Transcriptional Landscape and Clinical Utility of Enhancer Rnas for Erna-Targeted Therapy in Cancer

ARTICLE

https://doi.org/10.1038/s41467-019-12543-5 OPEN Transcriptional landscape and clinical utility of enhancer RNAs for eRNA-targeted therapy in cancer

Zhao Zhang 1,7, Joo-Hyung Lee1,7, Hang Ruan1,7, Youqiong Ye 1, Joanna Krakowiak 1, Qingsong Hu2, Yu Xiang1, Jing Gong1, Bingying Zhou3, Li Wang3, Chunru Lin2, Lixia Diao4, Gordon B. Mills5, Wenbo Li1,6*& Leng Han 1,6* 1234567890():,; Enhancer RNA (eRNA) is a type of noncoding RNA transcribed from the enhancer. Although critical roles of eRNA in gene transcription control have been increasingly realized, the systemic landscape and potential function of eRNAs in cancer remains largely unexplored. Here, we report the integration of multi-omics and pharmacogenomics data across large- scale patient samples and cancer cell lines. We observe a cancer-/lineage-speciﬁcity of eRNAs, which may be largely driven by tissue-speciﬁc TFs. eRNAs are involved in multiple cancer signaling pathways through putatively regulating their target genes, including clinically actionable genes and immune checkpoints. They may also affect drug response by within- pathway or cross-pathway means. We characterize the oncogenic potential and therapeutic liability of one eRNA, NET1e, supporting the clinical feasibility of eRNA-targeted therapy. We identify a panel of clinically relevant eRNAs and developed a user-friendly data portal. Our study reveals the transcriptional landscape and clinical utility of eRNAs in cancer.

1 Department of Biochemistry and Molecular Biology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX 77030, USA. 2 Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 3 State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, PR China. 4 Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 5 Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. 6 Center for Precision Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA. 7These authors contributed equally: Zhao Zhang, Joo-Hyung Lee, Hang Ruan. *email: [email protected]; [email protected]

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nhancer is a distal regulatory DNA that enhances the eRNAs in STAD, but for 64.8% of eRNAs in uterine corpus transcription of a target gene by interacting with target gene endometrial carcinoma (UCEC). Interestingly, the ubiquitous E 1 promoter . Traditionally considered to be DNA elements eRNAs have higher expression levels than the intermediately that nucleate transcription factor (TF) binding, enhancers were specific eRNAs (Wilcoxon test p-value < 2.2 × 10–16) and the recently found to also transcribe noncoding RNAs, which are cancer-type-specific eRNAs (Wilcoxon test p-value < 2.2 × 10–16, referred to as enhancer RNAs (eRNAs)2. Tens of thousands of Supplementary Fig. 1C). This phenomenon is reminiscent of eRNAs have been identified in human cells, many of which were features of protein-coding genes, among which the housekeeping shown to play important roles in transcriptional circuitry to genes are generally expressed at high levels as compared with mediate the activation of target genes3. tissue-specific genes17. Numbers of cancer-type-specific eRNAs In human cancers, activation of oncogenes or oncogenic sig- showed a broad range, from 4 in colon adenocarcinoma (COAD) naling pathways often converges to enhancer activation and pro- to 987 in STAD (Fig. 1a). We still observed cancer-type-specific duction of eRNAs. For example, the activation of ESR1 can globally pattern even with a much more stringent cutoff (RPM ≥ 5, Sup- increase eRNA transcription in breast cancer4.Oncogene-induced plementary Fig. 1D), suggesting that the cancer-type-specific eRNAs can under certain circumstances directly promote tumor- patterns of eRNA expression is not due to expression levels. igenesis. For example, KLK3e, an androgen-induced eRNA reg- The expression similarity between any two tumor samples ulating gene KLK3, can scaffold the androgen receptor (AR)- further showed a strong cancer-type-specific pattern, in that associated protein complex to control AR-dependent gene samples from the same cancer type clustered together (Fig. 1b). expression in prostate cancer5. Tumor suppressors can also induce Furthermore, cancer types with similar histological features eRNAs to contribute to tumor repression processes. For example, clustered at higher levels of hierarchy, such as pan-kidney cancers18 TP53-induced eRNAs were found to be involved in p53-dependent (kidney renal clear cell carcinoma [KIRC], kidney renal papillary cell cycle arrest in multiple cancer cell lines6. Together these evi- cell carcinoma [KIRP] and kidney chromophobe [KICH]), pan- dences reveal significant roles of eRNAs in tumorigenesis and squamous cell carcinomas19 (bladder urothelial carcinoma [BLCA], suggest their clinical utility in eRNA-targeted therapy7. head and neck squamous cell carcinoma [HNSC], cervical The Encyclopedia of DNA Elements (ENCODE) project8, squamous cell carcinoma and endocervical adenocarcinoma Functional Annotation of the Mammalian Genome (FANTOM) [CESC] and lung squamous cell carcinoma [LUSC]), the project9, and Roadmap Epigenomics project10 have annotated a sarcomas20 (sarcoma [SARC] and uterine carcinosarcoma [UCS]), large number of regulatory elements, including enhancers, while and the neuronal cancers20 (glioblastoma multiforme [GBM], brain The Cancer Genome Atlas (TCGA) collected multi-omic data lower grade glioma [LGG], pheochromocytoma and paraganglioma and clinical information in ~10,000 tumor samples11. In addition [PCPG], skin cutaneous melanoma [SKCM], and uveal melanoma to patient samples, Cancer Cell Line Encyclopedia (CCLE)12 [UVM]). This cancer-type-specific pattern is further confirmed by collected omics data in ~1000 cancer cell lines. Furthermore, t-Distributed Stochastic Neighbor Embedding (t-SNE) analysis Cancer Therapeutics Response Portal (CTRP)13, and Genomics (Supplementary Fig. 1E), suggesting that eRNAs may be powerful of Drug Sensitivity in Cancer (GDSC)14 provided pharmacoge- biomarkers with clinical utility in specificcancertypes7. nomics data from ~ 500 anticancer compounds across > 1000 cancer cell lines. These data resources provide unique opportu- nities to characterize the expression landscape, functions and Analysis of transcription factor and eRNA relationship. TFs drug response of eRNAs across different cancer types. have been shown to mediate the biogenesis of eRNAs2,8; however, the global regulation of eRNAs is still unclear. Here, we collected human transcription factors (TFs) from JASPAR21, DBD22, Results AnimalTFDB23, and TF2DNA24, and calculated Spearman’s Dynamic expression landscape of eRNAs in human cancers. correlation between individual TF expression and individual We obtained enhancer annotations from ENCODE, FANTOM, eRNA expression in each cancer type. We defined TFs that show and Roadmap Epigenomics, and selected enhancers annotated in significant correlation (Rs ≥ 0.3; false discovery rate [FDR] <0.05) at least two datasets. Given the fact that eRNA transcription with an individual eRNA in a specific cancer type as its putative region could be wider than the enhancer ChIP-seq peaks15,we regulators, and further defined those putative regulators sig- defined the ± 3 kb regions around the middle point of these nificantly correlated to ≥ 25% of individual eRNAs in a specific annotated enhancers as potential eRNA-transcribing regions16. cancer type as putative master regulators. Taking breast invasive To avoid counting the transcriptional signal from known coding carcinoma (BRCA) as an example, we identified 84 putative genes, we excluded eRNA regions that overlap with known master regulators, including three well-known regulators, coding genes and lncRNAs (with 1 kb extension from both FOXA125 (highly correlated with 28.6% of all eRNAs in BRCA), transcription start site and transcription end site) (Supplementary ESR115 (29.2%), and GATA326 (25.3%, Fig. 2a and Supplementary Fig. 1A and Methods). To characterize the expression landscape Data 2). Applying this computational predictions, we have of eRNAs across human cancers, we mapped TCGA RNA-seq identified 845 putative master regulators across cancer types reads to eRNA regions and defined those eRNAs with average (Supplementary Fig. 2A and Supplementary Data 2). The expression value (reads per million, RPM) ≥1 as detectable eRNA majority of these putative master regulators (693/845, 82.0%) for each cancer type (Supplementary Fig. 1A and Methods). This exhibits strong expression correlation with a large number of analysis identified a total of 9108 detectable eRNAs in human eRNAs in only one or a few cancer types (i.e., <5), suggesting that cancers (Fig. 1a and Supplementary Fig. 1B). The number of the TF-eRNA correlation is tissue-specific and may imply direct detectable eRNAs ranged from 457 in liver hepatocellular carci- regulatory functions of these TFs in that cancer type (Supple- noma (LIHC) to 2267 in stomach adenocarcinoma (STAD) mentary Fig. 2B). For example, OLIG2 is a TF highly expressed in (Supplementary Fig. 1B, Supplementary Data 1). We classified brain and highly correlate with the expression of 33.5% of eRNAs these detectable eRNAs into three groups: 652 ubiquitous eRNAs in LGG, suggesting its potential importance in enhancer/eRNA expressed in ≥10 cancer types, 3124 intermediately specific control therein (Supplementary Fig. 2C). Our global analysis of eRNAs that are expressed in 2–9 cancer types, and 5332 cancer- TF-eRNA correlation indicates that cancer- and/or lineage- type-specific eRNAs that are expressed in only one cancer type specific patterns of eRNAs can be largely mediated by lineage- (Fig. 1a). The ubiquitous eRNAs account for 20.0% of detectable specific TFs.

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a Ubiquitous Intermediately specific Cancer type specific n = 652 n = 3124 n = 5332 Percentage (%)

GBM LGG UVM HNSC THCA DLBC THYM LUAD 6 LUSC MESO BRCA LIHC CHOL STAD PAAD PCPG 3 KIRC KIRP Expression (Log2) KICH ACC COAD READ BLCA CESC 0 UCEC UCS OV PRAD TGCT SARC SKCM b

Cancer type

Similarity (Rs) 01

GBM-LGG-PCPG Cancer type Neuronal ACC MESO UVM-SKCM BLCA OV BRCA PAAD CESC PCPG CHOL PRAD COAD READ

Pan-kidney DLBC SARC KICH-KIRP-KIRC GBM SKCM HNSC STAD KICH TGCT Pan-squamous KIRC THCA BLCA-HNSC-UCEC-LUSC KIRP THYM LGG UCEC LIHC UCS LUAD UVM LUSC

Sarcomas SARC-UCS

Fig. 1 Expression landscape of eRNAs in human cancers. a Expression profile of eRNA in human cancers. Red, green and blue bars, respectively, denote ubiquitous, intermediately specific and cancer-type-specific eRNAs. Pie charts reflect the percentage of each category of eRNAs in each cancer type. Abbreviation of each cancer type is listed in Supplementary Data 1. Scale bar denotes the expression level of eRNAs (log2). Organ icons made by Freepik (https://www.freepik.com/). b Expression similarity among tumor samples. Color bar denotes each cancer type. Scale bar denotes the similarity of eRNA expression among TCGA samples

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a FOXA1 40 ESR1

30 GATA3

20 Percentage (%) 10

0 3 7 8 4 5 6 9 2 1 X 20 21 10 12 15 22 11 13 14 16 17 18 19 Chromosome TF Putative master regulator b

THCA 45.5 45.4 43.1 42.3 30.5 35.9 46.837 49.5 38.6 27.5 36.9 48 34.2 34.240.3 39.1 35.3 41.2 27.3 33.2 38.5 28 35.5 46.3 41.8 37.5 37.1 28 28.2 52.5 44.4 29.227.1 40.3 34.9 34.4 33.2 42.7 29 27.7 56.5 37.3 28.8 52.4 30.6 26.4 33.8 25.2 THYM 38.3 32.9 36.4 33.9 31.9 30.4 36.428.9 31.6 42.3 36 38.8 33.3 34.636.3 27.4 30.6 33 28.6 27.7 37.2 32.8 35 35.2 32.3 36.9 37.2 33.9 34.1 36.9 30.131.3 34.7 36 33.6 37.2 33.8 37.7 31.7 33.8 40.2 37.5 36.9 36.7 UVM 57.2 27.3 57.4 50.9 54.5 54.5 55.756.8 58.5 54.7 51.5 33.9 54.5 55.556.2 58.949.6 49.4 58.9 58 59.8 43.6 52.3 47.5 56.6 57.2 36.7 57.4 41.7 39 55.9 43.947.3 55.5 55.9 44.5 55.3 54.5 41.9 58 47.7 57.8 58 GBM 52 54.1 53.6 46.9 34.7 4928.8 47.9 37.9 50.9 52.9 32.2 43.9 44.4 37.553.7 51.2 42.1 38.9 29.2 37.4 38.7 27.8 36.7 43.4 48.9 32.4 35.946.5 40.4 38.8 32.9 41.9 51 47.6 27.9 27.6 36.4 47.1 DLBC 32.6 26.329.5 33.5 27.6 26.3 30.930.3 26.4 30.7 27.9 31.4 28.5 30.1 29.626 28.8 28.6 31.3 26.4 29.4 29.8 29.8 32.5 28.5 30.8 3229.4 28.2 30.9 27.3 27.9 25.3 31.5 27.6 28.8 26.8 31.4 25.1 CHOL 59.9 69.1 27.9 40.7 40.7 65 28.8 34.1 27.7 68.7 61.1 66.7 51.264.1 59.1 31.7 68.9 62.2 39.9 62.6 39.6 46.6 40.4 73 27.3 40.2 65.1 26.2 51.8 70 63.8 32 50.8 67.2 43.3 32.9 71 40.9 KIRP 73.5 62.5 69.2 53.7 31 44 69.142.2 58.3 63.4 46.8 38 50.6 43.6 31.441.4 30.8 38.4 49.6 25.4 58 68.2 44.4 68.3 50.6 44.5 64.2 43 49.1 49.4 28.8 52.8 46 56 35.2 KIRC 62 63.6 59.8 3645.7 35.847.8 48.4 61.3 45.4 42.6 57.8 37.848 31.1 56.2 48.2 30 39 25.5 26.4 41.9 46.8 27.1 40.3 49.3 47.9 25.3 39 57.2 53.9 63.7 35.5 29.3 BRCA 35.1 31.9 34.7 37.836.8 39.128.1 31.5 29.7 35.9 30 38.633.4 32 38.4 34.4 30.4 27.9 28.2 33.4 36.6 33.3 35.3 30.8 32.8 29.1 33.4 42.5 34.7 25.9 34.1 27 30.7 36.6 STAD 74.9 4877.7 45.3 72.6 43.4 28.762.9 75.6 60.6 52.3 75 64.5 80.774.5 69.8 84.5 26.1 34.7 49.9 39.2 34 44.4 60.7 38.8 36.3 51.7 49.6 28.8 40.2 27.1 41.2 LUAD 43.1 40.6 39.3 51.4 26.6 27.644.4 52.5 56.5 35.9 52.1 42 36.431.5 2728 35.4 40.9 30.7 37.6 36.7 30.5 31.4 33 31 29.3 26.6 34.4 32.4 29.5 OV 49.2 42.1 57 56.7 56 30.958 58.7 63 48.1 67 26.4 33.36043.1 46.9 42.9 33.7 48 45.3 57.3 50.4 39.6 25.9 53.3 37.6 26.8 28.4 30.6 TGCT 50.9 41.8 27.3 34.6 39.1 30.6 39.9 65.8 41.8 28.5 48.8 61.7 62.5 39.8 25.3 57.5 32.429.2 34.8 27.2 54.9 49.9 59.2 57.6 51.9 66.1 59.8 LGG 41.4 49.2 48.4 27 41.2 27.3 33.8 27.9 28.234.3 26.536.2 33.2 39.5 34.5 32.4 27.3 43.6 32.5 31.1 37.6 32.3 25 35.5 33.5 25.1 SKCM 34.9 33.7 31.6 30.7 26.7 35.436.9 36.3 34.2 30.925.5 30.1 33.6 30.3 36.3 27.8 32.2 30.6 32.7 29.3 27.6 30.7 33 25.7 29.3 MESO 41.7 42.3 36.8 32.9 36 26.8 37.4 37.1 29.7 26.3 33 27.5 37.9 25.4 33.9 40.1 26.2 34.6 25.3 31.130.8 26.2 35.5 30.1 33 KICH 37.7 28.7 38.2 28.7 38 26.7 25.243.9 25.2 36.4 26.6 39.7 31.8 29.3 26 35.933.5 34.5 26.4 39.6 31.6 29.5 33.8 UCEC 31.3 25.5 28.6 29 26.2 27.1 28.6 30.2 26.2 25.529.3 27 29.1 36.2 27.1 26.4 26.6 28.1 25.9 27.5 SARC 32.9 32.937.1 32.8 30.136.6 34.3 27.9 28.3 29.1 32.4 25.5 30.4 25.7 33.8 28.7 31.2 37 26.6 29.3 PCPG 25.7 34.3 29.4 25.3 27.9 26.9 33.131.8 35.3 28.7 28.9 36.5 25.8 26.7 28.3 33.7 25.1 30.3 26.6 31.7 PRAD 27.7 36.9 34.8 26.3 25.3 25.7 30 30.2 27.2 32.6 25.6 29.8 25 27.9 28.9 28.3 25.7 29 25 READ 38.9 39.8 35.2 27 27.4 40 25.2 28.3 27.6 26.7 28.5 32 27.2 25.4 25.4 26.1 37.6 27.4 BLCA 37.3 32.3 36.9 27.2 26.9 36.9 28.6 26.4 32.9 37.3 32.8 35.5 29.7 34.9 32.7 35.6 30 28.6 COAD 41.1 54.3 38.8 50.9 25.8 47.4 29.6 30.4 29.4 31.4 43 37.1 30.2 30.2 50.5 27 LUSC 29.8 31.9 30.7 37.6 25.8 27.9 28 30.2 32.9 34.3 29.9 25.6 32.5 32.1 PAAD 44.1 43.1 45 28.3 29 37.7 25.8 26.3 38.9 28 LIHC 29.3 28.9 26.7 29.8 30 26 28.7 ACC 28.4 28.7 SP1 SP3 BBX SON MGA JRKL AFF1 ELF2 TET2 LHX4 HSF4 MZF1 BDP1 RFX7 BPTF PMS1 IKZF4 CHD2 MBD6 ADNP RBAK RERE POGZ TIGD4 ARID2 NFXL1 BAZ2A BAZ2B NFAT5 RC3H1 THAP6 DMTF1 CREB1 NR2C2 GON4L KCTD7 RCOR3 MYSM1 ARID4A ARID4B MLLT10 BCLAF1 AHCTF1 SETDB1 GATAD1 SNAPC4 PRDM10 PRDM15 CREBZF CREBBP KIAA2018 GATAD2B GTF2IRD2 GTF2IRD2B

Percentage (%) 40 80

Fig. 2 Putative regulation of eRNA biogenesis in cancer. a Putative regulators of eRNAs in BRCA. Each dot represents one transcription factor (TF). Red dots denote putative master regulators signiﬁcantly correlated to ≥25% of individual eRNAs. Three well-known TFs (ESR1, GATA3, and FOXA1) are highlighted. X-axis is chromosome; y-axis is percentage of eRNAs correlated to each TF. b General putative master regulators in human cancers. X-axis is symbol of putative master regulator and y-axis is cancer type. General putative master regulators involved in genomic instability are marked by red. Number in cells denotes the percentage of eRNAs regulated by each master regulator. Scale bar denotes percentage of eRNA correlated with TFs (x axis) across cancer types (y axis)

We further identified 54 general putative master regulators that regulating inflammation28,29, is highly correlated with eRNAs in 17 play significant roles in ≥ 10 cancer types (Fig. 2b). We performed cancer types, ranging from 27.0% in READ to 63.4% in KIRP. These GO analyses and observed that these TFs are enriched in the general master regulators are enriched in functions related to functional categories related to transcriptional process (Supplemen- genomic instability, which provides a potential explanation to a tary Fig. 2D-2E). These general master regulators can be classified previously observed positive correlation between somatic copy into 17 families base on Pfam annotation (https://pfam.xfam.org/), number alteration and enhancer activation in many cancer types30. and they are significantly enriched in four families, including MDB, ARID, GTF2I, and MYB (FDR <0.05, Supplementary Fig. 2F). More importantly, we manually examined the functions of these TFs and Putative effects of eRNAs on signaling pathways.Itremainsa found 35.2% (19/54) of them are associated with genomic instability challenge to establish the direct interaction between eRNA and its (Fig. 2b). For example, NR2C2, which can mediate genomic target genes. We built a global eRNA-gene regulatory network rearrangements by a telomere-related pathway27,ishighlycorrelated across cancer types based on the physical distance (≤ 1MB) and co- with eRNAs in 20 cancer types, ranging from 26.3% in PRAD to expression between individual eRNAs and their putative target 53.7% in KIRP. NFAT5, which can induce genomic instability by genes (Spearman’s correlation Rs ≥ 0.3, FDR < 0.05, Supplementary

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Fig. 3A)2.Weidentified 11,593 (56.5% of all protein-coding genes) Putatively regulation of eRNAs on CAGs and ICs. Based on the putative target genes that are significantly correlated with 88.8% finding that eRNAs were tightly associated with cancer signaling (8086/9108) of eRNAs in at least one cancer type. High- pathways and drug-associated pathways, we further asked if throughput chromosome conformation capture (Hi-C) data can eRNAs were directly linked to cancer therapy. We collected 135 reveal the interaction between an enhancer and its target gene, clinically actionable genes (CAGs), and observed that 107 of them while active enhancer usually produce eRNA31–33.Therefore,we (79.3%) were correlated to eRNAs in at least one cancer type investigated Hi-C interaction for all putative eRNA-gene connec- (distance ≤ 1MB, Spearman’s correlation Rs ≥ 0.3 and FDR < 0.05, tions across 20 tissues, and observed that more than 80% eRNA- Supplementary Data 4). Among these, 36 clinically actionable gene connections are supported by significant Hi-C interactions in genes are correlated with eRNAs in at least five cancer types at least one tissue (Supplementary Fig. 3B). The proportion of Hi- (Fig. 4a), suggesting that these genes are potentially regulated by C supported eRNA-gene connection is significant higher than the eRNAs in multiple cancers. Increased numbers of samples background of random pairs (permutation test, bootstrap = enhance the ability to detect and analyze molecular data. In 10,000, p < 0.0001, Supplementary Fig. 3B). To explore the reg- particular, the pan-cancer analysis will help to identify master ulatory roles of eRNAs in cancer, we collected 229 genes involved events that play a critical functional role in different tumor in 10 cancer signaling pathways, including Myc, PI3K, and p53 contexts36–38. Furthermore, 91.7% of these correlations (33/36) pathways34 (Supplementary Data 3). The majority (185/229, could be supported by Hi-C interactions in at least one tissue, 80.8%) of these genes are correlated with eRNAs in at least one which further support the potential regulatory roles of eRNAs on cancer type (Fig. 3a and Supplementary Data 3). For example, all clinically actionable genes (Fig. 4b and Supplementary Fig. 4A). six genes in the p53 pathway (i.e., TP53, MDM2, MDM4, ATM, For example, MDM2- and MDM2-associated eRNA (MDM2e, CHEK2,andRPS6KA3) were found to correlate with eRNAs in at ENSR00000053727) are positively correlated in 12 cancer types, least one cancer type (Supplementary Fig. 3C and Supplementary and Hi-C data supports their chromatin interaction in 20 tissues Data 3). In support of this, most eRNA-gene associations in (Fig. 4c and Supplementary Fig. 4A). MYC- and MYC-associated pathways (91.9%, 170/185) were found to form chromatin inter- eRNA (MYCe, ENSR00000333355) are positively correlated in six action by Hi-C (Supplementary Fig. 3D and 3E), including cancer types, and Hi-C data supports their interaction in all 20 MAML2-associated eRNA (hereafter we will refer to eRNAs based tissues (Fig. 4d and Supplementary Fig. 4A). on their associated, putative target gene, i.e., MAML2e, We further investigated the relationship between individual ENSR00000043746) and MAML2, CDK6-associated eRNA eRNAs and immune checkpoints (ICs, Supplementary Data 5), (CDK6e, ENSR00000215101) and CDK6,andTCF7L2-associated and observed six checkpoints were correlated with eRNAs in at eRNA (TCF7L2e, ENSR00000033597) and TCF7L2 (Supplemen- least five cancer types (Fig. 4e). All these putative eRNA- tary Fig. 3F). Our results suggested important roles played by checkpoints interactions again were supported by Hi-C data in eRNAs in regulating various cancer signaling pathways. at least one tissue (Fig. 4fandSupplementaryFig.4B).For To further understand the meaningful contributions of eRNAs example, CD200- and CD200-associated eRNA (CD200e, in cancer signaling pathways on drug response, we calculated ENSR00000156542) are positively correlated in 12 cancer types, eRNA expression levels across ~1000 cancer cell lines from the and Hi-C data supports the interactions in all 20 tissues (Fig. 4g Cancer Cell Line Encyclopedia (CCLE), and then analyzed and Supplementary Fig. 4B). Taken together, our analysis showed Spearman’s correlation between eRNA expression levels and drug putative interactions between eRNAs and clinically actionable sensitivity of these cells (Area Under Curve [AUC]), which is genes and/or immune checkpoints, suggesting the potentially available from the Cancer Therapeutics Response Portal (CTRP). clinical utility of eRNAs in cancer therapy. We identified 512 eRNAs in all 10 cancer signaling pathways, the expression of which displayed high correlation with 63 anticancer drugs (FDR < 0.0535,Fig.3b and Supplementary Fig. 3G), Characterizing the functional roles of eRNA in cancer.To suggesting significant roles of eRNAs in the response to anticancer further characterize the functional roles of eRNAs in cancer, we drugs. For example, 217 eRNAs are highly correlated with examined the differentially expressed eRNAs (|fold change| >1.5 belinostat, a drug that targets the Notch pathway. Among these, and FDR <0.05) across 16 cancer types with ≥5 tumor-normal 32.7% (71/217) of their putative target genes are within the Notch paired samples (Supplementary Data 6). Overall, there were more pathway (Supplementary Fig. 3H), such as PSEN2-associated upregulated eRNAs in tumor samples, ranging from 22.0% in eRNA (PSEN2e, ENSR00000257043),RBX1-associated eRNA thyroid carcinoma (THCA) to 68.9% in cholangiocarcinoma (PBX1e, ENSR00000257043), and NOTCH4 associated eRNA (CHOL), with a median of 42.2%. The downregulated eRNAs (NOTCH4e, ENSR00000320261). More interestingly, the putative ranged from 1.9% in STAD to 27.9% in KICH, with a median of target genes of the remaining eRNAs (146/217, 67.4%) are in cross- 9.9% (Fig. 5a). Taking BRCA as an example, we identified 208 pathways, such as MDM2-associated eRNA (MDM2e, upregulated eRNAs and 166 downregulated eRNAs (Fig. 5b). ENSR00000053727) in the p53 pathway, CDK6-associated eRNA Among these, one eRNA located ~ 90 kb downstream of the (CDK6e, ENSR00000215101) in the cell cycle pathway, and oncogene NET139, which we referred to as NET1-associated eRNA RNF43-associated eRNA (RNF43e, ENSR00000096250) in the (NET1e, ENSR00000023843), showed the largest expression Wnt pathway (Supplementary Fig. 3H). We further confirmed this alteration between tumor and normal samples (fold change = 5.8, eRNA-drug connection using another drug database, Genomics of FDR = 3.7 × 10–13,Fig.5b and Supplementary Fig. 5A). NET1e Drug Sensitivity in Cancer (GDSC), and observed some similar exhibited much higher expression levels in BRCA, including all pattern (Supplementary Fig. 3I and 3J). Indeed, belinostat subtypes (Supplementary Fig. 5B). High level of NET1e was asso- treatment could alter the expression of 46 eRNAs (35.7%) within ciated with worse survival (log-rank test p-value = 0.0004, Fig. 5c). the target pathway and 83 eRNAs (64.3%) in a cross-pathway in Of interest, NET1 gene itself is not associated with the breast A549 cells (Supplementary Fig. 3K). Taken together, our results cancer patient’s survival (Supplementary Fig. 5C), suggesting that suggest a strong association between eRNAs and anticancer drugs, NET1e may be a predictor irrelevant to NET1 in breast cancer either within the target pathway or through a cross-pathway. It will patients. NET1e was highly correlated with NET1 across all BRCA an important future direction to examine the molecular basis of subtypes (Fisher’stransformation,Rs’ = 0.45, p′ = 1.58 × 10–4), eRNA-gene-drug correlation, and potential roles eRNAs played in including the basal subtype (Spearman’s correlation, Rs = 0.53, modulating cancer cell drug response. p = 1.95 × 10–11, Supplementary Fig. 5D). We further examined

NATURE COMMUNICATIONS | (2019) 10:4562 | https://doi.org/10.1038/s41467-019-12543-5 | www.nature.com/naturecommunications 5 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-12543-5

a MESO OV LUAD LUSC

LIHC PAAD LGG PCPG

PRAD KIRP RTK/RAS Cell cycle Wnt KIRC READ

100

n SARC

KICH c 50

e Myc P Hippo 0 HNSC SKCM

STAD GBM p53 PI3K

DLBC TGCT

TGF beta COAD Nrf2 Notch THCA

CHOL THYM

CESC UCEC eRNA BRCA UCS BLCA eRNA putative target gene ACC UVM

b eRNAs Pathways

Cell cycle Hippo Myc Notch Nrf2 PI3K RTK/RAS TGF−beta p53 Wnt

# of drug-eRNA interaction 1 10 100 ML320 RITA HLI 373 Alvocidib SNS−032 Dinaciclib Nutlin−3 Apicidin Merck60 XL765 KU−60019 Vorinostat Entinostat PI−103 Belinostat PRIMA−1 AT7867 AZD7762

ETP−46464 Interaction type CHIR−99021 PHA−793887 Gefitinib SMER−3 AZD1480 L−685458 OSI−027 KU−55933 Tacedinaline Serdemetan BYL−719 Sirolimus AZD8055 Pifithrin−mu AZD6482 MK−2206 TGX−221 ZSTK474 Panobinostat Ruxolitinib SCH−529074 PRIMA−1−Met Rigosertib SB−525334 SB−431542 LY−2157299 Momelotinib GDC−0941 TG−101348 RO4929097 Pandacostat Within pathway GSK2636771 Temsirolimus NVP−BSK805 KU−0063794 Semagacestat NVP−BEZ235 BRD−A94377914 BRD−K80183349 BRD−K66532283 BRD−K11533227 Cross pathway BRD−K24690302 BRD−K85133207 N9−isopropylolomoucine Drugs

Fig. 3 Putatively regulatory network and drug response of eRNAs on signaling pathways. a eRNA and putative target gene in 10 cancer signaling pathways. Red nodes in outer circle denote eRNAs that regulate genes in cancer signaling pathways across cancer types. Blue nodes in middle circle denote putative target gene across cancer signaling pathways. Blue bars in inner circle denote percentage of eRNA putative target genes across each pathway. Magenta links denote correlation between eRNAs and their putative target genes. b Association between eRNAs (top) and drugs (bottom) across different cancer signaling pathways. Orange and blue links, respectively, denote within- and cross-pathway relationships

NET1e signaling in MCF7, a breast cancer cell line (Fig. 5d). This NET1e upregulation by two different sgRNAs, which interestingly region harbors classical enhancer features, such as the enrichment led to strong upregulation of NET1 mRNA (Fig. 5e). Consistent of histone H3K4me1 modification; it also exhibits strong enrich- with the role of NET1 as a breast cancer oncogene39, CRISPR- ment of active enhancer markers such as histone H3K27ac mod- SAM induction of NET1e increased cell proliferation significantly ification and binding of transcription co-factor p300 (Fig. 5d). (Fig. 5e). To delineate a role of the eRNA per se, we designed There are multiple p300 binding peaks densely distributed in the three locked nucleic acid GapmeR (LNAs) to knockdown NET1e. NET1e region, indicating it might be a potential super-enhancer in With efficient reduction of NET1e expression, we found that cell MCF7. NET1e transcription was also detected by GRO-seq data in proliferation was significantly reduced in both MCF7 cells MCF7 cells (Fig. 5d). Furthermore, we observed a chromatin (Fig. 5f) and MCF7 cells with CRISPR-SAM treatment (Fig. 5g). interaction between NET1e and NET1 by RNA Pol II ChIA-PET21, These data, together with their chromatin looping (Fig. 5d), suggesting a direct interaction for regulation. suggested that NET1e contributes to breast cancer progression via To further characterize NET1e, we applied CRISPR activation upregulation of the important breast cancer oncogene NET1.In (CRISPR/dCas9-SAM)40 to epigenetically induce NET1e expres- addition, knockdown of NET1e did not significantly impact cell sion in MCF7 cells (Fig. 5e). We successfully achieved >30-fold proliferation in non-breast cancer cell lines, including MCF10A

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a b NOTCH2 BRCA2 PTEN EGFR c PTCH1 MDM2 (TSS) MDM2e MDM4 Chr12 AKT2 MDM2 XPO1 MEN1 TSC2 TMPRSS2 RB1 TSC1 EZH2 MYC O/E MSH2 MET FGFR1 0>52.5 ETV6 ASXL1 RAF1 MAPK3

Clinically actionable gene HRAS CDKN1B d PDGFRA MYC (TSS) MYCe NOTCH1 JAK2 Chr8 CTNNB1 CDK12 CCND1 AKT1 SMO KIT ESR1 BRD3

01020 O/E OV ACC UCS LGG UVM GBM LIHC KIRP KICH KIRC BLCA LUSC STAD LUAD DLBC THCA TGCT PAAD # of tissues with SARC PRAD CESC READ BRCA CHOL THYM HNSC UCEC PCPG COAD SKCM MESO Hi-C interaction 0>52.5 FDR 10-5 10-10 Rs 0.5 0.9

efPDL2 HAVCR1 g

PLEC CD200 (TSS) CD200e Chr3 PDL1 CD200 CD200 PDL2

PDL1

HAVCR1 Immune checkpoints BTLA BTLA PLEC

ACC UCS LGG 0 10 20 UVM LIHC GBM KIRP KICH KIRC O/E

BLCA LUAD STAD DLBC LUSC PAAD THCA TGCT PRAD BRCA CHOL READ SARC CESC PCPG HNSC UCEC THYM COAD SKCM MESO # of tissues with Hi-C interaction –5 –10 0>52.5 Rs 0.5 0.9 FDR 10 10

Fig. 4 Putatively regulatory effects of eRNAs on clinically actionable genes and immune checkpoints. a Correlation between eRNAs and clinically actionable genes in human cancers. Red dots denote significantly correlation. X-axis is cancer type and y-axis is symbol of gene. b Number of tissues with Hi-C interactions between clinically actionable genes and their eRNAs. c Hi-C interaction between MDM2 and MDM2e in blade. d Hi-C interaction between MYC and MYCe in lung. e Correlation between eRNA and cancer immune checkpoints in human cancers. f Number of tissues with remarkable Hi-C interaction between cancer immune checkpoints and their eRNA. g Hi-C interaction between CD200 and CD200e in ovary. Scale bars denote spearman correlation (Rs)in(a) and (e), and Hi-C O/E value in (b), (c), and (g), respectively and Hela, in which NET1e shows low expression level when we knocked down NET1e in MCF7 (Fig. 5f, g), therefore we (Supplementary Fig. 5E and 5F), suggesting a specific effect of could not test drug response in NET1e KD cells. Of interest, NET1 NET1e in breast cancer growth. It supports a minimal off-target is not significantly correlated with BEZ235 (FDR = 0.15) and effects/toxicity of NET1e LNA and the potential to target cancer- obatoclax (FDR = 0.80). Taken together, these results revealed specific eRNAs for effective treatment. More importantly, that NET1e is an oncogenic eRNA in BRCA and may be a expression of NET1e is negatively correlated (sensitive, Spear- promising target for eRNA therapy. man’s correlation, FDR <0.05) with 14 and 15 compounds response (AUC) while positively correlated (resistance, Spear- Identification of clinically relevant eRNAs.Clinicalrelevanceis man’s correlation, FDR < 0.05) with 56 and 31 compounds used to define cancer-related clinical features, including association response (AUC) in CTRP and GDSC, respectively, which with survival, differential expression among subtypes, stages, grade, suggested that altered expression of NET1e could influence and different groups of smoking history41–43. To further investigate response to these drugs (Fig. 5h and Supplementary Data 7). the clinical utility of eRNAs, we identified 5715 clinically relevant Indeed, in situ overexpression of NET1e led to the resistance of eRNAs (i.e., associated with clinical relevance) that account for MCF7 cells to a PI3K inhibitor, BEZ235 (Fig. 5i), and a BCL-2 62.7% (5715/9108) of the total detectable eRNAs in cancers (Fig. 6a Inhibitor, Obatoclax (Fig. 5j) in MCF7 cells. We also examined and Supplementary Data 8). For example, TAOK1-associated the effects for the other three drugs (CHIR-99021, BX-795, and eRNA (TAOK1e, ENSR00000092917), which putatively targets the (5Z)-7-Oxozeaenol) and observed a similar trend but not Hippo signaling pathway gene TAOK144, is associated with overall statistically significant. Cells showed strong growth inhibition survival in KIRC (Fig. 6b, log-rank test, FDR = 7.97 × 10–5);

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ab c UCEC BLCA 100 >10.0 Expression 1.0 Expression high THCA BRCA NET1e in tumor (RPM) Expression low 20 0.8 STAD CHOL 7.5 >40 50 0.6 READ COAD Fold change

Percentage (%) 5.0 5.0 0 0.4 2.5 –Log10 (FDR) PRAD HNSC 2.5 0.0 Probability of survival 0.2 −2.5 Logrank test LUSC p = 0.0004 KICH −5.0 0.0 Upregulated 0.0 0 50 100 150 200 Downregulated LUAD KIRC <−5.0 −2.5 0.0 2.5 >5.0 Months LIHC No significant KIRP Fold change

d e NET1e expression NET1 expression p = 0.003 4 p = 0.002 Chr 10 100 p = 0.0001 p = 0.0001 hg38 5,410,000 5,420,000 5,420,000 5,430,000 5,450,000 5,460,000 5,470,000 5,480,000 5,490,000 5,500,000 3 10 NET1 NET1e 2 1 H3K4me1 1 Relative expression Relative expression 0 0 Control NET1e NET1e Control NET1e NET1e H3K27ac gRNA gRNA #1 gRNA#2 gRNA gRNA #1 gRNA#2

6 p300 Con gRNA p p = 0.0001

NET1e gRNA #1 = 0.0003 NET1e gRNA #2 Gro-seq 4

ChIA-PET 2 Normalized cell confluence

0 0244872 Hours

f 3 g 5 NET1e expression Con gRNA Con LNA NET1e expression NET1e gRNA Con LNA Con gRNA NET1e LNA NET1e gRNA NET1e LNA p = 0.0001 p = 0.002 4 p = 0.015 1 p = 0.0002 1 2 3

0.5 2 0.5 1

1 Relative expression Relative expression Normalized cell confluence Normalized cell confluence 0 0 0 0 Con gRNA Con gRNA 0244872NET1e gRNA NET1e gRNA 0 244872 Con LNA NET1e LNA Con LNA NET1e LNA Hours Hours

h Piperlongumine ij 6 GDSC

BEZ235 Obatoclax 0 Control Control

NET1e OE NET1e OE

100 −6 100 Afatinib 6 ML210 CTRP –log10 (FDR) 50 50 % cell survival % cell survival

0 0 00.022 0 0.001 1 Dosage Dosage −6 Austocystin

Fig. 5 Functional characterization of NET1e in cancer. a Expression alterations of eRNAs in human cancers. Red, blue and gray, respectively, denote percentage of upregulated, downregulated and no signiﬁcant expression of eRNAs in each cancer type. b Differentially expressed eRNAs in BRCA. Scale bar denotes the fold change between tumor and normal samples. c Survival curves for NET1e in BRCA (top quarter in red vs. bottom quarter in blue). d Epigenetic features and expression of NET1e and NET1 region in MCF7. e Overexpression of NET1e in MCF7 and its functional consequences. Overexpression of NET1e by CRISPR/dCas9-SAM (top left) and NET1 (top right), and cell growth assay with overexpression of NET1e in MCF7 (bottom). f Knockdown NET1e by LNA (left) and cell growth assay (right) in MCF7. g Knockdown NET1e by LNA after activation (left) and cell growth assay (right) in MCF7. h NET1e-associated drugs in GDSC and CTRP database. i Response curve of compound BEZ235 in NET1e overexpressed cells and control. j Response curve of compound Obatoclax in NET1e overexpressed cells and control. Error bars in (e), (f), (g), (i), and (j) indicate mean ± SD

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EN1-associated eRNA (EN1e, ENSR00000122295), which puta- (Fig. 6g). In the eRNA-expression module, users can explore the tively targets the BRCA-basal marker gene EN145,ishighly expression of eRNA across TCGA cancer types and samples and expressed in the BRCA-basal subtype (Fig. 6c, Analysis of variance the eRNA location by Ensembl ID or genomic location. The [ANOVA], FDR < 2.2 × 10–16); CELF2-associated eRNA (CELF2e, clinical relevance module aims to help users identify clinically ENSR00000024385), which putatively targets the tumor suppressor relevant eRNAs, including those that showed differentially gene CELF246, is highly expressed in stage III STAD (Fig. 6d, expressed patterns between tumor and normal samples among ANOVA, FDR = 4.7 × 10–7); APH1A-associated eRNA (APH1Ae, different groups of cancer subtypes, stages, and grades and dif- ENSR00000013533), which putatively targets the oncogene ferent categories of patient smoking history, and in association APH1A47, is highly expressed in grade-3 LIHC (Fig. 6e, ANOVA, with patient survival times. The target genes module allows users FDR = 6.4 × 10–5); and SCRIB-associated eRNA (SCRIBe, to identify eRNA target genes (Supplementary Fig. 6A). We also ENSR00000232146), which putatively targets the oncogene integrated the drug response data from GDSC and CTRP, which SCRIB48, is differentially expressed among patients with LUAD allows users to investigate whether an eRNA shows sensitivity or according to different categories of smoking history (Fig. 6f, resistance to drugs (Supplementary Fig. 6B and 6C). In addition, ANOVA, FDR < 4.3 × 10–3). These results suggest that appreciable eRic provides a download module, which allows users to down- levels of eRNAs are clinically valuable. load the expression, clinical relevance, targeted genes, and drug response data. This valuable resource will be of signiﬁcant interest to the research community49. A comprehensive data resource to explore eRNAs in cancer. We developed a user-friendly data portal, eRNA in cancer (eRic) (https://hanlab.uth.edu/eRic), to facilitate broad access to these Discussion data by the biomedical community. eRic includes four modules: eRNA are increasingly realized to play important roles in the expression, clinical relevance, target genes and drug response regulation of gene transcriptional circuitry in human cancers. We

a Grade 290 113 344 866 526 108 70 42 515 371 Number

Smoking 76 50 163 57 53 28 292 337 79

Stage 73 272 137 55 111 94 36 53 140 427 604 54 258 85 28 24 118 97 1627 503 557 218 58 38 1000

Subtype 260 783 101 265 633 963 467 97 460 592 48 1581 348 500 Survival 149 34 105 112 33 15 48 103 96 620 249 696 75 137 58 82 35 29 29 42 132 52 109 30 84 65 105 32 100 Clinically relevant events 0 OV ACC UCS LGG UVM LIHC GBM KIRP KICH KIRC BLCA LUAD LUSC DLBC STAD TGCT THCA PAAD BRCA CHOL READ CESC PRAD SARC THYM HNSC UCEC PCPG COAD SKCM MESO Cancer

b TAOK1e ENSR00000092917 c EN1e ENSR00000122295 d CELF2e ENSR00000024385 e APH1Ae ENSR00000013533 f SCRIBe ENSR00000232146 1.0 5 –7 –3 FDR = 4.7 × 10 –5 FDR = 4.3 × 10 FDR < 2.2 × 10−16 FDR = 6.4 × 10 4 3.0 0.8 6 2.0 3 0.6 2.0 4 2 0.4 log2 RPM log2 RPM log2 RPM

log2 RPM 1.0 Logrank −5 2 1 1.0 0.2 FDR = 7.97 × 10

Probability of survival High 0.0 Low 0 0 0.0 0 20 40 60 80 100 120 G1 G2 G3 CRM Her2

Months Basal Smoker Stage I Stage II Stage III Stage IV Luminal B Luminal A Non−smoker Normal_like CRM < 15 years CRM > 15 years

g Expression landscape Clinical relevance Target genes Drug response

eRNA

eRNA eRNA

Target gene eRNA

Fig. 6 Clinically relevant eRNAs and the eRNA data portal in cancer (eRic). a Number of clinically relevant eRNAs across different cancer types. For survival analyses, we use cox model to analyze the relationship between eRNA expression and survival time, and considered FDR < 0.05 as signiﬁcant. For other clinical relevance, we used Student’s t test to test the difference between two groups and analysis of variance (ANOVA) to test the difference among multiple groups, and considered FDR < 0.05 as signiﬁcant. Scale bar denotes number of clinically relevant eRNAs. b–f Examples of clinically relevant eRNAs for patient survival time (b), cancer subtype (c), tumor stage (d), tumor grade (e), and patient smoking history (f). The boxes in (c–f) show the median ± 1 quartile, with whiskers extending from the hinge to the smallest or largest value within 1.5 interquartile range from the box boundaries. g The four modules in eRic: expression landscape, clinical relevance, putative target genes, and drug response

NATURE COMMUNICATIONS | (2019) 10:4562 | https://doi.org/10.1038/s41467-019-12543-5 | www.nature.com/naturecommunications 9 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-12543-5 developed a computational pipeline to reveal the global expres- utilization of the expression landscape and clinical relevance of sion landscape of eRNAs across multiple cancer types. By inte- eRNAs by the broad biomedical community, we have built a data grating multi-omics data from TCGA, CCLE, ENCODE, portal, eRic, offering a comprehensive resource for further inves- FANTOM, Roadmap Epigenomics, and 4D Nucleome projects, as tigation of eRNA expression landscape, clinical relevance, target well as pharmacogenomics datasets from GDSC and CTRP, we genes, functions in tumorigenesis or response to anticancer drugs. have revealed novel insights on the expression landscape and This is the first data portal in eRNA field and will be a valuable clinical utility of eRNAs in cancer (Supplementary Fig. 7 and resource for further investigation of cancer therapy that targets Supplementary Table 1). We demonstrated a strong cancer-type- eRNAs. In particular, the related data will help the researchers to specific expression pattern of many eRNAs, suggesting that identify key eRNAs in cancer patients, and to select the appropriate eRNAs may be powerful diagnostic and/or prognostic markers in cancer cell lines for their functional investigations. cancer therapy. The cancer-type-specific pattern is aligned well with the previous studies characterizing the regulatory elements Methods (e.g., enhancers) based on ATAC-seq50, as well as activated Data collection. We downloaded RNA-seq BAM files, clinical features and the enhancers30. The sequencing depth may still cause some batch mRNA expression matrix from TCGA data portal (https://portal.gdc.cancer.gov/)11. 4 effects. For those cancer types with 75/76 bp pair-end reads, GRO-seq data and ChIP-seq for MCF7 were collected from our previous paper . ChIA-PET data were obtained from WASHU EpiGenome Browser (https:// including OV, STAD, and GBM, only STAD showed the relative epigenomegateway.wustl.edu/)21. RNA-seq data of cancer cell lines were down- more eRNAs. In contrast, COAD, READ, and UCEC with 76 bp loaded from the Cancer Cell Line Encyclopedia (https://portals.broadinstitute.org/ single-end reads have relative fewer eRNAs. The majority (25 out ccle/about)46. Drug sensitivity datasets were downloaded from GDSC14 and CTRP51. of 31) of cancer types with 48/50 bp pair-end reads showed vast The Hi-C interactions across 20 human tissues were downloaded from http:// promoter.bx.psu.edu/public/HiCPlus/matrix/52. Clinically actionable genes were difference in detectable eRNAs ranging from 457 in LIHC to 1790 collected from previous literatures37,38,53, and cancer immune checkpoints were in TGCT (Supplementary Data 1). The number of eRNAs is not collected from a previous literature54. RNA-seq data for A549 treated by belinostat correlated with the sequencing depth in these cancer types (Rs = (GSE96649) was downloaded from Gene Expression Omnibus (https://www.ncbi. 0.02, p = 0.93). These results suggested that the tissue-specific nlm.nih.gov/geo/), and processed by Hisat2 software55 and SAMtools toolkit56. pattern of eRNAs is robust. We identified a series of transcription factors as the potential regulators for eRNA biogenesis, which Quantification of eRNA expression. Annotation of enhancers were collected from Ensembl (https://useast.ensembl.org/)57, FANTOM (http://fantom.gsc.riken. greatly expanded our knowledge about eRNA biogenesis. Inter- jp/index.html)9, and Roadmap Epigenomics (http://www.roadmapepigenomics. estingly, we observed that the general putative master regulators org/)10. The annotation from ENCODE and Roadmap considered H3K4me1 and of eRNAs, including NR2C2 and NFAT5, displayed an intriguing H3K27ac marks57, and annotation from FANTOM considered CAGE marks. We enrichment of functions in modulating genomic instability, sug- combined all three datasets and used those enhancers annotated in at least two 58 gesting a potential mechanistic link between eRNA expression/ datasets. Annotation of protein-coding genes was collected from GENECODE and UCSC Genome Browser59 (hg38). We used the ± 3 kb of the middle loci of biogenesis and genome instability. enhancer to define eRNA region16. We also filtered out those eRNA regions that Integrative analysis showed that more than 80% genes in the overlapped with known coding regions and lncRNAs (with 1 kb extension from canonical cancer signaling pathways are highly correlated with both transcription start site and transcription end site). In particular, the ~ 500 bp specific eRNAs in at least one cancer type, suggesting potentially (uaRNA) region was also excluded from our analysis. We also excluded all blacklist regions, including rRNA repeats. important regulatory roles of eRNA in cancer. Due to the lack of We downloaded RNA-seq BAM files from TCGA data portal (https://portal. Hi-C data in large number of tumor tissues, we can only confirm gdc.cancer.gov/)11. The RNA-seq raw data were processed by TCGA consortium as the eRNA-gene connections support by Hi-C interaction in at described on the official website (https://docs.gdc.cancer.gov/Data/ least one normal tissue. It will be more appropriate to use the Hi- Bioinformatics_Pipelines/Expression_mRNA_Pipeline/). We downloaded BAM fi files for our downstream analysis. We mapped RNA-seq data to these eRNA C data in matched samples to con rm the regulatory roles of regions and calculated the expression level as RPM60 for each eRNA in each eRNAs. We also observed associations between eRNAs and sample. We normalized eRNA expression by reads per million. We averaged all anticancer drugs, either within the target pathway or through a RPMs annotated to the eRNA from all samples in a cancer type, and defined those cross-pathway. Furthermore, many clinically actionable genes eRNAs with average expression level (RPM) ≥1 as a detectable eRNA. We converted different genomic versions of the human genome by liftover59.We and immune checkpoints were putatively regulated by eRNAs, present t-SNE analysis using R package Rtsne61. Our method may only detect a emphasizing the clinical utility of eRNA in anticancer treatment. subset of polyadenylated eRNAs at their steady state since TCGA and CCLE only Nevertheless, our integrative analysis demonstrated the putative included the poly(A) RNA-seq. Our method may not distinguish functional eRNAs regulatory roles of eRNAs, and further experiments are necessary from those may just be the side effect of active enhancer. to confirm their regulatory roles. We demonstrated the functional importance of an individual Biogenesis of eRNAs. We collected TFs from multiple TF resources, including 21 22 eRNA, NET1e, which is highly expressed in breast cancer. JASPAR (http://jaspar.genereg.net/) , DBD (http://www.transcriptionfactor.org/) , AnimalTFDB (http://bioinfo.life.hust.edu.cn/AnimalTFDB/)23, and TF2DNA CRISPR activation of NET1e accelerated cell growth in MCF7, (http://www.fiserlab.org/tf2dna_db/)24. We identified putative regulators of eRNAs suggesting its oncogenic effect in cancer cell lines, while NET1e based on the co-expression between individual eRNA and each TF in a given cancer LNA specifically decreased cell proliferation in MCF7, and has type, and considered Spearman’s correlation Rs ≥ 0.3 and FDR < 0.05 as significant. fi shown limited or no off-target effects and toxicity. More For each cancer type, TFs that signi cantly correlated with more than 25% of the detectable eRNAs were defined as master regulators. Master regulators that exist in importantly, in situ overexpression of NET1e will lead to drug more than 10 cancer types were defined as general master regulators. The functional resistance to BEZ235 and Obatoclax in MCF7 cells. To our enrichment analyses of these general master regulator were performed by DAVID62 knowledge, this is the first evidence showing that eRNA could and GSEA63. affect drug response in cancer. Taken together, our results suggest the promising clinical importance of NET1e. eRNA putative target genes and drugs. We identified eRNA putative target RNA-target drugs are now becoming a major new branch of genes based on close distance (≤ 1MB) and co-expression (Spearman’s correlation fi Rs ≥ 0.3 and FDR < 0.05) between individual eRNAs and their putative target genes pharmaceuticals. For example, US FDA has approved the rst in each cancer type58.Wefiltered out eRNAs located in the intronic regions of siRNA drug in 2018 (i.e., to use Patisiran infusion for the treatment target genes for correlation analysis. We collected 229 genes associated with 10 of peripheral nerve disease like polyneuropathy), and there are cancer signaling pathways34: p53, PI3K, Myc, RTK/RAS, cell cycle, Wnt, TGF beta, extensive ongoing efforts to target disease-relevant RNAs in the Nrf2, Notch, and Hippo. Due to the lack of Hi-C data in large number of tumor fi tissue, we used 20 Hi-C data from normal tissues52 to confirm the putative eRNA- pharmaceutical industry. We identi ed an appreciable number of gene connections. Hi-C interaction was evaluated by O/E value, which is calculated clinically relevant eRNAs and further demonstrated their clinical as observed value (estimated with normalized mapped reads) divided by expected utility in diagnostic and/or eRNA-targeted therapy. To facilitate value (estimated with a genome-wide model of interaction probability over the

10 NATURE COMMUNICATIONS | (2019) 10:4562 | https://doi.org/10.1038/s41467-019-12543-5 | www.nature.com/naturecommunications NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-12543-5 ARTICLE genomic distance)64. We also estimated the Hi-C interactions based on random Reporting summary. Further information on research design is available in eRNA-gene pairs throughout genome as background, and performed permutation the Nature Research Reporting Summary linked to this article. test (bootstrap = 10,000) to compare with eRNA-gene pairs with the background of random pairs. For those eRNAs identified in TCGA, we examined their expression across ~1000 cancer cell lines in CCLE. GDSC and CTRP collected drug Data availability fi response data across >1000 cancer cell lines. We used matched cell lines to calculate All accession codes, unique identi ers, or web links for publicly available datasets are fi the Spearman’s correlation between eRNA expression in CCLE and drug response described in the paper. All data supporting the ndings of the current study are listed in – value (AUC) of more than 500 anticancer drugs from CTRP and GDSC, and Supplementary Data 1 8, Supplementary Fig. 7, and our online data portal (https:// defined FDR < 0.05 as significant35. hanlab.uth.edu/eRic/).

Clinically relevant eRNAs. We used the Student’s t test to assess the statistical Code availability difference between tumor and paired normal samples and defined significantly All codes are available upon reasonable request. aberrant expression as |fold change| > 1.5 and FDR < 0.05. We used Student’s t test for two groups and analysis of variance (ANOVA) for multiple groups to assess the statistical difference of patient smoking history and cancer subtype, stage, and Received: 28 November 2018; Accepted: 16 September 2019; grade (FDR <0.05). Only groups with ≥ 5 samples were included in these analyses. We used the univariate Cox model or log-rank test to assess whether eRNA expression was associated with the overall survival times of cancer patients and considered FDR < 0.05 as significant.

Lentivirus generation.Amixtureof3μgofpsPAX2,1μg of pMD2.G, and 4 μgof References target sgRNA vector was transfected into 293T cells using Lipofectamine 2000 (Life 1. Blackwood, E. M. & Kadonaga, J. T. Going the distance: a current view of – Technologies). After 16 h, the media was changed, and the supernatants were collected enhancer action. Science 281,60 63 (1998). at 48 and 72 h posttransfection for two independent infections. The collected super- 2. Kim, T.-K. et al. Widespread transcription at neuronal activity-regulated – natants were filtered using 0.45 μm syringe filter (Fisher) and used to infect MCF7 cells enhancers. Nature 465, 182 187 (2010). after being mixed with polybrene (final concentration of 8 μgml−1, Sigma). Target 3. Li, W., Notani, D. & Rosenfeld, M. G. Enhancers as non-coding RNA cells were incubated in complete media with an equal amount of lentiviral particle- transcription units: recent insights and future perspectives. Nat. Rev. Genet. containing media for 24 h for each infection. After the second infection, the cells were 17, 207–223 (2016). selected over at least one week with selection markers to achieve a stable line. 4. Li, W. et al. Condensin I and II complexes license full estrogen receptor α- dependent enhancer activation. Mol. Cell 59, 188–202 (2015). 5. Hsieh, C.-L. et al. Enhancer RNAs participate in androgen receptor-driven Cell culture and transfection. We originally purchased MCF7 and MCF10A cells looping that selectively enhances gene activation. Proc. Natl Acad. Sci. USA from American Type Culture Collection. We maintained the MCF7 and Hela cells 111, 7319–7324 (2014). in Dulbecco’s modified Eagle’s medium (DMEM) (Corning) media, supplemented 6. Melo, C. A. et al. ERNAs are required for p53-dependent enhancer activity with 10% fetal bovine solution (FBS) (GenDEPOT) and maintained the MCF10A – cells in DMEM/F-12 (Corning) supplemented with 5% horse serum, 20 ng ml−1 and gene transcription. Mol. 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