Hepatocellular carcinoma (HCC) is one of the most common human malignancies with poor prognosis and urgent unmet medical need. Aberrant expression of multiple members of the miR-17 family are frequently observed in HCC, and their overexpression promotes tumorigenic properties of HCC cells. However, whether pharmacologic inhibition of the miR-17 family inhibits HCC growth remains unknown. In this study, we validated that the miR-17 family was upregulated in a subset of HCC tumors and cell lines and its inhibition by a tough decoy inhibitor suppressed the growth of Hep3B and HepG2 cells, which overexpress the miR-17 family. Furthermore, inhibition of the miR-17 family led to a global derepression of direct targets of the family in all three HCC cell lines tested. Pathway analysis of the deregulated genes indicated that the genes associated with TGFβ signaling pathway were highly enriched in Hep3B and HepG2 cells. A miR-17 family target gene signature was established and used to identify RL01-17(5), a lipid nanoparticle encapsulating a potent anti-miR-17 family oligonucleotide. To address whether pharmacologic modulation of the miR-17 family can inhibit HCC growth, RL01-17(5) was systemically administrated to orthotopic Hep3B xenografts. Suppression of Hep3B tumor growth in vivo was observed and tumor growth inhibition correlated with induction of miR-17 family target genes. Together, this study provides proof-of-concept for targeting the miR-17 family in HCC therapy. Mol Cancer Ther; 16(5); 905–13. ©2017 AACR.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths, with over 500,000 deaths each year worldwide (1). During the last decade, extensive progress has been made in understanding the molecular mechanisms of HCC and many potential targets for therapeutic intervention have been identified (2, 3). However, sorafenib, a small-molecule targeting multiple tyrosine kinases, remains the only targeted drug approved for the treatment of HCC. As sorafenib treatment only increases patient survival by an average of a few months, there is an urgent need to develop more effective therapies (4).

miRNAs are a class of small noncoding RNAs that control gene expression posttranscriptionally by regulating mRNA translation or stability. Deregulation of miR expression contributes to cancer initiation and progression by directly or indirectly controlling the expression of key genes involved in cancer-associated pathways (5, 6). Accumulating evidence has linked deregulation of miR expression to HCC (7). For example, miR-21 is upregulated in human HCC tumors and regulates HCC cell proliferation, migration, and anchorage-independent growth. Furthermore, inhibition of miR-21 suppressed HCC tumor growth in vivo (8). In addition, ectopic expression of miR-221 in the liver promoted liver tumorigenesis and in vivo delivery of an anti-miR-221 oligonucleotide repressed tumor growth (9, 10). These studies suggest that HCC-associated miRNAs may represent a novel group of targets for therapeutic intervention.

The miR-17-92 cluster and its paralogs (miR-106a-363 and miR-106b-25) are known to act as oncogenes (11). These clusters encode 15 individual miRs that can be classified into four families based on their seed sequences: miR-17 (miR-17, miR-20a, miR-20b, miR-93, miR-106a, and miR106b), miR-18 (miR-18a and miR-18b), miR-19 (miR-19a, miR-19b-1, and miR19b-2) and miR-25 family (miR-25, miR-92a-1, miR-92a-2, and miR-363). Among them, the miR-17 family is the most well studied in HCC. Ectopic expression of miR-17 and the passenger miR-17-3p in the liver synergistically induced the development of HCC in a mouse model (12). Another member of the miR-17 family, miR-106b, was shown to play an important role in promoting human HCC cell proliferation (13). In addition, both miR-17 and 106b promoted human HCC cell migration and cancer metastasis (14, 15). These studies highlighted a potential strategy to treat HCC by inhibiting the miR-17 family.

In this study, we validated that inhibition of the miR-17 family–suppressed HCC cell growth in vitro using a genetic tough decoy (TuD) approach (16). We further demonstrated that systemic lipid nanoparticle (LNP)-based delivery of an anti-miR-17 family oligonucleotide significantly inhibited orthotopic Hep3B tumor growth in vivo. Thus, our findings demonstrate initial proof-of-concept that targeting the miR-17 family using anti-miRs may be a valid therapeutic approach for the treatment of HCC.

Cell culture and stable cell line generation

THLE-2 and HCC cell lines including Hep3B, HepG2, PLC/PRF/5, SK-HEP-1, SNU-398, SNU-449, and SNU-475 were acquired from ATCC where cell lines were authenticated by short tandem repeat profiling. HuH-7 cell line was acquired from Japanese Collection of Research Bioresources Cell Bank where cell lines were authenticated by short tandem repeats profiling and isoenzyme detection. All the cell lines were obtained in 2013. THLE-2 cells were cultured with bronchial epithelial growth medium (Lonza) and HCC cells were grown in ATCC-formulated Eagle minimum essential medium supplemented with 10% FBS. To generate stable cell lines overexpressing miR-17 or negative control (NC) TuD, 1.0 × 105 HCC cells were incubated with viral particles encoding miR-17 or NC TuD (Sigma-Aldrich) at multiplicity of infection 5 in medium with 10.0 μg/mL polybrene at 37°C for 48 hours. Cells were then selected by culturing in medium supplemented with 1.0 μg/mL puromycin (Life Technologies). After 2 weeks in culture, puromycin-resistant colonies were pooled to establish stable cell lines. All cell lines were used at the fifth through fifteen passage in culture for this study. Cell cultures were routinely checked for mycoplasma contamination with LookOut Mycoplasma qPCR detection Kit (Sigma-Aldrich).

In vitro proliferation assays

To determine the effects of miR-17 family inhibition on HCC cell growth, miR-17- and NC TuD–expressing cells were plated onto a 96-well plate at 500 cells/well. On day 0, 2, 4, 6, and 8 after plating, cell growth was assessed using CellTiter-Glo Luminescent Cell Viability Assay kit (Promega). The cell growth rates were normalized and expressed as fold change from day 0.

Transfection

All oligonucleotides were designed and synthesized at Regulus Therapeutics. These oligonucleotides had phosphorothioate linkages between the nucleosides that form the backbone and were modified at 2′-hydroxyl group of the ribose rings. Cells were reverse transfected with oligonucleotides at the indicated concentrations in the presence of RNAiMAX (Life Technologies) according to manufacturer's instructions and plated onto 24-well plates (1.0 × 105 cells/well). Twenty-four hours posttransfection, cells were harvested for target gene analysis.

Real-time quantitative PCR

Total RNA and miRNAs were prepared from cells and tissues using the miRNeasy miRNA isolation kit (Qiagen) according to the manufacturer's instructions. cDNA and miR cDNA synthesis were performed with high capacity RNA to cDNA kit and TaqMan MicroRNA Reverse Transcription kit respectively (Life Technologies). Real-time PCR was performed using the ViiA7 (Life Technologies) or BioMarker HD System (Fluidigm). miRNA copy numbers were calculated from standard curves generated using synthetic miRNAs (Integrated DNA Technologies).

Gene expression profiling

Microarray profiling of Hep3B RNAs was performed using Affymetrix HG-U133Plus2 arrays. Briefly, starting with 1.0 μg total RNA, amplified biotin-labeled cRNA targets were produced using the Enzo Target Labeling method. Fragmented, biotin-labeled cRNA was hybridized to the Affymetrix GeneChip Human Genome U133 Plus 2.0 Arrays overnight at 45°C for 16 to 18 hours according to the manufacturer's recommendations. The staining, washing (GeneChip Fluidics Station 450, EukGE-WS2v5_450 script), and scanning (GCS 3000 7G with the GeneChip AutoLoader) was performed according to the manufacturer's recommendations. Microarray data were GC Robust Multiarray Average (GCRMA) normalized using R and the bioconductor.org package gcrma (17). For HepG2 and SK-HEP-1 RNAs, gene expression profiling was done using RNA-Sequencing. mRNA expression profiles were determined using next-generation sequencing (NGS) on the Illumina HiSeq 2000 platform producing 50-bp paired-end reads. STAR alignment was used to align the reads to human genome or transcriptome (18) and Cufflinks was used to quantify gene abundances (19). Gene-level counts were then normalized with the R/Bioconductor package limma using the voom/variance stabilization method (20). All the gene expression data were deposited in GEO under the accession number GSE93832.

Bioinformatics analysis

Both Affymetrix and RNA-Seq data were quality controlled for outliers using principal component analysis (PCA). Differential gene expression analysis between transcriptome profiles of miR-17 versus NC-TuD was performed using the R/Bioconductor package limma (Linear Models for Microarray Data), that includes methods for RNA-Seq data analysis. TargetScan 6.2 was used to identify putative conserved targets of miR-17 (21, 22). Derepression in a cell line was defined as upregulation >1.3 fold (P <0.05). An absolute fold-change threshold of >1.2 (P < 0.05) was used to select genes for pathway analysis. MetaCore (Thomson Reuters) version 6.23 was used for pathway analyses.

Formulation of LNPs

Cationic lipid RL01 was synthesized by Regulus Therapeutics. 1,2-Distearoyl-sn-glycero-3-phosphatidylcholine (DSPC), and 1,2-dimyristoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy(polyethylene glycol)-2000] (DMPE-PEG2000) were purchased from Avanti Polar Lipids. Cholesterol was purchased from Sigma. LNPs encapsulating anti-miR-17(5) or control oligonucleotide were prepared by mixing oligonucleotide in citrate buffer with lipid mixture in ethanol. Lipid mixture consisted of the cationic lipid RL01, DSPC, cholesterol, and DMPE-PEG2000. LNPs thus obtained were diluted with PBS buffer (pH 7.4). They were then purified by tangential flow filtration, concentrated, and subjected to sterile filtration through a 0.8/0.2-μm syringe filters into sterile vials and stored at 4°C. Physicochemical properties were then characterized. The lipid nanoparticles RL01-17(5) used in this study had a mean particle size of approximately 100 nm with a polydispersity index (PDI) of 0.1. HPLC-determined anti-miR-17(5) concentration was 2.6 mg/mL and lipid content was 57%. The percentage of encapsulated anti-miR-17(5) was determined to be 98% as measured by the Ribogreen assay.

Animal studies

All animal studies were carried out under approved Institutional Animal Care and Use Committee protocols held at Regulus Therapeutics. Male SCID/Beige mice between 8 and 10 weeks of age were purchased from Taconic Biosciences. To establish orthotopic liver tumors, 1.0 × 106 Hep3B cells suspended in a 1:1 ratio of PBS and Matrigel (BD Biosciences) were injected into the left lateral lobe of the liver. To determine miR-17 family target derepression, a single dose of PBS or anti-miR-17(5) (20.0 mg/kg) was injected subcutaneously and RL01-ctrl (2.0 mg/kg) or LNP RL01-17(5) (2.0 mg/kg) was injected intravenously approximately 3 weeks after tumor implantation. Tumor tissues were harvested 24 hours postdosing for analysis. For the efficacy study, one week postimplantation, animals were randomized into groups based on serum AFP values to ensure that each group had similar initial tumor burdens. Animals were then tail vein injected with 100 μL of PBS or 0.5, 1.0, 2.0 mg/kg of anti-miR-17(5) encapsulated in RL01 three times per week (Monday, Wednesday, and Friday) for 3 weeks. Seventy-two hours after the last dose, animals were sacrificed, tumors were dissected, and weighed. RNAs were isolated from the tumor tissues to determine pharmacodynamics.

Statistical analysis

The results (mean ± SD) were subjected to statistical analysis by Student t test or one-way ANOVA with a significance threshold of P < 0.05.

The miR-17 family was upregulated in HCC tumors and cell lines

To determine expression of the miR-17 family members in HCC tissues, we analyzed their levels in the miR expression profiling data published by Burchard and colleagues (NCBI GEO under accession number GSE22058) consisting of 79 liver tumors and 94 adjacent nontumor liver samples, including 78 matched tumor–liver pairs. A subset of HCC tumor tissues showed elevated levels of individual miR-17 family members (Fig. 1A) as well as combined miR-17 family expression (Fig. 1B) compared with the normal liver tissues (miR-20b was not reported in the database). To further determine whether the miR-17 family is upregulated in HCC cells, we measured copy numbers of each family member in a panel of 8 human HCC cell lines. miR-20a was the highest expressed and miR-20b was the least expressed family member across the panel. Consistent with the public database, most of the family members were upregulated in at least a subset of HCC cell lines compared with the immortalized human hepatocyte THLE-2 (Fig. 1C). The overall copy numbers of entire miR-17 family were also determined. Compared with THLE-2, the miR-17 family was modestly upregulated in HuH-7 and SNU398 and significantly upregulated in Hep3B and HepG2 (Fig. 1D).

Suppression of the miR-17 family inhibited HCC cell proliferation

To functionally characterize the miR-17 family in HCC, we utilized lentiviral vector–based miRNA inhibitor TuD to inhibit endogenous miR-17 family activity. miR-17 TuD was able to inhibit activity of each miR-17 family member as demonstrated in a luciferase reporter assay performed in the HeLa cells (Supplementary Fig. S1). Three HCC cell lines, Hep3B, HepG2, and SK-HEP-1 with high, medium, and low levels of miR-17 family, were chosen to generate stable cell lines that expressed miR-17 or NC TuD. Similar levels of miR-17 TuD were expressed across the three lines as determined by qPCR (Supplementary Fig. S2). Interestingly, miR-17 TuD significantly suppressed the growth rate of Hep3B and HepG2 but had no effect on SK-HEP-1 cells (Fig. 2). No obvious cell death was noticed in either cell line (Supplementary Fig. S3). These results confirm that the miR-17 family has a vital function in regulating the growth of HCC cells.

Gene expression profiling and pathway analysis

Having shown a critical role of the miR-17 family in regulating HCC cell growth, we then performed gene expression profiling of the miR-17 and NC TuD cell lines to investigate the mechanisms by which it regulates cell proliferation. Inhibition of miR-17 family resulted in global derepression of the transcripts that contain putative miR-17–binding sites in their 3′-UTRs in all three cell lines generated (Fig. 3A). Many previously reported miR-17 family target genes including TGFBR2 (23, 24), CDKN1A (25), E2F1 (26), and CASPAS7 (27) were confirmed in at least one cell line. Additional potential miR-17 family target genes with well-known functions in controlling cell proliferation were also identified. Among them was BTG3, a tumor suppressor and a downstream target of TP53 and RASD1, a dexamethasone-inducible Ras-related protein.

We then performed MetaCore (Thomson Reuters) pathway analysis using genes differentially expressed between miR-17 and NC TuD cell lines (fold change >1.2 or ←1.2, P < 0.05; Fig. 3B). Interestingly, genes involved in cancer-associated pathways including cell-cycle regulation of G1–S transition, cytoskeleton and extracellular matrix remodeling, epithelial-to-mesenchymal transition, and TGF-β signaling were highly enriched in Hep3B and HepG2 cells, the two cell lines in which miR-17 TuD inhibited cell proliferation. In contrast, these pathways were not affected in SK-HEP-1 cells where miR-17 TUD has no growth-inhibitory effect. Among these, the TGF-β signaling pathway was the only one shared by both Hep3B and HepG2 cells. Consistently, TGFBR2 was statistically and significantly derepressed in Hep3B and HepG2 but not SK-HEP-1 cells. Specific scrutiny of the MetaCore pathway for TGF-β signaling revealed that additional genes downstream of TGFBR2 including ZFYVE9 and CDKN1A were also derepressed in Hep3B and HepG2 cells (Supplementary Fig. S4). ZFYVE9 (also known as SARA: Smad anchor for receptor activation) is critical for the recruitment of SMAD 2/3 to the appropriate subcellular membrane compartment and in close proximity to the activated TGFBR1 (28). Together, these results indicate that inhibition of the miR-17 family results in a robust global target engagement in HCC cells and it regulates HCC cell proliferation partly through suppression of the TGF-β signaling pathway.

miR-17 family target gene signature

By comparing these profiling results, we identified 40 genes derepressed by miR-17 TuD in at least two out of the three cell lines and containing putative conserved miR-17 family binding site in their 3′-UTRs. The top 20 genes were selected for further validation. We transfected Hep3B, HepG2, and SK-HEP-1 cells with 3 different anti-miR-17 family oligonucelotides and 4 control oligonucleotides. All 20 genes were derepressed by the anti-miR-17 family oligonucleotides in the cell contexts that were tested. However, 9 genes were also nonspecifically derepressed by at least one control oligonucleotide in at least one cell context. The other 11 genes which included BTG3, C7ORF43, CROT, LIMK1, MINK1, NAGK, NKIRAS1, PLEKHA3, PTPN4, TBC1D9, and TGFBR2 were chosen and designated as the miR-17 family target gene signature. Dose-dependent derepression of these targets upon a representative anti-miR-17 family oligonucleotide treatment are shown in Fig. 4A–C. The log2 of the fold changes of these 11 genes was averaged and the average was named the miR-17 family target gene signature score (Fig. 4D). Interestingly, the gene signature scores correlated well with miR-17 family expression levels. The miR-17 family gene signature score was then used as a pharmacodynamics readout for determination of miR-17 family inhibition.

LNP-mediated delivery of anti-miR-17 family oligonucleotide suppresses Hep3B tumor growth in vivo

To identify a potent anti-miR-17 family oligonucleotide, we designed and synthesized 40 anti-miR-17 family oligonucleotides. These oligonucleotides were tested by transfection in Hep3B cells and their potencies were determined by the induction of the miR-17 family gene signature score. Fig. 5A shows examples of the miR-17 family gene signature score obtained by representative anti-miR-17 oligonucleotides. anti-miR-17(5) was identified as the most potent anti-miR-17 family oligonucleotide. One of the biggest challenges for RNA-based therapy in oncology is inefficient delivery of oligonucleotide to the tumor cell. No target derepression was demonstrated in orthotopic Hep3B tumors when nonformulated or “naked” anti-miR-17(5) was systemically administrated to the tumor-bearing mice (Fig. 5B). LNPs represent one of the most advanced platforms for systemic delivery of oligonucleotide to tumor tissues (29, 30). We therefore developed a cationic LNP formulation RL01-17(5) by complexing anti-miR-17(5) with the primary lipid RL01, DSPC, cholesterol and PEG-lipid. As expected, the LNP-mediated delivery of anti-miR-17(5) but not control oligonucleotide derepressed miR-17 family targets in orthotopic Hep3B tumors (Fig. 5B). We next sought to test the antitumor effect of anti-miR-17(5) in the orthotopic Hep3B tumor model. Hep3B tumor-bearing mice were administered with 0.5, 1.0, or 2.0 mg/kg RL01-17(5) three times a week for 3 weeks. Twenty-four hours after the last dose, tumors were harvested and weighed. As shown in Fig. 5C, RL01-17(5) dose-dependently reduced tumor weight. At 2.0 mg/kg dose level, 57% of tumor growth inhibition was demonstrated (P < 0.05). Consistently, RL01-17(5) dose-dependently derepressed miR-17 family targets in the tumor tissues as indicated by the increase in the miR-17 family gene signature score (Fig. 5D). The formulation was well-tolerated at 2 mg/kg with no gross clinical abnormalities. Necropsy revealed no macroscopic changes in organ weights (liver, kidney, spleen) and there were no significant changes in body weight. Overall, these results suggest that systemic delivery of LNP-encapsulated anti-miR-17 family oligonucleotide led to significant derepression of miR-17 family targets in the Hep3B tumors and resulted in the suppression of tumor growth.

All six members of the miR-17 family (miR-17, 20a, 20b, 93, 106a, and 106b) have been shown to function as onco-miRs in multiple malignancies (12, 13, 31–37). As they share the same seed sequence, they likely target common genes and function redundantly. We showed that each member of the family was upregulated in at least a subset of HCC tumors and cell lines. Although inhibition of individual members of the miR-17 family could inhibit tumor cell growth, it may be necessary to target the entire family to achieve the maximum effect. Using a miR-17 TuD, which inhibited all members of the miR-17 family, we showed that miR-17 family inhibition suppressed growth of Hep3B and HepG2 cells that overexpress the miR-17 family but not in SK-HEP-1 cells that express relatively low levels of the miR-17 family. These results imply that levels of the miR-17 family may be used as a predictive biomarker to determine which HCC cell line or more importantly which HCC tumor will respond to miR-17 family inhibition. An increasing number of clinical trials for targeted cancer therapy have used patient selection strategies to identify patients who are most likely to respond to specific molecularly targeted agents. For example, EML-ALK rearrangement in patients with non–small cell lung cancer and BRAFV600E mutation in patients with melanoma were selected for trial of crizotinib and vemurafenib (38). If the current findings can be expanded to a broader panel of HCC cell lines, miR-17 family expression levels may be used as a valuable biomarker for patient selection in future development of miR-17 family–based therapeutic.

The TGF-β signaling pathway is a well-known regulator of cell proliferation, apoptosis, angiogenesis, and metastasis in various cancers (39). It has been shown in multiple biological contexts that the miR-17 family regulates the TGF-β signaling pathway by modulating expression of its components and downstream target genes including TGFBR2, CDKN1A, and BIM. Multiple members of the miR-17 family blunted TGF-β response by targeting TGFBR2 to promote cancer cell angiogenesis and migration (23, 24). Overexpression of miR-93 and 106b of the miR-106b-25 cluster in gastric cancer cells caused a muted response to TGF-β by downregulating CDKN1A expression (25). In MYCN-amplified neuroblastoma cells, inhibition of miR-17 blocked cell cycle and induced dramatic apoptosis through CDKN1A and BIM upregulation (40). Interestingly, while TGFBR2, CDKN1A, and ZFYVE9 were derepressed by miR-17 TuD, we did not observe significant derepression of BIM, one of the most potent proapoptotic factors, in Hep3B and HepG2 cells. This may explain a more proproliferative rather than an antiapoptotic effect of the miR-17 family in HCC cell lines.

miRs generally fine-tune gene expression, with modest effects on a number of their direct targets. In addition, regulation of targets by miRs is often cell context-dependent due to a variety of factors such as cell type–specific expression levels of the miR and/or the mRNA (41), differential isoform expression and alternative polyadenylation (42), and presence of RNA-binding proteins (43). Therefore, demonstration of target engagement upon miR inhibition in preclinical models and in the clinic is quite difficult and potentially inaccurate if only a few target genes are evaluated. To overcome this challenge, we validated a list of 11 genes from 40 putative conserved target genes in 3 HCC cell lines and established the miR-17 family target gene signature. We demonstrated that the gene signature score inversely correlated with miR-17 family expression levels upon miR-17 family inhibition. We then used the gene signature score as a readout in our effort to identify the potent anti-miR-17 family oligonucleotide anti-miR-17(5). With further validation, this gene signature score has the potential to be used as a pharmacodynamics readout for future development of anti-miR-17 family–based HCC therapeutic.

One of the major challenges for RNA-based therapies in oncology is inefficient delivery of oligonucleotide to tumor cells. Several delivery platforms have been explored and LNP-based formulations have emerged as one of the most promising strategies. Clinical trials in liver cancer using LNP formulations have demonstrated initial proof-of-concept in humans (29). Among lipid-based nanoparticles, the cationic LNP has been the most commonly used. We used a cationic lipid RL01-based LNP formulation to successfully deliver anti-miR-17 family oligonucleotide to orthotopic Hep3B tumors and demonstrated that miR-17 family inhibition by synthetic oligonucleotides is sufficient to suppress HCC tumor growth in vivo. However, it is worth noting that with the current LNP formulation, the gene signature scores achieved in vivo remained lower than the maximum score demonstrated in vitro by transfection. This indicates an incomplete penetration of anti-miR-17 oligonucleotide into the tumor with the current LNP formulation. Additional efforts will be required to develop an improved and suitable delivery system for therapeutic purposes.

In summary, we have observed that miR-17 family inhibition by genetic tools suppressed growth of Hep3B and HepG2 cells that overexpress miR-17 family and developed a novel miR-17 family gene signature that can be used as a pharmacodynamic readout of miR-17 family inhibition in human HCC cells. We have further demonstrated that pharmacologic inhibition of the miR-17 family modulated the miR-17 gene signature and suppressed orthotopic Hep3B tumor growth in vivo. Altogether, this study provides proof-of-concept for targeting the miR-17 family in HCC therapy.

All authors were employees of Regulus Therapeutics Inc. at the time of this work.

Conception and design: X. Huang, P. Karmali, M. Walls, X. Yang, B.N. Chau, S. Zabludoff

Development of methodology: X. Huang, J. Magnus, V. Kaimal, P. Karmali, M. Sorourian, R. Lee, B.N. Chau, S. Zabludoff

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): X. Huang, J. Magnus, P. Karmali, J. Li, M. Walls, R. Prudente, E. Sung, R. Lee

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): X. Huang, J. Magnus, V. Kaimal, P. Karmali, J. Li, M. Walls, E. Sung, H. Estrella, A. Pavlicek, S. Zabludoff

Writing, review, and/or revision of the manuscript: X. Huang, J. Magnus, V. Kaimal, P. Karmali, R. Lee, E.C. Lee, B.N. Chau, A. Pavlicek, S. Zabludoff

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): X. Huang, J. Magnus, P. Karmali, M. Sorourian, R. Lee

Study supervision: X. Huang, J. Magnus, P. Karmali, M. Walls, X. Yang, S. Zabludoff

Other (contributed to data generation): S. Davis

The authors thank all their colleagues at Regulus Therapeutics for their thoughtful insights and contributions.

This work was funded by Regulus Therapeutics, Inc.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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