Thread 2: Network models | Nature Genetics

Cellular networks are complex systems that are perturbed in cancer. Therefore, integrating mutation data with robust gene interaction networks is a key step toward modeling the faulty logic

at work in cancer cells. MAIN Cellular networks consist of genes that mutually regulate one another. Consequently, genomic alterations in one gene may propagate to disrupt functions in a

diverse array of molecular processes encoded by the system. Networks can therefore help in linking together seemingly very different events, just as a circuit diagram allows the

understanding of how either of two light switches, when flipped on, can turn on a lamp at the other side of the room. Two patients with the same cancer subtype may carry completely different

mutations, but knowing the way gene switching works can identify the underlying causal mechanism that leads to the same disease in both individuals. In addition to being databased in a

generic way, findings of cancer-specific genomic alterations can also be leveraged to gain an understanding of the context in which recurrent mutations exert their effects. This can be

achieved by linking mutational data into databases that catalog known functional relationships among gene products, for example, in protein-protein interactions, transcriptional regulation

and pathway-based relationships. However, current representations of the functional interactions governing cellular networks are fraught with false positives and false negatives and often

fail to account for experimental systems, cellular contexts and repeatability of the observations. Therefore, algorithms that incorporate network information must assess the robustness of

solutions and be designed to perform well in the face of uncertainty. Identifying and integrating explicit gene interaction networks is a key step toward building computational models that

represent the perturbed gene regulatory systems of cancer cells with high fidelity. PROPAGATING EFFECTS OF MUTATIONS ON A GENE-GENE INTERACTION NETWORK In Hofree _et al._ (_Nat. Methods_

doi:10.1038/nmeth.2651), network integration was achieved by combining new data with previously constructed gene-gene interaction networks. Integration of the two data sets was achieved by

coupling genes (on the basis of an associated confidence statistic) if both had previously been reported in the literature to participate in the same biological process, for example, in

protein-protein interaction, biochemical reaction or shared signaling logic. NETWORK-BASED STRATIFICATION OF TUMOR MUTATIONS Matan Hofree, John Shen, Hannah Carter, Andrew Gross & Trey

Ideker _Nature Methods_ 10.1038/nmeth.2651 For each patient independently we project the mutation profiles onto a human gene interaction network obtained from public databases28, 29, 30.

Next, the technique of network propagation31 is applied to spread the influence of each mutation profile over its network neighborhood (Fig. 1b). The result is a 'network-smoothed'

profile in which the state of each gene is no longer binary but reflects its network proximity to the mutated genes in that patient along a continuous range [0, 1]. Following this

'network smoothing', patient profiles are clustered into a predefined number of subtypes (_k = _2, 3, ... 12) using the unsupervised technique of non-negative matrix

factorization32 (NMF; Fig. 1c). [...] Finally, to promote robust cluster assignments, we use the technique of consensus clustering33, in which the above procedure is repeated for 1,000

different subsamples in which subsets of 80% of patients and genes are drawn randomly without replacement from the entire data set. The results of all 1,000 runs are aggregated into a

(patient × patient) co-occurrence matrix, which summarizes the frequency with which each pair of patients has cosegregated into the same cluster. This co-occurrence matrix is then clustered

a second time to recover a final stratification of the patients into clusters/subtypes (Fig. 1d). DETERMINING THE EFFECTS OF THE ALTERED CANCER GENOME ON PROTEIN PHOSPHORYLATION SIGNALING

Phosphorylation of protein substrates by protein kinase enzymes is a central signal transduction mechanism used to regulate many cancer-relevant processes. In Reimand _et al._ (_Sci. Rep._

doi:10.1038/srep02651), numerous mutations were found to eliminate phosphorylation sites and, thus, abolish phosphorylation signaling. Additional somatic mutations were found to affect

residues adjacent to phosphorylation sites and also caused rewiring of kinase signaling networks. By studying kinase target sequences in detail, the authors made predictions about mutations

leading to oncogenic gain or loss of signaling in kinase-based signal transduction networks. Recurrent mutations and signaling pathway enrichments within the network in cancer were also

analyzed, with mutations that affected signaling found in known cancer genes as well as in novel candidate cancer genes and pathways. THE MUTATIONAL LANDSCAPE OF PHOSPHORYLATION SIGNALING IN

CANCER Jüri Reimand, Omar Wagih & Gary Bader _Scientific Reports_ 10.1038/srep02651 To investigate cancer mutations in phosphorylation signaling, we collected 87,060 experimentally

determined phosphosites in 10,185 human proteins and integrated these with 241,701 missense single nucleotide variants from the TCGA pan-cancer project. Including ±7 residues of phosphosite

flanking regions and covering 7% of protein sequence, we found 16,840 phosphorylation-related SNVs (pSNVs) in 5,859 genes and 89% of all samples—over 17 times more pSNVs than previously

discovered7. According to another measure of pSNV importance, 1,427 direct pSNVs replace the central phosphorylated residue and thus disrupt phosphorylation; such mutations are

under-represented on the whole, although frequently seen in known cancer genes such as _TP53_ and _CTNNB1_ (79 cancer genes, _p_ = 4.2e - 18, Fisher's exact test). In total, we predict

a specific signaling mechanism for 29% of pSNVs (4,800) through either direct pSNVs or kinase network rewiring. A high-confidence network includes 392 pSNVs in 534 interactions, and

comprises only top-scoring kinase binding sites for signaling gain and experimentally determined kinase-substrate signaling loss (Fig. 3a). MAPPING CANCER DRIVERS TO A NETWORK OF FUNCTIONAL

GENE INTERACTIONS COMPREHENSIVE IDENTIFICATION OF MUTATIONAL CANCER DRIVER GENES ACROSS 12 TUMOR TYPES David Tamborero _et al.Scientific Reports_ 10.1038/srep02650 We provide a list of 291

high confidence cancer driver genes (HCDs) acting on 3,205 tumors from 12 different cancer types by combining the results of four computational approaches [...] designed to find signals in

genes reflecting the positive selection on cells during tumor emergence and evolution5–9. When HCDs are mapped to a functional interaction network (see Methods), they appear enriched for

biological processes within 5 broad modules—Chromatin remodeling, mRNA processing, Cell signaling/proliferation, Cell adhesion, DNA repair/Cell cycle—which loosely correspond to both

established and emergent cancer hallmarks (Fig. 3). These novel driver candidates appear alongside other well-established cancer genes. One may thus hypothesize that as more tumor genomes

are sequenced, new lowly recurrent mutational drivers in these modules will emerge. This idea is further illustrated in Figure 4a, where, for example, well-known cancer genes within the cell

cycle pathway are schematically represented together with poorly established HCDs. Examples of novel cell cycle driver candidates include ATR, a kinase which phosphorylates p53 and other

proteins, [and] PIK3CG and PIK3CB, within the PIK3-AKT signaling pathway, which appear to complement the tumorigenic role of PIK3CA. EFFECTS OF CANCER MUTATIONS ON REGULATORY RNA NETWORKS

MicroRNAs (miRNAs) modulate transcript (mRNA) stability and translation by binding to complementary sites on transcripts. miRNAs and their mRNA targets form a many-to-many network. Thus,

genomic mutations that perturb components of miRNA-mRNA networks may cause complex disruptions in cellular regulatory networks. ANALYSIS OF MICRORNA-TARGET INTERACTIONS ACROSS DIVERSE CANCER

TYPES Anders Jacobsen _et al.Nature Structural & Molecular Biology_ 10.1038/nsmb.2678 RECURRENCE OF TARGET ASSOCIATIONS ACROSS CANCER TYPES To explore the hypothesis that individual

miRNA-target relationships are active in multiple cancer types and may regulate common cancer traits, we developed a method and rank-based statistical score, the REC score. The method ranks

miRNA-mRNA expression associations in the context of miRNA and cancer type and evaluates the null hypothesis that no association exists between the miRNA-mRNA pair in all cancer types (Fig.

1 and Online Methods). GLOBAL ANALYSIS OF INTERACTIONS USING PUBLIC DATA SETS To further analyze whether recurrent pan-cancer miRNA-mRNA associations capture miRNA regulatory relationships,

we evaluated the extent to which the REC score could predict mRNA expression changes induced by experimental perturbation of miRNAs _in vitro_. In all analyzed miRNA perturbation

experiments, we found that these REC target mRNAs were significantly downregulated or upregulated after miRNA overexpression or inhibition, respectively (Fig. 3, range of _P_ values:

0.06–1.9 × 10-13, one-tailed Wilcoxon's rank-sum test, 7 < _n_ < 179), consistent with the hypothesis that the recurrent pan-cancer miRNA-mRNA associations capture miRNA

regulatory relationships. These 143 putative recurring target interactions formed a network of 40 evolutionarily conserved miRNAs and 72 target mRNAs (Fig. 4b). TESTING THE ROBUSTNESS OF

NETWORK ASSIGNMENT Classification of patterns of mutation and structural rearrangement into coherent modules that are significantly different from one another is sensitive to the input data

used. In Ciriello _et al._ (_Nat. Genet._ doi:10.1038/ng.2762), a number of controls were performed to test the robustness of modular classification. EMERGING LANDSCAPE OF ONCOGENIC

SIGNATURES ACROSS HUMAN CANCERS Giovanni Ciriello _et al.Nature Genetics_ 10.1038/ng.2762 VALIDATION OF THE MODULARITY OPTIMIZATION METHOD We tested our approach on two well-characterized

data sets frequently used as benchmarks for network modularity detection. The first network is known as the Southern Women Event Participation network33. It represents women's

attendance of social events in the Deep South, using data collected by Davis and colleagues in the 1930s to study social stratification. For this network, our approach was able to identify

the two-module structure of the network (Supplementary Fig. 12) that coincides with the solution proposed by Guimerá and colleagues34 and, except for one woman, with the subjective solution

proposed by the ethnographers that conducted the study. ROBUSTNESS OF THE SUBCLASSES. The robustness of the subclasses was assessed by removal of different percentages of samples and

reclassification of the reduced data sets. During each run, hierarchical stratification obtained with the reduced data set was mapped to the original one by mapping each module from the

reduced classification to the module from the original classification that maximizes the overlap. We found that sample assignment to each subclass was robust in that it varied little upon

systematic subsampling (Supplementary Fig. 6). VALIDATION OF THE RESULTS IN INDEPENDENT DATA SETS EMERGING LANDSCAPE OF ONCOGENIC SIGNATURES ACROSS HUMAN CANCERS Giovanni Ciriello _et

al.Nature Genetics_ 10.1038/ng.2762 Closer inspection of the distribution of selected functional events showed a striking inverse relationship between copy number alterations and somatic

mutations at the extremes of genomic instability, particularly in highly altered tumors (Fig. 2c). Such tumors had either a large number of somatic mutations or a large number of copy number

alterations, never both. We refer to this trend as the cancer genome hyperbola. Tumors in the C class and M class were positioned along the two axes of this hyperbola. Whereas individual

tumor types (defined by tissue of origin) had varying proportions of copy number alterations and mutations, none had high numbers of both. We verified this approximately inverse relationship

by adding 907 tumor samples from 6 additional tumor types to the pan-cancer set of 3,299 samples (Supplementary Fig. 4). In this larger data set, we also identified two major classes, one

primarily dominated by mutations and the other primarily dominated by copy number alterations, with a remarkably similar set of characteristic functional events. AUTHOR INFORMATION AUTHORS

AND AFFILIATIONS * Donnelly Centre, University of Toronto https://www.nature.com/nature Jüri Reimand & Gary Bader * Sage Bionetworks https://www.nature.com/nature Adam Margolin *

University Pompeu Fabra https://www.nature.com/nature Abel Gonzalez-Perez, David Tamborero & Nuria Lopez-Bigas * University of Texas MDAnderson GDAC https://www.nature.com/nature John

Weinstein * University of California, Santa Cruz https://www.nature.com/nature Joshua Stuart * Nature Genetics https://www.nature.com/nature Myles Axton Authors * Jüri Reimand View author

publications You can also search for this author inPubMed Google Scholar * Gary Bader View author publications You can also search for this author inPubMed Google Scholar * Adam Margolin

View author publications You can also search for this author inPubMed Google Scholar * Abel Gonzalez-Perez View author publications You can also search for this author inPubMed Google

Scholar * David Tamborero View author publications You can also search for this author inPubMed Google Scholar * Nuria Lopez-Bigas View author publications You can also search for this

author inPubMed Google Scholar * John Weinstein View author publications You can also search for this author inPubMed Google Scholar * Joshua Stuart View author publications You can also

search for this author inPubMed Google Scholar * Myles Axton View author publications You can also search for this author inPubMed Google Scholar CORRESPONDING AUTHORS Correspondence to Jüri

Reimand, Gary Bader, Adam Margolin, Abel Gonzalez-Perez, David Tamborero, Nuria Lopez-Bigas, John Weinstein, Joshua Stuart or Myles Axton. SUPPLEMENTARY INFORMATION SUPPLEMENTARY FIGURES

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