A data-driven interactome of synergistic genes improves network-based cancer outcome prediction
Metastases at distant sites ( e . g . in bone , lung , liver and brain ) is the major cause of death in breast cancer patients 1 ., However , it is currently difficult to assess tumor progression in these patients using common clinical variables ( e . g . tumor size , lymph-node status , etc . ) 2 ., Therefore , for 80% of these patients , chemotherapy is prescribed 3 ., Meanwhile , randomized clinical trials showed that at least 40% of these patients survive without chemotherapy and thus unnecessarily suffer from the toxic side effect of this treatment 3 , 4 ., For this reason , substantial efforts have been made to derive molecular classifiers that can predict clinical outcome based on gene expression profiles obtained from the primary tumor at the time of diagnosis 5 , 6 ., An important shortcoming in molecular classification is that ‘cross-study’ generalization is often poor 7 ., This means that prediction performance decreases dramatically when a classifier trained on one patient cohort is applied to another one 8 ., Moreover , the gene signatures found by these classifiers vary greatly , often sharing only few or no genes at all 9–11 ., This lack of consistency casts doubt on whether the identified signatures capture true ‘driver’ mechanisms of the disease or rather subsidiary ‘passenger’ effects 12 ., Several reasons for this lack of consistency have been proposed , including small sample size 11 , 13 , 14 , inherent measurement noise 15 and batch effects 16 , 17 ., Apart from these technical explanations , it is recognized that traditional models ignore the fact that genes are organized in pathways 18 ., One important cancer hallmark is that perturbation of these pathways may be caused by deregulation of disparate sets of genes which in turn complicates marker gene discovery 19 , 20 ., To alleviate these limitations , the classical models are superseded by Network-based Outcome Predictors ( NOP ) which incorporate gene interactions in the prediction model 21 ., NOPs have two fundamental components: aggregation and prediction ., In the aggregation step , genes that interact , belong to the same pathway or otherwise share functional relation are aggregated ( typically by averaging expressions ) into so called “meta-genes” 22 ., This step is guided by a supporting data source describing gene-gene interactions such as cellular pathway maps or protein-protein interaction networks ., In the consequent prediction step , meta-genes are selected and combined into a trained classifier , similar to a traditional classification approach ., Several NOPs have been reported to exhibit improved discriminative power , enhanced stability of the classification performance and signature and better representation of underlying driving mechanisms of the disease 18 , 23–25 ., In recent years , a range of improvements to the original NOP formulation has been proposed ., In the prediction step , various linear and nonlinear classifiers have been evaluated26 , 27 ., Problematically , the reported accuracies are often an overestimation as many studies neglected to use cross-study evaluation scheme which more closely resembles the real-world application of these models 7 ., Also for the aggregation step , which is responsible for forming meta-genes from gene sets , several distinct approaches are proposed such as clustering 23 and greedy expansion of seed genes into subnetworks 18 ., Moreover , in addition to simple averaging , alternative means by which genes can be aggregated , such as linear or nonlinear embeddings , have been proposed 17 , 28 ., Most recent work combines these steps into a unified model 8 , 29 ., Recent efforts that extend these concepts to sequencing data by exploiting the concept of cancer hallmark networks have also been proposed 30 ., Despite these efforts and initial positive findings , there is still much debate over the utility of NOPs compared to classical methods , with several studies showing no performance improvement 21 , 31 , 32 ., Perhaps even more striking is the finding that utilizing a permuted network 32 or aggregating random genes 10 performs on par with networks describing true biological relationships ., Several meta-analyses attempting to establish the utility of NOPs have appeared with contradicting conclusions ., Notably , Staiger et al . compared performance of nearest mean classifier 33 in this setting and concluded that network derived meta-genes are not more predictive than individual genes 21 , 32 ., This is in contradiction to Roy et al . who achieved improvements in outcome prediction when genes were ranked according to their t-test statistics compared to their page rank property 34 in PPI network 28 , 35 ., It is thus still an open question whether NOPs truly improve outcome prediction in terms of predictive performance , cross-study robustness or interpretability of the gene signatures ., A critical—yet often neglected—aspect in the successful application of NOPs is the contribution of the biological network ., In this regard , it should be recognized that many network links are unreliable 36 , 37 , missing 38 or redundant 39 and considerable efforts are being made to refine these networks 38 , 40–42 ., In addition , many links in these networks are experimentally obtained from model organisms and therefore may not be functional in human cells 43–45 ., Finally , most biological networks capture only a part of a cell’s multifaceted system 46 ., This incomplete perspective may not be sufficient to link the wide range of aberrations that may occur in a complex and heterogeneous disease such as breast cancer 47 , 48 ., Taken together , these issues raise concerns regarding the extent to which the outcome predictors may benefit from inclusion of common biological networks in their models ., In this work , we propose to construct a network ab initio that is specifically designed to improve outcome prediction in terms of cross-study generalization and performance stability ., To achieve this , we will effectively turn the problem around: instead of using a given biological network , we aim to use the labelled gene expression datasets to identify the network of genes that truly improves outcome prediction ( see Fig 1 for a schematic overview ) ., Our approach relies on the identification of synergistic gene pairs , i . e . genes whose joint prediction power is beyond what is attainable by both genes individually 49 ., To identify these pairs , we employed grid computing to evaluate all 69 million pairwise combinations of genes ., The resulting network , called SyNet , is specific to the dataset and phenotype under study and can be used to infer a NOP model with improved performance ., To obtain SyNet , and allow for rigorous cross-study validation , a dataset of substantial size is required ., For this reason , we combined 14 publicly available datasets to form a compendium encompassing 4129 survival labeled samples ., To the best of our knowledge , the data combined in this study represents the largest breast cancer gene expression compendium to date ., Further , to ensure unbiased evaluation , sample assignments in the inner as well as the outer cross-validations folds are kept equal across all assessments throughout the paper ., In the remainder of this paper , we will demonstrate that integrating genes based on SyNet provides superior performance and stability of predictions when these models are tested on independent cohorts ., In contrast to previous reports , where shuffled versions of networks also performed well , we show that the performance drops substantially when SyNet links are shuffled ( while containing the same set of genes ) , suggesting that SyNet connections are truly informative ., We further evaluate the content and structure of SyNet by overlaying it with known gene sets and existing networks , revealing marked enrichment for known breast cancer prognostic markers ., While overlap with existing networks is highly significant , the majority of direct links in SyNet is absent from these networks explaining the observed lack of performance when NOPs are guided by the phenotype-unaware networks ., Interestingly , SyNet links can be reliably predicted from existing networks when more complex topological descriptors are employed ., Taken together , our findings suggest that compared to generic gene networks , phenotype-specific networks , which are derived directly from labeled data , can provide superior performance while at the same time revealing valuable insight into etiology of breast cancer ., We first evaluated NOP performance for three existing methods ( Park , Chuang and Taylor ) and the Group Lasso ( GL ) when supplied with a range of networks , including generic networks , tissue-specific networks and SyNet ., As a baseline model , we used a Lasso classifier trained using all genes in our expression dataset ( n = 11748 ) without network guidance ., The Lasso exhibits superior performance among many linear and non-linear classifiers evaluated on our expression dataset ( see S3 for details ) ., The AUC of the four NOPs , presented in Fig 2 , clearly demonstrates that SyNet improves the performance of all NOPs , except for the Park method in which it performs on par to the Correlation ( Corr ) network ., Notably , SyNet is inferred using training samples only , which prevents “selection bias” in our assessments 50 ., Furthermore , comparison of baseline model performance ( i . e . Fig 2 , rightmost bar ) and other NOPs supports previous findings that many existing NOPs do not outperform regular classifiers that do not use networks 8 , 21 , 32 ., The GL clearly outperforms all other methods , in particular when it exploits the information contained in SyNet ., This corroborates our previous finding 8 that existing methods which construct meta-genes by averaging are suboptimal ( see S1 for a more extensive analysis ) ., The GL using the Corr network also outperforms the baseline model , albeit non-significantly ( p~0 . 6 ) , which is in line with previous reports 23 ., It should be noted that across all these experiments an identical set of samples is used to train the models so that any performance deviation must be due to differences in, ( i ) the set of utilized genes or, ( ii ) the integration of the genes into meta-genes ., In the next two sections , we will investigate these factors in more details ., Networks only include genes that are linked to at least one other gene ., As a result , networks can provide a way of ranking genes based on the number and weight of their connections ., One explanation for why NOPs can outperform regular classifiers is that networks provide an a priori gene ( feature ) selection 32 ., To test this hypothesis and determine the feature selection capabilities of SyNet , we compare classification performances obtained using the baseline classifier ( i . e . Lasso ) that is trained using enclosed genes in each network ., While this classifier performs well compared to other standard classifiers that we investigated ( see S3 for details ) , it cannot exploit information contained in the links of given network ., So , any performance difference must be due to the genes in the network ., The number of genes in each network under study is optimized independently by varying the threshold on the weighted edges in the network and removing unconnected genes ( see section “Regular classifiers and Network based prediction models” for network size optimization details ) ., The edge weight threshold and the Lasso regularization parameter were determined simultaneously using a grid search cross-validation scheme ( see S5 for details ) ., Fig 3 provides the optimal performances for 12 distinct networks along with number of genes used in the final model ( i . e . genes with non-zero Lasso coefficients ) ., We also included the baseline model where all genes ( n = 11748 ) are utilized to train Lasso classifier ( rightmost bar ) ., The results presented in Fig 3a demonstrate that SyNet is the only network that performs markedly better than the baseline model which is trained on all genes ., Interestingly , we observe that SyNet is the top performing network while utilizing a comparable number of genes to other networks ., The second-best network is the Corr network ., We argue that superior performance of SyNet over the Corr network stems from the disease specificity of genes in SyNet which helps the predictor to focus on the relevant genes only ., It should be noted that the data on which SyNet and the Corr network are constructed are completely independent from the validation data on which the performance is based due to our multi-layer cross-validation scheme ( see Methods and S5 ) which avoids selection bias 50 ., We conclude that dataset-specific networks , in particular SyNet which also exploits label information , provides a meaningful feature selection that is beneficial for classification performance ., Our result show that none of the tissue-specific networks outperform the baseline ., Despite the modest performance , it is interesting to observe that performance for these networks increases as more relevant tissues ( e . g . breast and lymph node networks ) are utilized in the classification ., Additionally , we observe that tissue-specific networks do not outperform the generic networks ., This may be the result of the fact that generic networks predominantly contain broadly expressed genes with fundamental roles in cell function which may still be relevant to survival prediction ., A similar observation was made for GWAS where SNPs in these widely-expressed genes can explain a substantial amount of missed heritability 51 ., In addition to classifier performance , an important motivation for employing NOPs is to identify stable gene signatures , that is , the same genes are selected irrespective of the study used to train the models ., Gene signature stability is necessary to confirm that the identified genes are independent of dataset specific variations and therefore are true biological drivers of the disease under study ., To measure the signature consistency , we assessed the overlap of selected genes across all repeats and folds using the Jaccard Index ., Fig 3b shows that a Lasso trained using genes preselected by SyNet , identifies more similar genes across folds and studies compared to other networks ., Surprisingly , despite the fact that the expression data from which SyNet is inferred changes in each classification fold , the signature stability for SyNet is markedly better than for generic or tissue-specific networks that use a fixed set of genes across folds ., Therefore , our results demonstrate that synergistic genes in SyNet truly aid the classifier to robustly select signatures across independent studies ., The ultimate goal of employing NOPs compared to classical models that do not use network information is to improve prognosis prediction by harnessing the information contained in the links of the given network ., Therefore , we next aimed to assess to what extent also connections between the genes , as captured in SyNet and other networks , can help NOPs to improve their performance beyond what is achievable using individual genes ., As before , we utilized identical datasets ( in terms of genes , training and test samples ) in inner and outer cross-validation loops to train all four NOPs as well as the baseline model which uses Lasso trained using all genes ( n = 11748 ) ., Our results presented in Fig 4a , clearly demonstrate that compared to other NOPs under study , GL guided by SyNet achieves superior prognostic prediction for unseen patients selected from an independent cohort ., To confirm that NOP performance using SyNet is the result of the network structure , we also applied the GL to a shuffled version of SyNet ( Fig 4a ) ., We observe a substantial deterioration of the AUC , supporting the conclusion that not only the genes , but also links contained in SyNet are important to achieve good prediction ., Moreover , this observation rules out that the GL by itself is able to provide enhanced performance compared to standard Lasso ., The result of a similar assessment for the Corr network is given in S12 ., Additionally , we found that SyNet remains predictive even when the dataset is down sampled to 25% of samples ( see S13 for details ) ., We also evaluated a recently developed set of subtype-specific networks for breast cancer 52 and found that SyNet markedly outperforms these networks in predictive performance ( see S18 for details ) ., We next assessed the performance gain of the network-guided model compared to a Lasso model that cannot exploit network information ., To this end , the GL was trained based on each network whereas the Lasso is was trained based on the genes present in the network ., Fig 4b demonstrates the results of this analysis ., We find that the largest gain in GL performance is achieved when using SyNet ( Fig 4b , x-axis ) , indicating that the links between genes in SyNet truly aid classification performance beyond what is obtained as a result of the feature selection capabilities of Lasso ., Fig 4c provides the Kaplan-Meier plot when each patient is assigned to a good or poor prognostic class according to frequency of predicted prognosis across 10 repeats ( ties are broken by random assignment to one of the classes ) for Lasso as well as Group Lasso ., Result of this analysis suggests that superior performance of the GL compared to the Lasso is mostly stemming from GLs ability to better discern the patients with poor prognosis ., An important property of an outcome predictor is to exhibit constant performance irrespective of the dataset used for training the model ( i . e . performance stability ) ., This is a highly desirable quality , as concerns have been raised regarding the highly variable performances of breast-cancer classifiers applied to different cohorts 7 , 53 ., To measure performance stability , we calculated the standard deviation of the AUC for Lasso and GL ., The y-axis in Fig 4b represents the average difference of standard deviation for Lasso and GL across all evaluated folds and repeats ( 14 folds and 10 repeats ) ., Based on this figure , we conclude that a NOP model guided by SyNet not only provides superior overall performance , it also offers improved stability of the classification performance ., Finally , we investigated the importance of hub genes in SyNet ( genes with >4 neighbors ) and observe that a comparable performance can be obtained with a network consisting of hub genes exclusively at the cost of reduced performance stability ( see S14 for details ) ., Moreover , we did not observe performance gain for a model that is governed by combined links from multiple networks ( either by intersection or unification , see S15 for details ) ., We further confirmed that the performance gain of the network-guided GL is preserved when networks are restricted to have equal number of links ( see S7 for details ) , or when links with lower confidence are included in the network ( see S16 for details ) ., We also considered the more complex Sparse Group Lasso ( SGL ) , which offers an additional level of regularization ( see S1 Text for details ) ., No substantial difference between GL and SGL performance was found ( see S8 for details ) ., Likewise , we did not observe substantial performance differences when the number of genes , group size and regularization parameters were simultaneously optimized in a grid search ( see S9 for details ) ., Together , these findings can be considered as the first unbiased evidence of true classification performance improvement in terms of average AUC and classification stability by a NOP ., Many curated biological networks suffer from an intrinsic bias since genes with well-known roles are the subject of more experiments and thus get more extensively and accurately annotated 54 ., Post-hoc interpretation of the features used by NOPs , often by means of an enrichment analysis , will therefore be affected by the same bias ., SyNet does not suffer from such bias , as its inference is purely data driven ., Moreover , since SyNet is built based on gene pairs that contribute to the prediction of clinical outcome , we expect that the genes included in SyNet not only relate to breast cancer; they should play a role in determining how aggressively the tumor behaves , how advanced the disease is or how well it responds to treatment ., To investigate the relevance of genes contained in SyNet in the development of breast cancer and , more importantly , clinical outcome , we ranked all pairs according to their median Fitness ( Fij ) across 14 studies and selected the top 300 genes ( encompassing 3544 links ) ., This cutoff was frequently chosen by the GL as the optimal number of genes in SyNet ( see section “SyNet improves NOP performance” ) ., Fig 5 visualizes this network revealing three main subnetworks and a few isolated gene pairs ., We performed functional enrichment for all genes as well as for the subcomponents of the three large subnetworks in SyNet using Ingenuity Pathway Analysis ( IPA ) 55 ., IPA reveals that out of 300 genes in SyNet , 287 genes have a known relation to cancer ( 2e-06<p<1e-34 ) of which 222 are related to reproductive system disease ( 2e-06<p<1e-34 ) ., Furthermore , according to IPA analysis , the top five upstream regulators of genes in SyNet ( orange box , Fig 5 ) are CDKN1A , E2F4 , RABL6 , TP53 and ERBB2 , all of which are well known players in the development of breast cancer56–60 ., The mean degree of the 300 genes in SyNet is 24 , but there are 12 genes which have a degree of 100 or above: ASPM 61 , BUB162 , CCNB2 63 , CDKN3 64 , CENPA 65 , DLGAP5 66 , KIF23 67 , MCM10 68 , MELK 69 , RACGAP1 70 , TTK 71 and UBE2C 72 ., All these genes play a vital role in progression through the cell cycle and mitosis , by ensuring proper DNA replication , correct formation of the mitotic spindle and proper attachment to the centromere ., In addition to a clear involvement of genes linked to breast cancer generically , IPA also finds clear indications that the genes in SyNet are relevant to clinical outcome and prognosis of the disease ., For instance , the most highly enriched cluster ( Fig 5; green cluster ) is found by IPA to be associated to histological grade of the tumor ( p = 6e-201 ) ., The histological grade , which is based on the morphological characteristics of the tumor , has been shown to be informative for the clinical behavior of the tumor and is one of the best-established prognostic markers 73 ., Notably , the blue cluster is enriched for genes involved in tamoxifen resistance ( p<2e-3 ) , one of the important treatments of ER-positive breast cancer ., Two other sub-clusters ( yellow and purple in Fig 5 ) , contain genes from distinctly different biological processes than the main cluster ., In these clusters we also observe clear hub genes: SLC7A7 and CD74 in the yellow and ACKR1 and MFAP4 in the purple cluster ., ACKR1 is a chemokine receptor involved in the regulation of the bio-availability of chemokine levels and MFAP4 is involved in regulating cell-cell adhesion ., The recruitment of cells , as regulated by chemokines , and reducing cell-cell adhesion both play an important role in the process of metastasis ., CD74 has also been linked to metastasis in triple negative breast cancer 74 ., Metastasis , and not the primary tumor , is the main cause of death in breast cancer 3 ., IPA highly significantly identifies the SyNet genes as upstream regulators of canonical pathways implicated in breast cancer ( Fig 5 ) , such as Cell Cycle Control of Chromosomal Replication ( 8e-18 ) , Mitotic Roles of Polo-Like Kinase ( 4e-15 ) , Role of CHK Proteins in Cell Cycle Checkpoint Control ( 6e-12 ) , Estrogen-mediated S-phase Entry ( 2e-11 ) , and Cell Cycle: G2/M DNA Damage Checkpoint Regulation ( 5e-10 ) ., Although all cancer cells deregulate cell cycle control , the degree of dysregulation may contribute to a more aggressive phenotype ., For instance , it is recognized that the downregulation of certain checkpoint regulators is related to a worse prognosis in breast cancer75 , 76 ., In summary , SyNet predominantly appears to contain genes relevant to two main processes in the progression of breast cancer: increased cell proliferation and the process of metastasis ., Although many genes have not previously been specifically linked to breast cancer prognosis , their role in regulating different stages of replication and mitosis points to a genuine biological role in the progression and prognosis of breast cancer ., We next sought to investigate the similarity between SyNet and existing biological networks that directly or indirectly capture biological interactions ., To enable a comparison with networks of different sizes , we compare the observed overlap ( both in terms of genes as well as links ) to the distribution of expected overlap obtained by shuffling each network 1000 times ( while keeping the degree distribution intact ) ., Overlap is determined for varying network sizes by thresholding the link weights such that a certain percentage of genes or links remains ., Results are reported in terms of a z-score in Fig 6 ., Fig 6a shows that for the majority of networks a significantly higher than expected number of SyNet genes is contained in the top of each network ., The overlap is especially pronounced for the tissue-specific networks , in particular the Breast-specific and Lymph node-specific networks , supporting our observation that SyNet contains links that are relevant for breast cancer ., The enrichment becomes even more significant when considering the overlap between the links ( Fig 6b ) ., In this respect , SyNet is also clearly most similar to the Breast-specific and Lymph node-specific networks ., We confirmed that these enrichments are not only driven by the correlation component of SyNet by repeating this analysis with a variant of the SyNet network without the correlation component ( i . e . only average and synergy of gene pairs are used for pair-ranking; see S10 for details ) ., It should moreover be noted that , although a highly significant overlap is observed , the vast majority of SyNet genes and links are not present in the existing networks , explaining the improved performance obtained with NOPs using SyNet ., Specifically , out of the 300 genes in SyNet , only 142 are contained within the top 25% of genes ( n = 1005 ) in the Breast-specific network , and 151 in the top 25% of genes ( n = 1290 ) in the Lymph node-specific network ., Similarly , out of the 3544 links in SyNet , only 1182 are contained within the top 25% of links ( n = 12500 ) in the Breast-specific network , and 617 in the top 25% ( n = 12500 ) of the Lymph node-specific network ( see S11 for details ) ., We further confirmed that the overall trend in observed overlaps between SyNet and other networks does not change when the size of these networks ( in terms of the number of links ) are increased or reduced ( see S17 for details ) ., In addition to direct overlap , we also aimed to investigate if genes directly connected in SyNet may be indirectly connected in existing networks ., To assess this for each pair of genes in SyNet , we computed several topological measures characterizing their ( indirect ) connection in the biological networks ., We included degree ( Fig 7a ) , shortest path ( Fig 7b ) and Jaccard ( Fig 7c ) ( see S1 Text for details ) ., To produce an edge measure from degree and page rank ( which are node based ) , we computed the average degree and page rank of genes in a pair respectively ., Furthermore , we produced an expected distribution for each pair by computing the same topological measures for one of the genes and another randomly selected gene ., The results from this analysis supports our previous observation that the information contained in the links of SyNet is markedly—yet only partially—overlapping with the information in the existing networks ., Notably , the similarity increases for networks of increased relevance to the tissue in which the gene expression data is measured ( i . e . breast tissue ) ., Encouraged by the overlap with existing biological networks , we next asked whether links in SyNet can be predicted from the complete collection of topological measures calculated based on existing networks ., To this end , we characterized each gene-pair by a set of 12 graph-topological measures that describe local and global network structure around each gene-pair ., In addition to the degree , shortest path and Jaccard , we included several additional graph-topological measures including direct link , page rank ( with four betas ) , closeness centrality , clustering coefficient and eigenvector centrality ( see S1 Text for details ) ., While converting node-based measures to edge based measures , in addition to using the average , we also used the difference between the score for each gene in the pair , similar to our previous work 77 ., We applied these measures to all 10 networks in our collection yielding a total of 210 features ., The gene-pairs are labeled according to their presence or absence in SyNet ., Inspection of this dataset using the t-SNE 78 reveals that the links in SyNet occupy a distinct part of the 2D embedding obtained ( Fig 8a ) ., We trained a Lasso and assessed classification performance in a 50-fold cross validation scheme where in each fold 1/50 of pairs in SyNet is kept hidden and the rest of pairs is utilized to train the classifier ., To avoid information leakage in this assessment , we removed gene pairs from the training set in case one of the genes is present in the test set ., Based on this analysis we find that a simple linear classifier can reach ~85% accuracy in predicting the synergistic gene relationships from SyNet ( Fig 8b , rightmost bar ) ., The contribution from generic networks is notably smaller than for the tissue-specific networks ., In particular the networks relevant to breast cancer are highly informative , to the extent that combining multiple networks no longer improves prediction performance ., Further investigation of feature importance revealed that the page rank topological measure was commonly used as a predictive marker across folds ., Apparently , while direct overlap between SyNet and existing networks is modest , the topology of the relevant networks ( i . e . breast-specific and lymph node-specific networks ) are highly informative for the links contained in SyNet ., This corroborates findings from Winter et al . in which the page rank topological measure was proposed to identify relevant genes in outcome prediction 34 , 35 , 79 ., Although the principle of using existing knowledge of the cellular wiring diagram to improve performance , robustness and interpretability of gene expression classifiers appears attractive , contrasting reports on the efficacy of such approach have appeared in literature 21 , 28 , 35 ., Consensus in this field has particularly been frustrated by an evaluation of a limited set of sub-optimal classifiers 21 , 23 , 28 , 35 , small sample size 18 , 24 , 26 , or the use of standard K-fold cross-validation instead of cross-study evaluation schemes , which results in inflated performance estimates 24 , 26 ., For this reason , it remained unclear if network-based classification , and in particular network-based outcome prediction , is beneficial ., Here , we present a rigorously cross-validated procedure to train and evaluate Group Lasso-based NOPs using a variety of networks , including tissue-specific networks in particular , which have not been evaluated in the context of NOPs before ., Based on our analyses , we conclude that none of the existing networks achieve improved performance compared to using properly regularized classifiers trained on all genes ., In this work we therefore present a novel gene network , called SyNet , which is computationally derived directly from the survival-labeled sam
Robustly predicting outcome for cancer patients from gene expression is an important challenge on the road to better personalized treatment ., Network-based outcome predictors ( NOPs ) , which considers the cellular wiring diagram in the classification , hold much promise to improve performance , stability and interpretability of identified marker genes ., Problematically , reports on the efficacy of NOPs are conflicting and for instance suggest that utilizing random networks performs on par to networks that describe biologically relevant interactions ., In this paper we turn the prediction problem around: instead of using a given biological network in the NOP , we aim to identify the network of genes that truly improves outcome prediction ., To this end , we propose SyNet , a gene network constructed ab initio from synergistic gene pairs derived from survival-labelled gene expression data ., To obtain SyNet , we evaluate synergy for all 69 million pairwise combinations of genes resulting in a network that is specific to the dataset and phenotype under study and can be used to in a NOP model ., We evaluated SyNet and 11 other networks on a compendium dataset of >4000 survival-labelled breast cancer samples ., For this purpose , we used cross-study validation which more closely emulates real world application of these outcome predictors ., We find that SyNet is the only network that truly improves performance , stability and interpretability in several existing NOPs ., We show that SyNet overlaps significantly with existing gene networks , and can be confidently predicted ( ~85% AUC ) from graph-topological descriptions of these networks , in particular the breast tissue-specific network ., Due to its data-driven nature , SyNet is not biased to well-studied genes and thus facilitates post-hoc interpretation ., We find that SyNet is highly enriched for known breast cancer genes and genes related to e . g . histological grade and tamoxifen resistance , suggestive of a role in determining breast cancer outcome .
Cancer is caused by disrupted activity of several pathways ., Therefore , to predict cancer patient prognosis from gene expression profiles , it may be beneficial to consider the cellular interactome ( e . g . the protein interaction network ) ., These so-called Network based Outcome Predictors ( NOPs ) hold the potential to facilitate identification of dysregulated pathways and delivering improved prognosis ., Nonetheless , recent studies revealed that compared to classical models , neither performance nor consistency ( in terms of identified markers across independent studies ) can be improved using NOPs ., In this work , we argue that NOPs can only perform well when supplied with suitable networks ., The commonly used networks may miss associations specially for under-studied genes ., Additionally , these networks are often generic with low coverage of perturbations that arise in cancer ., To address this issue , we exploit ~4100 samples and infer a disease-specific network called SyNet linking synergistic gene pairs that collectively show predictivity beyond the individual performance of genes ., Using a thorough cross-validation , we show that a NOP yields superior performance and that this performance gain is the result of the wiring of genes in SyNet ., Due to simplicity of our approach , this framework can be used for any phenotype of interest ., Our findings confirm the value of network-based models and the crucial role of the interactome in improving outcome prediction .
medicine and health sciences, genetic networks, breast tumors, statistics, protein interaction networks, cancers and neoplasms, oncology, mathematics, forecasting, network analysis, genome analysis, research and analysis methods, computer and information sciences, genomics, mathematical and statistical techniques, gene expression, breast cancer, proteomics, prognosis, biochemistry, diagnostic medicine, gene regulatory networks, gene identification and analysis, genetics, biology and life sciences, physical sciences, computational biology, gene prediction, statistical methods