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Downloaded from TCGA (Provisional) (Accessed on 10.10.2018) Dimitrakopoulos et al. BMC Genomics (2021) 22:592 https://doi.org/10.1186/s12864-021-07876-9 RESEARCH ARTICLE Open Access Multi-omics data integration reveals novel drug targets in hepatocellular carcinoma Christos Dimitrakopoulos1,2,3†, Sravanth Kumar Hindupur4,5†, Marco Colombi4, Dritan Liko4, Charlotte K. Y. Ng6,7, Salvatore Piscuoglio6,8,9, Jonas Behr1,2, Ariane L. Moore1,2, Jochen Singer1,2, Hans-Joachim Ruscheweyh1,2, Matthias S. Matter6, Dirk Mossmann4, Luigi M. Terracciano6, Michael N. Hall4* and Niko Beerenwinkel1,2* Abstract Background: Genetic aberrations in hepatocellular carcinoma (HCC) are well known, but the functional consequences of such aberrations remain poorly understood. Results: Here, we explored the effect of defined genetic changes on the transcriptome, proteome and phosphoproteome in twelve tumors from an mTOR-driven hepatocellular carcinoma mouse model. Using Network- based Integration of multi-omiCS data (NetICS), we detected 74 ‘mediators’ that relay via molecular interactions the effects of genetic and miRNA expression changes. The detected mediators account for the effects of oncogenic mTOR signaling on the transcriptome, proteome and phosphoproteome. We confirmed the dysregulation of the mediators YAP1, GRB2, SIRT1, HDAC4 and LIS1 in human HCC. Conclusions: This study suggests that targeting pathways such as YAP1 or GRB2 signaling and pathways regulating global histone acetylation could be beneficial in treating HCC with hyperactive mTOR signaling. Keywords: HCC, mTOR signaling, NetICS, Omics Background While DNA sequencing has enabled a comprehensive Liver cancer is the second leading cause of cancer- characterization of tumor genomes and stratification of related deaths worldwide, and hepatocellular carcinoma patients, translating such information into treatment (HCC) accounts for approximately 90% of primary liver strategies has remained a major challenge. A limitation cancer cases [1]. Approximately 50% of HCC tumors ex- of relying entirely on genomic data to determine a thera- hibit loss of the tumor suppressors Pten, Tsc1,orTsc2 peutic strategy is that it ignores functionally-related, leading to aberrant PI3K–AKT–mTOR signaling. How- non-mutated genes that could also encode potential ever, the effector pathways via which mTOR promotes drug targets. In addition, different mutations across can- tumorgenicity are widely unknown. We generated an cer patients (genetic divergence) could result in the same mTOR-driven HCC mouse model, by liver-specific dele- pathways being activated (functional convergence) [4]. tion of the tumor suppressors Pten and Tsc1 [2, 3], to in- Apart from somatic mutations, tumorigenesis can be vestigate the molecular and cellular mechanisms of regulated by the levels of specific miRNAs, mRNAs, pro- mTOR-driven tumorgenicity. teins and protein phosphorylation. miRNAs are key reg- ulators of the transcriptome and can act as either * Correspondence: [email protected]; [email protected] oncogenes or tumor suppressors. Common mechanisms †Christos Dimitrakopoulos and Sravanth Kumar Hindupur are first authors. that can dysregulate miRNA expression in human can- 4 Biozentrum, University of Basel, 4056 Basel, Switzerland cers include amplification, deletion or epigenetic 1Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland changes [5]. Transcriptomic and proteomic analyses Full list of author information is available at the end of the article © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Dimitrakopoulos et al. BMC Genomics (2021) 22:592 Page 2 of 26 have been performed to stratify HCC patients into clinically-relevant groups [6–8]. However, to further A. understand the effect of a genetic aberration or dysregu- lated gene expression (possibly due to aberrant miRNA expression) it is necessary to identify the mediators com- mon to diverse alterations. Distinct genomic aberrations (in different tumors) are expected to converge function- ally on the same downstream protein, referred to here as a ‘mediator’. To identify such mediators, it is essential to Control L-dKO integrate omics data, i.e., the genome, transcriptome, proteome and phosphoproteome (commonly referred to B. Mouse 1 as multi-omics analysis), from diverse tumors. Muscle N1 N2 N3 Recently, multi-omics analysis has been informative in Muscle N1 N2 N3 Mouse 2 the characterization of tumors. For example, integration of DNA, RNA and phosphoproteomic data enabled Muscle N1 N2 N3 Mouse 3 stratification of prostate cancer patients and to identify individualized treatment options [9]. Computational Muscle N1 N2 N3 Mouse 4 methods that focus on the direct effect of genetic aberra- Muscle tissues 12 tumors from from 4 tions, i.e., the effect of a gene mutation on the encoded from 4 L-dKO mice L-dKO mice protein, have also been proposed [10]. However, a major drawback of these studies is that they rely solely on gen- omic analysis. New methods are necessary to integrate C. N1 N2 N3 Mouse 1 different types of omics data to identify dysregulated N1 N2 N3 Mouse 2 pathways. C1 C2 C3 In this study we use NetICS, a computational method C4 C5 C6 Mouse 3 to integrate multi-omic data (somatic mutations, miRNA N1 N2 N3 differential expression, transcriptomics, proteomics and N1 N2 N3 Mouse 4 phospho proteomics) from an mTOR-driven mouse 6controllivers 12 tumors from 4 L-dKO HCC tumor model [11], to understand the molecular Pooled (WT) mice mechanisms of mTOR-driven HCC. NetICS provides a Fig. 1 Experimental setting. A. Representative images of whole livers comprehensive framework that reveals how specific gen- from 20-week-old L-dKO (tumors are indicated with arrowheads) etic aberrations (i.e., deletion of the tumor suppressors and control mice. B. Three independent tumor samples were taken Pten and Tsc1) and tumor-specific changes in miRNA from each of four 20-week-old L-dKO mice. For each mouse, one expression can affect downstream mediators. NetICS muscle sample was used as the matched healthy tissue. This setting was used for exome sequencing. Each of the three tumor nodules employs a sample-specific network diffusion process that was compared against the matched muscle tissue sample. C. Three reveals the convergence of diverse changes in distinct tu- independent tumor samples were taken from each of four 20-week- mors (mutations and differentially expressed miRNAs) old L-dKO mice. Liver tissues from six cre-negative age- and sex- on common downstream mediators. The identified me- matched littermates were used as a control. This setting was used diators include transcription factors, kinases, phospha- for mRNA sequencing, miRNA sequencing, proteome and phosphoproteome quantification tases and deacetylases. While, some of the mediators are known oncogenes, others are novel oncogenic mediators. These mediators are potential, novel drug targets. negative, age- and sex-matched littermates) (Fig. 1C). We detected a total of 157 point mutations and small inser- Results tions/deletions in the twelve tumors (Table 1). Except for We isolated tumors from an HCC mouse model generated the originally introduced Pten and Tsc1 deletions (Fig. S1), by liver-specific deletion of Pten and Tsc1 (Fig. 1A). We no specific mutation was found in more than a single hereafter refer to this model as the liver-specific double- tumor (Fig. 2A-B). In contrast to somatic mutations, we knockout (L-dKO) mouse. We isolated twelve distinct detected mRNAs, miRNAs, proteins and phosphosites liver tumors, three each from four 20 week old L-dKO commonly dysregulated across multiple tumor samples mice. To detect somatic mutations, we compared exome (Fig. 2C-F). On average, 4,348 mRNA, 108 miRNAs, 2,389 sequence data from tumors and from matched muscle tis- proteins and 906 phosphosites were dysregulated in the sue (Fig. 1B). For all other analyses (RNA, miRNA, prote- tumor samples (Fig. 3A-D). ome and phosphoproteome), we compared the tumor To identify the downstream mediators, we used nodules to healthy liver tissue from six control mice (cre- NetICS, a network-based method that integrates multi- Dimitrakopoulos et al. BMC Genomics (2021) 22:592 Page 3 of 26 Table 1 Shown are the results of exome sequencing in the 12 tumor nodules. Relevant information are given such as the position in thechromosome, the reference and alternative alleles, the type of mutation, the amino acid substitution and variant allele frequency TUMOR_ NORMAL_ CHROM POS REF ALT GENE EFFECT Alteration Alteration Depth Variant SAMPLE SAMPLE
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