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Signal-Oriented Pathway Analyses Reveal a Signaling Complex As A Published OnlineFirst October 10, 2016; DOI: 10.1158/0008-5472.CAN-16-1740 Cancer Integrated Systems and Technologies Research Signal-Oriented Pathway Analyses Reveal a Signaling Complex as a Synthetic Lethal Target for p53 Mutations Songjian Lu1,2, Chunhui Cai1,2, Gonghong Yan3,4,5, Zhuan Zhou3,6, Yong Wan3,6, Vicky Chen1,2, Lujia Chen1,2, Gregory F. Cooper1,2, Lina M. Obeid7, Yusuf A. Hannun7, Adrian V. Lee2,3,4,5, and Xinghua Lu1,2 Abstract Defining processes that are synthetic lethal with p53 muta- methods-identified pathways were perturbed by SGA. In par- tions in cancer cells may reveal possible therapeutic strategies. ticular, our analyses revealed that SGA affecting TP53, PTK2, In this study, we report the development of a signal-oriented YWHAZ,andMED1 perturbed a set of signals that promote cell computational framework for cancer pathway discovery in this proliferation, anchor-free colony formation, and epithelial– context. We applied our bipartite graph–based functional mod- mesenchymal transition (EMT). These proteins formed a sig- ule discovery algorithm to identify transcriptomic modules naling complex that mediates these oncogenic processes in a abnormally expressed in multiple tumors, such that the genes coordinated fashion. Disruption of this signaling complex by in a module were likely regulated by a common, perturbed knocking down PTK2, YWHAZ, or MED1 attenuated and signal. For each transcriptomic module, we applied our weight- reversed oncogenic phenotypes caused by mutant p53 in a ed k-path merge algorithm to search for a set of somatic genome synthetic lethal manner. This signal-oriented framework for alterations (SGA) that likely perturbed the signal, that is, the searching pathways and therapeutic targets is applicable to all candidate members of the pathway that regulate the transcrip- cancer types, thus potentially impacting precision medicine in tomic module. Computational evaluations indicated that our cancer. Cancer Res; 76(23); 6785–94. Ó2016 AACR. Introduction Consortium [ICGC (3–5); see study by Djosta Nono and collea- gues (ref. 6) for a comprehensive review]. However, a majority of Cancer is driven by aberrations in cellular signaling pathways, the current methods concentrate on finding genes that are mutat- commonly caused by somatic genome alterations (SGA), such as ed above a baseline frequency according to a certain statistical somatic mutations and copy number alterations, and epigenetic model, without information regarding the functional impacts of alterations that affect signaling proteins (1, 2). Large-scale cancer mutation events, such as the biological processes perturbed by the genomic studies, such as The Cancer Genome Atlas (TCGA), mutation events. As such, it is difficult to organize candidate provide an unprecedented opportunity to comprehensively inves- drivers observed in different tumors into pathways. To address tigate cancer pathways perturbed by SGAs. this challenge, researchers have designed a family of algorithms Discovering cancer drivers and pathways is an active research that searches for a set of mutated genes that exhibit mutually area. Different algorithms have been designed to search for cancer exclusive patterns in tumors and identifies them as members of a driver genes using cancer genomic data availed by large-scale candidate pathway, based on the observation that SGA events projects such as TCGA and the International Cancer Genome affecting a pathway seldom co-occur in a tumor (7–12). This framework has limitations in that mutual exclusivity is neither a fi 1Department of Biomedical Informatics, University of Pittsburgh, Pitts- necessary nor a suf cient condition for genes involved in a burgh, Pennsylvania. 2Center for Causal Discovery, University of Pitts- pathway. That is, not all genes in a pathway show mutual 3 burgh, Pittsburgh, Pennsylvania. University of Pittsburgh Cancer exclusivity, and not all mutually exclusive genes are in a common Institute, Pittsburgh, Pennsylvania. 4Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsyl- pathway. Other researchers resort to the knowledge of biological vania. 5Magee-Womens Research Institute, Pittsburgh, Pennsylvania. networks, for example, protein–protein interaction (PPI) net- 6Department of Cell Biology, University of Pittsburgh, Pittsburgh, 7 works, to search for genes that are closely located within a biologic Pennsylvania. Department of Medicine, the State University of New – York at Stony Brook, Stony Brook, New York. network and are commonly perturbed by SGAs (13 16). The limitation of this approach is that PPI data can be noisy and that Note: Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/). member proteins of a pathway do not always physically interact. Importantly, the aforementioned driver identification or pathway S. Lu, C. Cai, and G. Yan contributed equally to this work. discovery algorithms do not take into account the functional Corresponding Authors: Xinghua Lu, University of Pittsburgh, 5607 Baum Blvd, impacts of SGA events, which is critical to determine whether an Pittsburgh, PA 15206. Phone: 412-624-3333; Fax: 412-624-5310; E-mail: SGA-perturbed gene is a driver and whether a set of SGAs perturb a [email protected]; and Adrian V. Lee, [email protected] common cellular signal. doi: 10.1158/0008-5472.CAN-16-1740 In this study, we developed a novel signal-oriented compu- Ó2016 American Association for Cancer Research. tational framework, which utilizes the intrinsic relation www.aacrjournals.org 6785 Downloaded from cancerres.aacrjournals.org on September 26, 2021. © 2016 American Association for Cancer Research. Published OnlineFirst October 10, 2016; DOI: 10.1158/0008-5472.CAN-16-1740 Lu et al. Figure 1. Identification of response modules predictive of clinical outcomes. A, Diagram illustrating the search for a response module by finding a densely connected bipartite graph. Tumors and DEGs annotated by a common GO term are represented as the nodes in the graph. An edge indicates that a gene is differentially expressed in a tumor. A response module consists of a set of co-differentially expressed genes in multiple tumors satisfying a specified connectivity. B, Volcano plot illustrating response modules that are predictive of patient outcomes. A row corresponds to a response module, and a red dot indicates the Kaplan–Meier P value of METABRIC patients dichotomized according to the state of the response module. The pink area indicates the upper 95% of P values that can be obtained when dividing patients conditioning on randomly drawn gene sets of the same size as the corresponding response module. C, Heatmap illustrating the averaged within-module expression values of response modules (response modules annotated with GO: 0000278, GO: 0000087, and GO: 0007067 were merged and are indicated by an asterisk). The pseudo-color represents the relative expression value of a response module in tumors by the number of SDs from the mean. The tumors were clustered into subgroups with group IDs indicated below the heatmap; the proportion of tumors belonging to PAM50 subtypes within a cluster are shown as colors. D, Kaplan–Meier survival curves of the 8 patient groups identified by clustering analysis. between the perturbation of a signaling pathway and the transcriptomic module as a means to discover the members of expression changes of downstream genes regulated by the a cancer pathway. pathway, to search for driver SGAs and reconstruct signaling Applying our framework to breast cancer tumors from TCGA, pathways. To this end, we first applied a bipartite graph–based we defined the most commonly perturbed transcriptomic mod- functional module discovery (BFMD) algorithm to identify ules and investigated the candidate pathways driving their aber- transcriptomic modules (17, 18) as signatures of cellular rant expression. We discovered a p53-centered signaling complex signals that are perturbed in tumors. We then applied a involving TP53, PTK2, YWHAZ, and MED1, which drives multiple weighted k-path merge (WKM) algorithm to identify a set of oncogenic processes in breast cancer and is associated with SGAs that perturb a common signal (12, 19) with respect to a worse clinical outcome. We show that disrupting the complex, 6786 Cancer Res; 76(23) December 1, 2016 Cancer Research Downloaded from cancerres.aacrjournals.org on September 26, 2021. © 2016 American Association for Cancer Research. Published OnlineFirst October 10, 2016; DOI: 10.1158/0008-5472.CAN-16-1740 Synthetic Lethal Targets for Treating p53 Mutations by knocking down PTK2, YWHAZ, and MED1 proteins, specifi- cancer cell line kit (ICBP45) of the National Cancer Institute cally attenuates or reverses the oncogenic phenotypes induced by (Bethesda, MD). The cell lines were obtained by the ICBP program mutant p53. Given that TP53 is the most frequently mutated gene in 2010 and were thawed 2 weeks before experiments (2013– (>40% of all types of tumors) and is associated with worse 2014). There were less than 10 passages between thawing of the outcome (1, 20), the effective targeting of TP53 mutation–medi- cells and the experiments described in this study. In general, ated signals could have a significant impact on a large number of mycoplasma testing is performed periodically on all cell lines patients with cancer. used in the laboratory (every 3 months), but in this case, this was The signal-oriented framework is a general approach that can be not necessary, as all
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