Large-scale gene network analysis reveals the significance of extracellular matrix pathway and homeobox genes in acute myeloid leukemia: an introduction to the Pigengene package and its applications
- Amir Foroushani1,
- Rupesh Agrahari1,
- Roderick Docking2,
- Linda Chang2,
- Gerben Duns2,
- Monika Hudoba3,
- Aly Karsan†2 and
- Habil Zare†1Email authorView ORCID ID profile
© The Author(s) 2017
Received: 18 August 2016
Accepted: 8 March 2017
Published: 16 March 2017
The distinct types of hematological malignancies have different biological mechanisms and prognoses. For instance, myelodysplastic syndrome (MDS) is generally indolent and low risk; however, it may transform into acute myeloid leukemia (AML), which is much more aggressive.
We develop a novel network analysis approach that uses expression of eigengenes to delineate the biological differences between these two diseases.
We find that specific genes in the extracellular matrix pathway are underexpressed in AML. We validate this finding in three ways: (a) We train our model on a microarray dataset of 364 cases and test it on an RNA Seq dataset of 74 cases. Our model showed 95% sensitivity and 86% specificity in the training dataset and showed 98% sensitivity and 91% specificity in the test dataset. This confirms that the identified biological signatures are independent from the expression profiling technology and independent from the training dataset.
(b) Immunocytochemistry confirms that MMP9, an exemplar protein in the extracellular matrix, is underexpressed in AML. (c) MMP9 is hypermethylated in the majority of AML cases (n=194, Welch’s t-test p-value <10−138), which complies with its low expression in AML.
Our novel network analysis approach is generalizable and useful in studying other complex diseases (e.g., breast cancer prognosis). We implement our methodology in the Pigengene software package, which is publicly available through Bioconductor.
Eigengenes define informative biological signatures that are robust with respect to expression profiling technology. These signatures provide valuable information about the underlying biology of diseases, and they are useful in predicting diagnosis and prognosis.
KeywordsGene expression Network analysis Leukemia Extracellular matrix Homeobox Hematological malignancy
Acute myeloid leukemia (AML) is an aggressive type of blood cancer and accounts for 1.2% of cancer deaths in the United States . It is the most common acute leukemia, which is characterized by the rapid growth of immature white blood cells. These cells interfere with the production of normal blood cells in the bone marrow. Without treatment, AML can lead to death within months after diagnosis . Myelodysplastic syndrome (MDS) are a set of less aggressive diseases; however, about 30 to 40% of MDS cases can transform into AML . Therefore, it is critical to delineate the exact mechanisms of this transformation .
Possible molecular mechanisms include genetic mutations [5, 6], chromosomal abnormalities , and epigenetic changes [8, 9]. For example, mutation and abnormal expression of mRNA splicing genes such as SRSF2  and SF3B1  are associated with the prognosis of MDS. Overexpression of Bcl-2 increases resistance of MDS cells to apoptosis , and it can play a role in the transformation into leukemia . Similarly, the abnormal expression of some miRNAs such as miR-125 and miR-155 can lead to aberrant self-renewal of HSC , a characteristic of AML.
Although investigating the differences between AML and MDS at the molecular level has provided valuable insight, the research in this area has only scratched the surface of the problem. In particular, the current knowledge is far from adequate for the development of strategies for preventing or predicting the transformation of MDS into AML . Researchers have proposed gene expression profiling as a systematic approach to explore the biology and clinical heterogeneity of MDS.
The classifier was based only on the 100 most differentially expressed genes. However, the biological processes in a hematopoietic cell often depend on the coordination of many more genes. Because the status of the cell is determined by the level of expression of hundreds of transcripts, restricting the analysis to only 100 genes could decrease the statistical power to a great extent . Also, a random gene might be considered differentially expressed due to biological or technical noise or due to the difference in the analyzed cell types. Such a gene would convolute a classification based on differentially expressed genes .
The produced data were inconsistent because of multiple platforms and approaches used across different institutions . For instance, if a signature was defined using the level of expression in a microarray dataset, it would be very challenging to interpret and use that signature in an RNA-Seq dataset produced in a different laboratory .
We hypothesized that gene network analysis addresses both of the above challenges because it models the interactions between genes in a comprehensive structure [20, 21] (Additional file 1: Note S1). Recently, Liu reviewed the computational methods that employ a gene network approach to identify biomarkers from high-throughput data . Gene networks provide a systematic way to organize complex data, and to identify biomarkers that are useful in improving diagnosis, prognosis and therapy of diseases.
The confusion matrices show the accuracy of our decision tree on the training (MILE) and test (BCCA) datasets
Full tree (155 genes)
Reduced tree (14 genes)
Mills et al. 
We identified 33 gene modules as clusters of genes that are coexpressed in the 202 AML cases from the MILE dataset  (Additional file 1: Note S2). The sizes of the modules vary in the range of 21 to 888, with a mean and median of 153 and 75, respectively (Additional file 1: Figure S1).
Analysis of gene modules
Overrepresentation analysis reveals that some of the modules are associated with canonical pathways and biological processes. For instance, module 6 is enriched with genes that are related to the cell cycle. That is, out of 421 genes in the Reactome cell cycle pathway , 81 (19%) are grouped in module 6, which consists of 255 genes (p-value of the hypergeometric test <10−37). Similarly, module 12 is associated with extracellular matrix, module 14 with cytotoxic pathway (CD8+ T cells), module 15 with DNA replication, and module 21 with translation (Additional file 1: Figures S2 and S3 and Additional file 2: Table S2).
Module 33 is the smallest module containing 21 genes. We named it HIST1 because almost all of its genes (20, 95%) encode proteins from the linker histone, or H1, family (Additional file 3: Table S1). Half of the 39 genes in module 28 are from the homeobox family. Considering that this module contains 10 HOXA and 9 HOXB genes, we named it HOXA&B module. It is highly enriched with the homeobox genes that have been reported to be associated with the development and prognosis of AML [27, 28] (Additional file 1: Figure S4, Additional file 3: Table S1 and Additional file 4: Table S3).
Eigengenes are associated with the disease
We summarized the biological information of each module in one eigengene (Additional file 5: Table S4). An eigengene of a module is a weighted average of expression of all genes in that module. The weights were adjusted such that the loss in the biological information is minimized (Methods) [24, 29]. In the MILE dataset, all module eigengenes present significantly different expression in AML vs. MDS. The adjusted Welch’s t-test p-values are in the range of 10−61 to 10−6, with a median of 10−24 (Additional file 1: Figure S5) .
These eigengenes were differentially expressed in AML vs. MDS cases
AML and MDS are different in their expression of extracellular matrix, HOXA, and HOXB genes
Misclassification of MDS was associated with risk factor
The International Prognostic Scoring System (IPSS) score  is the standard tool for MDS risk stratification . It ranges from 0 to 3.5, and a higher value indicates a poorer prognosis. There are 30 MDS cases (18%) in the MILE dataset with poor prognosis (IPSS ≥1.5). This set has a significant overlap with the 23 cases “misclassified” by our decision tree (Additional file 6: Table S5). Specifically, 15 MDS cases with poor prognosis show AML signatures and are classified as AML by the tree (hypergeometric test p-value <10−7). This suggests that underexpression of the extracellular genes and overexpression of the HOXA genes in an MDS case can be considered as a risk factor. Because transition into AML is more likely for such an MDS case, a monitoring assay can be developed based on these signatures.
Validating AML signatures in an independent dataset
We validated the performance of the tree on classifying 74 cases in the BCCA dataset. To this end, we inferred the values of extracellular matrix and HOXA&B eigengenes in the BCCA dataset (Methods). With the same above-mentioned thresholds that performed well for the MILE dataset, the tree correctly identified 51 (98%) of the AML-NK and 20 (91%) of the MDS cases. The high accuracy of our decision tree was helpful in correcting a clerical error in annotating the dataset. In particular, two BCCA cases (B118 and B129), originally labeled with MDS, have signatures very similar to AML (Additional file 5: Table S4). Interestingly, a second review revealed that their correct diagnosis is in fact tAML (therapy–related AML) and AML–M1, respectively.
Although the decision tree was trained using only AML-NK subtype in the MILE dataset, its performance in differentiating some other subtypes of AML from MDS in the BCCA dataset is remarkable. In particular, all of the four AML-t(8;21) cases (100%), all of the four AML cases with complex karyotype cases (100%), all of the four AML cases with 11q23 abnormality (100%), and 9 out of 11 AML-inv(16) cases (82%) are all correctly classified as AML. However, cases from other subtypes, such as AML-t(15;17), AML-M6, and tAML, do not always show strong extracellular or HOXA&B signatures of AML-NK and are frequently misclassified as MDS (Additional file 6: Table S5). This is expected, because these three subtypes of AML are distinct and too different from AML-NK. In particular, leukemic cells in AML-t(15;17) and AML-M6 are relatively more differentiated , and may produce some extracellular matrix proteins.
A minimal gene set for clinical testing
Considering the good performance of the decision tree, it is useful to develop a clinical test based on gene expression. The extracellular matrix and HOXA&B modules contain 113 and 42 genes, respectively. To infer the corresponding eigengenes, the expression of 155 genes are needed in total. If the number of genes is reduced without significant loss of accuracy, the test will be easier to use in clinical settings. Because the genes are correlated with each other in each module, shrinking the tree is expected to have little—or no—effect on the accuracy of classification.
Using a greedy approach, we excluded the majority of the 155 genes, and obtained a decision tree that need the expression values of only 14 genes (9%) (). The performance of the reduced tree is comparable to the original tree (Table 1). On the training set, the accuracy dropps by only 5% for AML and by 2% for MDS. On the test set, however, the reduced tree is as accurate as the full tree (Additional file 6: Table S5).
The significance of the extracellular matrix pathway in AML
The relationship between HOX genes and AML and their role in leukemogenesis are extensively studied [27, 27, 28]. Researchers have also explained the significance of the extracellular matrix pathway in the prognosis of cancers in general . However, its role in the development of AML and other leukemias is more complicated. In addition to regulating cell growth , proliferation , differentiation , and apoptosis , it also mediates the migration of hematopoietic stem cells through the vessels . Module 12 is enriched with extracellular matrix genes (Additional file 1: Figure S8). We investigated these genes, which defined a significant signature in our decision tree (Fig. 4).
Gene Ontology Cellular Component (GO-CC) analysis showed that 36 of 113 genes in module 12 code for proteins in the extracellular region (Additional file 1: Figure S8 and Additional file 7: Table S7). Moreover, 77 of the genes in this module are associated with at least one of the following categories: extracellular vesicular exosome (44 genes), extracellular region (36), extracellular space (30 genes), and plasma membrane (31 genes). We noted that 18 genes (16%) are located on chromosome 19. Almost all of these 113 genes are underexpressed in AML (Fig. 5 and Additional file 1: Figure S6). The enriched biological processes include: immune system process (adjusted p-value <10−9), killing by host of symbiont cells (<10−3), killing of cells in other organism involved in symbiotic interaction (<10−2), defense response to fungus (<10−3), antibacterial humoral response (<10−2), extracellular matrix disassembly (<10−2), and response to lipopolysaccharide (<10−2) (Additional file 8: Table S9) .
One particularly interesting gene from this module was MMP9, which had a relatively high contribution to the eigengene. Its weight is 0.92, the highest in the extracellular matrix pathway (Reactome ), and the eighth in the module (Additional file 7: Table S7). MMP9 is a member of the matrix metalloproteinase (MMP) family, which has 23 members.
They remodel and degrade the extracellular matrix by cleaving its components . In addition to MMP9, this module includes two other members of MMP family, namely MMP8 (weight = 0.91) and MMP25 (weight = 0.87). All of these three genes are underexpressed in AML (Additional file 1: Figure S9a). One way to confirm that these genes are silenced in AML would be to check epigenetic factors such as DNA methylation, which generally anticorrelates with gene expression . We compared 194 AML cases of Acute Myeloid Leukemia (LAML) dataset from The Cancer Genome Atlas (TCGA) with 368 control cases, and we confirmed that these three genes were heavily methylated in AML (Additional file 1: Figure S9b and Additional file 9).
Validating gene expression changes at the protein level
Validating the identified coexpression pattern in other AML-related datasets
The 113 genes in the extracellular matrix module are correlated and underexpressed in AML. To validate that the observed coexpression pattern is specifically associated with AML, we investigated the expression of these 113 genes in a large collection of human datasets. Specifically, we used Search-Based Exploration of Expression Compendium (SEEK)  to objectively compare the coexpression of these genes across a collection of 5210 datasets. SEEK automatically scored and ranked the datasets based on the significance of coexpression of our 113 genes. SEEK also computed empirical p-values to assess the statistical significance of scores. Specifically, random scores for each dataset was computed based on 5000 queries of 113 random genes, and a p-value was reported as the fraction of random scores that were higher than the reported score. The collection contains 61 AML-related datasets (1.2%), which mostly score high in the ranked list (Additional file 10: Table S8). In particular, all of the five top datasets are related to AML (GEO accession numbers: GSE15434 , GSE16015 , GSE12417 , GSE21261 , and GSE30599 ; with 251, 107, 405, 96, and 29 samples, respectively). The coexpression scores are 0.31, 0.29, 0.28, 0.28, and 0.27, respectively; and the adjusted empirical p-values are smaller than 10−37 for each of these five datasets. A hypergeometric test confirmed that the coexpression of the queried genes is significantly associated with AML (p-value <10−9). Thereby, our unbiased and objective SEEK analysis indicates that these genes define an expression signature that is specific to AML.
Generalizability to studying other cancers
The described pipeline can also be applied to analyze other types of cancers and answer different biological questions. To demonstrate this, we applied our approach to a prognostic question in breast cancer research. Is it possible to identify low-risk breast cancer cases based solely on gene expression and thereby avoid overtreating a subset of patients who likely would not benefit from the additional toxic therapy ? In this type of prognostic setting, the emphasis lies on achieving very high specificity for predicted low-risk cases. For instance, the TRANSBIG Consortium  considers a test to be clinically practicable and reliable for ER+ breast cancer only if at least 88% of cases classified as low-risk have more than a 10-year overall survival. However, the only clinical test with such high precision is Oncotype DX, which is applicable to only one clinical subtype of breast cancer, stage I ER+ tumors . Unfortunately, this method cannot be generalized to other breast cancer subtypes .
Accuracy of predicting breast cancer risk
Predicted low risk
Predicted medium risk
Predicted high risk
The smaller module is associated with translational control (Additional file 1: Figure S12). The expression of the majority of the genes (122, 63%) is correlated with poor prognosis. Notable genes include AKT1, GSK3B, MTOR, RAF1, and SRC from the epidermal growth factor receptor (ErbB) signaling pathway . In contrast, the high expression of 71 genes (37%) in this module—including 16 ribosome-related genes such as RPL22, RPL26, RPS27, RPS27A, RPL13A, RPL21 and RPLP0—correlate with good prognosis. This may be predicted, as the loss of function or abnormal expression of proteins involved in ribosomal biogenesis is associated with activation of the tumor suppressor p53 pathway [55, 56]. A possible mechanism of p53 activation could be through binding free (non-ribosome-bound) ribosomal proteins with MDM2, which modulates the inhibitory activity of MDM2 on p53 .
None of the 193 genes from the smaller module is in common with PAM50. This suggests that the corresponding eigengene can be considered as a novel biological signature to assess breast cancer prognosis, and it can be a basis for improving clinical tests. Overall, our model is biologically plausible because regulated cell cycle and controlled translation are generally associated with better prognostic outcome .
Biological processes in a cell often require coordination between multiple genes and proteins, not just one gene or a single protein. Accordingly, we used network analysis to delineate the differences in gene expression profiles of AML and MDS in a systematic and robust way (Additional file 1: Note S1). We compared the expression at the module level to minimize the effect of artifacts such as a random change in expression of an isolated gene and other biological or technical noise (Additional file 1: Figure S10).
The results of our study underline the association of the extracellular matrix pathway with AML, and also confirm that the overexpression of homeobox genes is a biological characteristic of AML. These two signatures are biologically related . Homeobox genes encode transcription factors that regulate the development of body structures during the embryonic period . They also have key roles in adult tissue remodeling and pathogenesis . In particular, specific homeobox genes can regulate the extracellular matrix through the expression of matrix-degrading proteinases . For instance, the expression of the HOXA3 and HOXB3 are upregulated during wound healing to remodel the extracellular matrix and to increase endothelial cell migration . Overexpression of HOXA7, which is associated with poor prognosis of AML , can modify the interactions between hematopoietic progenitor cells and the extracellular matrix in the bone marrow. This alteration can be responsible for blocking the differentiation process in AML cells .
Our decision tree can accurately predict the diagnosis in a validating dataset (BCCA) without the need to change the parameters that were fitted to the training dataset (MILE). Our results confirm that the model was not overfitted to the training dataset.
SEEK analysis confirms that the genes in the extracellular matrix module are coexpressed in several other AML-related datasets.
The three MMP genes in the extracellular matrix, MMP9, MMP8, and MMP25, are methylated in AML.
Immunocytochemistry showed that MMP9 is underexpressed in AML at the protein level.
MMP9 is an important gene in our analysis, and it has a distinct expression profile between the two diseases. MMP9 acts as a cell surface transducer by cleaving the extracellular matrix and other proteins, including chemokines, cytokines, and growth factor receptors. In this way, it can regulate key signaling pathways in cell growth, migration, invasion, inflammation, and angiogenesis . While MMP9 was previously reported to have a critical role in AML invasion and metastasis [66–69], the relationship between its expression and the prognosis of hematological malignancies is complicated. For instance, Aref et al. report that 43 pretreatment AML cases had significantly lower expression of MMP9 as compared to 10 controls. However, after chemotherapy, MMP9 was expressed significantly higher in relapsed cases as compared to complete remission cases .
In this context, the high expression of MMP9 in MDS, which we showed is more than AML, is interesting. Correspondingly, Travaglino et al. measured MMP2 and MMP9 in myeloid cells of 143 MDS cases using immunocytochemistry. They found that high MMP levels are associated with longer overall survival . One possible interpretation is that by deregulating the extracellular matrix, MMP9 may interrupt the survival signalling in MDS and lead to apoptosis. In contrast, lowering MMP9 expression may prolong the life of the MDS cells and facilitate the transition into AML. MMP9 processing of the matrix may also have an impact on blast cell invasion, dissemination, and homing . However, functional studies will be needed to determine the mechanism and impact of MMP9 on myeloid cancers. A competing theory would be that the observed differences in the extracellular matrix activity might be due to differences in the underlying cell-types.
Our approach has novel methodological contributions to gene expression analysis. While other scholars have used weights (loadings) of eigengenes to study genes in a module , we are the first to use values of eigengenes directly as biological signatures. We developed an approach to infer and compare eigengenes across datasets. Our approach is fundamentally different from applying PCA directly on the entire expression profile, which is not a promising approach because the first few PCs may not have enough information on the modules’ structure .
An analysis based on a limited number of genes with the best p-value can be convoluted by random, dramatic expression changes due to biological or technical noise . In contrast, because an eigengene is a weighted average expression of several genes, our systematic and holistic approach is much more robust than the alternative approaches that select one or a few genes from each module [72, 73]. We show that our methodology is generalizable and useful in studying other malignancies by applying it to several breast cancer datasets.
Eigengenes are robust informative biological signatures. They are useful in predicting the diagnosis and prognosis, and also, in delineating the molecular characteristics of diseases. For instance, we used large-scale network analysis to show that underexpression of particular genes in the extracellular matrix pathway is a specific characteristic of AML.
The AML gene expression datasets
We downloaded the expression profiles of 202 AML-NK and 164 MDS cases from Gene Expression Omnibus (GEO) (series number GSE15061) , Additional file 12. The dataset is part of the expression MIcroarray analysis for diagnosis of LEukaemia (MILE) series. For simplicity, we refer to this expression profile as the MILE dataset, which was used to train our model. To validate our model and test the accuracy of classification, we used RNA-Seq data from 133 AML and 22 MDS cases analyzed at British Columbia Cancer Agency. For simplicity, we refer to this expression profile as the BCCA dataset, which is independent from the MILE dataset. From the 133 AML cases, 52 were AML-NK and thus were comparable with the 202 cases from the MILE dataset (Additional file 6: Table S5). We used Sailfish (version 0.6.3)  to compute reads per kilobase per million mapped reads (RPKM) values  for each gene, and considered the natural logarithm of RPKM to measure gene expression.
Breast cancer datasets
We used 640 ER+ cases from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC)  discovery dataset for training. We evaluated the resulting model on 533 different cases from the METABRIC validation dataset. We also validated the prognostic value of the inferred biological signatures using 201 cases from a second independent dataset produced by Miller et al.  (GEO accession number GSE3494). The details of our analysis on these three datasets is presented in Additional file 1: Note S4.
Detailed description of the Pigengene methodology
Preprocessing The input to the Pigengene methodology includes two gene expression profiles corresponding to two biological conditions (e.g., AML and MDS in this paper). Optionally, the user can provide a validating dataset (e.g., BCCA dataset). The train and validation datasets do not need to be assayed using the same platform. Thas is, one dataset can be microarray and the other one can be RNA-Seq. Figure 1 shows the main steps of the Pigengene methodology. More specificity, the first step of the analysis is to exclude the genes that have too little variation or negligible expression across the conditions. This can be done using a differential expressed analysis, which computes a p-value for each gene with the null hypothesis that it is similarly expressed in the two conditions. Consistent with the common approach in the gene network analysis [78, 79], we kept only the top one-third genes with the best p-values in our analysis.
Constructing the coexpression network: We used the WGCNA package to construct a coexpression gene network, in which each node (vertex) is a gene and the edge (connection) between two genes is weighted based on the correlation between their expression values (Additional file 1: Note S2). WGCNA uses a hierarchical clustering approach to identify gene modules from the coexpression network.
Computing eigengenes: We used principal component analysis (PCA) to compute an eigengene for each module. First, we balanced the number of AML and MDS cases using oversampling, so that both disease types had comparable representatives in the analysis. Specifically, we repeated the data of each AML and MDS case 9 and 11 times, and obtained 1818 and 1804 samples from each type, respectively. Then, we applied the moduleEigengenes() function from the WGCNA package on the oversampled data. We ran it with the default parameters, and computed an eigengene for each of the modules identified earlier. This function computed the first principal component of each module, which maximized the explained variance ensuring the loss in the biological information was minimized. [24, 29] (Additional file 5: Table S4).
Inferring the decision tree: We use eigengenes as features to infer a decision tree (R package C50 version 0.1.0-24) . While the C50 package uses a heuristic approach to select the best set of features, its default arguments does not result in optimal performance when too many features are provided. The solutions include: 1) using a Bayesian network to determine the relationships of the modules with each other and with the type of hematological malignancy (Additional file 1: Note S3) , 2) using a feature scoring algorithm such as FeaLect , and 3) adjusting the C50 parameters, for example, enforcing the number of samples in each node to be at least 10%. The first and the third solutions are implemented in the Pigengene package through the bnNum argument of the one.step.pigengene() function and the minPerLeaf argument of the make.decision.tree() function, respectively. These two approaches resulted in the same decision tree presented in this paper (Fig. 4).
Inferring the values of eigengenes in an independent dataset: When a validation dataset is available (i.e., the BCCA dataset in our study), the values of the eigengenes need to be inferred in the validation dataset. We computed eigengenes using the MILE dataset, which is a microarray dataset. It was challenging to compute the values of the same eigengenes for BCCA cases because the BCCA dataset was produced using a different platform (i.e., RNA-Seq) . The simple approach of applying PCA on the BCCA data would fail; It would result in different weights (loadings), and the eigengenes would not be comparable between the two datasets. Instead, we inferred the values of the eigengenes for BCCA cases using the same weights obtained from the MILE dataset. Specifically, for each module, we identified the genes that are common in both datasets. Then, we scaled the expression of those genes by subtracting their mean and dividing by their standard deviation. We used the scaled expression values to compute the eigengene (the weighted average of expression) for each BCCA case. The project.eigen() function from our Pigengene package facilitates this approach.
Reducing the number of genes needed for the decision tree: Our decision tree used the eigengenes of HOXA&B and extracellular matrix modules, which were weighted averages of the expression of 42 and 155 genes, respectively. To reduce the number of genes, we repeated the following greedy procedure : We excluded the gene with the lowest absolute weight, inferred the eigengenes using the remaining genes, and used the updated eigengenes as input to the decision tree. In each iteration, we used the same tree structure and thresholds, and we measured the accuracy of classification. We repeated this procedure until the tree needed only 14 genes, because excluding any more genes would result in a significant decline in the accuracy of the classification. The sufficiency of these 14 related genes indicates that they contain the core biological information needed for classification. The compact.tree() function from our Pigengene package facilitates this approach.
“Pigengene”, a documented R package that implements our approach, is publicly available through Bioconductor: http://bioconductor.org/packages/Pigengene. The results presented in this paper can be reproduced using version 0.99.19. To apply our methodology in other studies, we strongly recommend using the most recent version. We encourage users to use the Bioconductor mailing list to send bug reports and seek technical help.
Acute myeloid leukemia
- AML–NK AML:
With normal karyotype
British Columbia cancer agency
Gene expression omnibus
Gene ontology cellular component
Hematopoietic stem cells
Prognostic scoring system
Molecular taxonomy of breast cancer international consortium
Microarray innovations in leukemia
Principal component analysis
Reads per kilobase per million mapped reads
Search-based exploration of expression compendium
The cancer genome atlas
We would like to thank the GSC sequencing and library generation teams for sequencing and Vennie Chou for immunohistochemistry. We thank Qian Zhu for her assistance in running SEEK. We used DNA methylation data generated by the TCGA Research Network: http://cancergenome.nih.gov/. We acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing high-performance computing (HPC) resources: http://www.tacc.utexas.edu.
Work in the lab of AK was funded by grants from the Terry Fox Research Institute (122869), CIHR (MOP-133455, MOP-97744), and Genome BC (121AML). AK is supported by the John Auston BC Cancer Foundation Clinical Investigator award. Work in the lab of HZ was supported by an internal grant from Texas State University.
Availability of data and materials
All data and code required to reproduce the presented results are available, either through publicly available repositories or as supplementary materials. Specifically, the MILE leukemia and Miller breast cancer datasets are available from GEO with accession numbers GSE3494 and GSE15061, respectively, and the METABRIC dataset is available from the European Genome-phenome Archive with study accession number EGAS00000000083. DNA methylation data are available from TCGA (LAML dataset). Additionally, eigengene values for the MILE and BCCA datasets are available as supplementary materials. The Pigengene software package is available through Bioconductor.
AK and HZ conceived the experiments, AF, HZ, RA, and RD conducted the experiments, LC, GD and MH acquired data, and AK analyzed the results. All authors reviewed the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Not applicable because no sequencing data or identifying information is being published.
Ethics approval and consent to participate
This study was approved by the University of British Columbia (UBC) BC Cancer Agency Research Ethics Board (UBC-BCCA REB) under protocol H13-02687 “Genomic analysis of molecular changes in myeloid malignancy”. The informed consent of participants was provided prior to specimen acquisition under the guidelines of the Leukemia/Bone Marrow Transplant Program at Vancouver General Hospital, as approved by the UBC-BCCA REB (protocol H04-61292). For historically-banked anonymized specimens (Legacy cell bank specimens), a waiver of consent was provided by the UBC-BCCA REB (protocol H09-01779). This protocol states: Genomic data obtained from these samples may be posted on access restricted sites as required for publications. This is covered by transfer contracts governed by our Technology Development Office. Transfer of material outside of the institution would also be covered by MTAs.
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