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Insights into Impact of DNA Copy Number Alteration and Methylation on the Proteogenomic Landscape of Human Ovarian Cancer via a Multi-omics Integrative Analysis*

  • Xiaoyu Song
    Affiliations
    Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY

    The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
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  • Jiayi Ji
    Affiliations
    Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY

    The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
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  • Kevin J. Gleason
    Affiliations
    Department of Public Health Sciences, The University of Chicago, Chicago, IL
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  • Fan Yang
    Affiliations
    Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado, Anschutz Medical Campus, Aurora, CO
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  • John A. Martignetti
    Affiliations
    Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
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  • Lin S. Chen
    Correspondence
    To whom correspondence may be addressed:Lin S. Chen Department of Public Health Sciences 5841 S Maryland Ave W258 The University of Chicago, Chicago, IL 60637. .
    Affiliations
    Department of Public Health Sciences, The University of Chicago, Chicago, IL
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  • Pei Wang
    Correspondence
    To whom correspondence may be addressed:Pei Wang, 1399 Park Ave, Floor 4, 420D, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY.
    Affiliations
    Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
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  • Author Footnotes
    * This work was supported by National Health Institute | National Cancer Institute U24 CA2109993 and P30 CA196521, by National Health Genome Research Institute R01 HG008980, by National Institute of General Medical Sciences R01 GM108711, and by Susan G. Komen Grant TDR16376189.
    This article contains supplemental Figures and Tables.
Open AccessPublished:June 21, 2019DOI:https://doi.org/10.1074/mcp.RA118.001220
      In this work, we propose iProFun, an integrative analysis tool to screen for proteogenomic functional traits perturbed by DNA copy number alterations (CNAs) and DNA methylations. The goal is to characterize functional consequences of DNA copy number and methylation alterations in tumors and to facilitate screening for cancer drivers contributing to tumor initiation and progression. Specifically, we consider three functional molecular quantitative traits: mRNA expression levels, global protein abundances, and phosphoprotein abundances. We aim to identify those genes whose CNAs and/or DNA methylations have cis-associations with either some or all three types of molecular traits. Compared with analyzing each molecular trait separately, the joint modeling of multi-omics data enjoys several benefits: iProFun experienced enhanced power for detecting significant cis-associations shared across different omics data types, and it also achieved better accuracy in inferring cis-associations unique to certain type(s) of molecular trait(s). For example, unique associations of CNAs/methylations to global/phospho protein abundances may imply posttranslational regulations.
      We applied iProFun to ovarian high-grade serous carcinoma tumor data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium and identified CNAs and methylations of 500 and 121 genes, respectively, affecting the cis-functional molecular quantitative traits of the corresponding genes. We observed substantial power gain via the joint analysis of iProFun. For example, iProFun identified 117 genes whose CNAs were associated with phosphoprotein abundances by leveraging mRNA expression levels and global protein abundances. By comparison, analyses based on phosphoprotein data alone identified none. A network analysis of these 117 genes revealed the known oncogene AKT1 as a key hub node interacting with many of the rest. In addition, iProFun identified one gene, BIN2, whose DNA methylation has cis-associations with its mRNA expression, global protein, and phosphoprotein abundances. These and other genes identified by iProFun could serve as potential drug targets for ovarian cancer.

      Graphical Abstract

      The initiation, progression, and metastasis of cancer often results from accumulation of DNA-level variations, such as DNA copy number alterations (CNAs)
      The abbreviations used are: CNA, copy number alteration; QT, quantitative trait; HGSOC, high-grade serous ovarian carcinoma; TCGA, The Cancer Genome Atlas; CPTAC, Clinical Proteomic Tumor Analysis Consortium; FDR, false discovery rate; eQTC, expression quantitative trait CNA; pQTC, global protein quantitative trait CNA; phQTC, phosphoprotein quantitative trait CNA; eQTM, expression quantitative trait methylation; pQTM, global protein quantitative trait methylation; phQTM, phosphoprotein quantitative trait methylation.
      1The abbreviations used are: CNA, copy number alteration; QT, quantitative trait; HGSOC, high-grade serous ovarian carcinoma; TCGA, The Cancer Genome Atlas; CPTAC, Clinical Proteomic Tumor Analysis Consortium; FDR, false discovery rate; eQTC, expression quantitative trait CNA; pQTC, global protein quantitative trait CNA; phQTC, phosphoprotein quantitative trait CNA; eQTM, expression quantitative trait methylation; pQTM, global protein quantitative trait methylation; phQTM, phosphoprotein quantitative trait methylation.
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      Motivated by these challenges and needs, in this work we propose to conduct integrative analysis of multiple types of omics data in order to achieve a systematic and comprehensive understanding of the functional mechanisms of DNA-level alterations in tumors. We propose a novel integrative analysis tool to screen for proteogenomic functional traits (iProFun) altered by CNAs and DNA methylations. Specifically, we are interested in (1) detecting genes with “cascading effects” on downstream molecular traits, i.e. a gene's DNA alterations have associations with its cis mRNA expression levels, and global and phospho protein abundances and (2) identifying associations unique to certain type(s) of molecular trait(s), in particular unique to global/phospho protein levels.
      Several mechanisms could result in association patterns of DNA alterations unique to the protein levels but that may not be reflected on mRNA levels. First, specific proteins may be more stable molecules and have longer half-lives than their corresponding RNAs. For example, the median mRNA half-life is ∼10 h in human cells (
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      Despite the urgent needs for integrative analysis methods and tools in biomedical research, the integration of data from multiple data types imposes tremendous statistical challenges, such as high dimensionality, complex gene–gene correlations, different scales and distributions among different types of omics data, and complete or partial overlapping of samples across platforms/data types (
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      ). To address these challenges, the iProFun method takes as input genome-wide summary statistics in assessing associations of CNAs and DNA methylations on each type of molecular trait and allows genes and molecular QTs to be arbitrarily correlated in the joint analysis. The iProFun method estimates the conditional density of each type of trait separately, allowing different scales and distributions among different data types. And, it also allows for sample correlations due to complete or partial overlapping of samples. Compared with the separate analyses of CNA and then methylation on each molecular trait, iProFun leverages data from multiple sources and borrows information across data types. By imposing rigorous assessment of false discovery rates (FDR), we have demonstrated that iProFun is able to largely boost power and maintain low FDR in identifying various types of cis-associations, in particular in the data types with relatively low sample sizes.
      We applied iProFun to the high-grade serous ovarian carcinoma (HGSOC) data from the cancer genome atlas (TCGA) and the genome-wide proteomic data measured in clinical proteomic tumor analysis consortium (CPTAC). HGSOC is the leading cause of gynecologic cancer death in the United States (
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      National Cancer Institute. (2019) SEER cancer statistics factsheets. Ovary cancer. http://seer.cancer.gov/statfacts/html/ovary.html,

      ). Thus, new treatments and an improved understanding of the biological basis of this cancer are desperately required. Using iProFun, we identified a collection of genes whose molecular functional traits at transcriptomic, proteomic, and/or phosphoproteomic levels were altered by somatic CNAs and DNA methylations. Some candidates in this list could serve as potential drug targets.

      DISCUSSION

      In this study, we introduced a novel integrative analysis tool, iProFun, to effectively detect proteogenomic functional traits altered by CNAs and DNA methylations by jointly modeling CNA, epigenome, transcriptome, global proteome, and phospho-proteome data. This integrative solution boosts power for detecting significant cis-associations and infers multi-omic association patterns by borrowing information across different omics data types.
      We applied iProFun to the HGSOC tumor data from TCGA and CPTAC. HGSOC is the leading cause of gynecologic cancer death in the United States (
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      • Pearson J.V.
      • Waddell N.
      • deFazio A.
      • Grimmond S.M.
      • Bowtell D.D.
      Whole-genome characterization of chemoresistant ovarian cancer.
      ,
      • Tothill R.W.
      • Tinker A.V.
      • George J.
      • Brown R.
      • Fox S.B.
      • Lade S.
      • Johnson D.S.
      • Trivett M.K.
      • Etemadmoghadam D.
      • Locandro B.
      • Traficante N.
      • Fereday S.
      • Hung J.A.
      • Chiew Y.E.
      • Haviv I.
      • Gertig D.
      • DeFazio A.
      • Bowtell D.D.
      Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome.
      ), understanding of the mechanisms of oncogenic alterations of HGSOC is still limited and there is still a lack of therapeutically actionable genomic alterations in tumors (
      • Tomao F.
      • D'Incalci M.
      • Biagioli E.
      • Peccatori F.A.
      • Colombo N.
      Restoring platinum sensitivity in recurrent ovarian cancer by extending the platinum-free interval: Myth or reality?.
      ,
      • Villalobos V.M.
      • Wang Y.C.
      • Sikic B.I.
      Reannotation and analysis of clinical and chemotherapy outcomes in the ovarian data set from the Cancer Genome Atlas.
      ). Novel strategies are needed to gain insights that could lead to new treatment targets. The proposed analysis is an attempt to address this challenge by further integrating proteomics information with genomics information. By leveraging all the available molecular-level information in iProFun, we are able to identify and prioritize important alterations in the genome that have multiple functional consequences. Considering the well-known, low efficiency (at less than 5%) of translating even pharma-selected candidate therapeutic oncology targets into clinical practice, admittedly the targets discovered by iProFun will still need to be further validated and tested. However, the method provides a new way to identify candidates with known functional consequences in tumor for cancer development, progression, and resistance. Results from this analysis may help to nominate or prioritize novel candidate drug targets for ovarian cancer patients.
      Specifically, using iProFun, we identified 117 CNAs that impact all levels of molecular QTs, i.e. mRNA, global, and phosphoprotein abundances, our definition of “cascade” cis-effects. This set should be enriched for biologically relevant cancer genes, as CNAs with preserved functional consequences are more likely to be cancer drivers. A network analysis of these 117 genes using the STRING database directed our attention to the gene AKT1, a key hub in the network, which interacts with many other cascade CNA genes (Supplemental Fig. S5). AKT1 is an effector in the PI3K/RAS pathway, which is deregulated in nearly half of all HGSOC cases (
      • Cancer Genome Atlas Research Network
      • et al.
      Integrated genomic analyses of ovarian carcinoma.
      ), and down-regulation of its phosphoprotein was found to be associated with poor survival outcomes in the original TCGA-CPTAC ovarian study (
      • Zhang H.
      • Liu T.
      • Zhang Z.
      • Payne S.H.
      • Zhang B.
      • McDermott J.E.
      • Zhou J.-Y.
      • Petyuk V.A.
      • Chen L.
      • Ray D.
      • Sun S.
      • Yang F.
      • Chen L.
      • Wang J.
      • Shah P.
      • Cha S.W.
      • Aiyetan P.
      • Woo S.
      • Tian Y.
      • Gritsenko M.A.
      • Clauss T.R.
      • Choi C.
      • Monroe M.E.
      • Thomas S.
      • Nie S.
      • Wu C.
      • Moore R.J.
      • Yu K.H.
      • Tabb D.L.
      • Fenyö D.
      • Bafna V.
      • Wang Y.
      • Rodriguez H.
      • Boja E.S.
      • Hiltke T.
      • Rivers R.C.
      • Sokoll L.
      • Zhu H.
      • Shih I.M.
      • Cope L.
      • Pandey A.
      • Zhang B.
      • Snyder M.P.
      • Levine D.A.
      • Smith R.D.
      • Chan D.W.
      • Rodland K.D.
      Integrated proteogenomic characterization of human high-grade serous ovarian cancer.
      ). While the impact of AKT1 copy number alteration was not discussed in the previous ovarian studies (
      • Zhang H.
      • Liu T.
      • Zhang Z.
      • Payne S.H.
      • Zhang B.
      • McDermott J.E.
      • Zhou J.-Y.
      • Petyuk V.A.
      • Chen L.
      • Ray D.
      • Sun S.
      • Yang F.
      • Chen L.
      • Wang J.
      • Shah P.
      • Cha S.W.
      • Aiyetan P.
      • Woo S.
      • Tian Y.
      • Gritsenko M.A.
      • Clauss T.R.
      • Choi C.
      • Monroe M.E.
      • Thomas S.
      • Nie S.
      • Wu C.
      • Moore R.J.
      • Yu K.H.
      • Tabb D.L.
      • Fenyö D.
      • Bafna V.
      • Wang Y.
      • Rodriguez H.
      • Boja E.S.
      • Hiltke T.
      • Rivers R.C.
      • Sokoll L.
      • Zhu H.
      • Shih I.M.
      • Cope L.
      • Pandey A.
      • Zhang B.
      • Snyder M.P.
      • Levine D.A.
      • Smith R.D.
      • Chan D.W.
      • Rodland K.D.
      Integrated proteogenomic characterization of human high-grade serous ovarian cancer.
      ), iProFun results suggest that down-regulation of AKT1 phosphopeptides are associated with DNA copy number losses of the gene. This potentially therapeutically targetable association/pathway, as well as the other 116 cascade CNA cis-associations, however, would not have been detected if only CNA phosphoproteomics data alone would have been analyzed due to the challenge of high-dimension and low sample size in such investigations (Fig. 5).
      Another interesting finding in iProFun results is the cascade effect of one methylation site cg10590292 within the bridging integrator 2 (BIN2) gene. BIN2, also called breast cancer-associated protein1, encodes a cytoplasmic protein, which influences podosome formation, motility, and phagocytosis via its interaction with the cell membrane and cytoskeleton (
      • Sánchez-Barrena M.J.
      • Vallis Y.
      • Clatworthy M.R.
      • Doherty G.J.
      • Veprintsev D.B.
      • Evans P.R.
      • McMahon H.T.
      Bin2 is a membrane sculpting n-bar protein that influences leucocyte podosomes, motility and phagocytosis.
      ) and relates to the innate immune system pathway. While the role of BIN2 in cancer is presently unknown, associations between up-regulation of BIN2 and favorable survival outcomes have been observed in all cervical, endometrial, breast, and ovarian cancers in TCGA studies (p value = 0.0001, 0.0006, 0.008, and 0.075, respectively (
      • Cancer Genome Atlas Network
      Comprehensive molecular portraits of human breast tumours.
      )). Our analysis further suggests that the expression levels as well as protein abundances of BIN2 were suppressed by DNA methylation in a subset of ovarian patients. Intriguingly, this implies a possible mechanism affecting immune invasion in ovarian tumors.
      A few other genes with biologically interesting association patterns identified by iProFun include CANX, RBM15, EML4, CDH6, MAP2, and KRT8. All have been previously shown to play different and important roles in cancers, but only CDH6 has been previously linked to ovarian cancer. CDH6 encodes a member of the cadherin superfamily. Cadherins are calcium-dependent cell adhesion proteins that play critical roles in cell differentiation and morphogenesis. CHD6 has been shown to be both highly differentially expressed in ovarian cancer and, taking advantage of its surface expression, demonstrated to be a unique therapeutic target for antibody–drug conjugates (
      • Bialucha C.U.
      • Collins S.D.
      • Li X.
      • Saxena P.
      • Zhang X.
      • Dürr C.
      • Lafont B.
      • Prieur P.
      • Shim Y.
      • Mosher R.
      • Lee D.
      • Ostrom L.
      • Hu T.
      • Bilic S.
      • Rajlic I.L.
      • Capka V.
      • Jiang W.
      • Wagner J.P.
      • Elliott G.
      • Veloso A.
      • Piel J.C.
      • Flaherty M.M.
      • Mansfield K.G.
      • Meseck E.K.
      • Rubic-Schneider T.
      • London A.S.
      • Tschantz W.R.
      • Kurz M.
      • Nguyen D.
      • Bourret A.
      • Meyer M.J.
      • Faris J.E.
      • Janatpour M.J.
      • Chan V.W.
      • Yoder N.C.
      • Catcott K.C.
      • McShea M.A.
      • Sun X.
      • Gao H.
      • Williams J.
      • Hofmann F.
      • Engelman J.A.
      • Ettenberg S.A.
      • Sellers W.R.
      • Lees E.
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      ).
      As already noted, in part, one of our reasons for targeting ovarian cancer in our studies was the current relatively bleak landscape for novel therapies. Thus, it is notable that our analyses have identified a number of therapeutic candidates. All of AKT1, KRT8, and MAP2 are druggable genes with approved drugs already on the market with indications for other tumors (
      • Cotto K.C.
      • Wagner A.H.
      • Feng Y.-Y.
      • Kiwala S.
      • Coffman A.C.
      • Spies G.
      • Wollam A.
      • Spies N.C.
      • Griffith O.L.
      • Griffith M.
      Dgidb 3.0: A redesign and expansion of the drug–gene interaction database.
      ). Our results suggest that integrating multiple-omics data to screen for genes whose DNA alterations have significant impact on functional molecular traits can be a very effective strategy to nominate candidate genes contributing to the disease. While we believe that CNAs and DNA methylations, which play key roles in disease etiology of cancer, should preserve functional consequences, not all genetic alteration events with functional impacts are disease relevant. Thus, after iProFun is performed to nominate disease relevant genes, further investigation leveraging additional information, such as patient outcome data and gene–gene interaction network could further help to pinpoint the most promising candidates. In addition, the observed protein unique associations in our paper may be identified due to various biological reasons as well as power issues. Future investigation, such as comparing half-lives of corresponding mRNA and protein levels in tumor cells, will help to reveal the underlying biological mechanisms for protein unique associations.
      Beyond the current studies, where we focused on the functional regulations of somatic CNA and DNA methylation, iProFun provides a general framework that can be easily extended to a wide range of applications. For example, by considering the same molecular quantitative trait from three subtypes as if it were from three omics data types with no overlapping samples, we could identify CNAs and methylations whose functional consequences are shared across subtypes versus CNAs and methylations whose functional consequences are unique to one or some of the subtypes. We could also extend the tool to germline variations, adding additional molecular QTs, and/or trans associations analysis. To provide a balanced perspective, we also note potential limitations of our analysis. First, iProFun is based on a linear regression framework and calculated the posterior probabilities using t-distributions, which might not be directly applicable to analyses that follow other distributions (e.g. χ2, F, or uniform distributions). Second, iProFun requires a relatively large number of genomic features, such as CNAs and DNA methylations, to estimate the density under the alternative distribution and robustly calculate the posterior probabilities. In cases where only a few genes are quantified, iProFun might not provide optimal results. Third, iProFun can be applied to genes that are measured across all data types of interest and, therefore, might define up fewer genes than separate analyses. Future studies could expand iProFun to incorporate more association analysis tools (e.g. provide a p-value-based algorithm) to overcome these limitations.
      Software implementing the proposed iProFun, as well as CNA, DNA methylation, mRNA, global, and phosphoprotein data used for this analysis, are available on Github https://github.com/songxiaoyu/iProFun.

      Data Availability

      The proteomic and phosphoproteomic data were obtained from the CPTAC Data Portal https://cptac-data-portal.georgetown.edu/cptacPublic/. The somatic CNA and mRNA data from the microarray platforms were downloaded from CPTAC publications (
      • Zhang H.
      • Liu T.
      • Zhang Z.
      • Payne S.H.
      • Zhang B.
      • McDermott J.E.
      • Zhou J.-Y.
      • Petyuk V.A.
      • Chen L.
      • Ray D.
      • Sun S.
      • Yang F.
      • Chen L.
      • Wang J.
      • Shah P.
      • Cha S.W.
      • Aiyetan P.
      • Woo S.
      • Tian Y.
      • Gritsenko M.A.
      • Clauss T.R.
      • Choi C.
      • Monroe M.E.
      • Thomas S.
      • Nie S.
      • Wu C.
      • Moore R.J.
      • Yu K.H.
      • Tabb D.L.
      • Fenyö D.
      • Bafna V.
      • Wang Y.
      • Rodriguez H.
      • Boja E.S.
      • Hiltke T.
      • Rivers R.C.
      • Sokoll L.
      • Zhu H.
      • Shih I.M.
      • Cope L.
      • Pandey A.
      • Zhang B.
      • Snyder M.P.
      • Levine D.A.
      • Smith R.D.
      • Chan D.W.
      • Rodland K.D.
      Integrated proteogenomic characterization of human high-grade serous ovarian cancer.
      ) and (
      • 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.L.
      • 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.
      Proteogenomics connects somatic mutations to signalling in breast cancer.
      ). The DNA methylation data were downloaded from the TCGA Firehose pipeline processed in July 2016 at the Broad Institute (http://gdac.broadinstitute.org/). The germline genotyping data were obtained from NCI's Genomic Data Commons https://gdc.cancer.gov/.

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