- Research article
- Open Access
- Open Peer Review
A computational model to predict bone metastasis in breast cancer by integrating the dysregulated pathways
- Xionghui Zhou1 and
- Juan Liu1Email author
https://doi.org/10.1186/1471-2407-14-618
© Zhou and Liu; licensee BioMed Central Ltd. 2014
- Received: 15 February 2014
- Accepted: 20 August 2014
- Published: 27 August 2014
Abstract
Background
Although there are a lot of researches focusing on cancer prognosis or prediction of cancer metastases, it is still a big challenge to predict the risks of cancer metastasizing to a specific organ such as bone. In fact, little work has been published for such a purpose nowadays.
Methods
In this work, we propose a Dysregulated Pathway Based prediction Model (DPBM) built on a merged data set with 855 samples. First, we use bootstrapping strategy to select bone metastasis related genes. Based on the selected genes, we then detect out the dysregulated pathways involved in the process of bone metastasis via enrichment analysis. And then we use the discriminative genes in each dysregulated pathway, called as dysregulated genes, to construct a sub-model to forecast the risk of bone metastasis. Finally we combine all sub-models as an ensemble model (DPBM) to predict the risk of bone metastasis.
Results
We have validated DPBM on the training, test and independent sets separately, and the results show that DPBM can significantly distinguish the bone metastases risks of patients (with p-values of 3.82E-10, 0.00007 and 0.0003 on three sets respectively). Moreover, the dysregulated genes are generally with higher topological coefficients (degree and betweenness centrality) in the PPI network, which means that they may play critical roles in the biological functions. Further functional analysis of these genes demonstrates that the immune system seems to play an important role in bone-specific metastasis of breast cancer.
Conclusions
Each of the dysregulated pathways that are enriched with bone metastasis related genes may uncover one critical aspect of influencing the bone metastasis of breast cancer, thus the ensemble strategy can help to describe the comprehensive view of bone metastasis mechanism. Therefore, the constructed DPBM is robust and able to significantly distinguish the bone metastases risks of patients in both test set and independent set. Moreover, the dysregulated genes in the dysregulated pathways tend to play critical roles in the biological process of bone metastasis of breast cancer.
Keywords
- Bone metastasis
- Breast cancer
- Dysregulated pathways
- Prediction model
- Immune system
Background
Metastasis is the main cause of death in breast cancer [1, 2], and bone is the organ suffering from metastasis most frequently [3]. Breast cancer patients with bone metastases may suffer marked decreased mobility, pathologic fractures, neurological damage and other symptoms, and the patients with high risks of bone metastases should take agents tailored treatments [4, 5]. Thus for cancer therapy, it is essential to identify the prognostic factors which can help to identify the patients with high risks of bone metastasis [4–6].
Because the ability of tumour cells metastasizing to a specific organ is an inherent genetic property [7, 8], it is possible to predict bone metastasis of breast cancer by using gene expression profiles [8]. However, up to now only several researches have attempted to identify bone metastasis related genes from gene expression data [3, 9–11], and only one in which [3] has made use of the identified genes as signature to construct classification model for predicting bone metastasis risk of breast cancer. What is more, the published work just considered very limited number of samples when selecting gene signatures and did not perform strict independent tests on any larger data set. As breast cancer is a heterogeneous disease, the characters associated with metastases may vary widely across different patients [1]. Insufficient patient samples would not cover all aspects of the metastases, thus gene signatures selected from small number of samples may not be credible enough. In fact, it has been found out that the gene signatures identified using one data set may perform badly on another data set [12–14].
The framework of DPBM prediction model.
Methods
Data sets and pre-processing
Breast cancer data sets
Data set | Bone metastasis samples | Metastasis samples | Samples |
---|---|---|---|
GSE2034 | 69 | 95 | 286 |
GSE2603 | 14 | 24 | 82 |
GSE12276 | 102 | 173 | 192 |
NKI295 | 53 | 84 | 295 |
We have also downloaded the human protein-protein interactions from the HIPPI (Human Integrated Protein-Protein Interaction rEference) [23], and the pathways from the Molecular Signatures Database (MSigDB) [24].
Selecting candidate genes by bootstrapping
As is known to all, t-test is a popular method used to select discriminative genes, thus it could be used in our work. However, t-test method requires that every sample must be attached with a class label. While in our work, for the reason that the clinical information of some patients is censored, not every sample can be assigned as either low-risk or high-risk of bone metastasis according to the widely used criterion that patients who are bone-metastasized within a threshold of years belong to high-risk group, and patients who are free of bone metastases and survive longer than the threshold belong to low-risk group, which results that some valuable samples not satisfying the criterion have to be removed from the training set if t-test method is used. Different with t-test method, however, the Cox proportional hazards regression can involve all samples into the calculation, thus it is more proper for our work to select the bone metastasis related genes.
In this work, we used a simple bootstrapping strategy to select candidate genes of which expression levels were significantly correlated with the bone metastasis risk. Concretely, we first randomly selected 3/4 of all the 380 samples from the training set; and then for each gene, we applied Cox proportional hazards regression to calculate the coefficient between the gene expression level and the bone metastasis risk across the chosen samples. The above procedure was repeated 400 times, and the genes with Cox p-values less than 0.05 in more than 80% of all runs were regarded as the candidate genes. For every selected gene, its averaged Cox coefficient and Cox p-value over all the 400 runs were set to be its final corresponding values for further calculations.
Identifying the dysregulated pathways
Where x stands for the size of intersection set; K represents the number of the candidate genes; N stands for the number of the genes in the pathway; and M represents the number of all genes in our calculation (the universal gene set). For a pathway, if the p-value is less than 0.05, then it is considered as the dysregulated one; and the genes belonging to the intersection set are called as dysregulated genes.
Constructing the DPBM
With the hypothesis that one dysregulated pathway may describe only one aspect of the bone metastasis mechanism, while all dysregulated pathways can provide a comprehensive view of the bone metastasis, we adopted the ensemble strategy [14] to construct DPBM to predict the bone metastases risks of breast cancer patients. We chose the dysregulated genes in each dysregulated pathway as features to construct a sub-model to distinguish the bone metastases risks of the patients, and all the sub-models were integrated as DPBM by majority voting strategy.
Where x i (x j ) represents the expression level of the dysregulated gene i (j) which has a positive (negative) Cox coefficient with metastasis risk. The higher the RiskScore is, the greater the risk of bone metastasis. We applied 10-fold cross validation test to set the proper threshold value of RiskScore. In each run, the n-th smallest riskScore value (n is the number of training patients free of bone metastases) in the training samples was set as the cut-off to determine the class labels of the test samples, based on which, the performance (log rank test) can be obtained. The final threshold value was set as the one with the best performance in ten runs. Any patient with RiskScore value greater than this threshold is considered as high-risk of bone metastasis by this sub-model, otherwise it is considered as low-risk of bone metastasis.
For a patient, if more than half sub-models vote for “high-risk of bone metastasis”, it will be finally predicted as “high-risk of bone metastasis” by DPBM, and vice versa. In order to assess the performance of DPBM, we used the log rank test to evaluate the significance of the risk differences between the patients in two groups. Kaplan Meier curves and the log rank test were performed using a tool (http://www.mathworks.com/matlabcentral/fileexchange/22317-logrank).
Topologically investigating dysregulated genes in PPI network
Protein-protein interaction network has been successfully applied to select signature genes [26]. For example, Hase et al. illustrated that the signature genes tended to have bigger degrees in the network [27]; and Yao et al. reported that the signature genes were usually with higher betweenness centralities in the network [28]. Thus we investigated two network topological coefficients (Degree and Betweenness Centrality) of the selected dysregulated genes by comparing with candidate genes (dysregulated genes excluded) and all genes in the PPI network (dysregulated genes excluded). The differences of the topological coefficients between the dysregulated genes and other two kinds of genes were tested by the Mann–Whitney-Wilcoxon non-parametric test for two unpaired groups. And the topology analysis of PPI network was performed by the Network Analyzer plug-in for Cytoscape [29].
Investigating dysregulated genes by functional analysis
DAVID [30] was applied to extract the GO Terms (Biological Processes) which were significantly enriched by the dysregulated genes and the ones with p-values less than 0.05 were set as enriched GO Terms. All enriched GO Terms were clustered into several functional groups by the functional annotation clustering method with the default threshold of enrichment score [30].
Results
Dysregulated pathways and genes
The dysregulated pathways
KEGG pathway | Enrichment p-value | Gene ID | Gene symbol | Cox coefficient | Cox p-value | Stability |
---|---|---|---|---|---|---|
Cytokine Cytokine Receptor Interaction | 0.029 | 355 | FAS | −0.42 | 0.0048 | 0.9925 |
1235 | CCR6 | −0.22 | 0.023 | 0.905 | ||
1439 | CSF2RB | −0.32 | 0.0046 | 0.9875 | ||
2322 | FLT3 | −0.28 | 0.015 | 0.93 | ||
3561 | IL2RG | −0.20 | 0.031 | 0.8125 | ||
3570 | IL6R | −0.37 | 0.027 | 0.85 | ||
3575 | IL7R | −0.23 | 0.0044 | 0.995 | ||
4982 | TNFRSF11B | −0.22 | 0.019 | 0.9125 | ||
6363 | CCL19 | −0.11 | 0.033 | 0.8025 | ||
6375 | XCL1 | −0.21 | 0.013 | 0.9575 | ||
7042 | TGFB2 | −0.24 | 0.016 | 0.9325 | ||
7422 | VEGFA | 0.15 | 0.031 | 0.83 | ||
Chemokine Signaling Pathway | 0.041 | 112 | ADCY6 | 0.34 | 0.032 | 0.8225 |
1235 | CCR6 | −0.22 | 0.023 | 0.905 | ||
3702 | ITK | −0.18 | 0.023 | 0.87 | ||
3717 | JAK2 | −0.48 | 0.0025 | 1 | ||
5579 | PRKCB1 | −0.39 | 0.021 | 0.8975 | ||
5613 | PRKX | −0.26 | 0.031 | 0.815 | ||
5829 | PXN | 0.34 | 0.021 | 0.8725 | ||
6363 | CCL19 | −0.11 | 0.033 | 0.8025 | ||
6375 | XCL1 | −0.21 | 0.013 | 0.9575 | ||
Cell Cycle | 0.012 | 894 | CCND2 | −0.27 | 0.013 | 0.96 |
1021 | CDK6 | −0.48 | 0.010 | 0.955 | ||
1869 | E2F1 | 0.32 | 0.0015 | 1 | ||
1870 | E2F2 | 0.33 | 0.031 | 0.8375 | ||
7042 | TGFB2 | −0.24 | 0.016 | 0.9325 | ||
8243 | SMC1A | 0.75 | 0.0085 | 0.98 | ||
9700 | ESPL1 | 0.25 | 0.010 | 0.97 | ||
10744 | PTTG2 | 0.46 | 0.011 | 0.975 | ||
Natural Killer Cell Mediated Cytotoxicity | 0.048 | 355 | FAS | −0.42 | 0.0048 | 0.9925 |
3002 | GZMB | −0.23 | 0.0067 | 0.985 | ||
3383 | ICAM1 | −0.32 | 0.022 | 0.8975 | ||
3821 | KLRC1 | −0.43 | 0.0073 | 0.97 | ||
3932 | LCK | −0.24 | 0.025 | 0.875 | ||
5579 | PRKCB1 | −0.39 | 0.021 | 0.8975 | ||
22914 | KLRK1 | −0.34 | 0.015 | 0.9325 | ||
T Cell Receptor Signaling Pathway | 0.046 | 917 | CD3G | −0.43 | 0.00097 | 0.9975 |
3702 | ITK | −0.18 | 0.023 | 0.87 | ||
3932 | LCK | −0.24 | 0.025 | 0.875 | ||
5788 | PTPRC | −0.21 | 0.024 | 0.8675 | ||
10892 | MALT1 | −0.43 | 0.015 | 0.905 | ||
29851 | ICOS | −0.41 | 0.018 | 0.915 | ||
Pancreatic Cancer | 0.027 | 1021 | CDK6 | −0.48 | 0.010 | 0.955 |
1869 | E2F1 | 0.32 | 0.0015 | 1 | ||
1870 | E2F2 | 0.33 | 0.031 | 0.8375 | ||
7042 | TGFB2 | −0.24 | 0.016 | 0.9325 | ||
7422 | VEGFA | 0.15 | 0.031 | 0.83 | ||
Non Small Cell Lung Cancer | 0.0095 | 1021 | CDK6 | −0.48 | 0.010 | 0.955 |
1869 | E2F1 | 0.32 | 0.0015 | 1 | ||
1870 | E2F2 | 0.33 | 0.031 | 0.8375 | ||
5579 | PRKCB1 | −0.39 | 0.021 | 0.8975 | ||
6256 | RXRA | 0.45 | 0.012 | 0.9525 | ||
Primary Immunodeficiency | 0.0014 | 3561 | IL2RG | −0.20 | 0.031 | 0.8125 |
3575 | IL7R | −0.23 | 0.0044 | 0.995 | ||
3932 | LCK | −0.24 | 0.025 | 0.875 | ||
5788 | PTPRC | −0.21 | 0.024 | 0.8675 | ||
29851 | ICOS | −0.41 | 0.018 | 0.915 |
Some cytokines have been reported to be related to breast invasion and metastasis site [31], while cytokine receptor interaction pathway has been found significant in our work. What is more, the dysregulated genes IL2RG, IL6R, IL7R and TGFB2 have been reported to be associated with metastasis site or prognosis [31], and CCR6 is associated with both live metastasis in breast cancer [32] and bone metastasis in human neuroblastoma [33].
Chemokines and their receptors have been shown to play critical roles in determining the metastatic destination of tumour cells [34]. In our work, the chemokine signalling pathway is also enriched with the candidate genes. In the meanwhile, among the nine dysregulated genes, Jak2 has been reported to be mediated by IL6 to involve in bone metastasis [35]; CCR6 is associated with bone metastasis [33]; PPKX regulates endothelial cell migration and vascular-like structure formation [36]; XCL1 and CCL19 are associated with organ specific metastasis [34, 37].
Cell cycle pathway plays an important role in tumorigenesis and cancer prognosis [38], and it has also been found to be dysregulated in our work. Among its dysregulated genes, CCND2 is differentially expressed between breast cancer patients with bone metastases and other patients [11]; E2F1 can regulate DZ13 to induce a cytotoxic stress response in tumour cells metastasizing to bone [39]; TGFB2 is related to the bone metastases development [40].
It is interesting that non-small cell lung cancer and pancreatic cancer pathways have also be found dysregulated in bone metastasis. In fact, lung is the organ with the second frequent metastasis for breast cancer [8], and it has been reported that some breast cancer would metastasize to pancreatic [41]. This phenomenon suggests that either lung cancer or pancreatic cancer might share some common mechanisms with bone metastasis of breast cancer, for the dysregulated genes E2F1 [39] and TGFB2 [40] in pancreatic cancer pathway have been shown to be also involved in bone metastasis process; while E2F2 gene, the family member of E2F1, has been found to be the dysregulated gene in the non-small cell lung pathway.
We have also found that three immune related pathways have been dysregulated in bone metastasis of breast cancer: natural killer cell mediated cytotoxicity pathway, T cell receptor signalling pathway and primary immunodeficiency pathway. In fact, some immune related genes are essential in bone metastasis of breast cancer [42–44], and their family members, such as FAS, IL2RG and IL7R, have shown dysregulated in our work and have been reported to be either metastasis related or bone metastasis related [31, 35, 45].
Now that references [3, 9–11] have published bone metastasis related genes, we merged all the reported genes and investigated the overlap with our dysregulated genes. It is surprising that there are only four common genes (Additional file 1: Figure S2) between two sets of genes. We thus investigated the functions of published genes and found that they are most enriched in ‘metabolic process’ (data not shown), while our dysregulated genes are mainly related to immune system. By literature investigation, we further found that the immune cells can play essential roles in bone metastasis or metastasis of cancer [42, 44], which illustrates that our dysregulated genes are related to some new biological mechanism of bone metastasis, compared to the reported genes.
Distinguishing bone metastasis risk by DPBM
Kaplan-Merier curves of the risk groups for breast cancer patients with bone metastasis-free survival. (a) Result in the training set. (b) Result in the test set. (c) Result in the independent set.
We noticed that different types of samples in any of the training, test and independent sets are imbalanced, which would lead to the overestimation problem. In order to address this issue, we also used random sampling methodology to choose the same number of cases from high-risk and low-risk groups and re-evaluated the DPBM on each of three data sets. We repeated the above process 1000 times, and the means of hazard ratios for training test and independent sets were 3.31 (p-value of 2.49E-04), 3.15 (p-value of 0.0082) and 2.48 (p-value of 0.015) respectively (Additional file 1: Table S2). The results further unveil the robustness of our model. In the meanwhile, the stable performance of the DPBM also indicates the reliability of the dysregulated genes identified by our method.
Topological analysis of dysregulated genes in PPI network
Comparison of the topological parameters in the PPI network among the three groups (Dysregulated genes, Candidate genes (except for the dysregulated genes) and All genes (except for the dysregulated genes) in the PPI network). (a) Comparison of the degrees. (b) Comparison of the betweenness centralities.
From Figure 3(a), it is clear that the dysregulated genes tend to have bigger degrees than the other two groups of genes, and the p-values of dysregulated vs candidate genes, dysregulated vs all genes are 2.29E-04 and 4.86E-07 respectively. Moreover, Figure 3(b) demonstrates that the betweenness centralities of the dysregulated genes are usually bigger than the other two groups of genes (with p-value = 1.17E-05 and p-value = 1.68E-08 separately).
From above results we can see that the dysregulated genes take up more important positions in the PPI network than the other genes, and tend to be essential genes for the bone metastasis.
Difference between bone and non-bone metastasis
We noticed that there are also some samples metastasized to other organs instead of bone in the data sets. By using the same strategy as we have done for bone metastasis, we have found nine dysregulated pathways and a total of 67 dysregulated genes related to non-bone metastases (metastases to other organs except for bone) (Additional file 1: Table S3). Therefore, we investigated the different functional groups to which these two kinds of genes belong, with the purpose of uncovering the biological mechanism of bone specific metastasis. By function annotating and clustering, the 35 dysregulated genes of bone metastasis were found to belong to 16 functional groups (Additional file 2: Table S4), and the 67 dysregulated genes of non-bone metastases were found to belong to 15 functional clusters (Additional file 3: Table S5).
By comparison, we found that these two kinds of genes shared a lot of common functional clusters. For example, cell differentiation related cluster, cell cycle related cluster, cell migration cluster, apoptosis related cluster, hormone stimulus related cluster, phosphate metabolic process and phosphorylation related cluster. As is known to all, cell differentiation, cell cycle, cell migration, and cell apoptosis are all famous caner hallmark related GO Terms that are related to cancer and cancer prognosis [46–48], while hormones are related to the risk of breast cancer and hormones-replacement therapy is a common therapy for breast cancer patients [49]. In addition, phosphorylation of some proteins have been reported to be related to breast cancer [50] and cancer prognosis [51].
The main difference between these two kinds of dysregulated genes was that dysregulated genes of bone metastasis are also enriched in biological processes associated with immune system, whereas dysregulated genes of non-bone metastases were not. The difference suggests that the immune system may be essential in the bone specific metastasis of breast cancer.
Comparing DPBM with other classification methods
Comparing DPBM with other methods
Training data set | Test data set | Independent data set | ||||
---|---|---|---|---|---|---|
AUC | Accuracy | AUC | Accuracy | AUC | Accuracy | |
DPBM | 0.76 | 0.64 | 0.60 | 0.60 | 0.61 | 0.66 |
SVM (RiskScore) | 0.72 | 0.71 | 0.58 | 0.59 | 0.60 | 0.60 |
SVM (dysregulated genes) | 0.75 | 0.75 | 0.55 | 0.54 | 0.60 | 0.59 |
SCC | 0.78 | 0.65 | 0.57 | 0.55 | 0.57 | 0.44 |
As far as we know, there is only one published work to construct a model for predicting bone metastases risks of cancer patients [3], by using SCC (shrunken centroids classifier) [52] method. Therefore, we also compared DPBM with SCC. Since the data set used in the original work is too small, we constructed SCC and evaluated its performances on our data sets (the training samples were labelled as high-risk or low-risk as described in Additional file 1: Supplementary Methods, and 35 dysregulated genes were used as features). The results are also listed in Table 3, from which we can see that our DPBM performs better than SCC that has been used in previous work [3].
Discussion and conclusions
Predicting the bone metastases risks for breast cancer patients is essential in cancer therapy, which is an urgent challenge now [5]. In this work, we have proposed a Dysregulated Pathway Based prediction Model (DPBM) to address this problem. We first selected the candidate genes (correlated with the bone metastasis) by bootstrapping strategy. Then we identified the dysregulated pathways enriched by the candidate genes. After that, we used the dysregulated genes in each dysregulated pathway to construct a sub-model to predict the bone metastasis risk separately. Finally, we combined all sub-models together by using majority voting strategy as an ensemble model, DPBM, to predict the risk of bone metastasis. Validation results on test set and independent set have shown the great prediction power of DPBM.
By literature investigation, most of the dysregulated pathways and dysregulated genes are related to bone metastasis. In addition, the dysregulated genes tend to have higher degrees and betweenness centralities in PPI network, suggesting that they play critical roles in the biological functions. By comparing the functional groups to which the dysregulated genes of bone and non-bone metastases belong, we found that the immune system may be essential in the bone specific metastasis of breast cancer.
All the results illustrate that the dysregulated genes may be good biomarker candidates. The facts that DPBM consistently performs well in both test set and independent set may be due to the following merits: (1) we used the pathways to filter the candidate genes, which can help to remove those genes less essential to the bone metastasis; (2) instead of selecting pathways or other functional gene sets via the activity differences between different phenotypes, we selected the dysregulated pathways enriched by the discriminative genes, which can help to preserve the useful information for classification and reduce noises; (3) we constructed one sub-model based on each dysregulated pathway, and then combined all sub-models by majority voting strategy. The ensemble classifier usually performs better than simple classifiers [53].
In this work, although we have collected 855 samples, the samples with the metastases to other specific organs are still insufficient, that is why we merged all samples with metastatic tumour of the other organs as one group (non-bone metastases group). This is reasonable for us to understand the difference between the bone metastasis and other organ metastases. Of course, if the samples with other organ metastases are sufficient, the differences among different metastases organs may also be well studied.
Declarations
Acknowledgements
This work was supported by the National Science Foundation of China [61272274, 60970063]; the program for New Century Excellent Talents in Universities [NCET-10-0644]; and the Fundamental Research Funds for the Central Universities [2012211020208].
Authors’ Affiliations
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