Recently, alternative approaches to those pursued using GWAS statistical approaches have started to emerge. ¹² The rationale for these methods is that genome-wide regulatory models representing causal molecular interactions in the cell—for example, transcription factors regulating their transcriptional targets or protein kinases activating their substrates—may help us identify a relatively small number of candidate genes, upstream of genetic programs that are dysregulated, which may be tested for genetic and epigenetic alterations (Figure 20-3 ).

Variants of such a genetic genomics approach were pioneered in plants ¹³ and metabolic disease ¹⁴ and have been used successfully in cancer-related studies. For instance, identification of the novel HUWE1-MYCN-DLL3 cascade in brain tumors was possible by reverse engineering posttranslational modulators of MYCN activity as well as its downstream targets using reverse engineering algorithms. ¹⁵ Similarly, the role of RUNX1 as a tumor suppressor mutated in T-cell acute lymphoblastic leukemia (T-ALL) was elucidated based on its most significant overlap with the TLX1 and TLX3 oncogene regulatory programs. ¹⁶ In some cases, a network-based view of cancer biology may allow elucidation of the dependency of a phenotype on an entire collection of genetic events, which would be virtually impossible to dissect using statistical approaches. For example, it was recently shown that deletion of any combination of 13 genetic loci distributed across the entire genome leads to functional inactivation of PTEN in glioma patients, via a novel interaction mechanism involving competitive endogenous RNA (ceRNA). ¹⁷ Indeed, cancer systems biology applications have exploded over the past 3 years, with studies ranging from the study of key drivers of tumorigenesis in melanoma ^18,19 to the dissection of tyrosine kinase signals downstream of ERBB receptors. ^20,21

Such a regulatory-model–driven view of cancer biology is thus emerging as an important systems-level contribution to the study of this disease. By taking a more holistic view of tumor-related processes, anchored in gene regulatory mechanisms, cancer systems biology mediates the genetic and the genomic views of cancer to provide novel insight into its mechanisms. Specifically, the proponents of these approaches argue that among all genetic and epigenetic alterations in a tumor, those contributing to its initiation, progression, or drug sensitivity cannot affect regulatory interactions in a random way but must co-segregate within specific regulatory subnetworks that are thus globally dysregulated across different samples of a given tumor subtype. Hence, if the full complement of regulatory interactions regulating the behavior of a specific cancer cell population were known, then it should be able to use its structure to separate driver from passenger alterations. The example of RUNX1 in T-ALL ¹⁶ is particularly revealing in this case. Here, the functional role of RUNX1 mutations could only be elucidated after determining that its targets are virtually overlapping with those of two previously established oncogenes, TLX1 and TLX3. ²² Without this regulatory insight, it would have been impossible to identify these mutations as statistically significant across the full repertoire of genes.

A key issue, then, is how to assemble accurate and comprehensive repertoires of molecular interactions to create a quantitative regulatory model that may be interrogated to elucidate drivers of tumor-related phenotypes. This is an important question, because virtually all cancer-related publications today contain appealing graphical presentations of molecular pathways in cancer. These bona fide models could provide a starting point for a systems-level study of cancer, as proposed, for instance, by pathway-wide association study (PWAS) strategies. ²³

Unfortunately, knowledge of molecular pathways governing physiological and tumor-related traits is still very poor. Indeed, canonical cancer pathways are more reflective of the researcher’s desire to understand biological processes as a relatively linear and interpretable set of events than of the true complexity of cellular regulation. Specifically, these representations have two major limitations. First, they are not context specific. For instance, the EGFR pathway would be identically represented for a glioma and for a lung-cancer cell.

Second, they constitute a manually curated collection of published facts, of which several are actually incorrect, and which represents less than 1% of the total complement of regulatory interactions in the cell. Hence, their use introduces a strong bias toward what is already known (prior knowledge). Indeed, in the absence of a prior hypothesis, interrogation of canonical cancer pathways has been largely unsuccessful in the elucidation of novel tumor-related mechanisms. To understand the difference between a true regulatory network and a canonical cancer pathway, consider Figure 20-4 , A, showing the differential phosphorylation of canonical EGFR pathway proteins in the H1650 cell line, where EGFR has an activating mutation, compared to the average of all cell lines. In contrast, Figure 20-4, B, shows the differentially phosphorylated proteins for the same cell lines in a signal transduction network, inferred de novo from a large-scale collection of phosphopeptide profiles of non–small-cell lung adenocarcinoma. ²⁴ Whereas the pathway-based representation provides no clue that the EGFR pathway may be dysregulated, the network-based representation shows a clear hyperphosphorylated protein pattern surrounding both EGFR and MET.

In the following, we discuss the idea of a simultaneous, de novo reconstruction of context-specific gene regulatory networks from large-scale molecular profile data, and of the genetic and epigenetic variability they harbor and mediate. A classic systems biology workflow generally involves three steps: First is acquisition of molecular profiles for a variety of molecular species, several of which represent gene products, from mRNA to phosphopeptide abundance, as well as of genetic and epigenetic alterations. Second is data integration and reconstruction of the regulatory models for the specific cellular context of interest. The final step is regulatory model interrogation, using genetic and genomic signatures that represent the cellular states of interest. Given the abundance and prior coverage of molecular profile data for cancer, we concentrate on the two latter steps.