Parkinson’s disease (PD) is a prime example of a complex and heterogeneous disorder, characterized by multifaceted and varied motor- and non-motor symptoms and different possible interplays of genetic and environmental risk factors. While investigations of individual PD-causing mutations and risk factors in isolation are providing important insights to improve our understanding of the molecular mechanisms behind PD, there is a growing consensus that a more complete understanding of these mechanisms will require an integrative modeling of multifactorial disease-associated perturbations in molecular networks. Identifying and interpreting the combinatorial effects of multiple PD-associated molecular changes may pave the way towards an earlier and reliable diagnosis and more effective therapeutic interventions. This review provides an overview of computational systems biology approaches developed in recent years to study multifactorial molecular alterations in complex disorders, with a focus on PD research applications. Strengths and weaknesses of different cellular pathway and network analyses, and multivariate machine learning techniques for investigating PD-related omics data are discussed, and strategies proposed to exploit the synergies of multiple biological knowledge and data sources. A final outlook provides an overview of specific challenges and possible next steps for translating systems biology findings in PD to new omics-based diagnostic tools and targeted, drug-based therapeutic approaches. Parkinson’s disease (PD) is one of the most common age-related, neurodegenerative disorders. In spite of 200 years of research on PD since its first published description by James Parkinson : These approaches quantify the statistical over-representation of a list of genes, proteins or metabolites among the members of a pathway using a statistical test . The input list usually corresponds to the biomolecules which displayed a differential abundance in an omics dataset between a condition of interest  as compared to a control condition, according to a chosen test statistic and significance threshold.Geneset enrichment analysis (GSEA): GSEA methods avoid the need for defining a significance threshold and instead assign ranking scores to all biomolecules in the analyzed omics data to test whether the members of a pathway are ranked unexpectedly high or low among them .Network module-based pathway analysis (NMPA): These algorithms exploit prior knowledge from molecular interaction networks to improve the scoring of pathway associations for omics profiling data. Typical NMPA methods first identify dense sub-network regions enriched in biomolecules undergoing activity changes , and, in a second step, quantify associations of these network modules with known pathways.Network topology-based pathway analysis (NTPA): Similar to NMPA approaches, NTPA methods exploit molecular network information to obtain more robust and sensitive pathway association scores, but they avoid the initial module identification step and directly quantify pathway associations using graph-based statistics to assess the network distances and multiplicity of interconnections between the biomolecules of interest and pathway members mapped onto the network.Apart from selecting a pathway collection, researchers also need to choose between a wide range of statistical analysis approaches. In general, these omics-based pathway and geneset enrichment analysis methods can be grouped into four main categories , NMPA and NTPA are hybrid approaches that combine aspects of both network and pathway analysis methods, and provide pathway rankings as the main output rather than altered sub-networks without known pathway annotations. As an important limitation, this also means that NMPA and NTPA approaches will not identify altered network regions that cannot be linked to any known cellular pathway. Moreover, a potential drawback in comparison to classical pathway analysis methods is that NMPA and NTPA statistics often rely heavily on the correctness and comprehensiveness of the underlying molecular interaction data. Similar to classical enrichment analyses, biases, noise, errors and incompleteness of the data used for network-based enrichment analyses can result in false-negative and false-positive findings. While sufficient high-quality molecular interaction data is typically available for the human species and common model organisms like mouse, rat, baker’s yeast and fruit fly, corresponding interaction data resources for other studied organisms may still be too incomplete for an effective application of these network analyses. Finally, similar to ORA approaches, NMPA and NTPA methods rely on differential expression thresholds, which need to be defined by the user.t test or the non-parametric Mann–Whitney U test) or moderated statistical tests with improved feature variance estimation .substantia nigra), using a weighted meta-analysis to combine effect size estimates for pathway-representing genesets across multiple independent datasets . For example, an NTPA method revealed shared transcriptomics pathway alterations in the brain in PD and during adult aging . As an alternative or extension to investigations based on pre-defined pathways, molecular network analyses have the potential to provide more detailed, comprehensive and novel findings for systems-level omics investigations. Network analyses do not require a time-consuming prior curation of cellular process annotations and avoid subjective judgments on the relevance of specific genes/proteins for a particular molecular function. They can exploit an extensive resource of interaction data from public databases, including STRING : These methods aim to identify sub-networks within a genome-scale molecular or regulatory network that undergo co-ordinated activity changes in a biological condition of interest. Such co-ordinated network changes are characteristic for complex diseases, which tend to involve perturbations in the activity of entire molecular network regions rather than only in a few genes or proteins : Causal reasoning  approaches use manually curated directional relationships, e.g., gene regulatory relationships or protein signaling cascades, to infer the root molecular causes for a set of observed condition-specific downstream changes in an omics dataset. While these directional relationships are often referred to as “causal relationships”, the underlying data are mostly correlational rather than causal and have to be interpreted with caution. By constructing a signed, directed interaction graph  from a list of known directional relationships between interacting molecules, a CRA method can track back through the graph from the molecules that underwent measured activity alterations in the omics data to their known or putative upstream regulators. These regulators are then scored as potential drivers of the observed downstream changes by evaluating the overall consistency of the activating and inhibitory regulation patterns in the graph with the measured data , whereas NPA tools are applicable to both regulatory and molecular interaction networks (represented by directed and undirected graphs). However, as illustrated in Fig. For network analyses in general, the result quality largely depends on how complete and correct the underlying network is, and corresponding methods may therefore not be applicable to model organisms with limited publicly available regulatory and interaction data. However, certain network analysis approaches, like the CRE method  from PD cases and controls, the machine learning-based NPA approach, GenePEN, identified a connected sub-network signature within a genome-scale protein–protein interaction network with significant predictive power to distinguish between biospecimens from patients and unaffected subjects  or combining the original attributes into more robust derived features . These approaches are also called “dimension reduction methods”, because they reduce the number of dimensions of the input data  in order to address multiple common statistical issues during following analyses, previously summarized under the notion “curse of dimensionality” Bellman . MoreoveTable A major benefit of machine learning and visualization techniques for the analysis of omics data is their broad applicability. While different functional omics data types require different lower-level pre-processing methods, the higher-level machine learning and visualization tools discussed here are applicable across almost all types of pre-processed molecular data and often also support the integrative analysis of diverse omics types. Thus, given a pre-processed functional omics dataset, e.g., normalized microarray data, RNAseq read counts or mass-spectrometry-derived protein or metabolite abundance data, a wide variety of machine learning tools can be applied directly to identify clustering patterns  to build predictive models for the classification of new data samples , or to visually explore and interpret the data .It is often recommendable to start the analysis of a normalized omics dataset with a simple visualization before using machine learning tools for automated pattern identification. Inspecting a 2D or 3D projection of the data can often reveal the presence of outliers, biases and other irregularities which are not always detected by automated quality control pipelines. The most commonly used approach for obtaining a low-dimensional visual representation of high-dimensional omics data is a principal component analysis (PCA). A benefit of PCA visualizations is that by design they tend to capture most of the variance in the data. However, in contrast to other dimension reduction methods like multidimensional scaling , then unsupervised clustering approaches should be applied. These methods will identify sub-groups of samples that are similar to each other in terms of their omics profiles but differ significantly from other identified sub-groups. The relevant algorithms can be grouped into hierarchical clustering methods  that could overshadow unrelated biomedically relevant differences between patient sub-groups of interest . Therefore, more recent approaches integrate prior biological knowledge into the cluster analysis, e.g., using gene/protein functional annotations and information from disease-related pathways, in order to aggregate measurements for functionally related, disease-associated biomolecules and determine more robust and relevant clustering patterns  or measures of disease severity  and whole-blood samples. These efforts were motivated by the observation that even after the onset of visible motor symptoms, the currently used clinical diagnostic criteria for PD (UK Parkinson’s Disease Society Brain Bank criteria) only reached around 76% specificity in recent studies . The model involved the three metabolites mannose, threonic acid, and fructose as predictive features and was reported to provide a sensitivity of 0.79 and a specificity of 0.8. Additional studies including patients with other neurological disorders and larger numbers of samples from multiple cohorts are still needed to assess the predictive value of the signature for differential diagnosis.Recently, a new metabolomics signature in CSF was proposed by Trezzi et al., using a non-targeted gas chromatography-mass spectrometry approach to study the CSF metabolome of 44 early-stage, untreated idiopathic PD patients in comparison with 43 age- and gender-matched unaffected controls  patients, an interesting alternative approach using an integrative analysis of whole-blood gene expression data from IPD patients, familial PD patients with the a et al. . By firsn = 205 PD, n = 233 controls, n = 48 other neurodegenerative diseases) . The model was trained to differentiate between PD and unaffected controls rather than between PD and other neurologic disorders, and further analyses are needed to evaluate the potential of extending the model towards differential diagnostic applications, reducing the number of required genetic probes and increasing the generalization performance.More recently, Shamir et al. presented a whole-blood gene expression signature for idiopathic PD, derived from microarray data analysis of 486 subjects , 17 patients with progressive supranuclear palsy (PSP) and 39 unaffected controls , Alzheimer’s disease (n = 10), dementia with Lewy body (n = 5) and unaffected controls (n = 10). The authors determined a multifactorial marker signature using logistic regression, which was reported to provide a sensitivity of 78% and a specificity of 95% for discriminating between PD and the other disorders. Given the limited sample sizes in this study and the lack of an external replication, the authors acknowledge that their preliminary findings will have to be validated in a larger and independent population of patients.A further cross-disease comparative machine learning analysis presented by Abdi et al.  involvedFinally, a comparative machine learning analysis across multiple neurodegenerative disorders has also been performed by Ishigami et al., who used MALDI-TOF profiling of CSF peptides and proteins from 37 PD patients, 32 MSA patients and 26 control subjects with other neurological disorders (OND) , have provided new means to pinpoint the main affected cellular pathways and gain a more detailed understanding of pathological changes in the disease. In order to translate the resulting knowledge and research efforts into improved diagnostic models and preclinical drug intervention studies, a variety of challenges still have to be overcome.In recent years, the discovery of multiple PD-causing mutations and risk factor variants and the growth of public data resources for PD research, e.g., through the Parkinson’s Progression Markers Initiative (substantia nigra) is regarded as the main affected tissue in PD and only post-mortem omics data are available for this brain region, one of the main challenges for omics-based biomarker modeling is to identify reliable surrogate markers in peripheral tissues or body fluids. One possible strategy to address this in the future could be to use the non-lesional access to the brain during deep brain stimulation (DBS) surgery, by capturing cells spontaneously adhering to the DBS stylet for omics profiling , provide a further possibility to increase the statistical power for comparative analyses at no added cost, but they are rarely used in practice. Bioinformatics methods can also facilitate biomarker discovery for the early pre-motor phase of PD, e.g., by combining analyses of molecular data from in vitro an in vivo models of early-stage PD with measurements from biospecimen of untreated de novo patients to pinpoint shared early-stage disease-associated changes. These integrative analyses could complement ongoing studies on the follow-up of at-risk cohorts, applying omics profiling analyses to biospecimens collected prior to the conversion to PD to discover presymptomatic molecular dysregulations.For the specific goal of modeling and estimating the future progression of PD using omics data, time-series measurements from longitudinal studies will need to be collected in larger quantities and probably also at smaller time intervals. Current longitudinal studies for PD typically involve between one to two follow-up investigations per year. While important changes in the disease course may occur during shorter time periods, the burden for the patient through blood draws and clinical assessments needs to be minimized. Since many longitudinal studies so far only collect limited molecular data, a more comprehensive molecular phenotyping may currently deserve higher priority than narrowing the time interval between follow-up investigations. For the statistical analysis of time series data from corresponding studies, similar strategies to increase the statistical power can be applied as discussed for cross-sectional analyses in this review, e.g., using dimension reduction approaches, prior knowledge on interrelationships between biomolecules from pathways/networks and the literature, and integrative omics analyses to identify coordinated alteration trends over time. Representative examples of relevant time series analysis approaches for omics data have been presented by Wachter and Beißbarth  and Lee in silico target prioritization approaches (Aerts et al. Apart from the exploration of new omics measurements for biomarker modeling, the same data will also provide an important resource for systems biology analyses dedicated to the discovery, validation and characterization of PD drug targets. Network analyses including the causal reasoning approaches discussed in this review can help to identify pathological activity alterations in key regulatory proteins, and provide a starting point to prioritize candidate protein drug targets for further analyses. These investigations can be integrated with other more generic in silico methods to predict this property are available as a prior filter for subsequent experimental testing (Kortagere et al. Finally, apart from their possible roles in the identification and preclinical validation of a drug target, systems biology approaches can also support the discovery of relevant drug-like small molecule binders. For example, a variety of systems-level approaches for drug repositioning have been developed (Dudley et al. In summary, computational systems biology approaches support experimental biomedical investigations by helping to prioritize candidate biomarkers, drug targets and binding compounds for subsequent validation, and providing insights into the mechanisms of molecular network and pathway dysregulations. For PD research specifically, integrative and comparative omics analyses that exploit prior biological knowledge can help to address current limitations in the available disease models and omics sample sizes, and to find surrogate markers for molecular changes in the brain. These computational systems-level analyses do not represent an alternative to targeted experimental studies of individual genes and proteins, but rather both targeted and systems-level approaches provide complementary information that will, collectively, help to pave the way towards improved biomarker signatures and new viable drug targets.


Furthermore, we extend our method to additional chemotherapy-regimens and cancer types to demonstrate its accuracy and generalizability. We propose that our model can be utilized to prioritize patients for specific chemotherapy-regimens as a part of treatment planning.Despite recent advances in discovering a wide array of novel chemotherapy agents, identification of patients with poor and favorable chemotherapy response prior to treatment administration remains a major challenge in clinical oncology. To tackle this challenge, we present a generalizable genome-wide computational framework pathCHEMO that uncovers interplay between transcriptomic and epigenomic mechanisms altered in biological pathways that govern chemotherapy response in cancer patients. Our approach is tested on patients with lung adenocarcinoma who received adjuvant standard-of-care doublet chemotherapy , identifying seven molecular pathway markers of primary treatment response and demonstrating their ability to predict patients at risk of carboplatin-paclitaxel resistance in an independent patient cohort (log-rank  Nusrat Epsi et al. present pathCHEMO, a computational framework for uncovering transcriptomic and epigenomic pathways of chemoresistance in cancer that has the potential to improve clinical decision-making. They apply pathCHEMO to lung adenocarcinoma data from public databases, and identify seven molecular pathways implicated in carboplatin-paclitaxel resistance. The majority of patients with LUAD lack clinically actionable mutations and are commonly administered a doublet chemotherapy  to improve response rates and survival5. Most recently, treatment for LUAD has also included immune checkpoint inhibitors, yet they are not curative for most patients6. The heterogeneity of response to the standard-of-care therapies and rapidly emerging treatment resistance remain major challenges in lung cancer management. Prioritization of patients based on their risk of developing resistance prior to therapy administration would improve disease course and enhance informed clinical decision making at large.Lung adenocarcinoma (LUAD) is a major cause of cancer-related death in the United States with a five-year survival rate of 17.7%12 have improved classification of LUAD based on markers of overall disease aggressiveness, including mutations in oncogenes , proto-oncogenes , and tumor suppressor genes . Despite being successful as prognostic markers of LUAD aggressiveness, they have not been associated with the complexity of therapeutic response yet14, suggesting that more complex mechanisms might be at play in this malignancy.In recent years, several successful attempts20. For example, studies focused on transcriptomic alterations have demonstrated that: MDR1 amplification is implicated in acquired resistance to anthracyclines, vinca alkaloids, and other antineoplastic chemotherapies in breast cancer15; over-expression of dihydrodiol dehydrogenase enzyme is central in resistance to cisplatin in ovarian cancer16; and higher genomic instability due to p53 inactivation is essential in resistance to platinum-based chemotherapy in ovarian cancer17. In parallel, epigenomic-centered studies have demonstrated that: genome-wide hypermethylation is implicated in resistance to antineoplastic fotemustine in melanoma18; hypermethylation of DKK3 leads to docetaxel resistance in non-small cell lung cancer19; and hypomethylation of MIR663A induce cyclophosphamide and docetaxel resistance in breast cancer20. Given the success of individual transcriptomic and epigenomic determinants of chemoresponse, a systematic genome-wide investigation of the interplay between transcriptomic and epigenomic mechanisms implicated in resistance can provide valuable predictive markers of predisposition to chemotherapy failure.Recently, multiple transcriptomic and epigenomic alterations have been highlighted to play a role in primary and secondary chemoresistance across various cancer types23, linear regression connecting DNA methylation sites and mRNA expression of the site-harboring genes24, network-based integration of mRNA expression and DNA methylation and/or copy number variations27. Even though successful in identifying clinically relevant signatures of disease progression, these methods have not yet fully explored the interplay between transcriptomic and epigenomic mechanisms altered in molecular pathways implicated in chemo response, which would shed light on complex molecular mechanisms that govern therapeutic resistance.In the past decade, several computational methods have been successfully applied to understand cancer initiation and progression through integration of transcriptomic and epigenomic data, including correlation of mRNA expression and DNA methylation and/or copy number variations28, which received adjuvant standard-of-care chemotherapy . pathCHEMO identifies seven molecular pathways altered on transcriptomic and epigenomic levels that differentiate patients with poor and favorable carboplatin–paclitaxel response. We demonstrate that the activity of these pathways as well as their representative read-out genes, can serve as molecular markers to identify patients at risk of resistance to carboplatin–paclitaxel in an independent patient cohort5  and can predict the risk of resistance to carboplatin–paclitaxel combination for new patients . We also confirm significant non-random predictive ability of our identified seven candidate pathways, when compared to seven pathways selected at random  and show that our approach outperforms other commonly utilized methods  in identifying patients at risk of resistance to chemotherapy (Area Under the Receiver Operating Characteristics (AUROC) = 0.98)30. In addition, we demonstrate that our model is independent of, and is not affected by commonly used covariates  and by the known signatures of lung cancer aggressiveness . Finally, we extend our approach to additional chemo combinations  and additional cancer types 31 and demonstrate accuracy and general applicability of our approach . We propose that our model can be used to pre-screen patients and prioritize them for specific chemotherapy treatments.In this work, we develop a generalizable computational framework to identify molecular pathways altered on transcriptomic  and epigenomic  levels that govern resistance to chemotherapy. We name our approach pathCHEMO—uncovering transcriptomic and epigenomic pathways implicated in CHEMOresistance. Our overall idea is that pathways that are altered on both mRNA expression and DNA methylation levels are more likely to capture complex relationships implicated in therapeutic resistance and overcome noise present in any single experiment or data type. In addition, our approach provides several important advantages that tackle complexity of treatment response. First, it uncovers molecular pathways altered on both transcriptomic and epigenomic levels, which increases the likelihood to identify functionally relevant alterations. Second, these pathways can be utilized as effective markers of primary chemoresistance to predict patients with poor and favorable response, even prior to therapy administration. Finally, it uncovers molecular pathways, rather than single determinants, thus providing potential functional candidates for therapeutic intervention to preclude or overcome resistance. Motivated by the need for markers of chemoresponse in lung cancer, we analyze profiles of patients with LUAD from The Cancer Genome Atlas (TCGA-LUAD)We have developed a genome-wide computational approach pathCHEMO that integrates mRNA expression and DNA methylation patient profiles to identify pathways altered on both transcriptomic and epigenomic levels   are compared for differences in their molecular profiles to capture the most prominent molecular signal. To assure that the comparison groups are balanced with respect to initial age, gender, disease stage at diagnosis , smoking status etc., we performed stratified sub-sampling (which identifies patient groups with similar distributions for these variables) and identified patients that experienced relapse within 1 year of carboplatin–paclitaxel start ; and patients that did not experience any events for more than 2 years  , in LUAD patients. For this, we have analyzed clinical and molecular profiles of patient with LUAD in the TCGA clinical cohortsion n = 4 (Supplet-test36 and re-confirmed with fold change (see Methods), which defined carboplatin–paclitaxel response differential gene expression signature . Pathway enrichment was performed using Gene Set Enrichment Analysis (GSEA)41. This analysis estimated Normalized Enrichment Score  for each of the 833 pathways, which reflects the extent to which each pathway is enriched in the treatment response signature, also referred to as pathway activity. A list of 833 pathways ranked by their enrichment  in the carboplatin–paclitaxel response differential expression signature defined carboplatin–paclitaxel response differential expression pathway signature and a list of 833 pathways ranked by their enrichment  in the carboplatin–paclitaxel response methylation signature defined carboplatin–paclitaxel response differential methylation pathway signature (see Methods). To account both for the pathways that have majority of their genes affected in the same direction  and pathways that have genes affected in different directions: some genes affected in one direction  and some in an opposite direction , we have performed both signed and absolute valued pathway enrichment analysis with their subsequent integration (see Methods), which defined carboplatin–paclitaxel response composite expression pathway signature   and epigenomic  levels and which would capture pathway genes affected: only on transcriptomic level, only on epigenomic level, or both levels , demonstrating robustness of our analysis regardless of the signature choice.To confirm that these identified seven molecular pathways are robust to the choice of the statistical methods used to define treatment response signatures, we have also performed our analysis using signatures defined using all DNA methylation sites and using non-parametric tests. First, we defined differential methylation signature with all DNA methylation sites considered , defined on either expression levels or methylation levels of the pathway genes (see Methods). The predictive ability was measured using Area under ROC (AUROC), which reflected how well each data type separates poor response and favorable response patients in the TCGA-LUAD patient cohort . Our analysis demonstrated that both expression levels (AUROC = 0.987) and methylation levels (AUROC = 0.965) of seven candidate pathways are highly predictive of poor response vs. favorable response separation  analysision Fig. , indicat43. An exception was Immune network for IgA production pathway, whose sites were heavily enriched in the gene body, indicating their potential interaction with alternative splicing machinery44  collected between 1996 to 2007, followed by treatment with carboplatin and a taxane  and monitored for further disease progression for 11 years. In this cohort, survival status during the clinical study (1996–2007) was used as a clinical endpoint and time to this event was calculated between the start of carboplatin–paclitaxel treatment to death (for patients with this event) or to follow-up (for censored patients). Similar to the analysis above, we evaluated activity levels of seven candidate pathways in each patient sample  and employed t-Distributed Stochastic Neighbor Embedding (t-SNE) clustering45, which stratified patients into two groups based on pathway activity levels  (see Methods).Our next essential step was to evaluate if the candidate molecular pathways can stratify patients based on the risk of failing chemotherapy in an independent, non-overlapping patient cohort Fig. . For thiels Fig. : one groels Fig. , which dp-value = 0.003). We paralleled this analysis with evaluation if patient groups stratified by our model are different in their treatment response compared to patient groups chosen at random, which were shown to be highly non-random .To evaluate non-randomness of this result, we compared predictive ability of our candidate seven pathways to the predictive ability of seven pathways selected at random (see Methods), which demonstrated that ability of the candidate seven pathways to predict carboplatin–paclitaxel response is highly non-random compared to 10,000 randomly selected pathways . This process is repeated for all patients (see Methods). LOOCV analysis demonstrated that our model has high accuracy in predicting poor and favorable carboplatin–paclitaxel response for a new incoming patient 47 LUAD tumor samples (n = 127) collected through surgery between 1996 and 2005 at Princess Margaret Cancer Centre, and Tang et al.5 provisional cohort, which includes LUAD tumor samples (n = 94) collected through surgery between 1996 and 2007 at The University of Texas MD Anderson Cancer Center. These negative control patient cohorts did not receive any subsequent treatment but were monitored for disease progression  was used as a clinical endpoint). Kaplan–Meier survival analysis on these datasets demonstrated that our candidate seven pathways did not separate patients based on the disease progression in both unstratified and stratified  analyses Der et al. , and Tang et al.  and are in fact specific for carboplatin–paclitaxel response.Finally, to determine that our candidate pathways specifically distinguish carboplatin–paclitaxel response and not disease aggressiveness, we have evaluated if the pathways can also separate patients based on their lung cancer aggressiveness. For this, we evaluated the predictive ability of our candidate pathways on the LUAD patient cohorts that did not receive any treatment after surgery (we used these cohorts as negative controls). These datasets , and random forest; and evaluated if our approach can be affected by commonly utilized covariates or known signatures of lung cancer aggressiveness.24 method, Epi2GenR, based on linear regression integration between DNA methylation and mRNA expression patient profiles, which identified 35 site-gene pairs as candidate markers of carboplatin–paclitaxel response. Second, our model was compared to Zhong et al.30 method based support vector machine (SVM) analysis, which identified 104 candidate genes. Finally, our model was evaluated against Yu et al.29 method PRES, based on random forest algorithm, which identified three candidates of carboplatin–paclitaxel response. We first compared ability of the identified candidates from each method to separate patients with poor and favorable carboplatin–paclitaxel response in the Tang et al. dataset using ROC analysis, which demonstrated advantage of pathCHEMO over other commonly utilized methods . Furthermore, we compared ability of these methods to predict response to carboplatin–paclitaxel in the Tang et al. validation set (as above), through Kaplan–Meier survival analysis  and Cox proportional hazards model , which confirmed that pathCHEMO outperformed other commonly used methods in its ability to predict therapeutic response.First, to measure the advantage of our model over other commonly utilized methods, we have compared predictive performance of our model (see Methods) to Panja et al.48 on the Tang et al. dataset (see Methods), which demonstrated that these covariates are not predictive of treatment response and do not affect predictive ability of our model , we have evaluated their effect through multivariable  Cox proportional hazards modeldel Fig. . Furtherdel Fig. .49 (54 prognostic markers); Beer et al.50 (50 prognostic markers); and Tang et al.5 (12 prognostic markers)  Fig.  and utilrs) Fig. .FGFR1OP, CCL22, CCR9, LSM7, PDE7A, CCT4, and POLR2C), which: first, accurately reflected activity levels of their corresponding pathways; second, were associated with treatment response; and finally, achieved identical accuracy in predicting patients at risk of carboplatin–paclitaxel resistance ; and finally, associated with carboplatin–paclitaxel response (see Methods). This analysis identified seven read-out genes   collected through surgery, for patients that received adjuvant cisplatin–vinorelbine, and demonstrated that three candidate pathways can predict poor and favorable cisplatin–vinorelbine response in patients with LUAD  using Kaplan–Meier survival analysis and Cox proportional hazards model .In order to test the general applicability of pathCHEMO, we applied our approach across additional chemotherapy combinations and cancer types. In particular, we extended pathCHEMO to: cisplatin–vinorelbine response in lung adenocarcinoma, cisplatin–vinorelbine response in lung squamous cell carcinoma, and folinic acid, fluorouracil, and oxaliplatin  response in colorectal adenocarcinoma   collected through surgery, for patients that received adjuvant cisplatin–vinorelbine treatment, and demonstrated that six candidate pathways can accurately predict poor and favorable cisplatin–vinorelbine response in patients with LUSC  .Next, we applied our approach to cisplatin–vinorelbine treated lung squamous cell carcinoma (TCGA-LUSC) patients   collected through surgery followed by adjuvant treatment with FOLFOX monitored for further disease progression , and demonstrated that five candidate pathways can predict poor and favorable FOLFOX response in patients with COAD . Interestingly, when evaluating overlaps between pathways across different chemo-treatments and cancers, we have noticed that even though some biological pathways might be overlapping  that received FOLFOX  combination  signalling events pathway52. Furthermore, chemokine receptors bind chemokines pathway is directly associated53 with cytokine and inflammatory response pathway, which modulates carboplatin resistance in ovarian cancer54. Finally, cell cycle mitotic pathway has been shown to be directly affected by paclitaxel55 and carboplatin–paclitaxel57 treatments in ovarian cancer. Thus, primary  dysregulation in these pathways might affect drug mechanism of action and can be utilized to identify patients at risk of resistance.We used pathCHEMO to elucidate mechanisms of resistance to carboplatin–paclitaxel chemotherapy in lung adenocarcinoma and identified seven molecular pathways implicated in resistance, including chemokine receptors bind chemokines, mRNA splicing, G alpha (s) signalling events, intestinal immune network for IgA production, metabolism of proteins, RNA degradation, and cell cycle mitotic pathways. Interestingly, paclitaxel resistance has been shown to be modulated by Hippo signaling pathway in breast cancer58, and disease progression in prostate cancer59. PKA has been known to contribute to lung cancer tumorigenesis by interacting with RAS oncogenic pathway and promoting epithelial-mesenchymal transition (EMT) during hypoxia. Several recent studies have confirmed the role of EMT as a key player in acquired  resistance to chemotherapy including acquired resistance to gemcitabine in pancreatic cancer60, to paclitaxel in ovarian cancer61, and to gefitinib in lung cancer62, emphasizing importance of further investigating EMT as a mechanism of primary resistance to chemotherapy in lung adenocarcinoma.Interestingly, one of the identified pathways, G alpha (s) signalling events pathway, is involved in mediation of extracellular signaling and activation of Protein Kinase A (PKA), a known player in cancer cell invasion and metastasis. Recently, PKA has been shown to play a central role in resistance to tamoxifen in breast cancer64. Recent studies have demonstrated that 50% of patients with metastatic lung adenocarcinoma, which were treated with erlotinib and acquired resistance to it, had a histological transformation to large cell neuroendocrine carcinoma (LCNEC), leading to increased metastatic burden and lethality66. Therefore, further investigation of the role of EMT and neuroendocrine markers and their interplay with transcriptomic and epigenomic molecular alterations are necessary for comprehensive understanding of complex mechanisms involved in resistance to chemotherapy and will contribute a central focus of our subsequent studies.In addition to EMT, the development of neuroendocrine phenotype has been shown to be a major emerging player in acquired therapeutic resistance in lung cancer28, Tang et al. (GSE42127)5, Der et al. (GSE50081)47, and Zhu et al. (GSE14814)4 datasets. The primary LUAD patient cohort, utilized for reconstruction of transcriptomic and epigenomic signatures of chemoresistance, was obtained from The Cancer Genome Atlas (TCGA-LUAD) project28 and downloaded from the Genomics Data Commons database  on February 2017. Clinical information  for these datasets were obtained from the TCGA GDC legacy archive .For this study, LUAD patient cohorts were obtained from publicly available data sources (Supplementary Table β (Beta) values, which were subsequently converted to M-values as suggested in67 when parametric analysis was utilized, using beta2m function in Lumi R package.For RNA-seq analysis, we normalized and stabilized variance for raw RNA-seq counts using DESeq2 R package. DNA methylation values for each site were reported as t-test (t.test function in R) in R studio version 3.3.2, such that differential expression signature was defined as a list of genes ranked on their differential expression  and differential methylation signature was defined as a list of genes based on the differential methylation of the corresponding site . We coupled this analysis with signatures defined based on a fold change, and obtained similar results. For DNA methylation signature, we performed analysis two ways: selected one CpG site per gene through the coefficient of variation analysis, where a site with the highest coefficient of variation was selected for each gene; and considered all CpG sites for signature reconstruction, yielding similar results.To determine molecular characteristics that differ between poor response and favorable response, we defined signatures of treatment response on transcriptomic  and epigenomic  levels between poor response and favorable response patient groups using two-sample two-tailed Welch 37 (http://software.broadinstitute.org/gsea/msigdb), which includes 833 pathways from REACTOME38, KEGG39, and BIOCARTA40 databases, and implemented pathway enrichment analysis using Gene Set Enrichment Analysis (GSEA)41, where differential expression and differential methylation signatures were used as a reference and collection of genes from each pathway was used as a query gene set. Normalized Enrichment Scores (NESs), and p-values were estimated using 1,000 gene permutations. This analysis estimated NESs for each of the 833 pathways, which reflects the extent to which each pathway is enriched in the treatment response signature and defines a so-called pathway activity. Positive NES would reflect pathway enrichment in the over-expressed part of the signature  and negative NES would reflect pathway enrichment in the under-expressed part of the signature . We refer to such pathway enrichment analysis as signed as it considers over- and under-expression of genes (with direction). Signed pathway enrichment analysis was performed on the differential methylation signature of treatment response in the similar manner.To identify molecular pathways altered on transcriptomic and epigenomic levels  and negative NESs reflect enrichment in the non-differentially expressed part of the signature (and are therefore not considered). This absolute valued pathway enrichment analysis discovers pathways whose genes might be changed in both directions (both over-expressed and under-expressed) as it estimates the enrichment in the differentially expressed tail of the signature (irrespective of sign). Such absolute valued pathway enrichment analysis defined NESs for each of 833 pathways, as above. Absolute valued pathway enrichment analysis was performed on the differential methylation signature of treatment response in the similar manner.Further, to overcome limitations of such  pathway enrichment analysis, which assumes that the pathway will be enriched only if majority of genes in the pathway are changed in the same direction , we performed absolute valued analysis. For this, the pathway enrichment analysis was run on the absolute valued differential expression signature, where signature p-value between signed and absolute valued pathway analyses . The advantage of such integration is two-fold: it captures pathways whose genes are strictly over-expressed or under-expressed in each pathway, and whose genes are changed in both directions , thus increasing the probability to identify functionally relevant molecular determinants. Similar logic applies to the methylation signatures. Such integration of signed and absolute valued NESs defined composite expression pathway signature and composite methylation pathway signature.The next essential step was to then integrate NESs from signed and absolute valued pathway enrichment analysis so that for each pathway a final integrative NES was defined as an NES with the lowest We have employed GSEA to compare composite expression pathway signature and composite methylation pathway signature to identify pathways that are affected on both transcriptomic and epigenomic levels . To assure that we can identify pathways which are over-expressed and under-methylated; under-expressed and over-methylated; differentially expressed and differentially methylated etc., each pathway signature was ranked based on the absolute values of their NESs and used for subsequent GSEA comparative analysis.For this pathway-based GSEA, we utilized composite expression pathway signature as a reference signature and top pathways from the composite methylation pathway signature as a query pathway set. To accurately define query pathway set, which should assure strongest enrichment between pathway signatures, we varied the threshold for the query pathway set between 0.001 and 0.05 (width of each step = 0.005) and estimated the strength of enrichment between the two signatures at each threshold. Since GSEA is a probabilistic algorithm, for each threshold, GSEA was run 100 times and average NES for the enrichment was reported. Threshold with the highest average NES then reflects the optimal threshold which corresponds to the strongest enrichment between the composite expression pathway signature and the composite methylation pathway signature, used for subsequent analysis. GSEA analysis between the composite expression pathway signature and the composite methylation pathway signature at the optimal threshold identified a set of pathways  of treatment response altered on both transcriptomic and epigenomic levels.p-value, which defined a final set of pathways  maximally non-overlapping non-redundant pathways used for subsequent analysis.One of the limitations of the pathways from the C2 collection is that they often represent a parent–child relationship, where a parent pathway  would encompass all genes in its child pathways . Such overlap produces data redundancy and can result in model overfitting as the same pathways are fit in the model repeatedly. To overcome this limitation and to eliminate pathways with heavy overlaps, we performed Fisher Exact Test (fisher.test function in R) and compared leading edge genes for each pair of pathways from our analysis  comparisons). From each group of parent–children pathways which shared a large number of overlapping genes, we selected one representative pathway with the NES corresponding to the lowest z-scored on genes or sites) in the discovery  cohort, which defined single-sample differential expression and single-sample differential methylation signatures, respectively. Each sample was then used for signed and absolute valued pathway enrichment analysis , where each single-sample signature was used as a reference and genes from each of seven candidate pathways were used as a query set thus producing a pathway activity signature for each patient. These single-sample expression and methylation pathway signatures were then used to evaluate predictive ability of seven pathways , using logistic regression modeling followed by ROC analysis. The logistic regression analysis was done using glm function and ROC analysis was done using pROC and ggplot2 packages in R.To examine if, in our candidate pathways, both data types  have equivalent ability to predict therapeutic response, we compared the performance of candidate pathways utilizing their activity levels based on expression only and activity levels based on methylation only, separately. To compare pathway performances based on each data type, we first scaled both expression and methylation data matrices  as a clinical endpoint . First, we estimated activity levels of the candidate pathways in the independent clinical Tang et al. cohort on a single-sample level, as above. The activity levels  of the candidate pathways were then subjected to t-Distributed Stochastic Neighbor Embedding (t-SNE) clustering45 (implemented through Rtsne package in R), a non-linear dimensionality reduction technique which chooses two similarity measures between pairs of points of high dimensional input space and low-dimensional embedding space. First, it constructs a probability distribution over the pairs of high dimensional space  in such a way that similar points are exhibited by nearby instances, while dissimilar points are exhibited by distant instances. Second, it constructs a similar probability distribution over the points in low-dimensional embedding space and tries to minimize the Kullback-Leibler divergence  between the high dimensional data and low-dimensional anticipated data at each point. Therefore, patients with similar pathway activity levels will be anticipated as nearby instances while patients with dissimilar pathway activity levels will be anticipated as dissimilar instances. The advantage of t-SNE lies in its ability to reduce dimensions from seven  to two and effectively identify groups of patients that share similar pathway activity levels. This analysis stratified patients into two groups: a group with overall increased composite pathways’ activities and a group with overall decreased composite pathways’ activities. We then evaluated if these patient groups differ in their response to carboplatin–paclitaxel treatment using Kaplan–Meier survival analysis, and Cox proportional hazards model via survival, ggplot2 and survminer R packages.To evaluate clinical significance of the candidate molecular pathways, we examined their ability to predict patients at risk of chemoresistance in an independent clinical cohort from the Tang et al.p-value was estimated as a number of times Kaplan–Meier log-rank p-value for seven candidate molecular pathways outperformed the results at random. We have also employed a second random model, where we evaluated the effect of selecting random patient groups.In order to evaluate if a random set of pathways can perform as well as our identified seven pathways, we compared the predictive ability of our seven candidate pathways to the predictive ability of seven pathways selected at random. For this, we built a random model, where seven pathways were selected at random and their activity levels were utilized to stratify patients based on their treatment response, with subsequent evaluation using Kaplan–Meier survival analysis. Random selection was done 10,000 times and the empirical Finally, to estimate the accuracy with which our model can predict treatment response for a new incoming patient, we simulated this process using leave-one-out cross-validation (LOOCV). In LOOCV, one patient is removed; and the model is trained on the rest of the patients. The patient that was removed is considered as a new incoming patient, subjected to predictive analysis, and is assigned a risk of developing resistance. This process was repeated for all patients. We implemented the predictive model for LOOCV using generalized linear modeling  through glm function and ggplot2 package in R.To assess advantages of our approach, we have compared its predictive performance to other commonly utilized approaches, including linear regression modeling, support vector machine, and random forest; and evaluated if our approach can be affected by commonly used covariates or known signatures of lung cancer aggressiveness.24 method, Epigenomic and Genomic mechanisms of treatment Resistance (Epi2GenR), which utilized linear regression to integrate DNA methylation and mRNA expression data; second, to Zhong et al.30 method, based on support vector machine (SVM) algorithm which utilized mRNA expression patient profiles; and finally, to Yu et al.29 method, Personalized REgimen Selection (PRES) method, based on random forest machine learning approach which utilizes mRNA expression patient profiles. We followed the selection and cross-validation techniques suggested in each of the above publications to carefully compare their performance to our approach. Epi2GenR utilized the same signature as utilized in our study. To apply SVM and PRES correctly, we split our validation set into 70:30 proportion subsets, where 70% of the validation set were used for model training and 30% for model validation. Predictive ability of the identified candidates from each of these methods was evaluated using ROC, Kaplan–Meier survival, and hazard ratio analyses through survival, survcomp, and survminer packages in R.To demonstrate the advantages of our approach over other commonly utilized methods, we compared its performance: first, to Panja et al.49, Beer et al.50, and Tang et al.5 described above) can predict therapeutic response or can affect predictive ability of the identified seven candidate pathways. For this, we utilized the multivariable Cox proportional hazards model48 (using coxph function in R) and stratified Kaplan–Meier survival analysis through survival, and survminer packages in R.Next, we evaluated if any of commonly used covariates  and known signatures of lung cancer aggressiveness . Likelihood-ratio test p-values were then combined with Pearson correlation p-values using Fisher’s method (metap R package) and utilized to make final gene selection. Visualization of the resulting read-outs was done using Cytoscape69.To identify pathway read-outs, we looked for genes inside each pathway, which were altered on transcriptomic and/or epigenomic levels ; correlated with pathway activity levels ; and associated with carboplatin–paclitaxel response  and additional cancer types  (Supplementary Table n = 8), for patients without neo-adjuvant treatment , who received adjuvant chemotherapies of interest and were further monitored for new tumor events (as defined above).For signature development, we utilized primary tumor samples from TCGA-LUAD/TCGA-LUSC/TCGA-COAD (4 (GSE14814), which included LUAD tumors obtained at surgery (n = 39), treated with adjuvant cisplatin–vinorelbine chemotherapy. In this cohort, lung cancer-related death was used as a clinical endpoint and time to event was calculated between the start of cisplatin–vinorelbine treatment and lung-cancer-related death (for patients with this event) or to follow-up (for censored patients), with maximum time to event/follow-up 3390 days.For clinical validation of response to cisplatin–vinorelbine combination in LUAD we utilized the Zhu et al. patient cohort4 (GSE14814), which were patient with LUSC whose tumors were obtained at surgery (n = 26) and who were treated with adjuvant cisplatin–vinorelbine chemotherapy. In this cohort, lung cancer-related death was used as a clinical endpoint and time to event was calculated between the start of cisplatin–vinorelbine treatment and lung-cancer-related death (for patients with this event) or to follow-up (for censored patients), with maximum time to event/follow-up 3318 days.For clinical validation of response to cisplatin–vinorelbine combination in lung squamous cell carcinoma (LUSC) we utilized a different subset of patients from the Zhu et al. patient cohort31 (GSE39582), which includes COAD tumors obtained at surgery (n = 23), treated with adjuvant FOLFOX chemotherapies. In this cohort, relapse-free survival  was used as a clinical endpoint and time to event was calculated between the start of FOLFOX treatment to relapse (for patients with this event) or to follow-up (for censored patients), with maximum time to event/follow-up 2790 days.Finally, for validation of FOLFOX combination in colorectal adenocarcinoma (COAD) we utilized Marisa et al. patient cohortTo investigate pathways overlaps, we employed Fisher Exact Test (fisher.test function in R) on the leading edge genes from the transcriptomic and epigenomic pathways . All resulting p-values are corrected for multiple comparisons using FDR.t-test36 for parametric estimates and fold change for non-parametric estimates. Signatures were compared using Gene Set Enrichment Analysis (GSEA)41, where NES and p-values were estimated using 1,000 gene/site/pathway permutations. Clustering of patients based on their pathway activity levels  was performed using t-Distributed Stochastic Neighbor Embedding (t-SNE) clustering45. To estimate the statistical difference in treatment response between patient groups, we used Kaplan–Meier survival analysis and Cox proportional hazards model. To estimate the association between gene and pathway activity levels, we employed Pearson correlation analysis. When needed, p-values were combined using Fisher’s method. Statistical significance of the overlap between sets was estimated using Fisher’s Exact Test. P-values were adjusted for multiple comparisons using FDR correction. To assure reproducibility of our results, we have deposited a SWEAVE executable document with necessary data objects for freely available download and analyses at http://license.rutgers.edu/technologies/2019-121_pathchemo70.Statistical analyses and data visualization were conducted using R studio version 3.3.2. To define differential expression signatures, we utilized two-sample two-tailed Welch Further information on research design is available in the Supplementary InformationDescription of additional supplementary filesSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting SummaryPeer Review File


POGZ, which is disrupted in many of the cancer datasets and has an unusually large number of predicted targets, from which the network analysis predicts membership of cancer related pathways. The results suggest close involvement in known cancer pathways of genes exhibiting consistent heterogeneous copy number disruption. Further experimental work would clarify their relevance to tumor biology. The results of the analysis presented in the database METAMATCHED, and included here as an R archive file, constitute a large number of predicted regulatory relationships and pathway memberships which we anticipate will be useful in informing such experiments.The copy numbers of genes in cancer samples are often highly disrupted and form a natural amplification/deletion experiment encompassing multiple genes. Matched array comparative genomics and transcriptomics datasets from such samples can be used to predict inter-chromosomal gene regulatory relationships. Previously we published the database METAMATCHED, comprising the results from such an analysis of a large number of publically available cancer datasets. Here we investigate genes in the database which are unusual in that their copy number exhibits consistent heterogeneous disruption in a high proportion of the cancer datasets. We assess the potential relevance of these genes to the pathology of the cancer samples, in light of their predicted regulatory relationships and enriched biological pathways. A network-based method was used to identify enriched pathways from the genes’ inferred targets. The analysis predicts both known and new regulator-target interactions and pathway memberships. We examine examples in detail, in particular the gene  Previously we have demonstrated that an analysis of matched array comparative genomics and transcriptomics human cancer datasets can reveal inter-chromosomal acting gene regulatory relationships –3. By rewithin a dataset and this occurs consistently for these same genes across many of the datasets and different cancer types. In this paper we investigate these unusual genes. We investigate their target genes, predicted by the meta-analysis of publically available cancer datasets, the biological pathways enriched in their lists of target genes, and their relevance to the cancer pathology of the samples. Why genes which have a highly variable, inconsistent copy number disruption amongst samples within a cancer dataset may, perhaps counter-intuitively, be of relevance to the cancer pathology is examined later in this introduction. Firstly we discuss the background to the meta-analysis and the pathway enrichment analysis.We noticed that there are genes in the database which have a highly variable copy number amongst samples Array comparative genomics (aCGH) microarrays detect gene deletions or gene amplifications (extra copies) by comparing gene copy numbers in the DNA extracted from test sample cells to the copy numbers in normal control cells. Transcriptomics experiments use microarrays that measure the abundance of mRNA. In matched experiments the two different types of measurement are performed on the same samples. Reviews of matched aCGH and transcriptomics experiments, their analysis and uses can be found in references  and 6]..6].In cancer samples the copy numbers of genes are often greatly altered and are in effect of a natural gene amplification/deletion experiment encompassing many genes. In matched experiments, transcriptomics data is also available for the same samples, and information can be extracted on how changes in a gene’s copy number affects that gene’s expression. The analysis can however be extended further, using the measurements to investigate whether a change in a given gene’s copy number, with an associated change in expression, affects the expression of any of the other genes in the dataset, hence inferring regulatory relationships.Inference of regulatory relationships from these experiments is not without difficulties. Relationships are masked by the noise in the data and further confounded by the biological complexity of the system being studied. In particular, unlike conventional gene knockdown experiments, the copy number of many genes are being perturbed simultaneously. Coamplification or codeletion of genes situated in the same region of the genome can produce many spurious relationships and it is for this reason that we concentrate on inter-chromosomal acting regulatory relationships.We validated the hypothesis that useful regulatory information can be extracted from matched datasets, experimentally in Goh et al.  and compThe meta-analysis predicts target lists for regulatory genes. In this paper, to augment this information, we investigate the enrichment of biological pathways in the target lists. To this end, we used a network-based pathway enrichment approach. An enrichment analysis based on the network of a pathway, rather than simply the gene set of the pathway, takes into consideration the interactions between the genes in the pathway. We use PathwayCommons network databases  and the The aim of the pathway enrichment analysis is three-fold. Firstly, statistically significance enrichments can help to validate the predictions of regulatory relationships generated by the meta-analysis of the matched datasets. Secondly, the enrichment of a regulatory gene’s predicted target gene list in a particular pathway can be used to augment current knowledge of that pathway. Thirdly, the enrichment analysis can be used to investigate the participation of the regulators in pathways implicated in cancers, which is the main focus of this paper.within a dataset will show the highest self aCGH/expression correlation, provided of course that there is a concomitant change in expression. Conversely, if a gene is not amplified or deleted in any sample in a dataset, or if it is amplified or deleted by a similar amount in all the samples, then little or no self aCGH/expression correlation will be detected and the analysis will be unable to reveal any regulatory relationships for the gene. This means that any oncogenes which are consistently amplified or deleted in cancer samples are unlikely to feature in the results.We are using cancer samples to predict regulatory relationships because they can be seen as a natural gene amplification/deletion experiment. That the disruptions are outside our control does mean however that inference about regulatory relationships will only be possible for certain genes. Those genes with high variation in their copy number amongst the samples within a dataset and this occurs consistently across many of the datasets and a very wide variety of different cancer types. There are 47 genes which have significant self aCGH/expression correlation (and predicted targets) in twenty or more datasets. In this paper we assess the potential relevance to the pathology of the cancer samples of genes exhibiting this unusual, consistent heterogeneous copy number disruption in light of their predicted regulatory relationships and enriched biological pathways. A prominent example of such a gene, that also has an unusually large number of target genes predicted by the meta-analysis, is POGZ (Pogo Transposable Element Derived With ZNF Domain) and we examine in detail some of the novel pathway involvements predicted for this interesting gene.We find that there are a number of genes which have a highly variable copy number amongst samples It may appear counter-intuitive that genes whose copy number disruption varies greatly between the different cancer samples within a dataset could be relevant to tumor biology. It would suggest that these genes are located in genomic regions which are prone to disruption in cancer cells, but this disruption occurs only sporadically amongst samples. And this inconsistency would suggest that the disruption of these regulators would have little relevance to the actual cancer pathology. It is known however that few genes are altered in more than 10% of tumors for any type of cancer  and therAlternatively, these genes may occur in genomic regions which are disrupted, not sporadically, but consistently in the later stages of tumor development. In general the samples within a dataset will come from tumors at different stages of the disease, so the disruption to these particular genes may reflect changes that take place during the course of tumor development. These points are examined further in the Discussion section.The aim of the work described in this paper was two-fold. Firstly, to augment the information held in the Metamatched database with a comprehensive pathway enrichment analysis. This has added predictions of pathway memberships, as well as predictions of regulatory relationships to the database, and we give examples of the usefulness of these predictions in the following. Secondly, the aim was to explore the relevance of the information in the database to cancer, concentrating on particular prominent regulators highlighted by the meta-analysis. We present arguments as to why these genes could play a role in tumor biology, which appears to be supported by the pathway enrichment results.In this section we first give a brief summary of the previously published meta-analysis method we use to predict gene regulatory relationships. We then describe the network-based pathway enrichment approach we use in this paper to analyse the results from the meta-analysis. We infer inter-chromosomal acting gene regulatory relationships from a meta-analysis of 45 matched aCGH/transcriptomics datasets, which are listed in We refer to a ‘regulating gene’ or ‘regulator’ as one whose up or down expression change has a direct or indirect effect on the up or down regulation of a ‘target gene’. Any gene whose mRNA expression levels are significantly correlated with changes in its own copy number is considered worth investigating as a potential regulating gene. A potential target gene of a regulating gene has expression levels which are significantly correlated with the copy number alterations of the regulating gene.p-value from the meta-analysis or by the maximum number of datasets where there is significant correlation between the target’s expression and the regulator’s aCGH.A gene appearing in a regulator’s list of predicted targets, does not mean that regulator is the most probable regulator for that target. Therefore, for each potential regulator, all predicted targets were assessed to see if the data indicated a more probable gene as its regulator. There are two possible criteria for assigning the best regulator for a target gene: either by minimum p-value < 0.05). Moreover this regulator is predicted to be the best regulator for the target out of all the regulators in the Metamatched analysis by way of both minimum p-value and having the maximum number of datasets where there is significant correlation between the target’s expression and the regulator’s aCGH. We use the short-hand ‘less stringent condition for best regulator’ to refer to assigning a regulator as the best regulator of a target by way of either minimum p-value or having the maximum number of datasets where there is significant correlation between the target’s expression and the regulator’s aCGH.In the following we use the short-hand ‘significant best target’ to refer to a predicted target of a regulator which is first of all significant , and a biological pathway annotation for the interaction and/or a Pubmed ID (PMID) . Not allThe analysis consisted of two steps. Firstly the target gene list of the regulator was analysed in order to find enrichment of local subnetworks within the given PathwayCommons network database. This step uses the network structure encapsulated in the PathwayCommons database. Then the set of enriched local subnetworks discovered in the first step were investigated for the presence of enriched biological pathways according to the pathway annotations in the PathwayCommons database. p-values. The LEAN method examines local subnetworks, each of which comprises a single centre gene and its direct neighbours in a network database. All possible local subnetworks (centre genes) are evaluated. Evaluation of a local subnetwork takes as input the p-values of the centre gene and its direct neighbours, calculates an enrichment score and uses a null distribution derived from random sampling to return an enrichment p-value. The parameter free and exhaustive aspect of the LEAN method makes it particularly suitable for a comprehensive analysis of large numbers of disparate gene lists.The local enrichment analysis (LEAN) method of Gwinner et al.  was usedp-values from the meta-analysis for this regulator. That is, the p-values for the correlation between the regulator’s aCGH and the target genes’ expression. For each target gene the minimum of the p-value for activation by the regulator and the p-value for repression by the regulator was selected.For a regulator, the input to the LEAN algorithm was the complete list of target gene p-value for the local subnetwork was less than 0.05.A gene was considered to be an enriched centre gene, at the centre of a significantly enriched subnetwork, if the LEAN The LEAN analysis of a regulator’s target gene list generates a number of significantly enriched local subnetworks for a given PathwayCommons network database. In this step the results of the network analysis are summarised and condensed in order to extract significantly enriched biological pathways.p-value < 0.05). The stringent rule was applied as to whether a regulator is predicted to be the best regulator for a target gene.The following procedure was carried out for each regulator in each PathwayCommons network database in which the regulator shows significant enrichment of its target list. For each significantly enriched local subnetwork found for the regulator, biological pathways associated with the subnetwork were recorded by noting the pathways assigned to the edges of the subnetwork in the PathwayCommons network database. Not all edges were included; only the edges attached to a gene for which the regulator is predicted to be the best regulator and where this regulator-target relationship is significant  between their copy number profile and gene expression profile in at least one of the datasets. Of the 15496 potential regulators, 1176 were found to have at least one significant predicted target . From the meta-analysis of 45 matched aCGH/transcriptomics datasets we infer inter-chromosomal acting gene regulatory relationships. The complete results of the analysis can be found in the METAMATCHED database [The target gene list of each regulator was analysed for enrichment of local subnetworks within four PathwayCommons network databases using the LEAN method . The setEach regulator will, in general, have a number of significantly enriched pathways besides a main pathway; the number ranges from 1 to 89 pathways, with a mean of 5. The 250 regulators between them have a total of 663 unique enriched pathways , POGZ (20), RBBP5 (16), WWOX (16), CPEB4 (9), MYC (8), PYGO2 (7), LYL1 (3), ZNF446 (3), HOXD9 (2), NIPAL4 (2), RAB3A (2) and HIST2H2BE (2). The numbers in brackets indicate the number of datasets in which the regulator has significant self aCGH/expression correlation.Thirteen regulators have this pathway as one of their enriched pathways, namely BCL9 (B Cell CLL/Lymphoma 9) has significant self-correlation in 21 datasets. It is known to be part of this pathway, being annotated with the pathway in Reactome.POGZ (Pogo Transposable Element Derived With ZNF Domain), a protein coding gene, has significant self-correlation in 20 of the datasets in Metamatched  domain, and a transposase-derived DDE domain [α from mitotic chromosome arms and for activation and dissociation of Aurora B kinase from chromosome arms at M phase [E domain . The HP1 M phase . POGZ ha M phase , and wit M phase , 66, and M phase .POGZ has six significant best targets associated with the pathway ‘Formation of the beta-catenin:TCF transactivating complex’ in the enrichment analysis. TTRAP is annotated with this pathway in Reactome, EP400 and YY1 are centres of enriched local subnetworks, and SMC4, KMT2A and NAT8L are part of enriched local subnetworks.POGZ appear in the pathway. POGZ is the best predicted regulator with the pathway.If the stringency of the best regulator condition is relaxed  [POGZ has also been found to affect the proliferation of mouse neural progenitor cells [Loss of function mutations in n cycles . POGZ muer (ASD) , 69, 70.or cells .CHD8, interacts with POGZ [POGZ expression is also correlated with CTNND2 (Catenin Delta 2) [POGZ’s involvement in the beta-catenin pathways predicted here by the pathway enrichment analysis of the regulatory relationships inferred from the meta-analysis of matched datasets.Another better characterised gene associated with ASD, ith POGZ  and is kith POGZ , and mutith POGZ . POGZ exDelta 2) , providiCHD8 is also implicated in cancer [POGZ in many of the cancer datasets used in our study and the concomitant changes in its expression, and the significant correlation of the expression changes of an unusually high number of targets, may indicate that POGZ has a functional role in cancer biology. The datasets in which POGZ showed significant aCGH/expression correlation corresponded to the following tissues and cancer pathologies: Breast , Head and Neck Squamous Cell, Lung Adenocarcinoma, Lymphoma, Multiple Myeloma, Osteosarcoma, Ovarian, Pancreatic, Prostate and Urothelial.Disruption of n cancer , as is tn cancer . Thus thRBBP5 (RB Binding Protein 5) has significant self-correlation in 16 datasets. It is known to be involved in signaling by Wnt, and in Reactome it is annotated with the pathway ‘Formation of the beta-catenin:TCF transactivating complex’, as is one of its two significant best targets, SETDB2. Its other significant best target is SLTM. If the less stringent condition for RBBP5 to be the best regulator is applied then a further significant best target, CDCA5, is included in the pathway.WWOX (WW Domain Containing Oxidoreductase) has significant self-correlation in 16 datasets. ‘Formation of the beta-catenin:TCF transactivating complex’, is an enriched pathway for WWOX due to its predicted significant best target LEF1. This prediction agrees with prior knowledge of WWOX in that it has a known regulatory relationship with LEF1 involving the beta-catenin pathway. That is, WWOX interacts with DVL2, inhibiting the function of DVL2 in controlling the transcriptional activity of LEF1, and also by interacting with a cofactor of the Wnt/beta-catenin pathway, BCL9-2, to enhance the activity of the beta-catenin-TCF/LEF transcription factor complexes [WWOX is also known to act as a tumor suppressor [omplexes , 79. WWOppressor .WWOX to be the best regulator is applied then five further significant best targets are included in the pathway, in addition to LEF1, namely SUV39H1, TLE1, SIN3A, KDM4B and RCL1. None are annotated with this pathway but they are associated with the pathway by the enrichment analysis as either enriched centres or connected to enriched centres. Overexpression of SUV39H1 is known to be associated with cell proliferation in cancer [SIN3A is downregulated in a variety of cancers and is thought to influence a specific step of tumorgenesis in part via the beta-catenin pathway [KDM4B overexpression contributes to the genesis of colorectal tumors via its role in beta-catenin mediated gene transcription [If the less stringent condition for n cancer . SIN3A i pathway . KDM4B ocription .CPEB4 (Cytoplasmic Polyadenylation Element Binding Protein 4) has significant self-correlation in 9 of the datasets and has ‘Formation of the beta-catenin:TCF transactivating complex’ as an enriched pathway through its significant best target TCF7L2. In Reactome CPEB4 is not connected to members of the pathway. Besides TCF7L2 it has four other significant best targets, namely NQO2, CES4A, TET1 and ZNF26. CES4A has no path to the pathway in Reactome, indicating that it may be a novel member of this pathway. If the less stringent condition is applied then the gene CROT is also a significant target of CPEB4 in this pathway.CPEB4 has been shown to have a role in oncogenesis through translational activation of mRNAs that are normally silenced [CPEB4 prevents cell invasion and migration in non-small cell lung cancer [CPEB4 expression can serve as a prognostic factor in invasive ductal breast carcinoma [CPEB1 regulates beta-catenin mRNA translation [silenced . Silencig cancer . High CParcinoma . The relnslation .MYC (MYC Proto-Oncogene) has significant self-correlation in 8 of the datasets and is known to be part of the pathway ‘Formation of the beta-catenin:TCF transactivating complex’. Similarly PYGO2 (Pygopus Family PHD Finger 2), with significant self-correlation in 7 datasets, is also annotated with this pathway in Reactome.POGZ (20), ANKRD46 (17), RBBP5 (16), KMT2E (9), DAAM2 (4), LYL1 (3), ZNF446 (3), HOXD9 (2), NIPAL4 (2), HIST2H2BE (2) and RAB3A (2).RUNX1 is a transcription factor, known to be essential for the maturation of megakaryocytes . It is kPOGZ (Pogo Transposable Element Derived With ZNF Domain): There is no path in Reactome from POGZ to members of the pathway ‘RUNX1 regulates genes involved in megakaryocyte differentiation’ however it has been shown that POGZ is highly expressed in mouse megakaryocyte erythroid progenitors [genitors .POGZ are associated with the pathway in the enrichment analysis. Of the five, KMT2A is annotated with this pathway in Reactome, EP400 and YY1 are at the centres of enriched local subnetworks, and SMC4 and TRRAP are part of enriched local subnetworks. Of the remaining 28, 11 do not currently appear in Reactome, one occurs but has no path to any members of the pathway, and 14 do have paths to members of this pathway. If the less stringent condition for a regulator to be the best regulator is applied then a further four significant best targets are associated with the pathway, namely MORF4L1, MORF4L2, MTA1 and YEATS2.Five of the thirty three significant best targets of ANKRD46 (Ankyrin Repeat Domain 46) is also not known to be part of this pathway, but is associated in this analysis by gene KAT2B being a significant best target. Whereas, RBBP5  and KMT2E (Lysine Methyltransferase 2E) are both annotated with this pathway in Reactome.By removing methyl groups from histone proteins Histone demethylases (HDMs) contribute to epigenetic regulation by reversing histone methylation  and giveDERL1 (21), MAN2A1 (13) and POLD3 (11). In Metamatched three regulators have this pathway enriched in their target lists: DERL1 (Derlin1) is a component of the endoplasmic reticulum-associated mechanism for the degradation of misfolded proteins. It demonstrates significant self correlation in 21 of the datasets and has 3 significant best targets, JMJD6, ZKSCAN7 and CCPG1. Its main pathway from the enrichment analysis is ‘HDMs demethylate histones’. DERL1 is not known to be part of this pathway, but in Reactome it has a path to members of the pathway by way of forming a complex with RAD23B, which in turn is connected to SUMO1. Significant best target JMJD6 is associated with the pathway. Of the two other significant best targets CCPG1 does not currently appear in Reactome whilst ZKSCAN7 does have a path to members of this pathway. If the stringency of the condition for a regulator to be a target’s best regulator is relaxed then POLD3 is also included as a significant best target. POLD3 (see below) is itself a potential regulator in the Metamatched analysis and interestingly its own target list is also enriched with the pathway ‘HDMs demethylate histones’.POLD3  plays an important role in DNA replication and repair. Depletion of POLD3 results in an increase in genome instability, by way of breaks, S-phase progression impairment and chromosome abnormalities [PHF2, is associated with this pathway. Four of the others have some path to the pathway and two do not appear in Reactome. POLD3 has a path to PHF2 via POLR2A. If the less stringent condition is applied then five further significant best targets are associated with the pathway, DOT1L, RARG, SUPT5H, TAF1L and PDCD7.malities . It has MAN2A1 (Mannosidase Alpha Class 2A Member 1) has self correlation in 13 datasets and one significant best target, KDM4A. MAN2A1 is not annotated with this pathway, but can be connected to members of the pathway in Reactome.Previously we have used matched aCGH/expression datasets to predict statistically significant gene regulatory relationships , validatThe data used in the analysis consists of more than three thousand cancer samples so in this paper we explore whether the results can provide insights into tumor biology. To this end we have carried out an exhaustive network-based pathway enrichment analysis. We concentrate on those genes that are unusual in manifesting copy number heterogeneity, and a concomitant change in expression, in many of the 45 datasets and a very wide variety of different cancer types.This consistent heterogeneity could indicate genes located in genomic regions which are prone to disruption in cancer cells, but this disruption occurs only sporadically amongst samples, suggesting little relevance to the actual cancer pathology. However it is known that genes are rarely altered in more than 10% of tumors for any type of cancer  and therAn alternative possibility is that these genes occur in genomic regions which are disrupted consistently in the later stages of tumor development. In general the samples within an experiment will come from tumors at different stages of the disease, so the disruption to these particular genes may reflect changes that take place during the course of tumor development. In this case the disruption to regulators and their pathways might have a role in later phases of the disease, for example in the development of metastases. Along with tumor heterogeneity, metastases are a major challenge facing cancer therapy .In some cases the pathway enrichment analysis predicts known down-stream target genes of a regulator, and some examples of this feature in the Results section. In other cases however known down-stream targets are not predicted. Such missing predictions may be due to lack of predictive power of the analysis. Alternatively they may result from cellular mechanisms able to compensate for a regulator’s copy number disruption and concomitant change in expression. For example, they may represent regulator-target relationships that are highly non-linear so that large changes in a regulator’s expression has little effect on down-stream components of the pathway. Activation of an alternative pathway, or pathways, that can compensate for the disruption to a pathway is another possible method for nullifying the effects of changes in a regulator’s expression.If cellular mechanisms are responsible, then the analysis presented here may be highlighting those regulator-target relationships that lack redundancy or any buffering process in the cell. They are relationships where disruption to the regulator’s expression, due to changes in its copy number, cannot be compensated for by the cell and so may have a real functional effect. The genetic diversity of tumors is a major problem in cancer therapy and the aim of personalized cancer therapy is to identify mutations that are clinically relevant. This involves distinguishing ‘driver’ mutations from neutral ‘passenger’ mutations that tend to accumulate during tumorgenesis , 96. If So despite the inconsistent disruption of the copy number across samples of the genes in this study, it is possible to hypothesize that these genes could be regulators that have a role in tumor biology. The pathway memberships predicted by the network analysis of the meta-analysis results do suggest a close involvement of some of these genes in known cancer pathways. This includes genes for which there is currently little or no information on their relevance to cancer, especially multiple types of cancer, but which have cancer related pathways enriched in the target lists predicted for them by the meta-analysis of matched experiments.WWOX and LEF1 in the beta-catenin pathway, and circumstantial validation, for example the enrichment of the target list of POGZ with the megakaryocytes and beta-catenin pathways.The pathway enrichment analysis shows some interesting examples of direct validation, for example the relationship between WWOX where applying the less stringent condition reveals further significant best targets known to be involved with beta-catenin pathways. Consequently the Metamatched database also provides the significant targets of a regulator found using the less stringent condition for a regulator to be the best regulator of a target.In order to reduce false positives we use high confidence levels and a stringent condition as to whether a regulator is the best regulator for a target in our analysis. Inevitably the stringency may remove some true positives. An example can be seen in the results for the regulator POGZ. It stands out in having self aCGH/expression correlation in a large number of datasets and cancer types, and also an unusually high number of predicted significant targets Click here for additional data file.S2 Filep-value < 0.1).A spreadsheet that summarises the Metamatched results for 2172 regulators that have at least one significant predicted target (adjusted (CSV)Click here for additional data file.S3 FileResults for 2172 genes, in R archive format. Unpacks as an R list object named ‘regulators.details’ with a format described in (RDATA)Click here for additional data file.S4 File(PDF)Click here for additional data file.S5 File(PDF)Click here for additional data file.