Large differences in global transcriptional regulatory programs of normal and tumor colon cells

Transcriptional regulation has an essential role for proper cell functioning. Gene regulatory programs establish and maintain specific cell states [1], ensure cell homeostasis and avoid metabolic disorders [2]. Genetic regulatory information encoded in DNA binding sites, such as enhancers and promoters, is interpreted by a network of transcription factors (TFs) [3]. Epigenetic events like DNA methylation or histone modifications are regulators of transcription [4, 5] and non-coding RNAs such as siRNAs and miRNAs are also involved in gene expression regulation at the post-transcriptional level [6].

Identification of global regulatory perturbations that actively participate in the initiation and maintenance of the tumor state is one of the major challenges in cancer biology [7]. Important processes intimately related to the neoplastic process, such as development and cell differentiation, are widely mediated by gene regulation [8]. Dysregulation of signaling pathways has also been related with tumor growth and cancer progression [9]. Although specific tumor genetic alterations are well described and annotated [10], comprehensive studies are required to obtain more information about the transcriptional programs involved in tumor development. Thus, a global analysis of regulatory network perturbations still remains a fundamental challenge for cancer biology [7].

Recent bioinformatics developments make use of large-scale gene expression datasets to infer genome-wide gene regulatory networks (GRN) [11]. Although not as accurate as methods based on experimental procedures and usually requiring subsequent validation, this approach to computationally-infer regulatory networks can be useful to predict in-vivo functions of specific cell types [12]. Diverse methodological approaches to infer GRNs have been proposed, such as regression-based methods, correlation, information-theoretic approaches and Bayesian networks [13]. Among all those, the ARACNe algorithm for the reconstruction of GRNs has been successfully applied to reverse-engineer large-scale transcriptional networks in B-cell leukemia [14, 15], neuroblastoma [16], T cell acute lymphoblastic leukemia [17] and prostate cancer [18]. These methodologies have also been applied to analyze and compare GRNs of several human tissues [19]. However, there are a limited number of studies about gene regulatory network inference in colon cancer cells, and these analyses were restricted to a small number of genes or used small sample sizes for the inference [20‐23].

The aim of our study is to infer GRNs from transcriptional data obtained for a large sample of stage II colon tumor cells and paired adjacent pathologically normal mucosa, as well as to perform a comprehensive analysis of the changes in the transcriptional regulatory programs related to the tumor phenotype.

In this study we have reverse-engineered the transcriptional regulatory networks of both pathologically normal and tumor colon cells obtained from the same set of patients. Using a large-scale gene expression microarray dataset, the ARACNe algorithm was applied to both tissue types independently. ARACNe gives preference to identify direct transcriptional regulatory interactions between TFs and their target genes. When both networks are compared, the most outstanding feature is the considerable loss of transcriptional interactions found in tumor cells (81%), with a global significant decrease in TFs (47%), target genes (60%). The fact that both normal and tumor samples belong to the same set of individuals, as well as the carefully performed experimental design to prevent biases between tissue types, strongly suggests that these large differences between networks are mainly due to the tumor phenotype.

Most of the TFs and target genes involved in disrupted interactions in the tumor network still maintain their expression levels, while only a minor proportion of lost edges may be explained by a complete loss of expression of one or both interactors. This expression silencing may be attributed either to genomic (e.g. DNA deletions, somatic mutations in promoter regions that hinder TF binding, transcript-truncating alterations, etc.) or epigenomic mechanisms (e.g. miRNA-associated transcript degradation, promoter hypermethylation, alterations in chromatin activation and repression marks, etc). On the other hand, disrupted interactions involving TFs and target genes that maintain expression levels in normal and tumor cells may be attributed to multiple reasons: presence or absence of a third-party molecule that could be acting as a post-translational modulator of the TF activity (i.e. phosphorylation, acetylation, ubiquitination) [36], alteration of key co-factors [1], or alterations in promoter regions that could create new TF-binding sites in target genes [37, 38]. The small set of genes involved in the loss of interactions through TFs or target gene silencing (~4%) is more likely to belong to currently known altered colon cancer pathways as the Wnt signaling and others, due to apparent under-expression. However, the vast majority of lost edges would not be easy to identify just by exploring the expression values of their TFs or targets genes. We think new and interesting undescribed mechanisms for molecular biology of colon cancer might be related to this gene deregulation without average gene expression change. A potential limitation may be the tumor cellular heterogeneity that could also be contributing to the observed loss of connectivity. While normal mucosa is a relatively homogeneous tissue among subjects, tumors are more heterogeneous, with diverse predominant cellular clones (epithelial, stromal and derived from the immune system). This could result in an apparent global loss of correlation if diverse transcriptional networks were mixed in the tumor.

The network of tumor cells also showed the emergence of a new set of transcriptional interactions that may have an essential role in tumor development and the acquisition of new cellular abilities. Recent studies have demonstrated that the activation of a small regulatory module is necessary and sufficient to initiate and maintain an aberrant phenotypic state in brain tumors [16]. Therefore, network inference approaches could prove effectively useful to uncover new modules and the master regulators that orchestrate malignant transformation. Among the TFs ranked at the top of the list of increased connectivity, our analysis identified colorectal cancer related genes: two oncogenes (MAFB[39] and GLI2[40]), proliferation-related genes (NOTCH3[41] and TGFB1[42]), epithelial-mesenchymal transition (SNAI2[43]) and the Wnt signaling genes SFRP4, TWIST1, SMARCA4 and DKK3, potentially involved in colorectal cancer angiogenesis [44]. One remarkable gene with increased activity in the tumor network was GREM1. This gene encodes a member of the bone morphogenic protein antagonist family and may play a role in regulating organogenesis, body patterning and tissue differentiation. Interestingly, GREM1 has been previously related with a locus strongly associated with increased colorectal cancer risk [45]. Moreover, increased expression of GREM1 has also been recently found in colorectal polyps [46], as well as in the dysplasia to carcinoma transition in colon tumors [47]. Therefore our results suggest that GREM1 may be mediating its tumorigenic effect by the activation of a large transcriptional program. Furthermore, encouraging results were obtained in the study of the relationship of somatic mutations in colorectal tumors in the set of relevant genes identified through our network approach. Though frequent mutation was independent of regulatory activity for TFs, we observed an association for target genes, with larger regulatory activity among mutated genes. Though this was a correlation analysis using external data from COSMIC database (we do not know if our tumors were actually mutated), it is suggestive that mutated genes trigger a regulatory control in the tumor. The presence of mutations combined with the alteration in their transcriptional regulatory connectivity postulate these genes as strong candidates to be involved in the pathogenesis of colon cancer, and even other type of tumors.

The analysis of network clusters has identified relevant sub-networks of highly connected genes specific of tumors. The regulatory network of normal cells is large and compact. Only 42 clusters have been identified with more than 10 genes. These clusters only account for 14% of the network genes, indicating that there is extensive regulation, but relatively low modularity. The tumor cell, however, has revealed 29 clusters that include 30% of their genes. This is consistent with a more modular organization of the regulatory machinery, which is also evident from the network representation (Figure 1). The functional analysis of these clusters has shown significant enrichment of known tumor-specific pathways: immune response, Wnt signaling, DNA replication, cell adherence, apoptosis, DNA repair, among others (Table 4). Some specific metabolism pathways appear also specifically captured by this analysis of sub-networks, which may be candidate for intervention: glycosphingolipid biosynthesis, tryptophan metabolism, glycosaminoglycan biosynthesis (chondroitin sulfate), beta-alanine metabolism, butanoate metabolism, glutathione metabolism. Obviously, all these functions are present in the normal cell, but they seem enhanced at the transcriptional level in the tumor, in such a way that a large cluster of related genes appear as a relevant entity. In this analysis we have generally focused on the gain of activity in the tumor network rather than on the lost interactions, given the massive loss of tumor network interactions that difficult to detect enriched functions. Despite this intrinsic limitation, we want to emphasize that the transcriptional loss found may influence the emergence of new functionality in the tumor cells. This finding may have a potential impact on the future of cancer molecular biology at level of further experiments and their corresponding biological interpretations.

The inference of GRNs has already been successfully applied to other malignances such as leukemia [14], breast cancer [48, 49] or ovarian tumors [50], with relevant findings regarding breast cancer metastasis prognostic markers or prioritization of druggable gene targets for ovarian cancer. In colorectal cancer some researchers have also explored the reconstruction of GRNs, but with limited approaches to one transcription factor [23] or only tumor tissue [21, 22]. To our knowledge, this is the first study in colon cancer that has simultaneously inferred networks for both tumor and adjacent normal cells obtained from the same set of individuals with a consistent methodology that makes both networks totally comparable.

We are aware that computational approaches of network reverse-engineering may suffer from intrinsic limitations. Therefore, we attempted a validation of the network to reinforce the validity of our study. An initial attempt to in-silico identify expected TF binding sites in targets was rejected because of the limited number and relative quality of the available TF positional weight matrices both in JASPAR [51] and TRANSFAC Public [52] databases. Other approach to validate the inferred regulatory networks would be to replicate our results in another colon cancer dataset. This has not been possible due to the lack of proper datasets to replicate the findings. The ARACNe’s authors emphasize in their papers that a hundred samples is the minimum sample size required to infer transcriptional networks with proper accuracy and they specifically discourage users to apply their algorithm on small datasets [15, 53]. The TCGA project [54] only provides 23 normal-tumor colon pairs available and we were unable to find a dataset with a more than 50 samples available after an exhaustive search in the most comprehensive public gene expression databases (GEO and ArrayExpress). Over the last decade, ChIP-on-chip and especially ChIP-Seq assays have become gold standard techniques for large-scale protein-DNA interaction identification. Therefore, ChIP-Seq and ChIP-on-chip datasets from the ENCODE project were used to validate interactions inferred by ARACNe. Since we restricted the potential set of TFs to be validated to those that had more than 20 interactions in the normal network and more than 500 experimentally observed peaks, only a very small part of the network could be tested. However, the obtained results were encouraging since 6 of the 16 tested TFs showed a good level of agreement. The large differences between the number of experimentally detected peaks and the number of inferred target genes for each one of the TFs may suggest a high rate of false negative interactions in our inferred networks, though it is not easy to interpret ChiP data, that provides may peaks that are not necessarily related to direct transcription interactions [55]. Failure in the validation of some TFs might also be partially influenced by the failure of the algorithm to completely remove indirect associations from the network due to high order interactions. In this direction, an extension of the ARACNe algorithm (hARACNe) specifically designed to deal with n-order interactions has been recently released, showing a significant increase in the quality and robustness of the inferred network [56]. Network deconvolution solutions over correlation-based networks have also proven to be successful for this purpose [57]. Due that the large heterogeneity of cell line tissues explored in the ENCODE project, we positively consider the overall observed level of agreement (38%), which is in the same range as previous studies found for other inferred transcriptional networks [14].

The inference of direct transcriptional networks at the whole-genome level has allowed us to detect a predominant loss of transcriptional activity in colon tumor cells, which has not been described before to the best of our knowledge. However, some specific TFs and biological processes related to colon cancer also increased the connectivity and became hubs in the dysregulated tumor network. These findings will allow a better comprehension of the transcriptional regulatory programs altered in colon cancer and could be an invaluable methodology to identify potential hubs with a relevant role in the field of cancer diagnosis, prognosis and therapy.