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Extracting Pathway-level Signatures from Proteogenomic Data in Breast Cancer Using Independent Component Analysis*

  • Wenke Liu
    Affiliations
    Institute for System Genetics, NYU School of Medicine, New York, New York 10016

    Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, New York, New York 10016
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  • Samuel H. Payne
    Affiliations
    Biology Department, Brigham Young University, Provo, Utah 84602
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  • Sisi Ma
    Correspondence
    To whom correspondence may be addressed:Institute for Health Informatics, University of Minnesota, 420 Delaware Street SE, MMC 912, Minneapolis, MN 55455. Tel.:612-625-8650;
    Affiliations
    Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota 55455
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  • David Fenyö
    Correspondence
    To whom correspondence may be addressed:Institute for System Genetics, NYU School of Medicine, 435 East 30th Street, New Science Building 9th Floor, New York, NY, 10016. Tel.:212–263-2216;
    Affiliations
    Institute for System Genetics, NYU School of Medicine, New York, New York 10016

    Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, New York, New York 10016
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  • Author Footnotes
    * We would like to acknowledge funding by the National Cancer Institute (NCI) through CPTAC award U24 CA210972 and a contract 13XS068 from Leidos Biomedical Research, Inc., and by a grant from the Shifrin-Myers Breast Cancer Discovery Fund.
    This article contains supplemental Figures and Tables.
Open AccessPublished:June 18, 2019DOI:https://doi.org/10.1074/mcp.TIR119.001442
      Recent advances in the multi-omics characterization necessitate knowledge integration across different data types that go beyond individual biomarker discovery. In this study, we apply independent component analysis (ICA) to human breast cancer proteogenomics data to retrieve mechanistic information. We show that as an unsupervised feature extraction method, ICA was able to construct signatures with known biological relevance on both transcriptome and proteome levels. Moreover, proteome and transcriptome signatures can be associated by their respective correlation with patient clinical features, providing an integrated description of phenotype-related biological processes. Our results demonstrate that the application of ICA to proteogenomics data could lead to pathway-level knowledge discovery. Potential extension of this approach to other data and cancer types may contribute to pan-cancer integration of multi-omics information.

      Graphical Abstract

      Breast cancer is the most common cancer among women, and although targeted therapies have helped to significantly reduced breast cancer mortality rate in the past decade, further improvement will require a comprehensive understanding of the molecular mechanisms of the disease (
      • Narod S.A.
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      Why have breast cancer mortality rates declined?.
      ,
      • Mendes D.
      • Alves C.
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      • Cardoso F.
      • Passos-Coelho J.L.
      • Costa L.
      • Andrade S.
      • Batel-Marques F.
      The benefit of HER2-targeted therapies on overall survival of patients with metastatic HER2-positive breast cancer – a systematic review.
      ). Recently, deep mass spectrometry based proteomic characterization of genomically annotated breast cancer samples by the Clinical Proteomic Tumor Analysis Consortium (CPTAC)
      The abbreviations used are: CPTAC, Clinical Proteomic Tumor Analysis Consortium; ICA, independent component analysis; PCA, principal component analysis; LC-MS/MS, liquid chromatography tandem mass spectrometry; FDR, false discovery rate; TCGA, The Cancer Genome Atlas; BRCA, breast invasive carcinoma; OV, ovarian serous cystadenocarcinoma.
      1The abbreviations used are: CPTAC, Clinical Proteomic Tumor Analysis Consortium; ICA, independent component analysis; PCA, principal component analysis; LC-MS/MS, liquid chromatography tandem mass spectrometry; FDR, false discovery rate; TCGA, The Cancer Genome Atlas; BRCA, breast invasive carcinoma; OV, ovarian serous cystadenocarcinoma.
      has marked the initial step of a proteogenomic integrative approach, in which recurrent mutations and copy number variations on the genomic level, expression profiles on the transcriptomic level and protein abundance and functional manifestations on proteomic level were measured for the same group of patient samples and examined in the same framework (
      • Mertins P.
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      ,
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      • Tian Y.
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      • Fenyö D.
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      • Sokoll L.
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      • Cope L.
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      • Chan D.W.
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      • CPTAC Investigators SA
      • Gillette M.A.
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      • Thangudu R.
      • Cai S.
      • Oberti M.
      • Paulovich A.G.
      • Whiteaker J.R.
      • Edwards N.J.
      • McGarvey P.B.
      • Madhavan S.
      • Wang P.
      • Chan D.W.
      • Pandey A.
      • Shih I.-M.
      • Zhang H.
      • Zhang Z.
      • Zhu H.
      • Cope L.
      • Whiteley G.A.
      • Skates S.J.
      • White F.M.
      • Levine D.A.
      • Boja E.S.
      • Kinsinger C.R.
      • Hiltke T.
      • Mesri M.
      • Rivers R.C.
      • Rodriguez H.
      • Shaw K.M.
      • Stein S.E.
      • Fenyo D.
      • Liu T.
      • McDermott J.E.
      • Payne S.H.
      • Rodland K.D.
      • Smith R.D.
      • Rudnick P.
      • Snyder M.
      • Zhao Y.
      • Chen X.
      • Ransohoff D.F.
      • Hoofnagle A.N.
      • Liebler D.C.
      • Sanders M.E.
      • Shi Z.
      • Slebos R.J.C.
      • Tabb D.L.
      • Zhang B.
      • Zimmerman L.J.
      • Wang Y.
      • Davies S.R.
      • Ding L.
      • Ellis M.J.C.
      • Townsend R.R.
      Integrated proteogenomic characterization of human high-grade serous ovarian cancer.
      ,
      • Zhang B.
      • Wang J.
      • Wang X.
      • Zhu J.
      • Liu Q.
      • Shi Z.
      • Chambers M.C.
      • Zimmerman L.J.
      • Shaddox K.F.
      • Kim S.
      • Davies S.R.
      • Wang S.
      • Wang P.
      • Kinsinger C.R.
      • Rivers R.C.
      • Rodriguez H.
      • Townsend R.R.
      • Ellis M.J.C.
      • Carr S.A.
      • Tabb D.L.
      • Coffey R.J.
      • Slebos R.J.C.
      • Liebler D.C.
      • NCI CPTAC S. A.
      • Gillette M.A.
      • Klauser K.R.
      • Kuhn E.
      • Mani D.R.
      • Mertins P.
      • Ketchum K.A.
      • Paulovich A.G.
      • Whiteaker J.R.
      • Edwards N.J.
      • McGarvey P.B.
      • Madhavan S.
      • Wang P.
      • Chan D.
      • Pandey A.
      • Shih I.-M.
      • Zhang H.
      • Zhang Z.
      • Zhu H.
      • Whiteley G.A.
      • Skates S.J.
      • White F.M.
      • Levine D.A.
      • Boja E.S.
      • Kinsinger C.R.
      • Hiltke T.
      • Mesri M.
      • Rivers R.C.
      • Rodriguez H.
      • Shaw K.M.
      • Stein S.E.
      • Fenyo D.
      • Liu T.
      • McDermott J.E.
      • Payne S.H.
      • Rodland K.D.
      • Smith R.D.
      • Rudnick P.
      • Snyder M.
      • Zhao Y.
      • Chen X.
      • Ransohoff D.F.
      • Hoofnagle A.N.
      • Liebler D.C.
      • Sanders M.E.
      • Shi Z.
      • Slebos R.J.C.
      • Tabb D.L.
      • Zhang B.
      • Zimmerman L.J.
      • Wang Y.
      • Davies S.R.
      • Ding L.
      • Ellis M.J.C.
      • Reid Townsend R.
      Proteogenomic characterization of human colon and rectal cancer.
      ). The collection of high-quality multi-omics data immediately led to the discovery of concordant gene amplification and protein phosphorylation in key pathways (
      • Mertins P.
      • Mani D.R.
      • Ruggles K.V.
      • Gillette M.A.
      • Clauser K.R.
      • Wang P.
      • Wang X.
      • Qiao J.W.
      • Cao S.
      • Petralia F.
      • Kawaler E.
      • Mundt F.
      • Krug K.
      • Tu Z.
      • Lei J.T.
      • Gatza M.L.
      • Wilkerson M.
      • Perou C.M.
      • Yellapantula V.
      • Huang K.
      • Lin C.
      • McLellan M.D.
      • Yan P.
      • Davies S.R.
      • Townsend R.R.
      • Skates S.J.
      • Wang J.
      • Zhang B.
      • Kinsinger C.R.
      • Mesri M.
      • Rodriguez H.
      • Ding L.
      • Paulovich A.G.
      • Fenyö D.
      • Ellis M.J.
      • Carr S.A.
      • NCI CPTAC
      Proteogenomics connects somatic mutations to signalling in breast cancer.
      ). At the same time, there is increasing demand for analytical approaches that could incorporate all data types and extract pathway level signatures. Because in all human patients “-omics” data sets the number of features far exceeds the number of samples, analysis of any single data type is already susceptible to “the curse of dimensionality,” and integration by simple concatenation of multi-omics data would be an even less desirable option. Our previous work has benchmarked the predictive power of multi-omics data sets for classifying breast cancer patients into different survival groups and showed that combined multi-omics data sets produced with data-driven fusion techniques were not able to outperform proteomic data alone (
      • Ma S.
      • Ren J.
      • Fenyö D.
      Breast cancer prognostics using multi-omics data.
      ). This result highlighted the possible redundancy among information contained in different biological levels and motivates us to explore other data fusion techniques that extract both concordant and complementary features from high-dimensional multi-omics data.
      In the current study, we applied independent component analysis to proteomic and transcriptomic data of 77 breast cancer samples to extract pathway-level molecular signatures. Independent component analysis (ICA) is an unsupervised learning method widely used in signal processing and has been applied to cancer genomics with notable success (
      • Frigyesi A.
      • Veerla S.
      • Lindgren D.
      • Höglund M.
      Independent component analysis reveals new and biologically significant structures in micro array data.
      ,
      • Engreitz J.M.
      • Daigle B.J.
      • Marshall J.J.
      • Altman R.B.
      Independent component analysis: mining microarray data for fundamental human gene expression modules.
      ,
      • Biton A.
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      • López-Bigas N.
      • Kamoun A.
      • Neuzillet Y.
      • Gestraud P.
      • Grieco L.
      • Rebouissou S.
      • de Reyniès A.
      • Benhamou S.
      • Lebret T.
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      • Allory Y.
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      • Radvanyi F.
      Independent component analysis uncovers the landscape of the bladder tumor transcriptome and reveals insights into luminal and basal subtypes.
      ). This approach decomposes the molecular profiles into linear combinations of non-Gaussian independent sources or components, each of which is comprised of weighted contributions from individual genes. Therefore, ICA reduces the dimensionality of original data by representing the molecular profile of each sample as weighted sum of several “meta-genes” or “meta-proteins,” and the weight of specific meta-gene/protein (mixing scores) in one sample reflects the “activity” of that component in the sample. Different from the more conventional dimension reduction method of principal component analysis (PCA), which seeks to find uncorrelated factors that explain the variance among the data, and works the best when the underlying components are normally distributed, ICA are able to discover more informative representations of high-dimensional biological signals, which are usually super-Gaussian and contain more close-to-zero values than a normally-distributed sample (
      • Hyvärinen A.
      Independent component analysis: recent advances.
      ). As clinical features are also available for the CPTAC samples, molecular signatures can be constructed from clusters of meta-genes/proteins that show activity patterns correlated with these clinical features. Further, taking advantage of a specific clinical feature as an “anchor,” this method may help extract patterns at different biological levels and across different cohorts, which may originate from the same cellular functionality (Fig. 1). The signatures extracted from different data sets were filtered based on their intrinsic stability and association with known clinical features (see Experimental Procedures below) and grouped into modules that showed similar correlation patterns to clinical features. Subsequent gene set enrichment analysis revealed the biological relevance of these modules to pathways such as HER2 signaling, mitosis, and histone modification. Our analysis has demonstrated that ICA was able to blindly extract information in the form of weighted gene combinations, which may be biologically meaningful at pathway level. With input from clinical features or other sample subgrouping indices, these signatures may be further integrated into multilevel models that provide insights into the molecular mechanisms of breast cancer.
      Figure thumbnail gr1
      Fig. 1.Data analysis workflow. Coefficients of independent components and their corresponding mixing scores are extracted from both proteome and transcriptome data sets for multiple randomly initiated runs. Each independent component is a pathway-level representation. Molecular signatures were identified as cluster centroids of these components, and clinical features were used to select biologically relevant signatures, which were further annotated with pathway analysis.

      DISCUSSION

      We have used independent component analysis to gain insights into the mechanisms of breast cancer and develop protein/gene modules as clinical signatures. Meta-proteins and meta-genes are extracted blindly from the data, and signatures are further selected based on consistency of meta-gene/protein clusters or the association between their activity scores and known clinical features. Gene set annotation revealed that several selected signatures contained biologically relevant information. A proteome signature (pr_02) characterizing strong activation of the Her2 pathway was recovered as a Her2-related meta-protein cluster. On the transcriptome level, a meta-gene enriched for genes in the 16q24 risk loci (tx_63) formed a stable signature that showed correlation with both ER status and Basal subtype. Stable signatures on both proteome and transcriptome levels (pr_04 and tx_46) were found to be heavily associated with the Luminal A index, suggesting that cell division and growth is specifically regulated in this subtype.
      As an unsupervised blind source separation method, independent component analysis has been applied to multiple biological data types. Consistent with previous reports, our results demonstrated that as an unsupervised learning method, ICA can extract biological meaningful information solely based on the intrinsic structures of the transcriptome and proteome data. Because of the underlying assumptions, this method would only yield satisfactory results when the “true” signals in the data do not follow Gaussian distribution, and it would fail to separate any mixture of normally distributed sources. Successful application in the current use case suggested that ICA have captured some aspects of the mechanistic processes underlying proteome and transcriptome profiles. It is reasonable to assume that the observed RNA and protein levels are the sum of several up-regulation and down-regulation modules, in which the distribution of individual gene levels deviate radically from the normal distribution that characterizes a noisy background.
      In the current use case, we have designated the number of components to be equal to the number of samples, as is usually assumed in blind source separation applications. It remains an open question whether all extracted signatures are reflective of some “true” biological processes. Moreover, the stochastic nature of the algorithm entails that, although all components were found simultaneously in each run without any component “privileged” over others (
      • Hyvarinen A.
      Fast and robust fixed-point algorithms for independent component analysis.
      ), there is no guarantee that all solutions found at local optima are practically useful features. Indeed, the fact that a lot of the extracted components were neither consistent nor recurrent in multiple runs suggested that they may arise from noise instead of stable signals. However, previous applications of ICA on biological data have showed that overestimating the number of independent sources gave rise to signatures driven by smaller gene groups without affecting the rediscovery of the most stable components, while underestimating the number of sources compromised signal detection (
      • Kairov U.
      • Cantini L.
      • Greco A.
      • Molkenov A.
      • Czerwinska U.
      • Barillot E.
      • Zinovyev A.
      Determining the optimal number of independent components for reproducible transcriptomic data analysis.
      ). It is possible that assuming the number of independent sources to be equal to the number of samples may result in noisy signals that are “unnecessary.” However, in an exploratory analysis setting where the number of samples is relatively small, this problem could be remedied by inspecting the stability and biological relevance of extracted signatures post hoc and extracting more components may lead to better separation of signals (supplemental Fig. S3). In the case of large sample size, the number of components will have to be determined by heuristics, and different choices may be compared in downstream analyses.
      Using clinical association (total significant associations >25 across all tested clinical variables) or signature stability (cluster silhouette >0.9) as selection criterion gave rise to two lists of potential signatures (Table II) with only a few overlapping members (pr_04, tx_46, tx_63), suggesting that the most clinically relevant signatures are not very stable under the current method. Other clustering method such as density-based clustering may be used to improve the estimation of stable signatures (
      • Himberg J.
      • Hyvärinen A.
      • Esposito F.
      Validating the independent components of neuroimaging time series via clustering and visualization.
      ,
      • Jahirabadkar S.
      • Kulkarni P.
      Clustering for high dimensional data: density based subspace clustering algorithms.
      ). On the other hand, it is possible that the most stable signatures described the housekeeping processes common in all samples, but they may also help reveal novel molecular mechanisms of breast cancer that are not previously linked to any phenotype. As a feature construction procedure, ICA could also facilitate knowledge integration from multiple data types. In addition to the integration of proteome and transcriptome signatures as demonstrated in this work, future studies could further apply ICA on omics data sets of multiple cancer types. Extracted molecular signatures may be grouped in another round of clustering procedure to reveal pan-cancer and cancer type specific mechanisms.

      Data Availability

      Proteome data of BRCA and OV are available at the CPTAC data portal (https://cptac-data-portal.georgetown.edu/cptac/s/S015; https://cptac-data-portal.georgetown.edu/cptac/s/S020). Transcriptome data are available at the NCI Genomics Data Commons (https://portal.gdc.cancer.gov) under project name TCGA-BRCA and TCGA-OV. Data tables are also available at the publication page (https://www.nature.com/articles/nature18003; https://www.nature.com/articles/nature10166).

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