A Multi-Method Approach for Proteomic Network Inference in 11 Human Cancers.

Protein expression and post-translational modification levels are tightly regulated in neoplastic cells to maintain cellular processes known as 'cancer hallmarks'. The first Pan-Cancer initiative of The Cancer Genome Atlas (TCGA) Research Network has aggregated protein expression profiles for 3,467 patient samples from 11 tumor types using the antibody based reverse phase protein array (RPPA) technology. The resultant proteomic data can be utilized to computationally infer protein-protein interaction (PPI) networks and to study the commonalities and differences across tumor types. In this study, we compare the performance of 13 established network inference methods in their capacity to retrieve the curated Pathway Commons interactions from RPPA data. We observe that no single method has the best performance in all tumor types, but a group of six methods, including diverse techniques such as correlation, mutual information, and regression, consistently rank highly among the tested methods. We utilize the high performing methods to obtain a consensus network; and identify four robust and densely connected modules that reveal biological processes as well as suggest antibody-related technical biases. Mapping the consensus network interactions to Reactome gene lists confirms the pan-cancer importance of signal transduction pathways, innate and adaptive immune signaling, cell cycle, metabolism, and DNA repair; and also suggests several biological processes that may be specific to a subset of tumor types. Our results illustrate the utility of the RPPA platform as a tool to study proteomic networks in cancer

Genome-scale Precision Proteomics Identifies Cancer Signaling Networks and Therapeutic Vulnerabilities

Mass spectrometry (MS) based-proteomics technology has been emerging as an indispensable tool for biomedical research. But the highly diverse physical and chemical properties of the protein building blocks and the dramatic human proteome complexity largely limited proteomic profiling depth. Moreover, there was a lack of high-throughput quantitative strategies that were both precise and parallel to in-depth proteomic techniques. To solve these grand challenges, a high resolution liquid chromatography (LC) system that coupled with an advanced mass spectrometer was developed to allow genome-scale human proteome identification. Using the combination of pre-MS peptide fractionation, MS2-based interference detection and post-MS computational interference correction, we enabled precise proteome quantification with isobaric labeling. We then applied these advanced proteomics tools for cancer proteome analyses on high grade gliomas (HGG) and rhabdomyosarcomas (RMS). Using systems biology approaches, we demonstrated that these newly developed proteomic analysis pipelines are able to (i) define human proteotypes that link oncogenotypes to cancer phenotypes in HGG and to (ii) identify therapeutic vulnerabilities in RMS. Development of high resolution liquid chromatography is essential for improving the sensitivity and throughput of mass spectrometry-based proteomics to genome-scale. Here we present systematic optimization of a long gradient LC-MS/MS platform to enhance protein identification from a complex mixture. The platform employed an in-house fabricated, reverse phase long column (100 µm x 150 cm, 5 µm C18 beads) coupled with Q Exactive MS. The column was capable of achieving a peak capacity of approximately 700 in a 720 min gradient of 10-45% acetonitrile. The optimal loading amount was about 6 micrograms of peptides, although the column allowed loading as many as 20 micrograms. Gas phase fractionation of peptide ions further increased the number of peptides identified by ~10%. Moreover, the combination of basic pH LC pre-fractionation with the long gradient LC-MS/MS platform enabled the identification of 96,127 peptides and 10,544 proteins at 1% protein false discovery rate in a postmortem brain sample of Alzheimer’s disease. As deep RNA sequencing of the same specimen suggested that ~16,000 genes were expressed, current analysis covered more than 60% of the expressed proteome. Isobaric labeling quantification by mass spectrometry has emerged as a powerful technology for multiplexed large-scale protein profiling, but measurement accuracy in complex mixtures is confounded by the interference from co-isolated ions, resulting in ratio compression. Here we report that the ratio compression can be essentially resolved by the combination of pre-MS peptide fractionation, MS2-based interference detection and post-MS computational interference correction. To recapitulate the complexity of biological samples, we pooled tandem mass tag (TMT) labeled E. coli peptides at 1 : 3 : 10 ratios, and added in ~20-fold more rat peptides as background, followed by the analysis of two dimensional liquid chromatography-MS/MS. Systematic investigation indicated that the quantitative interference was impacted by LC fractionation depth, MS isolation window and peptide loading amount. Exhaustive fractionation (320 x 4 h) can nearly eliminate the interference and achieve results comparable to the MS3-based method. Importantly, the interference in MS2 scans can be estimated by the intensity of contaminated y1 product ions, and we thus developed an algorithm to correct reporter ion ratios of tryptic peptides. Our data indicated that intermediate fractionation (40 x 2 h) and y1 ion-based correction allowed accurate and deep TMT protein profiling, which represents a straightforward and affordable strategy in isobaric labeling proteomics High throughput omics approaches provide an unprecedented opportunity for dissecting molecular mechanisms in cancer biology. Here we present deep profiling of whole proteome, phosphoproteome and transcriptome in two high-grade glioma mouse models driven by mutated receptor tyrosine kinase (RTK) oncogenes, platelet-derived growth factor receptor alpha (PDGFRA) and neurotrophic receptor tyrosine kinase 1 (NTRK1), analyzing 13,860 proteins (11,941 genes) and 30,431 phosphosites by mass spectrometry. Systems biology approaches identified numerous functional modules and master regulators, including 41 kinases and 26 transcription factors. Pathway activity computation and mouse survival curves indicate the NTRK1 mutation induces a higher activation of AKT targets, drives a positive feedback loop to up-regulate multiple other RTKs, and shows higher oncogenic potency than the PDGFRA mutation. Further integration of the mouse data with human HGG transcriptome data determines shared regulators of invasion and stemness. Thus, multi-omics integrative profiling is a powerful avenue to characterize oncogenic activity. There is growing emphasis on personalizing cancer therapy based on somatic mutations identified in patient’s tumors. Among pediatric solid tumors, RAS pathway mutations in rhabdomyosarcoma are the most common potentially actionable lesions. Recent success targeting CDK4/6 and MEK in RAS mutant adult cancers led our collaborator Dr. Dyer’s group to test this approach for rhabdomyosarcoma. They achieved synergistic killing of RAS mutant rhabdomyosarcoma tumor cells by combining MEK and CDK4/6 inhibitors in culture but failed to achieve efficacy in vivo using orthotopic patient derived xenografts (O-PDXs). To determine how rhabdomyosarcomas evade targeting of CDK4/6 and MEK, we collaborated to perform large-scale deep proteomic, phosphoproteomic, and epigenomic profiling of RMS tumors. Integrative analysis of these omics data detected that RMS tumor cells rapidly compensate and overcome CDK4/6 and MEK combination therapy through 6 myogenic signal transduction pathways including WNT, HH, BMP, Adenyl Cyclase, P38/MAPK and PI3K. While it is not feasible to target each of these signal transduction pathways simultaneously in RMS, we discovered that they require the HSP90 chaperone to sustain the complex developmental signal transduction milieu. We achieved specific and synergistic killing of RMS cells using sub-therapeutic concentrations of an HSP90 inhibitor (ganetespib) in combination with conventional chemotherapy used for recurrent RMS. These effects were seen in the most aggressive recurrent RMS orthotopic patient derived xenografts irrespective of RAS pathway perturbations, histologic or molecular classification. Thus, multi-omics integrative cancer profiling using our newly developed tools is powerful to identify core signaling transduction networks, tumor vulnerability (master regulators) for novel cancer therapy

Integrating biological knowledge into variable selection : an empirical Bayes approach with an application in cancer biology

Background: An important question in the analysis of biochemical data is that of identifying subsets of molecular variables that may jointly influence a biological response. Statistical variable selection methods have been widely used for this purpose. In many settings, it may be important to incorporate ancillary biological information concerning the variables of interest. Pathway and network maps are one example of a source of such information. However, although ancillary information is increasingly available, it is not always clear how it should be used nor how it should be weighted in relation to primary data. Results: We put forward an approach in which biological knowledge is incorporated using informative prior distributions over variable subsets, with prior information selected and weighted in an automated, objective manner using an empirical Bayes formulation. We employ continuous, linear models with interaction terms and exploit biochemically-motivated sparsity constraints to permit exact inference. We show an example of priors for pathway- and network-based information and illustrate our proposed method on both synthetic response data and by an application to cancer drug response data. Comparisons are also made to alternative Bayesian and frequentist penalised-likelihood methods for incorporating network-based information. Conclusions: The empirical Bayes method proposed here can aid prior elicitation for Bayesian variable selection studies and help to guard against mis-specification of priors. Empirical Bayes, together with the proposed pathway-based priors, results in an approach with a competitive variable selection performance. In addition, the overall procedure is fast, deterministic, and has very few user-set parameters, yet is capable of capturing interplay between molecular players. The approach presented is general and readily applicable in any setting with multiple sources of biological prior knowledge

Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps

The qualitative and quantitative evaluation of proteome changes that condition cancer development can be achieved with liquid chromatography–mass spectrometry (LC-MS). LC-MSbased proteomics strategies are carried out according to predesigned workflows that comprise several steps such as sample selection, sample processing including labeling, MS acquisition methods, statistical treatment, and bioinformatics to understand the biological meaning of the findings and set predictive classifiers. As the choice of best options might not be straightforward, we herein review and assess past and current proteomics approaches for the discovery of new cancer biomarkers. Moreover, we review major bioinformatics tools for interpreting and visualizing proteomics results and suggest the most popular machine learning techniques for the selection of predictive biomarkers. Finally, we consider the approximation of proteomics strategies for clinical diagnosis and prognosis by discussing current barriers and proposals to circumvent them.Research Council of Norway INFRASTRUKTUR-program (project number: 295910

Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps

Mass spectrometry; Proteomics; WorkflowsEspectrometría de masas; Proteómica; Flujos de trabajoEspectrometria de masses; Proteòmica; Fluxos de treballThe qualitative and quantitative evaluation of proteome changes that condition cancer development can be achieved with liquid chromatography–mass spectrometry (LC-MS). LC-MS-based proteomics strategies are carried out according to predesigned workflows that comprise several steps such as sample selection, sample processing including labeling, MS acquisition methods, statistical treatment, and bioinformatics to understand the biological meaning of the findings and set predictive classifiers. As the choice of best options might not be straightforward, we herein review and assess past and current proteomics approaches for the discovery of new cancer biomarkers. Moreover, we review major bioinformatics tools for interpreting and visualizing proteomics results and suggest the most popular machine learning techniques for the selection of predictive biomarkers. Finally, we consider the approximation of proteomics strategies for clinical diagnosis and prognosis by discussing current barriers and proposals to circumvent them.This research was funded by the Research Council of Norway INFRASTRUKTUR-program (project number: 295910)

Patient-level proteomic network prediction by explainable artificial intelligence

Understanding the pathological properties of dysregulated protein networks in individual patients’ tumors is the basis for precision therapy. Functional experiments are commonly used, but cover only parts of the oncogenic signaling networks, whereas methods that reconstruct networks from omics data usually only predict average network features across tumors. Here, we show that the explainable AI method layer-wise relevance propagation (LRP) can infer protein interaction networks for individual patients from proteomic profiling data. LRP reconstructs average and individual interaction networks with an AUC of 0.99 and 0.93, respectively, and outperforms state-of-the-art network prediction methods for individual tumors. Using data from The Cancer Proteome Atlas, we identify known and potentially novel oncogenic network features, among which some are cancer-type specific and show only minor variation among patients, while others are present across certain tumor types but differ among individual patients. Our approach may therefore support predictive diagnostics in precision oncology by inferring “patient-level” oncogenic mechanisms