Volume 10 Supplement 1

Selected articles from the 6th Translational Bioinformatics Conference (TBC 2016): medical genomics

Open Access

Identifying subtype-specific associations between gene expression and DNA methylation profiles in breast cancer

BMC Medical GenomicsBMC series – open, inclusive and trusted201710(Suppl 1):28

https://doi.org/10.1186/s12920-017-0268-z

Published: 24 May 2017

Abstract

Background

Breast cancer is a complex disease in which different genomic patterns exists depending on different subtypes. Recent researches present that multiple subtypes of breast cancer occur at different rates, and play a crucial role in planning treatment. To better understand underlying biological mechanisms on breast cancer subtypes, investigating the specific gene regulatory system via different subtypes is desirable.

Methods

Gene expression, as an intermediate phenotype, is estimated based on methylation profiles to identify the impact of epigenomic features on transcriptomic changes in breast cancer. We propose a kernel weighted l1-regularized regression model to incorporate tumor subtype information and further reveal gene regulations affected by different breast cancer subtypes. For the proper control of subtype-specific estimation, samples from different breast cancer subtype are learned at different rate based on target estimates. Kolmogorov Smirnov test is conducted to determine learning rate of each sample from different subtype.

Results

It is observed that genes that might be sensitive to breast cancer subtype show prediction improvement when estimated using our proposed method. Comparing to a standard method, overall performance is also enhanced by incorporating tumor subtypes. In addition, we identified subtype-specific network structures based on the associations between gene expression and DNA methylation.

Conclusions

In this study, kernel weighted lasso model is proposed for identifying subtype-specific associations between gene expressions and DNA methylation profiles. Identification of subtype-specific gene expression associated with epigenomic changes might be helpful for better planning treatment and developing new therapies.

Background

Altered gene expression that regulates cell growth and differentiation is a major component to transform normal cell into a cancer cell [1]. Expression of tumor suppressor genes or oncogenes affects many proteins that are turned on or off via gene silencing or gene activation, further inhibiting cell division and development and promoting the malignant phenotype of cancer cells, respectively [1]. In addition, other types of genomic data, including somatic mutations, copy number alterations (CNA), DNA methylation, or miRNA expression, are associated with cancer [25]. However, there are still huge gaps between genomic/epigenomic data and cancer as a phenotypic end-point to fully understand the complex mechanisms of cancer. Thus, transcriptomic changes could serve as a proxy to capture phenotypic variations in human cancer as an intermediate phenotype [68]. To identify genomics changes that are associated with functional changes in cancer, there have been many integrative analyses between genomic data and transcriptomic data. Many expression quantitative trait loci (eQTL) studies in cancer have been conducted to identify genomic variations that could explain the variance of the expression traits [9, 10]. In addition, associations between CNA data as a structural change and gene expression data were investigated to search genes associated with gene dosage in cancer [11, 12].

DNA methylation is one of the major mechanisms of epigenetic regulation that promotes or inhibits cancer related genes [13]. Cytosine methylation of CpG islands, which is the most common type of DNA methylation, occurs genome-wide in protein coding regions, including promoters, 5’ and 3’-UTRs, or exons, as well as in the intergenic regions [13]. CpG methylations are likely to occur in promoter regions located close to the start of transcription, and hypermethylation in the promoter regions is negatively associated with the transcript level [14]. For example, the hypermethylation of tumor suppressor genes, which is associated with their inhibition of transcription, is recognized as one of the key features of cancer pathogenesis [13]. On the contrary, CpG methylations in gene body regions are likely to be positively associated with transcript level [14]. To search relationships between epigenetic changes and transcriptomic changes in cancer, many integrative studies have been reported [1518]. Recently, numerous prediction models using machine learning to estimate the consequence of epigenetic changes on gene expression have been developed [1921]. In the previous study from Karlic et al [20], it reveals that predicting gene expression levels based on histone modifications is applicable. In addition, Cheng et al [21] has improved overall prediction performance of estimating gene expression levels. However, cancer is an extremely heterogeneous disease. Each cancer has many distinct subtypes and there are different genomic patterns based on different subtypes in cancer. Thus, there is a need to investigate subtype-specific epigenetic regulation mechanism in cancer.

In this study, we propose a novel method that incorporates subtype information to better explain gene expression variability based on methylation profiles. Inference of subtype-specific association patterns between gene expressions and DNA methylation features is challenging because the number of available samples in each subtype may not be large enough to produce reliable estimations. Therefore, separate estimation of association patterns on each subtype is not typically feasible. We address this issue by employing a kernel weighted lasso model that can incorporate information from samples in different subtypes while allowing subtype-specific estimations. As illustrated in Fig. 1, our proposed method requires two types of input: covariate matrix as commonly used in linear regression, and prior knowledge for differentiating observations. For the proper use of prior knowledge, a weighted kernel method is applied to be mixed with independent variables. Finally, the weighted lasso framework provides subtype-specific estimation method for gene expression level. To test the utility of the proposed method, we applied it to a breast cancer data set from The Cancer Genome Atlas (TCGA). TCGA has provided unprecedented opportunities to better understand the genetic architecture of cancers through integrating multi-omics data [7, 2230]. In particular, breast cancer has well-known distinct subtypes, including luminal A, luminal B, HER2 positive, and triple negative or basal-like type. Depending on subtypes in breast cancer, treatment and therapy approaches are different. Thus, identification of subtype-specific gene expressions associated with epigenetic changes might be useful for better planning treatment and developing new therapies.
Fig. 1

Overview of the proposed framework for identifying subtype-specific association patterns. For target gene estimation, our weighted lasso framework requires a covariate matrix and subtype information on samples. Note that four colors in Breast Cancer Subtype field correspond to subtypes, Luminal A, Luminal B, HER2 positive, and Triple Negative, respectively. With two inputs mixed from a kernel method, target genes in each of the subtypes are estimated based on DNA methylation features

Methods

Dataset

DNA methylation and gene expression data of 437 patients in breast cancer were obtained from TCGA (https://gdc.nci.nih.gov/). Gene expression data from RNA-seq consisted of 20,502 unique genes with upper quartile normalized RNA-Seq by Expectation-Maximization (RSEM) count estimates [31]. DNA methylation data was retrieved as a gene-level feature by choosing the probe least correlated with gene expression when genes were mapped with multiple methylation probes, from 485,577 methylation probes to 19,943 [25]. Numerical data were normalized by log (T + 1) where T was DNA methylation or gene expression level. Since the size of features and target genes to estimate was too large, part of them were filtered out. First, genes that were not members in any KEGG pathways were removed. This implies that we used genes that are known to be involved in certain molecular processes. Second, we removed trivial genes for which more than half of patient records were zero due to the unrecorded elements or experimental failures to measure expression level. Finally, we had 4,237 DNA methylation genes as features, and 4,062 genes for target gene estimation. Along with numerical data, breast cancer subtype information of all patients was also provided. The 437 observations are divided into four subtypes as shown in Table 1.
Table 1

Number of samples per subtype

HER2 Positive

Luminal A

Luminal B

Triple Negative

Total

16

306

42

73

437

Background on L 1-regularized linear regression

Suppose we have data (x i , y i ) for i = 1, 2, …, n, where x i  = (x i1 , …, x ip ) T p is a feature vector and y i is response for the i -th observation. In a linear regression model to predict the response based on the features, the ordinary least squares (OLS) estimates for the regression coefficients β p are obtained by minimizing residual squared error as follows.
$$ \boldsymbol{\beta} =\boldsymbol{argmi}{\boldsymbol{n}}_{\boldsymbol{\beta}}\ {\left(\boldsymbol{y}-\boldsymbol{X}\boldsymbol{\beta } \right)}^{\boldsymbol{T}}\left(\boldsymbol{y}-\boldsymbol{X}\boldsymbol{\beta } \right) $$
where X n × p is the covariate matrix for features, and y = (y 1, …, y n ) T . However, OLS estimates often have low bias but large variance; prediction accuracy can sometimes be improved by setting to 0 some coefficients [32]. Also, among a large number of predictors, determining a smaller subset of features that exhibits the strongest effects is more desirable. To satisfy the requirement, L 1 -regularized linear regression model, which is called lasso was proposed [32]. The lasso estimates are defined as:
$$ \boldsymbol{\beta} =\boldsymbol{argmi}{\boldsymbol{n}}_{\boldsymbol{\beta}}{\left(\boldsymbol{y}-\boldsymbol{X}\boldsymbol{\beta } \right)}^{\boldsymbol{T}}\left(\boldsymbol{y}-\boldsymbol{X}\boldsymbol{\beta } \right)+\boldsymbol{\lambda} \parallel \boldsymbol{\beta} {\parallel}_1 $$
where λ is a parameter for regulating the number of non-zero entries in the estimated β, and hence the sparsity of the coefficients. The parameter λ is typically determined through cross-validation. For the selection of a small number of effective features, L 1 -regulurized linear regression is known to be efficient.

Kernel weighted lasso for subtype-specific association network estimation

Gene expression, as an intermediate phenotype, is estimated based on DNA methylation profiles to identify the impact of epigenome on transcriptome in breast cancer. For understanding genomic mechanisms resulted from various breast cancer subtypes, we use weighted lasso with some modification in which subtype information of patients is incorporated using kernel-based weighting method. We concentrate on utilizing samples from various types of data. Especially in terms of small sample size problem, which is frequently encountered in the field of computational biology, our proposed method is exploited to enlarge the sample size by employing different types of samples. For example, samples resulted from a variety of breast cancer subtypes such as Luminal A, Luminal B, and Triple negative can be used in estimating a target gene whose subtype is HER2 positive.

As a response vector, \( {\boldsymbol{y}}^{\boldsymbol{g}}\in {\mathrm{\mathbb{R}}}^{{\boldsymbol{n}}_{\boldsymbol{s}}} \) denotes gene expression level of target gene g, where n s is the number of samples whose subtype is s. The covariate matrix \( {\boldsymbol{X}}_{\boldsymbol{s}}^{\boldsymbol{g}}\in {\mathrm{\mathbb{R}}}^{{\boldsymbol{n}}_{\boldsymbol{s}}\times {\boldsymbol{p}}^{\boldsymbol{g}}} \) is DNA methylation profile from samples whose subtype is s, where p g is the number of features for estimating target gene g. Note that the feature matrix X s g is changed over target genes, because for each target gene, we select DNA methylation features that are more likely to affect the target gene based on prior knowledge. Specifically, only DNA methylation genes that are extracted from those KEGG pathways where the target gene belongs to are selected for estimation. Finally, with modified lasso model, our proposed method for estimating the coefficients \( {\boldsymbol{\beta}}_{{\boldsymbol{s}}^{*}}^{\boldsymbol{g}} \) for subtype s * is defined as:
$$ {\boldsymbol{\beta}}_{{\boldsymbol{s}}^{*}}^{\boldsymbol{g}}=\boldsymbol{argmi}{\boldsymbol{n}}_{{\boldsymbol{\beta}}_{{\boldsymbol{s}}^{*}}^{\boldsymbol{g}}}{\displaystyle \sum_{\boldsymbol{s}}}{\boldsymbol{w}}_{{\boldsymbol{s}}^{*}}^{\boldsymbol{g}}\left(\boldsymbol{s}\right){\left({\boldsymbol{y}}_{\boldsymbol{s}}^{\boldsymbol{g}}-{\boldsymbol{X}}_{\boldsymbol{s}}^{\boldsymbol{g}}{\boldsymbol{\beta}}_{{\boldsymbol{s}}^{*}}^{\boldsymbol{g}}\right)}^{\boldsymbol{T}}\left({\boldsymbol{y}}_{\boldsymbol{s}}^{\boldsymbol{g}}-{\boldsymbol{X}}_{\boldsymbol{s}}^{\boldsymbol{g}}{\boldsymbol{\beta}}_{{\boldsymbol{s}}^{*}}^{\boldsymbol{g}}\right)+\boldsymbol{\lambda} \parallel {\boldsymbol{\beta}}_{{\boldsymbol{s}}^{*}}^{\boldsymbol{g}}{\parallel}_1 $$

Here, the weight \( {w}_{s^{*}}^g(s) \) for samples in subtype s when we estimate the coefficients of gene g in subtype s * is defined as K h (dist(s, s*)) where K h is a symmetric kernel function, h is the kernel bandwidth, and dist(s, s*) is a distance between samples from subtype s and s*. Note that the entire samples from all the subtypes are used for estimation of \( {\boldsymbol{\beta}}_{{\boldsymbol{s}}^{*}}^{\boldsymbol{g}} \) including samples from subtype s* but with different contribution to the final estimation. For the proper control of subtype-specific estimation, samples are learned at different rate based on target estimates.

The challenging problem is to set the geographical distance between heterogeneous samples to be applied in forms of kernel. We assumed that given two observations have different distribution over DNA methylation genes in which expression level is affected by subtype-specific molecular process. From the fact that two samples are not originated from the same distribution, the distance between them can be measured by conducting Kolmogorov Smirnov (K-S) test. K-S test is used to decide if given two samples come from a population with a specific distribution. The advantage of K-S test is that the distribution of the K-S test statistic itself does not depend on the underlying cumulative distribution function being tested. Taking advantage of this fact, it is intuitive to set the critical value as distance between two samples. Finally, kernel weighting is applied to weighted lasso regression based on the distance. Radical Basis Function (RBF) kernel of K h (d) = exp(−d 2/h) is used to give different weights to each observation based on their distance [33]. That is, \( {w}_{s^{*}}(s) \) is defined as exp(−distance 2/h) where distance is the critical value resulted from K-S test between samples from subtype s and s*, and h is the kernel bandwidth that is tuned through cross-validation.

Results

Prediction of gene expression level based on methylation profiles

As described in Methods, a covariate matrix X s g to estimate a target gene g is built by picking up methylation features from KEGG pathways that the target gene belongs to. The number of selected features p g varied across target genes, which is around 200 ~ 300 on average, 10 at minimum, and 1698 at maximum. Figure 2 shows the density plot for the number of features to predict target genes.
Fig. 2

Density plot for the number of DNA methylation features across all target genes. The number of methylation features ranges from 10 to 1698. For most of target genes, around 200 ~ 300 number of features are used for estimation

One of the advantages of our proposed method is that different sets of well-estimated genes having little prediction error can be obtained from subtype-specific estimation. It is observed that genes that might be sensitive to breast cancer subtype show prediction improvements when estimated using kernel weighted lasso. For validation of subtype-specific estimation over target genes, we pick up top 10 well-estimated genes over entire target genes as shown in the column Overall in Table 2, and then pick up top 10 better-estimated genes that have smaller prediction error in our proposed method than in the plain [34] lasso framework. We observe that most of the genes shown in four different subtypes do not appear in Overall. It means our proposed method is capable of recovering genes affected by breast cancer subtype that plain lasso cannot detect.
Table 2

Top 10 well-estimated gene list

Overall

HER2 positive

Luminal A

Luminal B

Triple negative

TRA2B

MMP1

PSMD3

PSMD3

ABCC12

HNRNPK

SPDYC

GPD1

CDC6

SLC18A2

RAB5B

ERBB2

ERBB2

RPL19

IVD

HNRNPK.1

ELOVL2

UGT1A6

ERBB2

ABCA12

HNRNPK.2

SERPINA5

BMPR1B

CACNG6

DNALI1

SEC11A

NPY1R

CTSE

PCK1

DEGS2

SF3A1

SEMA3E

CCL21

PSMB3

UCHL1

SRP14

AKR1B10

TAT

PIP4K2B

NEIL1

CDC42

UGT8

RPL19

CALML3

MAGOH

NRF1

EPO

ATP6V0A4

ABCC12

HGD

Genes having the smallest prediction error over all target genes are shown in column Overall, and genes that show prediction improvement when estimated using kernel weighted lasso over the plain lasso are shown for each subtype in the remaining columns

Furthermore, we examine pathway-based prediction performance over subtypes to identify the impact of our proposed method on pathway analysis. The performance on a pathway is measured by summing up error rates of target genes that belong to the pathway. In Table 3, 20 well-estimated KEGG pathways over entire subtypes are listed. And Table 4 represents top 10 pathways better estimated than the one without subtype information. We observe that commonly well-estimated pathways in Table 3 are not seen in Table 4. As discussed in [35], ERBB2 gene amplification and overexpression of the ERBB2 tyrosine kinase receptor is shown in breast cancer. [34] observed the upregulation of NPY1R is associated with ER+ breast cancer. Also, UCHL1 expression in invasive ductal carcinomas significantly correlated with the triple negative phenotype [36]. Previous researches show more than 5 genes at subtype columns are known to affect breast cancer subtype directly or indirectly. Especially genes in Triple negative are associated with breast cancer subtype progression.
Table 3

Top 20 well-estimated KEGG pathways

Overall

SPLICEOSOME

SNARE_INTERACTIONS_IN_VESICULAR_TRANSPORT

PROTEIN_EXPORT

VALINE_LEUCINE_AND_ISOLEUCINE_BIOSYNTHESIS

AMINOACYL_TRNA_BIOSYNTHESIS

UBIQUITIN_MEDIATED_PROTEOLYSIS

NON_HOMOLOGOUS_END_JOINING

DNA_REPLICATION

RNA_DEGRADATION

REGULATION_OF_AUTOPHAGY

NUCLEOTIDE_EXCISION_REPAIR

RENAL_CELL_CARCINOMA

BASAL_TRANSCRIPTION_FACTORS

GLYOXYLATE_AND_DICARBOXYLATE_METABOLISM

MISMATCH_REPAIR

OXIDATIVE_PHOSPHORYLATION

RNA_POLYMERASE

NOTCH_SIGNALING_PATHWAY

PROTEASOME

PARKINSONS_DISEASE

Table 4

Top 10 better-estimated KEGG pathways per subtype

HER2 positive

Triple negative

GLYCOLYSIS_GLUCONEOGENESIS

GLYCOLYSIS_GLUCONEOGENESIS

CITRATE_CYCLE_TCA_CYCLE

CITRATE_CYCLE_TCA_CYCLE

PENTOSE_PHOSPHATE_PATHWAY

PENTOSE_PHOSPHATE_PATHWAY

FRUCTOSE_AND_MANNOSE_METABOLISM

PENTOSE_AND_GLUCURONATE_INTERCONVERSIONS

GALACTOSE_METABOLISM

FRUCTOSE_AND_MANNOSE_METABOLISM

FATTY_ACID_METABOLISM

GALACTOSE_METABOLISM

STEROID_BIOSYNTHESIS

ASCORBATE_AND_ALDARATE_METABOLISM

PRIMARY_BILE_ACID_BIOSYNTHESIS

FATTY_ACID_METABOLISM

OXIDATIVE_PHOSPHORYLATION

STEROID_BIOSYNTHESIS

PURINE_METABOLISM

PRIMARY_BILE_ACID_BIOSYNTHESIS

Luminal A

Luminal B

GLYCOLYSIS_GLUCONEOGENESIS

GLYCOLYSIS_GLUCONEOGENESIS

PENTOSE_PHOSPHATE_PATHWAY

CITRATE_CYCLE_TCA_CYCLE

PENTOSE_AND_GLUCURONATE_INTERCONVERSIONS

PENTOSE_PHOSPHATE_PATHWAY

FRUCTOSE_AND_MANNOSE_METABOLISM

PENTOSE_AND_GLUCURONATE_INTERCONVERSIONS

GALACTOSE_METABOLISM

FRUCTOSE_AND_MANNOSE_METABOLISM

ASCORBATE_AND_ALDARATE_METABOLISM

GALACTOSE_METABOLISM

FATTY_ACID_METABOLISM

ASCORBATE_AND_ALDARATE_METABOLISM

STEROID_BIOSYNTHESIS

FATTY_ACID_METABOLISM

PRIMARY_BILE_ACID_BIOSYNTHESIS

STEROID_BIOSYNTHESIS

STEROID_HORMONE_BIOSYNTHESIS

PRIMARY_BILE_ACID_BIOSYNTHESIS

Subtype-specific prediction performance

Next, we compare the subtype-specific prediction performance of the proposed method with two baseline approaches: one in which each subtype data are learned separately with plain lasso framework, and the other for entire data learned equally without weighting using plain lasso. Figure 3 represents Root Mean Squared Error (RMSE) from 5-fold cross validation, resulted from each approach over entire target genes. Note that dotted horizontal line is the mean of error rates over entire genes estimated by plain lasso without kernel weighting. As seen in Fig. 3, our proposed method shows substantial prediction improvement over separate estimation approach. Especially in case of HER2 positive subtype that has the smallest number of samples of 16, our kernel-weighted approach outperforms separate estimation the most significantly. This result is as expected because our proposed method can effectively enlarge the sample size by incorporating information in samples from different subtypes. On the other hand, the largest subtype Luminal A with 307 samples does not show much performance improvement because the number of samples is already large enough for estimation. We find that the overall accuracy of our proposed method is comparable to the one for estimating a single common network (gray bars in Fig. 3) that does not produce subtype-specific association networks.
Fig. 3

Subtype-specific Root Mean Squared Error from 5-fold cross validation. Each bar represents the average prediction error obtained from the proposed method (red), separate estimation that uses only the corresponding subtype data (yellow), and a single common estimation ignoring the subtype information (gray). Our proposed method shows significantly improved performance over the separate estimation approach, and slightly better or comparable performance over single common estimation

Subtype-specific association network

We construct subtype-specific association networks by using the regression coefficients estimated by the proposed method. The node represents either methylation feature or gene expression feature and the edge represents the subtype-specific association. That is, if node A is associated with node B under specific subtype having non-zero coefficients, the edge is drawn. Figure 4 illustrates the resulting association network between DNA methylation and gene expression genes. The total number of edges in each subtype network is 31289, 31306, 31515, and 31385 for HER2 positive, Luminal A, Luminal B, and Triple negative, respectively, among which 29571 number of edges (88.82%) are common across all the subtypes as shown in the Venn diagram of Fig. 4 (gray region). To look into only subtype-specific edges in the network, common edges in at least two or more subtypes are not shown. The hub genes, which have a large number of associated genes are represented as bigger-sized nodes. The four types of subtype-specific edges are marked with the color of each region in the Venn diagram of Fig. 4. Among 4,061 genes, 2,063 subtype-specific features and 1,502 number of association are observed. The numbers of subtype-specific edges are 256, 326, 864, and 56 for HER2 positive, Luminal A, Luminal B, and Triple negative, respectively.
Fig. 4

Subtype-specific association networks between DNA methylation and gene expression, and Venn diagram for the number of edges in the network. An edge in a subtype-specific association network is drawn if methylation node A and gene expression node B have non-zero a regression coefficient resulted from kernel weighted lasso. The edges are colored based on their subtype-specific association. Venn diagram represents the number of edges occurring in each association network where intersection region stands for the number of edges appearing in more than two networks

We found that several hub methylation features in our subtype-specific association network are known to be involved in breast cancer progression. For example, LEP, the top hub methylation feature affecting the largest number of gene expressions with total degree of 9, is found to be associated with basal-like or luminal A breast cancer subtypes. Another example includes FGFR3 and FGFR4 that are known to be associated with breast cancer as revealed in [37]. Table 5 summarizes the top 5 hub methylation features and their subtype-specific degrees along with the supporting literature for the relevance of each feature in breast cancer.
Table 5

Top 5 hub methylation features in subtype-specific association network and their degrees

Gene Name

Total

HER2 positive

Luminal A

Luminal B

Triple negative

Literature

LEP

9

2

1

6

0

[63, 64]

RET

6

2

1

3

0

[65, 66]

FGFR3

6

1

0

5

0

[37, 67]

PLA2G2A

6

1

3

2

0

 

ADCY5

6

2

0

4

0

 

For each methylation node, the total number of connected edges that are present over four subtype-specific association networks is shown in the column Total. Remaining columns represent the degrees in the corresponding subtype-specific association network

Discussion

The proposed kernel weighted model allows subtype-specific prediction of gene expressions based on methylation data along with discovery of subtype-specific association patterns between them even when the number of samples per subtype is substantially small. The reduction in error across the subtype given by the model was the starkest in genes coding for GTPases, transcription factors, and splicing factors, and nucleic acid binding proteins. Given our model’s incorporation of factors at the transcriptome-epigenome level, incorporating such epigenetic signals into the model improved subtype prediction and recapitulates the importance of RNA processing mechanisms, transcription factors, and metabolic processes in determining subtype beyond the genomic level.

The RMSE over all subtypes using the proposed prediction model was lowest for genes coding for transcription factors, GTPases, and nucleic-acid binding proteins: TRA2B, HNRNPK, RAB5B, SEC11A, SF3A1, SRP14, CDC42, and NRF showed the lowest RMSE over all breast cancer subtypes. This is consistent with the fact that our kernel-weighted model incorporates epigenomic information and proof of concept of the potential of the incorporating previously-overlooked epigenomic information in cancer subtype classification. HnRNP K showed the second lowest prediction error over all subtypes in the kernel-weighted model; HnRNP K is a multifunctional protein that binds the TATA-box [38] and is associated with both oncogenic and tumor-suppressor pathways [39] by interacting with many kinases including ncRNAs to control the expression of target genes [40]. TRA2B, SF3A1, and NRF1 were splicing factors that showed significant improvement in subtype prediction when epigenomic data were incorporated. TRA2B showed the lowest prediction error over all subtypes and had previously been shown to be specifically induced in breast cancer, and more induced in invasive breast cancers compared to non-invasive breast cancers, perhaps by splicing CD44 isoforms [41]. When both TRA2A and TRA2B are eliminated, expression of full-length CHK1 protein is reduced [42]. Polymorphisms in SF3A1 have been found to be associated with slightly higher colorectal cancer risk [43] and breast cancer [44]. Lastly, NRF1, a splicing factor was shown to be part of a redox signaling pathway where PTEN and CDC25A were modified by reactive oxygen species, leading to activation of NRF1 and estrogen-induced growth of breast cancer cells [45] and NRF1 was previously included in a Bayesian model of transcription factors involved in estrogen receptor alpha (ER-a). In breast cancer cells with acquired resistance to tamoxifen, the ER-a network (of which NRF1 is a component) lost responsiveness to 17-beta-estradiol; this loss of responsiveness was mediated by epigenomic changes [46]. This indicates the fundamental importance of epigenomics in modifying the transcription and translation of multi-functional proteins and genes involved in the induction of an oncogenic phenotype.

The weighted estimation model also showed marked improvement in marking the influence of GTPases in accurately predicting breast cancer subtype. Two small GTPases, CDC42 (Rho) and RAB5B (Ras) were among the ten genes with smallest RMSE across all subtypes. CDC42 is a locally excitable GTPase which steers cells during chemotaxis [47] and induces the extension of filopodia [48]. In the developing mammary gland, overexpression of CDC42 induces hyperbranching, increased stromal thickness and collagen deposition, and elevated mRNA expression of extracellular matrix proteins in stromal cells [49]. MiR-1 binding with CDC42 (mediated by MALAT1) induced migration and invasion of breast cancer cells [50] and CDC42 activity has been implicated in the invasive phenotype [51]. CDC42 is overexpressed in a variety of tumor types and is activated by oncogenic Ras protein to instigate Ras-mediated tumorigenesis in colon cancer [52]. Another GTPase that showed improvement in predictivity after incorporating epigenetic modification was RAB5B, a Ras GTPase that participates in the early stages of endocytosis. The early endosomal autoantigen EEA1 was found in a yeast two-hybrid system to interact directly with RAB5B in a GTP-dependent matter, independent of intrinsic GTPase activity [53]; in tumor cells, exosomes tended to localize with EEA1 [54]. Suppression of RAB5A and RAB5B hampered the degradation of EGFR, an epidermal growth factor receptor [55]. RAB5B specifically interacts with LRRK2 (mutations in which are associated with autosomal-dominant Parkinson’s disease) and can rescue synaptic vesicle endocytosis defect induced by LRRK2 knockout [56]. Administration of paclitaxel at 60 ng/mL in breast cancer cells caused significant increase in the expression of the RAB family of genes in comparison to the control group. RAB5B with lost GTPase function in lymphocytes caused the formation of abnormal, giant hybrid organelles which showed changed morphology over time [57]. The influence of epigenomic data recapitulates the importance of incorporating multi-omics data when constructing complex disease models, subtypes, and classifications.

The network illustration (Fig. 4) implicated multiple levels and mechanisms by which epigenetic features impact subtype classification, especially on the histone, nucleosome, and cellular differentiation levels. HIST2H2AA4 is a variant of histone 2A (specifically, type 2-A) that is implicated in histone core octamer stabilization; Histone 2A forms a dimer with Histone 2B, and then forms a tetramer with the H3-H4 dimer [58]. It was found that HIST2H2AA4’s interaction with various linker histones, especially variants of H1. Among core histones, histone H2A has by far the maximum number of variants (totaling 19). The exact role of HIST2H2AA4 in the breast cancer phenotype merits additional investigation given that it was previously implicated in a study of genes that statistically distinguish the hyperthermic response of three breast cancer lines compared to normal mammary epithelial cells [59]. The interaction between an element of Collagen VI (COL6A6) and serine-threonine protein kinase AKT1 was also found to be meaningful in a search for significant networks that included epigenetic data. AKT1 encodes a serine-threonine protein kinase which is activated by platelet-derived growth factor which has been implicated in many cancers, with the highest incidence in breast cancer [60]. A subset of breast cancer specimens was found to only contain AKT1 as a driver alteration, although AKT1-mutants were also often found to contain mutations in other driver genes [61]. Down-regulation of the Collagen VI extracellular matrix by AKT1 and upregulation of MMP1 was found in human dermal fibrolasts [62]; our model incorporating epigenetic control also reduced error in MMP1 the most when predicting a HER2 positive subtype (Table 2).

In terms of the model accuracy for predicting the gene expression level, our proposed methodology shows performance improvement only to part of target genes, that is, the kernel weighted method does enhance the prediction accuracy for entire target genes. As shown in Fig. 3, large improvement over single common estimation in terms of prediction accuracy is not observed. That is because genes that are not sensitive to breast cancer subtype may not benefit much from the proposed method.

Conclusions

In this study, we proposed a novel method for identifying subtype-specific gene expressions based on DNA methylation profiles. To make it possible to provide subtype-specific association network, kernel weighted lasso model is applied in which breast cancer subtype information is employed in forms of kernel. We found our proposed method is able to discover subtype-sensitive genes that plain lasso framework could not detect (Table 2). Especially for the subtype with small sample size, it outperforms the separate estimation method substantially. Furthermore, our framework provides a subtype-specific network, which represents genomic association underlying breast cancer subtypes. From the perspective of observations, we assumed samples from different subtypes come from different distribution. The distance between samples from different subtypes are set based only on their distribution. Thus, for our future work, well-designed kernel that appropriately reflects association exerted between samples will enhance the performance, and can reveal the relationship between samples.

Declarations

Acknowledgements

We gratefully acknowledge the TCGA Consortium and all its members for the TCGA Project initiative, for providing sample, tissues, data processing and making data and results available. The results published here are in whole or part based upon data generated by The Cancer Genome Atlas pilot project established by the NCI and NHGRI. Information about TCGA and the investigators and institutions that constitute the TCGA research network can be found at http://cancergenome.nih.gov.

Funding

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT, and Future Planning (MSIP) [2016R1D1A1B03933875, 2015K2A1A2070917]. This project is also funded, in part, under a grant with the Pennsylvania Department of Health (#SAP 4100070267). The Department specifically disclaims responsibility for any analyses, interpretations or conclusions. This work was also funded by NIGMS grant P50GM115318. Publication charge of this article has been funded by NRF [2016R1D1A1B03933875] and Ajou University.

Availability of data and materials

The TCGA datasets used for analysis are publicly available at https://gdc.nci.nih.gov/.

Authors’ contributions

GL, KS, and DK designed and developed the research, and also formed the experiments. KS and DK provided experienced guidance. LB and SK collected medical literature and made up supporting materials to infer the results. GL, KS, DK, and LB wrote the manuscript and all authors read the manuscript and approved it.

Competing interest

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

About this supplement

This article has been published as part of BMC Medical Genomics Volume 10 Supplement 1, 2017: Selected articles from the 6th Translational Bioinformatics Conference (TBC 2016): medical genomics. The full contents of the supplement are available online at https://bmcmedgenomics.biomedcentral.com/articles/supplements/volume-10-supplement-1.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Software and Computer Engineering, Ajou University
(2)
Biomedical & Translational Informatics Institute, Geisinger Health System
(3)
The Huck Institute of the Life Sciences, Pennsylvania State University

References

  1. Croce CM. Oncogenes and cancer. N Engl J Med. 2008;358(5):502–11.View ArticlePubMedGoogle Scholar
  2. Davis-Dusenbery BN, Hata A. MicroRNA in Cancer: The Involvement of Aberrant MicroRNA Biogenesis Regulatory Pathways. Genes & cancer. 2010;1(11):1100–14.View ArticleGoogle Scholar
  3. Noushmehr H, Weisenberger DJ, Diefes K, Phillips HS, Pujara K, Berman BP, Pan F, Pelloski CE, Sulman EP, Bhat KP, et al. Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell. 2010;17(5):510–22.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Beroukhim R, Mermel CH, Porter D, Wei G, Raychaudhuri S, Donovan J, Barretina J, Boehm JS, Dobson J, Urashima M, et al. The landscape of somatic copy-number alteration across human cancers. Nature. 2010;463(7283):899–905.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Brennan CW, Verhaak RG, McKenna A, Campos B, Noushmehr H, Salama SR, Zheng S, Chakravarty D, Sanborn JZ, Berman SH, et al. The somatic genomic landscape of glioblastoma. Cell. 2013;155(2):462–77.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Stranger BE, Forrest MS, Dunning M, Ingle CE, Beazley C, Thorne N, Redon R, Bird CP, de Grassi A, Lee C, et al. Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science. 2007;315(5813):848–53.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Sohn KA, Kim D, Lim J, Kim JH. Relative impact of multi-layered genomic data on gene expression phenotypes in serous ovarian tumors. BMC Syst Biol. 2013;7 Suppl 6:S9.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Rosenwald A, Alizadeh AA, Widhopf G, Simon R, Davis RE, Yu X, Yang L, Pickeral OK, Rassenti LZ, Powell J, et al. Relation of gene expression phenotype to immunoglobulin mutation genotype in B cell chronic lymphocytic leukemia. J Exp Med. 2001;194(11):1639–47.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Whitington T, Gao P, Song W, Ross-Adams H, Lamb AD, Yang Y, Svezia I, Klevebring D, Mills IG, Karlsson R, et al. Gene regulatory mechanisms underpinning prostate cancer susceptibility. Nat Genet. 2016;48(4):387–97.View ArticlePubMedGoogle Scholar
  10. Li Q, Seo JH, Stranger B, McKenna A, Pe’er I, Laframboise T, Brown M, Tyekucheva S, Freedman ML. Integrative eQTL-based analyses reveal the biology of breast cancer risk loci. Cell. 2013;152(3):633–41.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Iranmanesh SM, Guo NL. Integrated DNA Copy Number and Gene Expression Regulatory Network Analysis of Non-small Cell Lung Cancer Metastasis. Cancer Inform. 2014;13 Suppl 5:13–23.PubMedPubMed CentralGoogle Scholar
  12. Pollack JR, Sorlie T, Perou CM, Rees CA, Jeffrey SS, Lonning PE, Tibshirani R, Botstein D, Borresen-Dale AL, Brown PO. Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors. Proc Natl Acad Sci U S A. 2002;99(20):12963–8.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Esteller M. Cancer epigenomics: DNA methylomes and histone-modification maps. Nat Rev Genet. 2007;8(4):286–98.View ArticlePubMedGoogle Scholar
  14. Portela A, Esteller M. Epigenetic modifications and human disease. Nat Biotechnol. 2010;28(10):1057–68.View ArticlePubMedGoogle Scholar
  15. Joung JG, Kim D, Kim KH, Kim JH. Extracting coordinated patterns of DNA methylation and gene expression in ovarian cancer. J Am Med Inform Assoc. 2013;20(4):637–42.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Li M, Balch C, Montgomery JS, Jeong M, Chung JH, Yan P, Huang TH, Kim S, Nephew KP. Integrated analysis of DNA methylation and gene expression reveals specific signaling pathways associated with platinum resistance in ovarian cancer. BMC Med Genet. 2009;2:34.Google Scholar
  17. Dudziec E, Gogol-Doring A, Cookson V, Chen W, Catto J. Integrated epigenome profiling of repressive histone modifications, DNA methylation and gene expression in normal and malignant urothelial cells. PLoS One. 2012;7(3):e32750.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Vangimalla RR, Jeong HH, Sohn KA. Integrative regression network for genomic association study. BMC Med Genet. 2016;9 Suppl 1:31.Google Scholar
  19. Li J, Ching T, Huang S, Garmire LX. Using epigenomics data to predict gene expression in lung cancer. BMC Bioinformatics. 2015;16 Suppl 5:S10.View ArticlePubMedGoogle Scholar
  20. Karlic R, Chung HR, Lasserre J, Vlahovicek K, Vingron M. Histone modification levels are predictive for gene expression. Proc Natl Acad Sci U S A. 2010;107(7):2926–31.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Cheng C, Yan KK, Yip KY, Rozowsky J, Alexander R, Shou C, Gerstein M. A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets. Genome Biol. 2011;12(2):R15.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Kim D, Joung JG, Sohn KA, Shin H, Park YR, Ritchie MD, Kim JH. Knowledge Boosting: A graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction. J Am Med Inform Assoc. 2014. doi:https://doi.org/10.1136/amiajnl-2013-002481.Google Scholar
  23. Kim D, Li R, Dudek SM, Frase AT, Pendergrass SA, Ritchie MD. Knowledge-driven genomic interactions: an application in ovarian cancer. BioData mining. 2014;7:20.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Kim D, Li R, Dudek SM, Ritchie MD. ATHENA: Identifying interactions between different levels of genomic data associated with cancer clinical outcomes using grammatical evolution neural network. BioData mining. 2013;6(1):23.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Kim D, Li R, Dudek SM, Ritchie MD. Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer. J Biomed Inform. 2015;56:220–8.View ArticlePubMedPubMed CentralGoogle Scholar
  26. Kim D, Li R, Dudek SM, Wallace JR, Ritchie MD. Binning somatic mutations based on biological knowledge for predicting survival: an application in renal cell carcinoma. Pac Symp Biocomput. 2015;20:96–107.Google Scholar
  27. Kim D, Li R, Lucas A, Verma S, Dudek S, Ritchie M: Using knowledge-driven genomic interactions for multi-omics data analysis: meta-dimensional models for predicting clinical outcomes in ovarian carcinoma. TBC 2015. 2015, (Accepted).Google Scholar
  28. Kim D, Shin H, Sohn KA, Verma A, Ritchie MD, Kim JH. Incorporating inter-relationships between different levels of genomic data into cancer clinical outcome prediction. Methods. 2014;67(3):344–53.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Kim D, Shin H, Song YS, Kim JH. Synergistic effect of different levels of genomic data for cancer clinical outcome prediction. J Biomed Inform. 2012;45(6):1191–8.View ArticlePubMedGoogle Scholar
  30. Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D. Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev Genet. 2015;16(2):85–97.View ArticlePubMedGoogle Scholar
  31. Li B, Ruotti V, Stewart RM, Thomson JA, Dewey CN. RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics. 2010;26(4):493–500.View ArticlePubMedGoogle Scholar
  32. Tibshirani R. Regression shrinkage and selection via the lasso: a retrospective. J R Stat Soc Ser B Stat Methodol. 2011;73(3):273–82.View ArticleGoogle Scholar
  33. Song L, Kolar M, Xing EP. Time-varying dynamic bayesian networks. In: Advances in Neural Information Processing Systems. 2009. p. 1732–40.Google Scholar
  34. Liu L, Xu Q, Cheng L, Ma C, Xiao L, Xu D, Gao Y, Wang J, Song H. NPY1R is a novel peripheral blood marker predictive of metastasis and prognosis in breast cancer patients. Oncol Lett. 2015;9(2):891–6.PubMedGoogle Scholar
  35. Sircoulomb F, Bekhouche I, Finetti P, Adelaide J, Ben Hamida A, Bonansea J, Raynaud S, Innocenti C, Charafe-Jauffret E, Tarpin C, et al. Genome profiling of ERBB2-amplified breast cancers. BMC Cancer. 2010;10:539.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Lien HC, Wang CC, Lin CH, Lu YS, Huang CS, Hsiao LP, Yao YT. Differential expression of ubiquitin carboxy-terminal hydrolase L1 in breast carcinoma and its biological significance. Hum Pathol. 2013;44(9):1838–48.View ArticlePubMedGoogle Scholar
  37. Cerliani JP, Vanzulli SI, Pinero CP, Bottino MC, Sahores A, Nunez M, Varchetta R, Martins R, Zeitlin E, Hewitt SM, et al. Associated expressions of FGFR-2 and FGFR-3: from mouse mammary gland physiology to human breast cancer. Breast Cancer Res Treat. 2012;133(3):997–1008.View ArticlePubMedGoogle Scholar
  38. Lynch M, Chen L, Ravitz MJ, Mehtani S, Korenblat K, Pazin MJ, Schmidt EV. hnRNP K binds a core polypyrimidine element in the eukaryotic translation initiation factor 4E (eIF4E) promoter, and its regulation of eIF4E contributes to neoplastic transformation. Mol Cell Biol. 2005;25(15):6436–53.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Gallardo M, Hornbaker MJ, Zhang X, Hu P, Bueso-Ramos C, Post SM. Aberrant hnRNP K expression: All roads lead to cancer. Cell Cycle. 2016;15(12):1552–7.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Barboro P, Ferrari N, Balbi C. Emerging roles of heterogeneous nuclear ribonucleoprotein K (hnRNP K) in cancer progression. Cancer Lett. 2014;352(2):152–9.View ArticlePubMedGoogle Scholar
  41. Watermann DO, Tang Y, Zur Hausen A, Jager M, Stamm S, Stickeler E. Splicing factor Tra2-beta1 is specifically induced in breast cancer and regulates alternative splicing of the CD44 gene. Cancer Res. 2006;66(9):4774–80.View ArticlePubMedGoogle Scholar
  42. Best A, James K, Dalgliesh C, Hong E, Kheirolahi-Kouhestani M, Curk T, Xu Y, Danilenko M, Hussain R, Keavney B, et al. Human Tra2 proteins jointly control a CHEK1 splicing switch among alternative and constitutive target exons. Nat Commun. 2014;5:4760.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Chen X, Du H, Liu B, Zou L, Chen W, Yang Y, Zhu Y, Gong Y, Tian J, Li F, et al. The Associations between RNA Splicing Complex Gene SF3A1 Polymorphisms and Colorectal Cancer Risk in a Chinese Population. PLoS One. 2015;10(6):e0130377.View ArticlePubMedPubMed CentralGoogle Scholar
  44. Michailidou K, Hall P, Gonzalez-Neira A, Ghoussaini M, Dennis J, Milne RL, Schmidt MK, Chang-Claude J, Bojesen SE, Bolla MK, et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet. 2013;45(4):353–61. 361e351-352.View ArticlePubMedPubMed CentralGoogle Scholar
  45. Okoh VO, Garba NA, Penney RB, Das J, Deoraj A, Singh KP, Sarkar S, Felty Q, Yoo C, Jackson RM, et al. Redox signalling to nuclear regulatory proteins by reactive oxygen species contributes to oestrogen-induced growth of breast cancer cells. Br J Cancer. 2015;112(10):1687–702.View ArticlePubMedPubMed CentralGoogle Scholar
  46. Shen C, Huang Y, Liu Y, Wang G, Zhao Y, Wang Z, Teng M, Wang Y, Flockhart DA, Skaar TC, et al. A modulated empirical Bayes model for identifying topological and temporal estrogen receptor alpha regulatory networks in breast cancer. BMC Syst Biol. 2011;5:67.View ArticlePubMedPubMed CentralGoogle Scholar
  47. Yang HW, Collins SR, Meyer T. Locally excitable Cdc42 signals steer cells during chemotaxis. Nat Cell Biol. 2016;18(2):191–201.View ArticlePubMedGoogle Scholar
  48. Nobes CD, Hall A. Rho, rac, and cdc42 GTPases regulate the assembly of multimolecular focal complexes associated with actin stress fibers, lamellipodia, and filopodia. Cell. 1995;81(1):53–62.View ArticlePubMedGoogle Scholar
  49. Bray K, Gillette M, Young J, Loughran E, Hwang M, Sears JC, Vargo-Gogola T. Cdc42 overexpression induces hyperbranching in the developing mammary gland by enhancing cell migration. Breast Cancer Res. 2013;15(5):R91.View ArticlePubMedPubMed CentralGoogle Scholar
  50. Chou J, Wang B, Zheng T, Li X, Zheng L, Hu J, Zhang Y, Xing Y, Xi T. MALAT1 induced migration and invasion of human breast cancer cells by competitively binding miR-1 with cdc42. Biochem Biophys Res Commun. 2016;472(1):262–9.View ArticlePubMedGoogle Scholar
  51. Stengel K, Zheng Y. Cdc42 in oncogenic transformation, invasion, and tumorigenesis. Cell Signal. 2011;23(9):1415–23.View ArticlePubMedPubMed CentralGoogle Scholar
  52. Makrodouli E, Oikonomou E, Koc M, Andera L, Sasazuki T, Shirasawa S, Pintzas A. BRAF and RAS oncogenes regulate Rho GTPase pathways to mediate migration and invasion properties in human colon cancer cells: a comparative study. Mol Cancer. 2011;10:118.View ArticlePubMedPubMed CentralGoogle Scholar
  53. Callaghan J, Nixon S, Bucci C, Toh BH, Stenmark H. Direct interaction of EEA1 with Rab5b. Eur J Biochem. 1999;265(1):361–6.View ArticlePubMedGoogle Scholar
  54. Koumangoye RB, Sakwe AM, Goodwin JS, Patel T, Ochieng J. Detachment of breast tumor cells induces rapid secretion of exosomes which subsequently mediate cellular adhesion and spreading. PLoS One. 2011;6(9):e24234.View ArticlePubMedPubMed CentralGoogle Scholar
  55. Chen PI, Kong C, Su X, Stahl PD. Rab5 isoforms differentially regulate the trafficking and degradation of epidermal growth factor receptors. J Biol Chem. 2009;284(44):30328–38.View ArticlePubMedPubMed CentralGoogle Scholar
  56. Shin N, Jeong H, Kwon J, Heo HY, Kwon JJ, Yun HJ, Kim CH, Han BS, Tong Y, Shen J, et al. LRRK2 regulates synaptic vesicle endocytosis. Exp Cell Res. 2008;314(10):2055–65.View ArticlePubMedGoogle Scholar
  57. Hirota Y, Kuronita T, Fujita H, Tanaka Y. A role for Rab5 activity in the biogenesis of endosomal and lysosomal compartments. Biochem Biophys Res Commun. 2007;364(1):40–7.View ArticlePubMedGoogle Scholar
  58. Ausio J. Histone variants--the structure behind the function. Brief Funct Genomic Proteomic. 2006;5(3):228–43.View ArticlePubMedGoogle Scholar
  59. Amaya C, Kurisetty V, Stiles J, Nyakeriga AM, Arumugam A, Lakshmanaswamy R, Botez CE, Mitchell DC, Bryan BA. A genomics approach to identify susceptibilities of breast cancer cells to “fever-range” hyperthermia. BMC Cancer. 2014;14:81.View ArticlePubMedPubMed CentralGoogle Scholar
  60. Kim MS, Jeong EG, Yoo NJ, Lee SH. Mutational analysis of oncogenic AKT E17K mutation in common solid cancers and acute leukaemias. Br J Cancer. 2008;98(9):1533–5.View ArticlePubMedPubMed CentralGoogle Scholar
  61. Rudolph M, Anzeneder T, Schulz A, Beckmann G, Byrne AT, Jeffers M, Pena C, Politz O, Kochert K, Vonk R, et al. AKT1 (E17K) mutation profiling in breast cancer: prevalence, concurrent oncogenic alterations, and blood-based detection. BMC Cancer. 2016;16:622.View ArticlePubMedPubMed CentralGoogle Scholar
  62. Bujor AM, Pannu J, Bu S, Smith EA, Muise-Helmericks RC, Trojanowska M. Akt blockade downregulates collagen and upregulates MMP1 in human dermal fibroblasts. J Invest Dermatol. 2008;128(8):1906–14.View ArticlePubMedGoogle Scholar
  63. Nyante SJ, Gammon MD, Kaufman JS, Bensen JT, Lin DY, Barnholtz-Sloan JS, Hu Y, He Q, Luo J, Millikan RC. Common genetic variation in adiponectin, leptin, and leptin receptor and association with breast cancer subtypes. Breast Cancer Res Treat. 2011;129(2):593–606.View ArticlePubMedPubMed CentralGoogle Scholar
  64. Yan W, Ma X, Gao X, Zhang S. Association Between Leptin (-2548G/A) Genes Polymorphism and Breast Cancer Susceptibility: A Meta-Analysis. Medicine (Baltimore). 2016;95(4):e2566.View ArticleGoogle Scholar
  65. Plaza-Menacho I, Morandi A, Robertson D, Pancholi S, Drury S, Dowsett M, Martin LA, Isacke CM. Targeting the receptor tyrosine kinase RET sensitizes breast cancer cells to tamoxifen treatment and reveals a role for RET in endocrine resistance. Oncogene. 2010;29(33):4648–57.View ArticlePubMedGoogle Scholar
  66. Spanheimer PM, Park JM, Askeland RW, Kulak MV, Woodfield GW, De Andrade JP, Cyr AR, Sugg SL, Thomas A, Weigel RJ. Inhibition of RET increases the efficacy of antiestrogen and is a novel treatment strategy for luminal breast cancer. Clin Cancer Res. 2014;20(8):2115–25.View ArticlePubMedPubMed CentralGoogle Scholar
  67. Jiang Y, Sun S, Wei W, Ren Y, Liu J, Pang D. Association of FGFR3 and FGFR4 gene polymorphisms with breast cancer in Chinese women of Heilongjiang province. Oncotarget. 2015;6(32):34023–9.PubMedPubMed CentralGoogle Scholar

Copyright

© The Author(s). 2017

Advertisement