A Multi-Method Approach for Proteomic Network Inference in 11 Human Cancers
[['The availability of proteomic datasets such as PANCAN11 where protein levels are measured across different conditions provides a unique opportunity to study the functions of proteins .', 'However , the analysis of function requires knowledge of interactions .', 'For instance , in the protein-folding domain , the function of a single residue during folding can be determined only by having knowledge about the residues it is interacting with .', 'Similarly , the function of a protein in the cell can only be understood by determining its interaction partners .', 'Therefore , the units of analysis are not the individual protein expression levels , but the interactions of proteins with other cellular entities .', 'Statistical techniques such as correlation can be used to study the interactions of proteins .', 'However , correlation between two proteins does not imply that they directly interact , because correlation may also be induced by chaining of correlation between a set of intervening , directly interacting proteins .', 'Such indirect correlations are called transitive interactions .', 'It was previously shown that the dominant correlations in a system can be the result of parallel transitive interactions [6] .', 'There are three main network motifs that lead to transitive interactions: fan-in , fan-out and cascade .', 'A fan-in is a case where there are direct interactions from proteins A and B to a third protein C but there is no interaction between A and B . A fan-out is the situation where there is a direct interaction from protein C to both A and B but there is no interaction between A and B . A cascade , on the other hand , is a chain event where there are direct interactions from A to B , and from B to C , but not from A to C . In all these three cases , if the two direct interactions are in the same direction ( both increasing or both decreasing ) , there is a transitive influence observed between the proteins that do not have a direct interaction .', 'Since biological pathways and signaling events contain many fan-in , fan-out and cascade network motifs , transitive effects occur widely across the network and have previously been shown to be a systematic source of false positive errors for many computational network inference methods [7] .', 'Thus , it is crucial to minimize transitive interactions when building network models from high-throughput datasets .', 'A wide suite of computational methods has been proposed in the literature for the identification of direct interactions in networks .', 'The common objective of many of these methods is to call a direct interaction between two entities if they are ‘not conditionally independent’ of each other given a set of other entities .', 'One simple example is the regression-based partial correlation approach .', 'Consider a three-variable system consisting of A , B , and C . When testing the existence of a direct interaction between A and B in this approach , measurements on A and B would first separately be regressed on the measurements on C , the residual vectors would be computed , and then the correlation between the residual vectors would be found .', 'If this ‘partial’ correlation is significantly different from zero , a direct interaction is called between A and B . Despite the similarity in the objective , these methods employ diverse inference procedures such as mutual information [8–11] , regression [12–14] , Gaussian graphical models [15 , 16] , and entropy maximization [17 , 18] .', 'The diversity of algorithms for inferring direct interactions , coupled with the absence of a robust off-the-shelf method , creates challenges for investigators that aim to generate hypotheses and eventually discover novel functional interactions among proteins .', 'We address this challenge by testing different families of network inference methods towards the goal of deriving guidance for the better-performing methods .', 'The RPPA platform , first introduced in Paweletz et al . [19] stands a good chance of becoming a widely used proteomics platform as greater numbers of reliable antibodies are being developed .', 'Here , we present a rigorous comparison of the performance of 13 commonly used network inference algorithms based on PANCAN11 , a pan-cancer RPPA dataset which contains levels of many proteins in a large number of samples , such that reasonably meaningful protein-protein correlations can be computed .', 'The goal of this comparison is two-fold: To investigate', '1 ) if the signal-to-noise ratio of the RPPA technology allows the discovery of known and novel protein-protein interactions ( PPIs ) , and', '2 ) to what extent algorithms that were originally developed for gene regulatory network inference accomplish the inference of PPIs .', 'Performance evaluation of PPI network inference for different cancers requires a ‘gold standard’ for each cancer type .', 'However , a true gold standard for human PPIs does not exist , let alone a separate one for each tumor type .', 'Most protein interactions in in vivo systems remain unknown or unproven and/or depend on physiological context .', 'Yet public knowledgebases that store collections of curated pathway and/or interaction data contain useful information .', 'For instance , Pathway Commons is a collection of publicly available and curated physical interactions and pathway data including biochemical reactions , complex assembly , transport and catalysis events [20] , aggregated from primary sources such as Reactome , KEGG and HPRD and conveniently represented in the BioPAX pathway knowledge representation framework [21–24] .', 'In this study , we adopted Pathway Commons as a benchmark , and evaluated the performance of 13 network inference methods ( Table', '1 ) in their capacity to retrieve ‘true’ PPIs from RPPA datasets of 11 cancer types .', 'We then used a group of high-performing methods to investigate the similarities and differences among the 11 cancer types in our dataset .', 'The workflow of this study ( Fig 1 ) , involves the parallel generation of two PPI network models , one from computational inference and one from the pathway knowledgebase .', 'On the inference side , multiple antibodies are assayed on an RPPA platform ( Step', '1 ) and the resulting dataset is normalized to generate a proteomic profile of the cohort such as PANCAN11 .', 'Computational network inference methods are then employed to create a network model with the inferred PPIs ( Step 2 ) .', 'On the knowledgebase side , various wet-lab experiments are performed to generate data , and the resulting information is stored in the scientific literature ( Step 3 ) .', 'Curators sift through the literature to distill multiple-layered information on PPIs ( Step 4 ) , and then this information is catalogued in knowledgebases such as Pathway Commons ( Step 5 ) .', 'A comparison of the PPI network models from the two sides reveals the level of fidelity at which the ‘true’ network is constructed by the computational methods ( Step 6 ) .', 'The mutual information-based methods listed in Table 1 infer unsigned undirected edges , whereas the edges inferred by the ‘correlation’ and ‘partial correlation’ family of methods are undirected but signed ( positive or negative ) .', 'An undirected positive edge between A and B means that the direction of influence is not known , but A and B generally exist at both high or both low levels among all tested experimental conditions .', 'A negative sign , thus , means that one is generally high when the other is low ( tending towards mutual exclusivity ) .', 'The use of positive/negative edges in this study refers to positive/negative signs of the weight .', 'Also , we use the words ‘edge’ and ‘interaction’ interchangeably throughout the manuscript .', 'The workflow of performance evaluation as described above involves certain caveats .', 'These are discussed in detail in the Discussion section and in S1A Text .', 'Here we discuss one of the caveats , the ascertainment bias in pathway knowledgebases ( Step 5 in Fig 1 ) .', 'Wet-lab experiments for PPI plausibly have over-representation of certain proteins due to the perceived interest in the field and ease of study .', 'In a recent paper , a Pearson correlation of 0 . 77 was reported for the correlation between the number of publications in which a protein was mentioned and the number of interactions reported for that protein in literature-curated data [25] .', 'This implies the potential existence of an ascertainment bias in pathway knowledgebases .', 'More documented interactions of a certain protein will exist if that protein is studied more intensively by the community .', 'The ascertainment bias in Pathway Commons precludes our benchmark network from being a perfect gold standard .', 'This and other caveats challenge the comparability of pathway models from a knowledgebase and network models from a computational algorithm .', 'Thus , it is necessary to be mindful of these caveats when interpreting the performance evaluation results in this study .'], ['We obtained network predictions for 11 tumor types listed in Table 2 by using the 13 network inference methods listed in Table 1 .', 'We employed precision–recall curves to first find the optimal parameter values for each method , and then to compare the performance of methods using their optimal values .', 'The precision-recall ( PR ) curves were constructed by first ranking an edge list based on significance , and then plotting precision and recall on the y and x axis respectively for cumulatively increasing numbers of the top ( the most significant ) edges from the list .', 'The trade-off between precision and recall at different cutoffs gives a reliable idea about the performance of a method , and this performance can be quantified with the area under the precision-recall curve ( AUPR ) .', 'The performance comparison for 13 methods was done separately for each tumor type .', 'For a given tumor type , our procedure involved two steps .', 'In the first step , we aimed to put all methods on an equal footing by finding each method’s optimal parameter values .', 'This was achieved by running each method multiple times with different parameter values obtained from a one- or two-dimensional grid , computing the AUPRs for the resulting gene lists , and then finding the parameter or parameter combination with the highest AUPR .', 'The parameters of each method and the design of the grid search are listed in Table A in S1E Text .', 'In the second step , the highest AUPR values from all methods were compared to determine the method with the best performance .', 'This procedure was repeated for each one of the 11 tumor types .', 'Therefore the best-performing method may be different for each one of the tumor types .', 'There is , however , a caveat concerning the computation of AUPRs from the entire span of the PR curves .', 'We observe in PR curves that ( 1 ) there is no significant difference among methods beyond a 10% recall level , and ( 2 ) the precision level of network predictions is very low when recall is 10% or higher , suggesting that network predictions are more likely to be affected by noise .', 'The PR curves for BRCA and GBM are shown in Fig 2A as representative examples of these two phenomena .', 'Therefore , we chose to use AUPR only from the 0–10% recall range ( i . e . limited-recall ) , and not from the entire recall range ( i . e . full-recall ) for the comparison of parameter configurations or the comparison of methods .', 'As the parameter configuration that optimizes AUPR in the limited-recall span can be different from that in the full-recall span , some methods were observed to have different PR curves for the limited-recall case ( Fig 2B ) .', 'The subsequent analysis is carried out with network predictions from the limited-recall case .', 'The optimal parameter values and the number of edges in the limited-recall case for each method and tumor type are shown in S5 and S6 Figs respectively .', 'After identifying the PR curves to compare the methods , we asked whether any particular method is a clear winner by being the best in all of the 11 tumor types .', 'The AUPR values in Fig 3A indicate that there is no single method that performs the best for all investigated tumor types .', 'The tumor types in this figure are ordered from left to right according to increasing coefficient of variation .', 'The differences in the tumor-wise AUPR means and variances indicate that the 11 tumor types are not equally amenable to network inference with RPPA data .', 'These differences could partially be explained by the different statistics of inferred networks such as average-node-degree and network density , which we found to be negatively correlated with AUPR ( Spearman r = –0 . 626 and –0 . 453 respectively ) ( S1B Text , S1 Fig ) .', 'Given the absence of a clear winner among the methods , we next asked what the overall best-performing methods were .', 'To achieve an overall comparison of the methods , we ranked them across all tumor types based on ( 1 ) overall AUPR and ( 2 ) overall AUPR rank .', 'For these two criteria , we computed respectively the sum of a method’s AUPR values in the investigated tumor types ( Fig 3B , left panel ) , and the sum of its AUPR ranks in the same tumor types ( Fig 3B , right panel ) .', 'The different-colored segments in horizontal bars correspond to tumor types as shown in the legend .', 'The numbers next to the horizontal bars indicate the rank of the method for the relevant criterion .', 'Higher AUPR values but lower AUPR ranks indicate better performance .', 'Therefore , the best rank of 1 is given to the highest overall AUPR and the lowest overall AUPR rank .', 'We observe in Fig 3B that the overall AUPR values ( left ) did not show as wide a variability across methods as the overall AUPR ranks ( right ) .', 'This might be due to the overfitting of the methods to the benchmark network , as each method was run with parameters that optimize performance ( AUPR ) against the same benchmark .', 'The small differences in overall AUPR values suggest that these methods may have a general capacity to achieve similar performance in other contexts as long as their respective parameter space is sufficiently explored .', 'However , such similarity in performance does not preclude the possibility that some methods consistently outperform others even if by small margins .', 'To investigate this possibility , we ordered the methods from top to bottom according to increasing overall AUPR rank .', 'This choice in the ordering shows that RIDGENET is the best-performing method overall .', 'Broken down by tumor type , RIDGENET is the best for BRCA , OV , UCEC , BLCA and KIRC; but is not as good as ARACNE variants for HNSC , LUSC , LUAD , GBM , COAD , and READ .', 'On the poor performance side , SIMPLEPARCOR has the worst rank according to both the overall AUPR and the overall AUPR rank ( Fig 3B ) .', 'We next investigated the level of similarity among the network predictions of all 13 methods .', 'One question here is whether the network predictions , as given by the inferred edge weights , would cluster the methods according to shared properties , such as the regularization technique , or the algorithm family listed in Table 1 .', 'To this end , we created one vector for each method by stacking the relevant edge weights from all 11 tumor types .', 'We then computed the Spearman correlation between each pair of methods , and also performed dimensionality reduction on the same vectors using principal component analysis ( PCA ) .', 'Unsupervised clustering on the Spearman correlation matrix ( hierarchical clustering with complete linkage and Euclidean distance ) and PCA on the edge weight matrix reveal concordant results in terms of the grouping of the methods ( Fig 3C and 3D ) .', 'We observe three major groups of methods in Fig 3C: ( 1 ) Mutual information-based methods ARACNE ( variants ) , CLR , MRNET , ( 2 ) correlation-based methods SPEARMANCOR and PEARSONCOR , and ( 3 ) partial correlation-based methods .', 'SIMPLEPARCOR from the third group can be considered an outlier compared with the other partial correlation methods .', 'Therefore , if we remove it as a separate group , the remaining partial correlation methods RIDGENET , LASSONET , ELASTICNET , PLSNET , GLASSO , GENENET can also be categorized as ‘regularized methods’ .', 'In the PCA plots , the 1st principal component ( PC ) primarily separates the correlation-based methods SPEARMANCOR and PEARSONCOR from the others , accounting for 53% of the variance ( Fig 3D ) .', 'Correlation methods are fundamentally different from other investigated methods because they do not attempt to eliminate transitive edges in any way .', 'This defect could predict poor performance for both SPEARMANCOR and PEARSONCOR .', 'However , the superior overall performance of the rank-based SPEARMANCOR compared with the value-based PEARSONCOR and several regularized methods ( Fig 3B ) could be due to the ability of SPEARMANCOR to capture nonlinear relationships and/or its robustness against outliers .', 'The 2nd PC ( 23 . 4% variance ) separates SIMPLEPARCOR , a method that is based on Gaussian graphical models and that employs the sub-optimal pseudo-inverse technique when the covariance matrix is singular .', 'Even when the covariance matrix is non-singular , the inversion of the covariance matrix without any regularization is known to introduce defects into the inference procedure unless the number of samples is at least twice the number of features [16] .', 'As the cohort sizes in this study are less than twice the number of antibodies ( 2*187 = 374 ) for 7 of the 11 tumor types ( Table 2 ) , it is not surprising that SIMPLEPARCOR has poor performance in these tumor types , hence the poorest overall performance by a margin ( Fig 3B ) .', 'Indeed , we can observe that the tumor types where SIMPLEPARCOR achieves relatively better ranks are BRCA , OVCA , KIRC , and UCEC , the four tumor types that have cohort size greater than 374 ( Fig 3A and 3B and Table 2 ) .', 'The 3rd PC ( 8 . 1% variance ) achieves the separation of mutual information methods from regularized methods .', 'Mutual information-based methods have the capability to model nonlinear relationships , but are not able to infer the direction of the relationship .', 'These two fundamental differences may account for the clear separation of these methods from the others .', 'Principal components can achieve a separation of regularization-based methods only at the 5th and 6th PC , which account for as little as 4% and 1 . 4% of the variance respectively ( Fig 3D ) .', 'The modest differences between overall AUPR values in the left panel of Fig 3B , and also the lack of a consistently best-performing method in all tumor types are reasons to refrain from recommending one method as the best off-the-shelf method for PPI inference .', 'Therefore , we propose a set of high performers by taking into consideration both the overall AUPR and the overall AUPR rank criteria .', 'The methods that rank in the top six according to both of these criteria are the same six methods: RIDGENET , ARACNE-M , ARACNE-A , LASSONET , CLR , and SPEARMANCOR ( Fig 3B ) .', 'This set of high performers , referred to as TOP6 from here on , includes representative methods from all algorithm families in Table 1 except for inverse covariance-based partial correlation methods .', 'This may be indicative of inverse covariance being a poor framework to model PPIs in cancer especially if the cohort size is not several times as large as the number of proteins .', 'In contrast , linear measures such as correlation and ( ℓ1- or ℓ2- regularized ) partial correlation , and also nonlinear measures such as mutual information are all represented in the set of high performers .', 'Although ARACNE-M and ARACNE-A differ only in the form of the threshold ( i . e . multiplicative or additive ) used to remove the weakest edge in a triplet , the networks inferred by these methods are a function of the user-specified threshold values ( S1F Text ) , and thus are not necessarily similar .', 'We next asked how the network predictions from the TOP6 methods cluster the 11 tumor types .', 'However , similar to the reduction from 13 methods to the TOP6 methods , it was necessary to apply a significance threshold for edges before performing the clustering .', 'P-values were not a viable option as significance scores because several methods did not return p-values .', 'Even if p-values were obtained from all methods , it would not be possible to combine the p-values in this study in a statistically sound way because all methods used the same data , hence violating the independence requirement .', 'Therefore , we resorted to an alternative method and used edge ranks as a nonparametric proxy for the importance of edges .', 'For a given tumor type , we computed ( 1 ) consensus edge ranks by taking the average of ranks from the TOP6 methods , and ( 2 ) consensus edge weights by taking the average of weights again from the TOP6 methods .', 'The consensus ranks served as a nonparametric proxy for our importance levels , while the consensus weights were used in the clustering steps .', 'Comparing consensus edge ranks obtained from the TOP6 methods with those obtained from all 13 methods ( ALL13 ) showed that the TOP6 methods yielded slightly higher AUPR than ALL13 against the Pathway Commons gold standard ( S3B Fig , S1C Text ) .', 'This finding confirmed the use of TOP6 as a superior choice over ALL13 .', 'The number of edges to use for the unsupervised clustering of tumor types was determined in the following way .', 'For a certain threshold , we extracted all edges from a given tumor type that have a consensus edge rank smaller ( more significant ) than the threshold level .', 'We then formed a matrix of edges by tumor types by combining extracted edges from all 11 tumor types .', 'Next , we computed the PCs constructed as a linear combination of the tumor-type vectors , and inspected the behavior of the percentage of variance explained by the first three PCs as the rank threshold was varied from 25 to 2000 .', 'We observed that the sum of the variance percentages from the first three PCs exhibited an inflection point at rank 425 , and thus determined 425 as the consensus rank threshold that determined significant and non-significant edges in each tumor type ( S4b Fig , S1D Text ) .', 'Using the consensus rank threshold of 425 , we investigated the natural groupings in the set of 11 tumor types when each tumor type was represented with the consensus edge weights obtained from the TOP6 methods .', 'The number of edges in each tumor type that pass the consensus rank threshold is shown in Table B in S1E Text .', 'The union set of these significant edges from the tested tumor types has 1008 edges .', 'We refer to this union set as the discovery set , and use it to perform PCA and hierarchical clustering of tumor types .', 'The edges in the discovery set and the corresponding weights in the 11 tumor types are given in S1A Table .', 'We note that , among the 187 antibodies in our dataset , all but STAT3_pY705 has at least one interaction in the discovery set ( N = 186 ) .', 'We see in the PCA that PC1 and PC2 jointly separate the 11 tumor types into three groups , and also that PC3 further breaks down one group into two to result in a total of four groups:', '1 ) COAD , READ;', '2 ) LUSC , LUAD , HNSC;', '3 ) GBM , KIRC; and', '4 ) OV , BRCA , BLCA , and UCEC ( Fig 4A ) .', 'These results are concordant with the clusters from hierarchical clustering ( Fig 4B dendrogram ) and also with the previously defined Pan-Cancer groups in the literature , as we elaborate below .', 'As for the first group , COAD and READ have previously been shown to cluster together in the Pan-Cancer subtypes defined both by RNA expression[27] and by protein expression[4] .', 'These tumors have also been shown to have common DNA-based drivers ( mutations and somatic copy number alterations ) , and hence have been treated as one disease [2 , 3 , 28] .', 'Our finding that COAD and READ have the highest percentage of shared PPIs in this study ( Fig 4B heat map ) is also in line with these observations .', 'Note that the order of tumor types in the heat map is taken from the dendrogram on the left , and that each cell represents the Jaccard index , i . e . the fraction of the intersection set over the union set of edges from two tumor types .', 'The tumors in the second group ( LUSC , LUAD , and HNSC ) have also been previously assigned to a single Pan-Cancer subtype in terms of protein expression[4] .', 'However , RNA expression and somatic copy-number alteration ( SCNA ) data types have divided these tumor types into two groups: ( 1 ) a squamous-like subtype including HNSC and LUSC , and ( 2 ) a separate LUAD-enriched group [3 , 27] .', 'In contrast to this separation where cell histology plays a more important role , both protein expression levels and PPI weights primarily separate these three tumor types based on tissue of origin: ( 1 ) lung-derived tumors LUAD and LUSC , and ( 2 ) a separate HNSC group ( Fig 4B dendrogram and [4] ) .', 'Tumors in the third and fourth groups ( GBM , KIRC , OV , UCEC , BRCA , and BLCA ) can be separated along a continuum in the PC3 dimension ( Fig 4A ) .', 'However , we can consider GBM and KIRC as a separate group as these two tumor types separate from the other four in the unsupervised clustering dendrogram in Fig 4B .', 'GBM and KIRC also cluster most closely among this set of 11 tumor types according to somatic copy-number alterations and protein expression levels [3 , 4] .', 'However , KIRC also shows an outlier behavior for PPI networks in that it exhibits the lowest fraction of shared PPIs with other tumor types ( Fig 4B ) .', 'GBM , on the other hand , has an outlier property by being on one extreme of the separation along the PC3 dimension .', 'This may reflect the fact that GBM samples arise from glial cells in the brain , a histological origin that shows marked differences from epithelial cells .', 'Indeed , GBM was previously shown to have a distinct cluster comprised of only GBM samples in terms of both RNA and protein expression levels [4 , 27] .', 'The fourth group contains OV , UCEC , BRCA , and BLCA; the first three of which are categorized as women’s cancers .', 'The proximity of women’s cancers in clustering results may point to female hormones , such as estrogen and progesterone , causing a similar profile of PPI weights .', 'BLCA is most similar to women’s cancers ( Fig 4B ) , but it also is on one extreme of the separation along the PC3 dimension .', 'This is concordant with the previously discovered Pan-Cancer subtypes because BLCA was shown to have the characteristic property of being one of the most diverse tumor types in the TCGA Pan-Cancer dataset .', 'It had samples in 7 major RNA expression subtypes , and histologies in squamous , adenocarcinoma , and other variants in bladder carcinoma [27] .', 'Next , we performed unsupervised clustering and community detection on the 1008 discovery set interactions to investigate patterns both among the interactions and also in the network formed by the interactions .', 'Unsupervised hierarchical clustering of the 1008 discovery set PPIs shows that these interactions form three main groups ( Fig 5 ) : ( 1 ) a positive dominant group where interactions generally have positive consensus weight and occurrence in multiple tumor types ( mean pan-cancer weight = 0 . 25 , mean pan-cancer recurrence = 4 . 42 , N = 136 , recurrence for an individual edge is computed over the binary values in S1B Table ) , ( 2 ) a negative dominant group where interactions generally have negative consensus weight ( mean pan-cancer weight = –0 . 099 , mean pan-cancer recurrence = 1 . 2 , N = 133 ) , and ( 3 ) a heterogeneous group ( mean pan-cancer weight = 0 . 093 , mean pan-cancer recurrence = 1 . 56 , N = 739 ) which is a mixture of positive and negative , and also recurrent and non-recurrent interactions ( S1A Table ) .', 'In this set of 1008 most significant edges , both the number and the overall weight of negative interactions are smaller with respect to positive interactions .', 'This may indicate either the lower prevalence of mutual exclusivity relationships for in vivo protein-protein interactions , or merely the difficulty of discovering negative PPIs from RPPA data .', 'We next visualized as networks the positive dominant , negative dominant and heterogeneous groups of interactions in order to gain insight on the related biological processes .', 'However , the number of interactions in the heterogeneous group ( N = 739 ) is too large to allow a clear interpretation of the results .', 'Thus , we investigated whether the complete set of 1008 edges could further be broken down into densely connected modules ( with high level of intra-module connectivity , and relatively lower levels of inter-module connectivity ) .', 'To this end , we employed five different community detection algorithms: ( 1 ) fast greedy modularity optimization[29] , ( 2 ) a spin-glass model from statistical mechanics coupled with simulated annealing for optimization[30] , ( 3 ) multi-level modularity optimization[31] , ( 4 ) an information theoretic approach that minimizes the expected description length of a random walker trajectory[32] , and ( 5 ) random walk-based Walktrap community finding algorithm[33] .', 'In the discovery set network , these algorithms detected 6 , 8 , 6 , 11 , and 22 modules respectively with similar and relatively high modularity scores ( range 0 . 41–0 . 44 , modularity due to [29] ) .', 'Even though the number of detected modules was variable across the methods , we defined a consensus measure to identify the agreement/disagreement between the five predictions .', 'For each antibody pair , the number of methods out of five , i . e . the frequency , of being predicted to be in the same module was utilized as a measure to quantify the level of method concordance .', 'The consensus matrix of frequencies formed this way revealed four robust ( Modules 1–4 ) , and two less robust ( Modules 5–6 ) modules among the discovery set interactions ( Fig 6 , S2A Table for consensus matrix , S2B Table for module membership of antibodies , S1A Table for module membership of interactions ) .', 'The four robust modules ( 1–4 ) discovered in Fig 6 are also the densely connected ones with per antibody averages of 7 . 72 , 8 . 24 , 9 . 24 , and 7 . 875 interactions respectively ( Table 3 ) .', 'Mapping the positive dominant , negative dominant , and heterogeneous group memberships onto Module 1 reveals that 88% ( 170/193 ) of the edges in Module 1 are from the heterogeneous group ( Fig 7 ) .', 'The major hubs in this module , N . Cadherin ( 22 edges ) , Mre11 ( 21 edges ) , and Bid ( 15 edges ) , have predominantly heterogeneous-group edges ( Table 3 ) .', 'Interactions in this module may play roles in cell cycle , DNA damage repair , apoptosis , hormone and receptor tyrosine kinase ( RTK ) signaling pathways .', 'Modules 2 and 4 are distinguishable from the other modules by having zero edges from the negative dominant group , and an abundance of edges from both the positive dominant and heterogeneous groups ( Table 3 , Fig 7 ) .', 'Interestingly , 91% ( 30/33 ) of the antibodies in Module 2 are phosphospecific .', 'On the contrary , only 6 . 2% ( 1/16 ) of the antibodies in Module 4 are phosphospecific .', 'These results raise the question whether significant correlations can only be found between antibodies of the same type ( i . e . phosphospecific with each other and non-phosphospecific with each other ) .', 'To address this question , we analyzed the interactions in Module 1 , which is the only module other than Module 2 that contains a subtantial number of phosphospecific antibodies ( N = 10 ) .', 'In this module , phosphospecific antibodies have 64 interactions , but only 14 of these are between two phosphospecific antibodies ( 22% ) ( S3 Table ) .', 'This result shows that it is possible to observe significant correlations between phosphospecific and non-phosphospecific antibodies , and suggests that there may be biological differences between Module 1 and Module 2 antibodies that lead to the differences in correlation patterns .', 'We next compared the biological functions of the phosphospecific antibodies in Module 1 and 2 to investigate potential differences .', 'Processes such as cell cycle , proliferation , RTK signaling , RAS/MAPK si
['Introduction', 'Results', 'Discussion', 'Methods']
['Protein expression and post-translational modification levels are tightly regulated in neoplastic cells to maintain cellular processes known as ‘cancer hallmarks’ .', 'The first Pan-Cancer initiative of The Cancer Genome Atlas ( TCGA ) Research Network has aggregated protein expression profiles for 3 , 467 patient samples from 11 tumor types using the antibody based reverse phase protein array ( RPPA ) technology .', 'The resultant proteomic data can be utilized to computationally infer protein-protein interaction ( PPI ) networks and to study the commonalities and differences across tumor types .', 'In this study , we compare the performance of 13 established network inference methods in their capacity to retrieve the curated Pathway Commons interactions from RPPA data .', 'We observe that no single method has the best performance in all tumor types , but a group of six methods , including diverse techniques such as correlation , mutual information , and regression , consistently rank highly among the tested methods .', 'We utilize the high performing methods to obtain a consensus network; and identify four robust and densely connected modules that reveal biological processes as well as suggest antibody–related technical biases .', 'Mapping the consensus network interactions to Reactome gene lists confirms the pan-cancer importance of signal transduction pathways , innate and adaptive immune signaling , cell cycle , metabolism , and DNA repair; and also suggests several biological processes that may be specific to a subset of tumor types .', 'Our results illustrate the utility of the RPPA platform as a tool to study proteomic networks in cancer .']
['Pan-cancer proteomic datasets from The Cancer Genome Atlas provide a unique opportunity to study the functions of proteins in human cancers .', 'Such datasets , where proteins are measured in different conditions and where correlations are informative , can enable the discovery of potentially causal protein-protein interactions , which may in turn shed light on the function of proteins .', 'However , it has been shown that the dominant correlations in a system can be the result of parallel transitive ( i . e . indirect ) interactions .', 'A wide suite of computational methods has been proposed in the literature for the discrimination between direct and transitive interactions .', 'These methods have been extensively tested for their performance in gene regulatory network inference due to the prevalence of mRNA data .', 'However , the understanding of the performance and limitations of these methods in retrieving curated pathway interactions is lacking .', 'Here , we utilize a high-throughput proteomic dataset from The Cancer Genome Atlas to systematically test different families of network inference methods .', 'We observe that most methods are able to achieve a similar level of performance provided their parameter space is sufficiently explored; but a group of six methods consistently rank highly among the tested methods .', 'The protein-protein interactions inferred by the high-performing methods reveal the pathways that are shared by or specific to different cancer types .']
['medicine and health sciences', 'squamous cell lung carcinoma', 'protein interactions', 'applied mathematics', 'protein interaction networks', 'carcinomas', 'cancers and neoplasms', 'simulation and modeling', 'oncology', 'algorithms', 'protein expression', 'mathematics', 'network analysis', 'head and neck tumors', 'molecular biology techniques', 'research and analysis methods', 'head and neck squamous cell carcinoma', 'computer and information sciences', 'gene mapping', 'proteins', 'lung and intrathoracic tumors', 'biological databases', 'proteomics', 'molecular biology', 'molecular biology assays and analysis techniques', 'gene expression and vector techniques', 'biochemistry', 'proteomic databases', 'database and informatics methods', 'biology and life sciences', 'squamous cell carcinomas', 'physical sciences']