Drug Response of Patient-Derived Lung Cancer Cells Predicts Clinical Outcomes of Targeted Therapy.

Intratumor heterogeneity leads to different responses to targeted therapies, even within patients whose tumors harbor identical driver oncogenes. This study examined clinical outcomes according to a patient-derived cell (PDC)-based drug sensitivity test in lung cancer patients treated with targeted therapies. From 487 lung cancers, 397 PDCs were established with a success rate of 82%. In 139 PDCs from advanced non-small-cell lung cancer (NSCLC) patients receiving targeted therapies, the standardized area under the curve (AUC) values for the drugs was significantly correlated with their tumor response (p = 0.002). Among 59 chemo-naive EGFR/ALK-positive NSCLC patients, the PDC non-responders showed a significantly inferior response rate (RR) and progression-free survival (PFS) for the targeted drugs than the PDC responders (RR, 25% vs. 78%, p = 0.011; median PFS, 3.4 months [95% confidence interval (CI), 2.8-4.1] vs. 11.8 months [95% CI, 6.5-17.0], p < 0.001). Of 25 EGFR-positive NSCLC patients re-challenged with EGFR inhibitors, the PDC responder showed a higher RR than the PDC non-responder (42% vs. 15%). Four patients with wild-type EGFR or uncommon EGFR-mutant NSCLC were treated with EGFR inhibitors based on their favorable PDC response to EGFR inhibitors, and two patients showed dramatic responses. Therefore, the PDC-based drug sensitivity test results were significantly associated with clinical outcomes in patients with EGFR- or ALK-positive NSCLC. It may be helpful for predicting individual heterogenous clinical outcomes beyond genomic alterations.

Tumor Microenvironment Modulation by Neoadjuvant Erlotinib Therapy and Its Clinical Impact on Operable EGFR-Mutant Non-Small Cell Lung Cancer.

PURPOSE: Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors have greatly improved survival in EGFR-mutant (EGFRm) non-small cell lung cancer (NSCLC); however, their effects on the tumor microenvironment (TME) are unknown. We assessed the changes induced by neoadjuvant erlotinib therapy (NE) in the TME of operable EGFRm NSCLC. MATERIALS AND METHODS: This was a single-arm phase II trial for neoadjuvant/adjuvant erlotinib therapy in patients with stage II/IIIA EGFRm NSCLC (EGFR exon 19 deletion or L858R mutations). Patients received up to 2 cycles of NE (150 mg/day) for 4 weeks, followed by surgery and adjuvant erlotinib or vinorelbine plus cisplatin therapy depending on observed NE response. TME changes were assessed based on gene expression analysis and mutation profiling. RESULTS: A total of 26 patients were enrolled; the median age was 61, 69% were female, 88% were stage IIIA, and 62% had L858R mutation. Among 25 patients who received NE, the objective response rate was 72% (95% confidence interval [CI], 52.4 to 85.7). The median disease-free and overall survival (OS) were 17.9 (95% CI, 10.5 to 25.4) and 84.7 months (95% CI, 49.7 to 119.8), respectively. Gene set enrichment analysis in resected tissues revealed upregulation of interleukin, complement, cytokine, transforming growth factor ß, and hedgehog pathways. Patients with upregulated pathogen defense, interleukins, and T-cell function pathways at baseline exhibited partial response to NE and longer OS. Patients with upregulated cell cycle pathways at baseline exhibited stable/progressive disease after NE and shorter OS. CONCLUSION: NE modulated the TME in EGFRm NSCLC. Upregulation of immune-related pathways was associated with better outcomes.

Molecular Subtypes and Tumor Microenvironment Characteristics of Small-Cell Lung Cancer Associated with Platinum-Resistance.

Although molecular subtypes of small-cell lung cancer (SCLC) have been proposed, their clinical relevance and therapeutic implications are not fully understood. Thus, we aimed to refine molecular subtypes and to uncover therapeutic targets. We classified the subtypes based on gene expression (n = 81) and validated them in our samples (n = 87). Non-SCLC samples were compared with SCLC subtypes to identify the early development stage of SCLC. Single-cell transcriptome analysis was applied to dissect the TME of bulk samples. Finally, to overcome platinum resistance, we performed drug screening of patient-derived cells and cell lines. Four subtypes were identified: the ASCL1+ (SCLC-A) subtype identified as TP53/RB-mutated non-SCLC representing the early development stage of SCLC; the immune activation (SCLC-I) subtype, showing high CD8+/PD-L1+ T-cell infiltration and endothelial-to-mesenchymal transition (EndMT); the NEUROD1 (SCLC-N) subtype, which showed neurotransmission process; and the POU2F3+ (SCLC-P) subtype with epithelial-to-mesenchymal transition (EMT). EndMT was associated with the worst prognosis. While SCLC-A/N exhibited platinum sensitivity, the EndMT signal of SCLC-I conferred platinum resistance. A BET inhibitor suppressed the aggressive angiogenesis phenotype of SCLC-I. We revealed that EndMT development contributed to a poor outcome in SCLC-I. Moreover, heterogenous TME development facilitated platinum resistance. BET inhibitors are novel candidates for overcoming platinum resistance.

Splicing signature database development to delineate cancer pathways using literature mining and transcriptome machine learning.

Alternative splicing (AS) events modulate certain pathways and phenotypic plasticity in cancer. Although previous studies have computationally analyzed splicing events, it is still a challenge to uncover biological functions induced by reliable AS events from tremendous candidates. To provide essential splicing event signatures to assess pathway regulation, we developed a database by collecting two datasets: (i) reported literature and (ii) cancer transcriptome profile. The former includes knowledge-based splicing signatures collected from 63,229 PubMed abstracts using natural language processing, extracted for 202 pathways. The latter is the machine learning-based splicing signatures identified from pan-cancer transcriptome for 16 cancer types and 42 pathways. We established six different learning models to classify pathway activities from splicing profiles as a learning dataset. Top-ranked AS events by learning model feature importance became the signature for each pathway. To validate our learning results, we performed evaluations by (i) performance metrics, (ii) differential AS sets acquired from external datasets, and (iii) our knowledge-based signatures. The area under the receiver operating characteristic values of the learning models did not exhibit any drastic difference. However, random-forest distinctly presented the best performance to compare with the AS sets identified from external datasets and our knowledge-based signatures. Therefore, we used the signatures obtained from the random-forest model. Our database provided the clinical characteristics of the AS signatures, including survival test, molecular subtype, and tumor microenvironment. The regulation by splicing factors was additionally investigated. Our database for developed signatures supported retrieval and visualization system.

Conserved miR-370-3p/BMP-7 axis regulates the phenotypic change of human vascular smooth muscle cells.

Endothelial dysfunction and inflammatory immune response trigger dedifferentiation of vascular smooth muscle cells (SMCs) from contractile to synthetic phenotype and initiate arterial occlusion. However, the complex vascular remodeling process playing roles in arterial occlusion initiation is largely unknown. We performed bulk sequencing of small and messenger RNAs in a rodent arterial injury model. Bioinformatic data analyses reveal that six miRNAs are overexpressed in injured rat carotids as well as synthetic-type human vascular SMCs. In vitro cell-based assays show that four miRNAs (miR-130b-5p, miR-132-3p, miR-370-3p, and miR-410-3p) distinctly regulate the proliferation of and monocyte adhesion to the vascular SMCs. Individual inhibition of the four selected miRNAs strongly prevents the neointimal hyperplasia in the injured rat carotid arteries. Mechanistically, miR-132-3p and miR-370-3p direct the cell cycle progression, triggering SMC proliferation. Gene ontology analysis of mRNA sequencing data consistently reveal that the miRNA targets include gene clusters that direct proliferation, differentiation, and inflammation. Notably, bone morphogenic protein (BMP)-7 is a prominent target gene of miR-370-3p, and it regulates vascular SMC proliferation in cellular and animal models. Overall, this study first reports that the miR-370-3p/BMP-7 axis determines the vascular SMC phenotype in both rodent and human systems.

Patient-derived cell-based pharmacogenomic assessment to unveil underlying resistance mechanisms and novel therapeutics for advanced lung cancer.

BACKGROUND: A pharmacogenomic platform using patient-derived cells (PDCs) was established to identify the underlying resistance mechanisms and tailored treatment for patients with advanced or refractory lung cancer. METHODS: Drug sensitivity screening and multi-omics datasets were acquired from lung cancer PDCs (n = 102). Integrative analysis was performed to explore drug candidates according to genetic variants, gene expression, and clinical profiles. RESULTS: PDCs had genomic characteristics resembled with those of solid lung cancer tissues. PDC molecular subtyping classified patients into four groups: (1) inflammatory, (2) epithelial-to-mesenchymal transition (EMT)-like, (3) stemness, and (4) epithelial growth factor receptor (EGFR)-dominant. EGFR mutations of the EMT-like subtype were associated with a reduced response to EGFR-tyrosine kinase inhibitor therapy. Moreover, although RB1/TP53 mutations were significantly enriched in small-cell lung cancer (SCLC) PDCs, they were also present in non-SCLC PDCs. In contrast to its effect in the cell lines, alpelisib (a PI3K-AKT inhibitor) significantly inhibited both RB1/TP53 expression and SCLC cell growth in our PDC model. Furthermore, cell cycle inhibitors could effectively target SCLC cells. Finally, the upregulation of transforming growth factor-ß expression and the YAP/TAZ pathway was observed in osimertinib-resistant PDCs, predisposing them to the EMT-like subtype. Our platform selected XAV939 (a WNT-TNKS-ß-catenin inhibitor) for the treatment of osimertinib-resistant PDCs. Using an in vitro model, we further demonstrated that acquisition of osimertinib resistance enhances invasive characteristics and EMT, upregulates the YAP/TAZ-AXL axis, and increases the sensitivity of cancer cells to XAV939. CONCLUSIONS: Our PDC models recapitulated the molecular characteristics of lung cancer, and pharmacogenomics analysis provided plausible therapeutic candidates.

Fluid Shear Stress Regulates the Landscape of microRNAs in Endothelial Cell-Derived Small Extracellular Vesicles and Modulates the Function of Endothelial Cells.

Blood fluid shear stress (FSS) modulates endothelial function and vascular pathophysiology. The small extracellular vesicles (sEVs) such as exosomes are potent mediators of intercellular communication, and their contents reflect cellular stress. Here, we explored the miRNA profiles in endothelial cells (EC)-derived sEVs (EC-sEVs) under atheroprotective laminar shear stress (LSS) and atheroprone low-oscillatory shear stress (OSS) and conducted a network analysis to identify the main biological processes modulated by sEVs' miRNAs. The EC-sEVs were collected from culture media of human umbilical vein endothelial cells exposed to atheroprotective LSS (20 dyne/cm2) and atheroprone OSS (±5 dyne/cm2). We explored the miRNA profiles in FSS-induced EC-sEVs (LSS-sEVs and OSS-sEVs) and conducted a network analysis to identify the main biological processes modulated by sEVs' miRNAs. In vivo studies were performed in a mouse model of partial carotid ligation. The sEVs' miRNAs-targeted genes were enriched for endothelial activation such as angiogenesis, cell migration, and vascular inflammation. OSS-sEVs promoted tube formation, cell migration, monocyte adhesion, and apoptosis, and upregulated the expression of proteins that stimulate these biological processes. FSS-induced EC-sEVs had the same effects on endothelial mechanotransduction signaling as direct stimulation by FSS. In vivo studies showed that LSS-sEVs reduced the expression of pro-inflammatory genes, whereas OSS-sEVs had the opposite effect. Understanding the landscape of EC-exosomal miRNAs regulated by differential FSS patterns, this research establishes their biological functions on a system level and provides a platform for modulating the overall phenotypic effects of sEVs.

Bioinformatics services for analyzing massive genomic datasets.

The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and ensuing computational problems. In Korea, the amount of genomic data has been increasing rapidly in the recent years. Leveraging these big data requires researchers to use large-scale computational resources and analysis pipelines. A promising solution for addressing this computational challenge is cloud computing, where CPUs, memory, storage, and programs are accessible in the form of virtual machines. Here, we present a cloud computing-based system, Bio-Express, that provides user-friendly, cost-effective analysis of massive genomic datasets. Bio-Express is loaded with predefined multi-omics data analysis pipelines, which are divided into genome, transcriptome, epigenome, and metagenome pipelines. Users can employ predefined pipelines or create a new pipeline for analyzing their own omics data. We also developed several web-based services for facilitating downstream analysis of genome data. Bio-Express web service is freely available at https://www.bioexpress.re.kr/.

Human cytomegalovirus induces and exploits Roquin to counteract the IRF1-mediated antiviral state.

RNA represents a pivotal component of host-pathogen interactions. Human cytomegalovirus (HCMV) infection causes extensive alteration in host RNA metabolism, but the functional relationship between the virus and cellular RNA processing remains largely unknown. Through loss-of-function screening, we show that HCMV requires multiple RNA-processing machineries for efficient viral lytic production. In particular, the cellular RNA-binding protein Roquin, whose expression is actively stimulated by HCMV, plays an essential role in inhibiting the innate immune response. Transcriptome profiling revealed Roquin-dependent global down-regulation of proinflammatory cytokines and antiviral genes in HCMV-infected cells. Furthermore, using cross-linking immunoprecipitation (CLIP)-sequencing (seq), we identified IFN regulatory factor 1 (IRF1), a master transcriptional activator of immune responses, as a Roquin target gene. Roquin reduces IRF1 expression by directly binding to its mRNA, thereby enabling suppression of a variety of antiviral genes. This study demonstrates how HCMV exploits host RNA-binding protein to prevent a cellular antiviral response and offers mechanistic insight into the potential development of CMV therapeutics.

Identification of tumor suppressor miRNAs by integrative miRNA and mRNA sequencing of matched tumor-normal samples in lung adenocarcinoma.

The roles of miRNAs in lung cancer have not yet been explored systematically at the genome scale despite their important regulatory functions. Here, we report an integrative analysis of miRNA and mRNA sequencing data for matched tumor-normal samples from 109 Korean female patients with non-small-cell lung adenocarcinoma (LUAD). We produced miRNA sequencing (miRNA-Seq) and RNA-Seq data for 48 patients and RNA-Seq data for 61 additional patients. Subsequent differential expression analysis with stringent criteria yielded 44 miRNAs and 2322 genes. Integrative gene set analysis of the differentially expressed miRNAs and genes using miRNA-target information revealed several regulatory processes related to the cell cycle that were targeted by tumor suppressor miRNAs (TSmiR). We performed colony formation assays in A549 and NCI-H460 cell lines to test the tumor-suppressive activity of downregulated miRNAs in cancer and identified 7 novel TSmiRs (miR-144-5p, miR-218-1-3p, miR-223-3p, miR-27a-5p, miR-30a-3p, miR-30c-2-3p, miR-338-5p). Two miRNAs, miR-30a-3p and miR-30c-2-3p, showed differential survival characteristics in the Tumor Cancer Genome Atlas (TCGA) LUAD patient cohort indicating their prognostic value. Finally, we identified a network cluster of miRNAs and target genes that could be responsible for cell cycle regulation. Our study not only provides a dataset of miRNA as well as mRNA sequencing from the matched tumor-normal samples, but also reports several novel TSmiRs that could potentially be developed into prognostic biomarkers or therapeutic RNA drugs.

GEMiCCL: mining genotype and expression data of cancer cell lines with elaborate visualization.

Database URL: GEMiCCL is available at https://www.kobic.kr/GEMICCL/.

ChimerDB 3.0: an enhanced database for fusion genes from cancer transcriptome and literature data mining.

Fusion gene is an important class of therapeutic targets and prognostic markers in cancer. ChimerDB is a comprehensive database of fusion genes encompassing analysis of deep sequencing data and manual curations. In this update, the database coverage was enhanced considerably by adding two new modules of The Cancer Genome Atlas (TCGA) RNA-Seq analysis and PubMed abstract mining. ChimerDB 3.0 is composed of three modules of ChimerKB, ChimerPub and ChimerSeq. ChimerKB represents a knowledgebase including 1066 fusion genes with manual curation that were compiled from public resources of fusion genes with experimental evidences. ChimerPub includes 2767 fusion genes obtained from text mining of PubMed abstracts. ChimerSeq module is designed to archive the fusion candidates from deep sequencing data. Importantly, we have analyzed RNA-Seq data of the TCGA project covering 4569 patients in 23 cancer types using two reliable programs of FusionScan and TopHat-Fusion. The new user interface supports diverse search options and graphic representation of fusion gene structure. ChimerDB 3.0 is available at http://ercsb.ewha.ac.kr/fusiongene/.

MONGKIE: an integrated tool for network analysis and visualization for multi-omics data.

BACKGROUND: Network-based integrative analysis is a powerful technique for extracting biological insights from multilayered omics data such as somatic mutations, copy number variations, and gene expression data. However, integrated analysis of multi-omics data is quite complicated and can hardly be done in an automated way. Thus, a powerful interactive visual mining tool supporting diverse analysis algorithms for identification of driver genes and regulatory modules is much needed. RESULTS: Here, we present a software platform that integrates network visualization with omics data analysis tools seamlessly. The visualization unit supports various options for displaying multi-omics data as well as unique network models for describing sophisticated biological networks such as complex biomolecular reactions. In addition, we implemented diverse in-house algorithms for network analysis including network clustering and over-representation analysis. Novel functions include facile definition and optimized visualization of subgroups, comparison of a series of data sets in an identical network by data-to-visual mapping and subsequent overlaying function, and management of custom interaction networks. Utility of MONGKIE for network-based visual data mining of multi-omics data was demonstrated by analysis of the TCGA glioblastoma data. MONGKIE was developed in Java based on the NetBeans plugin architecture, thus being OS-independent with intrinsic support of module extension by third-party developers. CONCLUSION: We believe that MONGKIE would be a valuable addition to network analysis software by supporting many unique features and visualization options, especially for analysing multi-omics data sets in cancer and other diseases. .

lncRNAtor: a comprehensive resource for functional investigation of long non-coding RNAs.

MOTIVATION: A number of long non-coding RNAs (lncRNAs) have been identified by deep sequencing methods, but their molecular and cellular functions are known only for a limited number of lncRNAs. Current databases on lncRNAs are mostly for cataloging purpose without providing in-depth information required to infer functions. A comprehensive resource on lncRNA function is an immediate need. RESULTS: We present a database for functional investigation of lncRNAs that encompasses annotation, sequence analysis, gene expression, protein binding and phylogenetic conservation. We have compiled lncRNAs for six species (human, mouse, zebrafish, fruit fly, worm and yeast) from ENSEMBL, HGNC, MGI and lncRNAdb. Each lncRNA was analyzed for coding potential and phylogenetic conservation in different lineages. Gene expression data of 208 RNA-Seq studies (4995 samples), collected from GEO, ENCODE, modENCODE and TCGA databases, were used to provide expression profiles in various tissues, diseases and developmental stages. Importantly, we analyzed RNA-Seq data to identify coexpressed mRNAs that would provide ample insights on lncRNA functions. The resulting gene list can be subject to enrichment analysis such as Gene Ontology or KEGG pathways. Furthermore, we compiled protein-lncRNA interactions by collecting and analyzing publicly available CLIP-seq or PAR-CLIP sequencing data. Finally, we explored evolutionarily conserved lncRNAs with correlated expression between human and six other organisms to identify functional lncRNAs. The whole contents are provided in a user-friendly web interface. AVAILABILITY AND IMPLEMENTATION: lncRNAtor is available at http://lncrnator.ewha.ac.kr/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

hiPathDB: a human-integrated pathway database with facile visualization.

One of the biggest challenges in the study of biological regulatory networks is the systematic organization and integration of complex interactions taking place within various biological pathways. Currently, the information of the biological pathways is dispersed in multiple databases in various formats. hiPathDB is an integrated pathway database that combines the curated human pathway data of NCI-Nature PID, Reactome, BioCarta and KEGG. In total, it includes 1661 pathways consisting of 8976 distinct physical entities. hiPathDB provides two different types of integration. The pathway-level integration, conceptually a simple collection of individual pathways, was achieved by devising an elaborate model that takes distinct features of four databases into account and subsequently reformatting all pathways in accordance with our model. The entity-level integration creates a single unified pathway that encompasses all pathways by merging common components. Even though the detailed molecular-level information such as complex formation or post-translational modifications tends to be lost, such integration makes it possible to investigate signaling network over the entire pathways and allows identification of pathway cross-talks. Another strong merit of hiPathDB is the built-in pathway visualization module that supports explorative studies of complex networks in an interactive fashion. The layout algorithm is optimized for virtually automatic visualization of the pathways. hiPathDB is available at http://hiPathDB.kobic.re.kr.