malignant cellular growth via regulation of CIP2A expression and protein phosphatase 2A inhibition

HOX-mediated LMO2 expression in embryonic mesoderm is recapitulated in acute leukaemias

The Lim Domain Only 2 (LMO2) leukaemia oncogene encodes an LIM domain transcriptional cofactor required for early haematopoiesis. During embryogenesis, LMO2 is also expressed in developing tail and limb buds, an expression pattern we now show to be recapitulated in transgenic mice by an enhancer in LMO2 intron 4. Limb bud expression depended on a cluster of HOX binding sites, while posterior tail expression required the HOX sites and two E-boxes. Given the importance of both LMO2 and HOX genes in acute leukaemias, we further demonstrated that the regulatory hierarchy of HOX control of LMO2 is activated in leukaemia mouse models as well as in patient samples. Moreover, Lmo2 knock-down impaired the growth of leukaemic cells, and high LMO2 expression at diagnosis correlated with poor survival in cytogenetically normal AML patients. Taken together, these results establish a regulatory hierarchy of HOX control of LMO2 in normal development, which can be resurrected during leukaemia development. Redeployment of embryonic regulatory hierarchies in an aberrant context is likely to be relevant in human pathologies beyond the specific example of ectopic activation of LMO2

Pre-Clinical Drug Prioritization via Prognosis-Guided Genetic Interaction Networks

The high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Patient population heterogeneity and unpredictable physiology complicate pre-clinical cancer modeling efforts. We hypothesize that gene networks associated with cancer outcome in heterogeneous patient populations could serve as a reference for identifying drug effects. Here we propose a novel in vivo genetic interaction which we call ‘synergistic outcome determination’ (SOD), a concept similar to ‘Synthetic Lethality’. SOD is defined as the synergy of a gene pair with respect to cancer patients' outcome, whose correlation with outcome is due to cooperative, rather than independent, contributions of genes. The method combines microarray gene expression data with cancer prognostic information to identify synergistic gene-gene interactions that are then used to construct interaction networks based on gene modules (a group of genes which share similar function). In this way, we identified a cluster of important epigenetically regulated gene modules. By projecting drug sensitivity-associated genes on to the cancer-specific inter-module network, we defined a perturbation index for each drug based upon its characteristic perturbation pattern on the inter-module network. Finally, by calculating this index for compounds in the NCI Standard Agent Database, we significantly discriminated successful drugs from a broad set of test compounds, and further revealed the mechanisms of drug combinations. Thus, prognosis-guided synergistic gene-gene interaction networks could serve as an efficient in silico tool for pre-clinical drug prioritization and rational design of combinatorial therapies

Mutations in ASXL1 are associated with poor prognosis across the spectrum of malignant myeloid diseases

The ASXL1 gene is one of the most frequently mutated genes in malignant myeloid diseases. The ASXL1 protein belongs to protein complexes involved in the epigenetic regulation of gene expression. ASXL1 mutations are found in myeloproliferative neoplasms (MPN), myelodysplastic syndromes (MDS), chronic myelomonocytic leukemia (CMML) and acute myeloid leukemia (AML). They are generally associated with signs of aggressiveness and poor clinical outcome. Because of this, a systematic determination of ASXL1 mutational status in myeloid malignancies should help in prognosis assessment

FHL2 interacts with CALM and is highly expressed in acute erythroid leukemia

The t(10;11)(p13;q14) translocation results in the fusion of the CALM (clathrin assembly lymphoid myeloid leukemia protein) and AF10 genes. This translocation is observed in acute myeloblastic leukemia (AML M6), acute lymphoblastic leukemia (ALL) and malignant lymphoma. Using a yeast two-hybrid screen, the four and a half LIM domain protein 2 (FHL2) was identified as a CALM interacting protein. Recently, high expression of FHL2 in breast, gastric, colon, lung as well as in prostate cancer was shown to be associated with an adverse prognosis. The interaction between CALM and FHL2 was confirmed by glutathione S-transferase-pulldown assay and co-immunoprecipitation experiments. The FHL2 interaction domain of CALM was mapped to amino acids 294–335 of CALM. The transcriptional activation capacity of FHL2 was reduced by CALM, but not by CALM/AF10, which suggests that regulation of FHL2 by CALM might be disturbed in CALM/AF10-positive leukemia. Extremely high expression of FHL2 was seen in acute erythroid leukemia (AML M6). FHL2 was also highly expressed in chronic myeloid leukemia and in AML with complex aberrant karyotype. These results suggest that FHL2 may play an important role in leukemogenesis, especially in the case of AML M6

PrognoScan: a new database for meta-analysis of the prognostic value of genes

<p>Abstract</p> <p>Background</p> <p>In cancer research, the association between a gene and clinical outcome suggests the underlying etiology of the disease and consequently can motivate further studies. The recent availability of published cancer microarray datasets with clinical annotation provides the opportunity for linking gene expression to prognosis. However, the data are not easy to access and analyze without an effective analysis platform.</p> <p>Description</p> <p>To take advantage of public resources in full, a database named "PrognoScan" has been developed. This is 1) a large collection of publicly available cancer microarray datasets with clinical annotation, as well as 2) a tool for assessing the biological relationship between gene expression and prognosis. PrognoScan employs the minimum <it>P</it>-value approach for grouping patients for survival analysis that finds the optimal cutpoint in continuous gene expression measurement without prior biological knowledge or assumption and, as a result, enables systematic meta-analysis of multiple datasets.</p> <p>Conclusion</p> <p>PrognoScan provides a powerful platform for evaluating potential tumor markers and therapeutic targets and would accelerate cancer research. The database is publicly accessible at <url>http://gibk21.bse.kyutech.ac.jp/PrognoScan/index.html</url>.</p

A Differentiation-Based Phylogeny of Cancer Subtypes

Histopathological classification of human tumors relies in part on the degree of differentiation of the tumor sample. To date, there is no objective systematic method to categorize tumor subtypes by maturation. In this paper, we introduce a novel computational algorithm to rank tumor subtypes according to the dissimilarity of their gene expression from that of stem cells and fully differentiated tissue, and thereby construct a phylogenetic tree of cancer. We validate our methodology with expression data of leukemia, breast cancer and liposarcoma subtypes and then apply it to a broader group of sarcomas. This ranking of tumor subtypes resulting from the application of our methodology allows the identification of genes correlated with differentiation and may help to identify novel therapeutic targets. Our algorithm represents the first phylogeny-based tool to analyze the differentiation status of human tumors