Discovering long noncoding RNA predictors of anticancer drug sensitivity beyond protein-coding genes

Possible Involvement of Long Non-Coding RNAs GNAS-AS1 and MIR205HG in the Modulation of 5-Fluorouracil Chemosensitivity in Colon Cancer Cells through Increased Extracellular Release of Exosomes

A growing number of studies have suggested the involvement of long non-coding RNAs as the key players in not just the initiation and progression of the tumor microenvironment, but also in chemotherapy tolerance. In the present study, generated 5-FU-resistant SW480/DR cells were analyzed via cDNA microarray for its aberrant lncRNAs and mRNAs expression in comparison with the 5-FU-susceptible SW480/DS cells. Among the 126 lncRNAs described, lncRNAs GNAS-AS1, MIR205HG, and LOC102723721 have been identified to be significantly upregulated, while lncRNs lnc-RP11-597K23.2.1-2, LOC100507639, and CCDC144NL-AS1 have been found to be significantly downregulated. In the meantime, bioinformatic analysis through gene ontology studies of aberrantly expressed mRNAs revealed "regulated exocytosis", among others, as the biological process most impacted in SW480/DR cells. To investigate, exosome purification was then carried out and its characterization were validated via transmission electron microscopy and nanoparticle tracking analysis. Interestingly, it was determined that the 5-FU-resistant SW480/DR cells secretes significantly higher concentration of extracellular vesicles, particularly, exosomes when compared to the 5-FU-susceptible SW480/DS cells. Based on the lncRNA-mRNA interaction network analysis generated, lncRNA GNAS-AS1 and MIR205HG have been identified to be potentially involved in the incidence of 5-FU resistance in SW480 colon cancer cells through promoting increased release of exosomes into the intercellular matrix. Our study hopes not only to provide insights on the list of involved candidate lncRNAs, but also to elucidate the role exosomes play in the initiation and development of 5-FU chemotherapy resistance in colon cancer cells.

Co-Expression Network Analysis Unveiled lncRNA-mRNA Links Correlated to Epidermal Growth Factor Receptor-Tyrosine Kinase Inhibitor Resistance and/or Intermediate Epithelial-to-Mesenchymal Transition Phenotypes in a Human Non-Small Cell Lung Cancer Cellular Model System

We investigated mRNA-lncRNA co-expression patterns in a cellular model system of non-small cell lung cancer (NSCLC) sensitive and resistant to the epithelial growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) erlotinib/gefitinib. The aim of this study was to unveil insights into the complex mechanisms of NSCLC targeted therapy resistance and epithelial-to-mesenchymal transition (EMT). Genome-wide RNA expression was quantified for weighted gene co-expression network analysis (WGCNA) to correlate the expression levels of mRNAs and lncRNAs. Functional enrichment analysis and identification of lncRNAs were conducted on modules associated with the EGFR-TKI response and/or intermediate EMT phenotypes. We constructed lncRNA-mRNA co-expression networks and identified key modules and their enriched biological functions. Processes enriched in the selected modules included RHO (A, B, C) GTPase and regulatory signaling pathways, apoptosis, inflammatory and interleukin signaling pathways, cell adhesion, cell migration, cell and extracellular matrix organization, metabolism, and lipid metabolism. Interestingly, several lncRNAs, already shown to be dysregulated in cancer, are connected to a small number of mRNAs, and several lncRNAs are interlinked with each other in the co-expression network.

Identification of Interpretable Clusters and Associated Signatures in Breast Cancer Single-Cell Data: A Topic Modeling Approach

Topic modeling is a popular technique in machine learning and natural language processing, where a corpus of text documents is classified into themes or topics using word frequency analysis. This approach has proven successful in various biological data analysis applications, such as predicting cancer subtypes with high accuracy and identifying genes, enhancers, and stable cell types simultaneously from sparse single-cell epigenomics data. The advantage of using a topic model is that it not only serves as a clustering algorithm, but it can also explain clustering results by providing word probability distributions over topics. Our study proposes a novel topic modeling approach for clustering single cells and detecting topics (gene signatures) in single-cell datasets that measure multiple omics simultaneously. We applied this approach to examine the transcriptional heterogeneity of luminal and triple-negative breast cancer cells using patient-derived xenograft models with acquired resistance to chemotherapy and targeted therapy. Through this approach, we identified protein-coding genes and long non-coding RNAs (lncRNAs) that group thousands of cells into biologically similar clusters, accurately distinguishing drug-sensitive and -resistant breast cancer types. In comparison to standard state-of-the-art clustering analyses, our approach offers an optimal partitioning of genes into topics and cells into clusters simultaneously, producing easily interpretable clustering outcomes. Additionally, we demonstrate that an integrative clustering approach, which combines the information from mRNAs and lncRNAs treated as disjoint omics layers, enhances the accuracy of cell classification.

TheMarker: a comprehensive database of therapeutic biomarkers

Distinct from the traditional diagnostic/prognostic biomarker (adopted as the indicator of disease state/process), the therapeutic biomarker (ThMAR) has emerged to be very crucial in the clinical development and clinical practice of all therapies. There are five types of ThMAR that have been found to play indispensable roles in various stages of drug discovery, such as: Pharmacodynamic Biomarker essential for guaranteeing the pharmacological effects of a therapy, Safety Biomarker critical for assessing the extent or likelihood of therapy-induced toxicity, Monitoring Biomarker indispensable for guiding clinical management by serially measuring patients' status, Predictive Biomarker crucial for maximizing the clinical outcome of a therapy for specific individuals, and Surrogate Endpoint fundamental for accelerating the approval of a therapy. However, these data of ThMARs has not been comprehensively described by any of the existing databases. Herein, a database, named 'TheMarker', was therefore constructed to (a) systematically offer all five types of ThMAR used at different stages of drug development, (b) comprehensively describe ThMAR information for the largest number of drugs among available databases, (c) extensively cover the widest disease classes by not just focusing on anticancer therapies. These data in TheMarker are expected to have great implication and significant impact on drug discovery and clinical practice, and it is freely accessible without any login requirement at: https://idrblab.org/themarker.

Multi-Omics Mining of lncRNAs with Biological and Clinical Relevance in Cancer

In this review, we provide a general overview of the current panorama of mining strategies for multi-omics data to investigate lncRNAs with an actual or potential role as biological markers in cancer. Several multi-omics studies focusing on lncRNAs have been performed in the past with varying scopes. Nevertheless, many questions remain regarding the pragmatic application of different molecular technologies and bioinformatics algorithms for mining multi-omics data. Here, we attempt to address some of the less discussed aspects of the practical applications using different study designs for incorporating bioinformatics and statistical analyses of multi-omics data. Finally, we discuss the potential improvements and new paradigms aimed at unraveling the role and utility of lncRNAs in cancer and their potential use as molecular markers for cancer diagnosis and outcome prediction.

Methylation-Regulated Long Non-Coding RNA Expression in Ulcerative Colitis

Long non-coding RNAs (lncRNAs) have been shown to play a role in the pathogenesis of ulcerative colitis (UC). Although epigenetic processes such as DNA methylation and lncRNA expression are well studied in UC, the importance of the interplay between the two processes has not yet been fully explored. It is, therefore, believed that interactions between environmental factors and epigenetics contribute to disease development. Mucosal biopsies from 11 treatment-naïve UC patients and 13 normal controls were used in this study. From each individual sample, both whole-genome bisulfite sequencing data (WGBS) and lncRNA expression data were analyzed. Correlation analysis between lncRNA expression and upstream differentially methylated regions (DMRs) was used to identify lncRNAs that might be regulated by DMRs. Furthermore, proximal protein-coding genes associated with DMR-regulated lncRNAs were identified by correlating their expression. The study identified UC-associated lncRNAs such as MIR4435-2HG, ZFAS1, IL6-AS1, and Pvt1, which may be regulated by DMRs. Several genes that are involved in inflammatory immune responses were found downstream of DMR-regulated lncRNAs, including SERPINB1, CCL18, and SLC15A4. The interplay between lncRNA expression regulated by DNA methylation in UC might improve our understanding of UC pathogenesis.

Refined expression quantitative trait locus analysis on adenocarcinoma at the gastroesophageal junction reveals susceptibility and prognostic markers

Objectives: This study aimed to explore cell type level expression quantitative trait loci (eQTL) in adenocarcinoma at the gastroesophageal junction (ACGEJ) and identify susceptibility and prognosis markers. Methods: Whole-genome sequencing (WGS) was performed on 120 paired samples from Chinese ACGEJ patients. Germline mutations were detected by GATK tools. RNA sequencing (RNA-seq) data on ACGEJ samples were taken from our previous studies. Public single-cell RNA sequencing (scRNA-seq) data were used to produce the proportion of epithelial cells. Matrix eQTL and a linear mixed model were used to identify condition-specific cis-eQTLs. The R package coloc was used to perform co-localization analysis with the public data of genome-wide association studies (GWASs). Log-rank and Cox regression tests were used to identify survival-associated eQTL and genes. Functions of candidate risk loci were explored by experimental validation. Results: Refined eQTL analyses of paired ACGEJ samples were performed and 2,036 potential ACGEJ-specific eQTLs with East Asian specificity were identified in total. ACGEJ-gain eQTLs were enriched at promoter regions more than ACGEJ-loss eQTLs. rs658524 was identified as the top eQTL close to the transcription start site of its paired gene (CTSW). rs2240191-RASAL1, rs4236599-FOXP2, rs4947311-PSORS1C1, rs13134812-LOC391674, and rs17508585-CDK13-DT were identified as ACGEJ-specific susceptibility eQTLs. rs309483-LINC01355 was associated with the overall survival of ACGEJ patients. We explored functions of candidate eQTLs such as rs658524, rs309483, rs2240191, and rs4947311 by experimental validation. Conclusion: This study provides new risk loci for ACGEJ susceptibility and effective disease prognosis biomarkers.

Long non-coding RNAs are essential for Schistosoma mansoni pairing-dependent adult worm homeostasis and fertility

The trematode parasite Schistosoma mansoni causes schistosomiasis, which affects over 200 million people worldwide. Schistosomes are dioecious, with egg laying depending on the females' obligatory pairing with males. Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nucleotides with low or no protein-coding potential that have been involved in other species with reproduction, stem cell maintenance, and drug resistance. In S. mansoni, we recently showed that the knockdown of one lncRNA affects the pairing status of these parasites. Here, we re-analyzed public RNA-Seq data from paired and unpaired adult male and female worms and their gonads, obtained from mixed-sex or single-sex cercariae infections, and found thousands of differentially expressed pairing-dependent lncRNAs among the 23 biological samples that were compared. The expression levels of selected lncRNAs were validated by RT-qPCR using an in vitro unpairing model. In addition, the in vitro silencing of three selected lncRNAs showed that knockdown of these pairing-dependent lncRNAs reduced cell proliferation in adult worms and their gonads, and are essential for female vitellaria maintenance, reproduction, and/or egg development. Remarkably, in vivo silencing of each of the three selected lncRNAs significantly reduced worm burden in infected mice by 26 to 35%. Whole mount in situ hybridization experiments showed that these pairing-dependent lncRNAs are expressed in reproductive tissues. These results show that lncRNAs are key components intervening in S. mansoni adult worm homeostasis, which affects pairing status and survival in the mammalian host, thus presenting great potential as new therapeutic target candidates.

A promising new cancer marker: Long noncoding RNA EGFR-AS1

Cancer consists of a group of diseases with the salient properties of an uncontrolled cell cycle, metastasis, and evasion of the immune response, mainly driven by the genomic instability of somatic cells and the physicochemical environment. Long noncoding RNAs (lncRNAs) are defined as noncoding RNAs with a length of more than 200 nucleotides. LncRNA dysregulation participates in diverse disease types and is tightly associated with patient clinical features, such as age, disease stage, and prognosis. In addition, an increasing number of lncRNAs have been confirmed to regulate a series of biological and pathological processes through numerous mechanisms. The lncRNA epidermal growth factor receptor antisense RNA 1 (EGFR-AS1) was recently discovered to be aberrantly expressed in many types of diseases, particularly in cancers. A high level of EGFR-AS1 was demonstrated to correlate with multiple patient clinical characteristics. More importantly, EGFR-AS1 was found to be involved in the mediation of various cellular activities, including cell proliferation, invasion, migration, chemosensitivity, and stemness. Therefore, EGFR-AS1 is a promising marker for cancer management. In this review, we introduce the expression profile, molecular mechanisms, biological functions, and clinical value of EGFR-AS1 in cancers.

Predicting clinical response to everolimus in ER+ breast cancers using machine-learning

Endocrine therapy remains the primary treatment choice for ER+ breast cancers. However, most advanced ER+ breast cancers ultimately develop resistance to endocrine. This acquired resistance to endocrine therapy is often driven by the activation of the PI3K/AKT/mTOR signaling pathway. Everolimus, a drug that targets and inhibits the mTOR complex has been shown to improve clinical outcomes in metastatic ER+ breast cancers. However, there are no biomarkers currently available to guide the use of everolimus in the clinic for progressive patients, where multiple therapeutic options are available. Here, we utilized gene expression signatures from 9 ER+ breast cancer cell lines and 23 patients treated with everolimus to develop and validate an integrative machine learning biomarker of mTOR inhibitor response. Our results show that the machine learning biomarker can successfully distinguish responders from non-responders and can be applied to identify patients that will most likely benefit from everolimus treatment.

Single-cell RNA-seq analyses show that long non-coding RNAs are conspicuously expressed in Schistosoma mansoni gamete and tegument progenitor cell populations

Schistosoma mansoni is a flatworm that causes schistosomiasis, a neglected tropical disease that affects over 200 million people worldwide. New therapeutic targets are needed with only one drug available for treatment and no vaccine. Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nucleotides with low or no protein-coding potential. In other organisms, they have been shown as involved with reproduction, stem cell maintenance and drug resistance, and they tend to exhibit tissue-specific expression patterns. S. mansoni expresses thousands of lncRNA genes; however, the cell type expression patterns of lncRNAs in the parasite remain uncharacterized. Here, we have re-analyzed publicly available single-cell RNA-sequencing (scRNA-seq) data obtained from adult S. mansoni to identify the lncRNAs signature of adult schistosome cell types. A total of 8023 lncRNAs (79% of all lncRNAs) were detected. Analyses of the lncRNAs expression profiles in the cells using statistically stringent criteria were performed to identify 74 lncRNA gene markers of cell clusters. Male gamete and tegument progenitor lineages clusters contained most of the cluster-specific lncRNA markers. We also identified lncRNA markers of specific neural clusters. Whole-mount in situ hybridization (WISH) and double fluorescence in situ hybridization were used to validate the cluster-specific expression of 13 out of 16 selected lncRNA genes (81%) in the male and female adult parasite tissues; for one of these 16 gene loci, probes for two different lncRNA isoforms were used, which showed differential isoform expression in testis and ovary. An atlas of the expression profiles across the cell clusters of all lncRNAs detected in our analysis is available as a public website resource (http://verjolab.usp.br:8081). The results presented here give strong support to a tissue-specific expression and to a regulated expression program of lncRNAs in S. mansoni. This will be the basis for further exploration of lncRNA genes as potential therapeutic targets.

Comprehensive Analysis of Key mRNAs and lncRNAs in Osteosarcoma Response to Preoperative Chemotherapy with Prognostic Values

OBJECTIVE

Osteosarcoma is one of the most common types of bone sarcoma with a poor prognosis. However, identifying the predictive factors that contribute to the response to neoadjuvant chemotherapy remains a significant challenge.

METHODS

A public data series (GSE87437) was downloaded to identify differentially expressed genes (DEGs) and differentially expressed lncRNAs (DElncRNAs) between osteosarcoma patients that do and do not respond to preoperative chemotherapy. Subsequently, functional analysis of the transcriptome expression profile, regulatory networks of DEGs and DElncRNAs, competing endogenous RNAs (ceRNA) and protein-protein interaction networks were performed. Furthermore, the function, pathway, and survival analysis of hub genes was performed and drug and disease relationship prediction of DElncRNA was carried out.

RESULTS

A total of 626 DEGs, 26 DElncRNAs, and 18 hub genes were identified. However, only one gene and two lncRNAs were found to be suitable as candidate gene and lncRNAs respectively.

CONCLUSION

The DEGs, hub genes, candidate gene, and candidate lncRNAs screened out in this context were considered as potential biomarkers for the response to neoadjuvant chemotherapy of osteosarcoma.

Long Non-Coding RNA ANRIL as a Potential Biomarker of Chemosensitivity and Clinical Outcomes in Osteosarcoma

Osteosarcoma has a poor prognosis due to chemo-resistance and/or metastases. Increasing evidence shows that long non-coding RNAs (lncRNAs) can play an important role in drug sensitivity and cancer metastasis. Using osteosarcoma cell lines, we identified a positive correlation between the expression of a lncRNA and ANRIL, and resistance to two of the three standard-of-care agents for treating osteosarcoma-cisplatin and doxorubicin. To confirm the potential role of ANRIL in chemosensitivity, we independently inhibited and over-expressed ANRIL in osteosarcoma cell lines followed by treatment with either cisplatin or doxorubicin. Knocking-down ANRIL in SAOS2 resulted in a significant increase in cellular sensitivity to both cisplatin and doxorubicin, while the over-expression of ANRIL in both HOS and U2OS cells led to an increased resistance to both agents. To investigate the clinical significance of ANRIL in osteosarcoma, we assessed ANRIL expression in relation to clinical phenotypes using the osteosarcoma data from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) dataset. Higher ANRIL expression was significantly associated with increased rates of metastases at diagnosis and death and was a significant predictor of reduced overall survival rate. Collectively, our results suggest that the lncRNA ANRIL can be a chemosensitivity and prognosis biomarker in osteosarcoma. Furthermore, reducing ANRIL expression may be a therapeutic strategy to overcome current standard-of-care treatment resistance.

LncTx: A network-based method to repurpose drugs acting on the survival-related lncRNAs in lung cancer

Despite the fact that an increased amount of survival-related lncRNAs have been found in cancer, few drugs that target lncRNAs are approved for treatment. Here, we developed a network-based algorithm, LncTx, to repurpose the medications that potentially act on survival-related lncRNAs in lung cancer. We used eight survival-related lncRNAs derived from our previous study to test the efficacy of this method. LncTx calculates the shortest path length (proximity) between the drug targets and the lncRNA-correlated proteins in the protein-protein interaction network (interactome). LncTx contains seven different proximity measures, which are calculated in the unweighted or weighted interactome. First, to test the performance of LncTx in predicting correct indication of drugs, we benchmarked the proximity measures based on the accuracy of differentiating anticancer drugs from non-anticancer drugs. The closest proximity weighted by clustering coefficient (closestCC) has the best performance (AUC around 0.8) compared to other proximity measures across all survival-related lncRNAs. The majority of the other six proximity measures have decent performance as well, with AUC greater than 0.7. Second, to evaluate whether LncTx can repurpose the drugs effectively acting on the lncRNAs, we clustered the drugs according to their proximities by hierarchical clustering. The drugs with smaller proximity (proximal drugs) were proved to be more effective than the drugs with larger proximity (distal drugs). In conclusion, LncTx enables us to accurately identify anticancer drugs and can potentially be an index to repurpose effective agents acting on survival-related lncRNAs in lung cancer.

A Long Noncoding RNA, GAS5 Can Be a Biomarker for Docetaxel Response in Castration Resistant Prostate Cancer

While functional studies of long noncoding RNAs (lncRNAs) have mostly focused on how they influence disease diagnosis and prognosis, the pharmacogenomic relevance of lncRNAs remains largely unknown. Here, we test the hypothesis that the expression of a lncRNA, grow arrest-specific 5 (GAS5) can be a biomarker for docetaxel response in castration resistant prostate cancer (CRPC) using both prostate cancer (PCa) cell lines and CRPC patient datasets. Our results suggest that lower GAS5 expression is associated with docetaxel resistance in both PCa cell lines and CRPC patients. Further experiments also suggest that GAS5 is downregulated in docetaxel resistant CRPC cell lines, which reinforces its potential as a biomarker for docetaxel response. To examine the underlying biological mechanisms, we transiently knockdown GAS5 expression in PCa cell lines and then subject the cells to docetaxel treatment overtime. We did not observe a decrease in docetaxel induced growth inhibition or apoptosis in the siRNA treated cells. The findings suggest that there is no direct causal relationship between change in GAS5 expression and docetaxel response. Subsequently, we explored the indirect regulation among GAS5, ATP binding cassette subfamily B member 1 (ABCB1), and docetaxel sensitivity. We showed that transient knockdown GAS5 did not lead to significant changes in ABCB1 expression. Therefore, we rule out the hypothesis that GAS5 directly down regulate ABCB1 that lead to docetaxel sensitivity. In conclusion, our work suggests that GAS5 can serve as a predictive biomarker for docetaxel response in CRPC; however, the exact mechanism behind the observed correlation remain to be elucidated.

Oncogene or tumor suppressor? Long noncoding RNAs role in patient's prognosis varies depending on disease type

Functional studies of long noncoding RNAs (lncRNAs) are often performed in the context of only a single cancer type. However, the tissue-specific expression patterns of lncRNAs raise the question of whether lncRNA associations identified in one cancer type are relevant to other cancer types. Here, we examine the relationships between the expression levels of 50 cancer-related lncRNAs and survival data from 24 types of cancer in The Cancer Genome Atlas (TCGA) with the goal of identifying prognosis related lncRNAs. Our results suggest that high expression levels of certain lncRNAs are consistently associated with worse/better survival in a number of cancers, while other lncRNAs have different prognostic roles in different types of cancer. Our analysis also identifies 20 novel unadjusted associations that have not been reported before. In addition, in low-grade glioma (LGG), prognostic-related lncRNAs are identified after conditioning on known clinical biomarker and common therapy, revealing that 2 lncRNAs, FOXP4-AS1, and NEAT1, are associated with temozolomide response-a standard-of-care in LGG. Pathway analysis suggests NF-kB/STAT3 signaling pathway enrichment in LGG patients with high NEAT1 expression and DNA repair/myc gene set enrichment in LGG patients with high expression of FOXP4-AS1. Our work demonstrates the context dependency of lncRNAs across cancer types and highlights a number of lncRNAs as potential novel cancer prognosis markers.

Leveraging Single-Cell Approaches in Cancer Precision Medicine

Cancer precision medicine aims to improve patient outcomes by tailoring treatment to the unique genomic background of a tumor. However, efforts to develop prognostic and drug response biomarkers largely rely on bulk 'omic' data, which fails to capture intratumor heterogeneity (ITH) and deconvolve signals from normal versus tumor cells. These shortcomings in measuring clinically relevant features are being addressed with single-cell technologies, which provide a fine-resolution map of the genetic and phenotypic heterogeneity in tumors and their microenvironment, as well as an improved understanding of the patterns of subclonal tumor populations. Here we present recent advances in the application of single-cell technologies, towards gaining a deeper understanding of ITH and evolution, and potential applications in developing personalized therapeutic strategies.

Long non-coding RNAs regulate the hallmarks of cancer in HPV-induced malignancies

High-risk human papillomavirus (HPV) is the most frequent sexually transmitted agent worldwide and is responsible for approximately 5% of human cancers. Identifying novel biomarkers and therapeutic targets for these malignancies requires a deeper understanding of the mechanisms involved in the progression of HPV-induced cancers. Long non-coding RNAs (lncRNAs) are crucial in the regulation of biological processes. Importantly, these molecules are key players in the progression of multiple malignancies and are able to regulate the development of the different hallmarks of cancer. This review highlights the action of lncRNAs in the regulation of cellular processes leading to the typical hallmarks of cancer. The regulation of lncRNAs by HPV oncogenes, their targets and also their mechanisms of action are also discussed, in the context of HPV-induced malignancies. Overall, accumulating data indicates that lncRNAs may have a significant potential to become useful tools for clinical practice as disease biomarkers or therapy targets.

Machine Learning and Systems Biology Approaches to Characterize Dosage-Based Gene Dependencies in Cancer Cells

Mapping of cancer survivability factors allows for the identification of novel biological insights for drug targeting. Using genomic editing techniques, gene dependencies can be extracted in a high-throughput and quantitative manner. Dependencies have been predicted using machine learning techniques on -omics data, but the biological consequences of dependency predictor pairs has not been explored. In this work we devised a framework to explore gene dependency using an ensemble of machine learning methods, and our learned models captured meaningful biological information beyond just gene dependency prediction. We show that dosage-based dependent predictors (DDPs) primarily belonged to transcriptional regulation ontologies. We also found that anti-sense RNAs and long- noncoding RNA transcripts display DDPs. Network analyses revealed that SOX10, HLA-J, and ZEB2 act as a triad of network hubs in the dependent-predictor network. Collectively, we demonstrate the powerful combination of machine learning and systems biology approach can illuminate new insights in understanding gene dependency and guide novel targeting avenues.

Individualized lncRNA differential expression profile reveals heterogeneity of breast cancer

Long non-coding RNAs (lncRNAs) play key regulatory roles in breast cancer. However, population-level differential expression analysis methods disregard the heterogeneous expression of lncRNAs in individual patients. Therefore, we individualized lncRNA expression profiles for breast invasive carcinoma (BRCA) using the method of LncRNA Individualization (LncRIndiv). After evaluating the robustness of LncRIndiv, we constructed an individualized differentially expressed lncRNA (IDElncRNA) profile for BRCA and investigated the subtype-specific IDElncRNAs. The breast cancer subtype-specific IDElncRNA showed frequent co-occurrence with alterations of protein-coding genes, including mutations, copy number variation and differential methylation. We performed hierarchical clustering to subdivide TNBC and revealed mesenchymal subtype and immune subtype for TNBC. The TNBC immune subtype showed a better prognosis than the TNBC mesenchymal subtype. LncRNA PTOV1-AS1 was the top differentially expressed lncRNA in the mesenchymal subtype. And biological experiments validated that the upregulation of PTOV1-AS1 could downregulate TJP1 (ZO-1) and E-Cadherin, and upregulate Vimentin, which suggests PTOV1-AS1 may promote epithelial-mesenchymal transition and lead to migration and invasion of TNBC cells. The mesenchymal subtype showed a higher fraction of M2 macrophages, whereas the immune subtype was more associated with CD4 + T cells. The immune subtype is characterized by genomic instability and upregulation of immune checkpoint genes, thereby suggesting a potential response to immunosuppressive drugs. Last, drug response analysis revealed lncRNA ENSG00000230082 (PRRT3-AS1) is a potential resistance biomarker for paclitaxel in BRCA treatment. Our analysis highlights that IDElncRNAs can characterize inter-tumor heterogeneity in BRCA and the new TNBC subtypes indicate novel insights into TNBC immunotherapy.

Cis- and Trans-Acting Expression Quantitative Trait Loci of Long Non-Coding RNA in 2,549 Cancers With Potential Clinical and Therapeutic Implications

Many cancer risk loci act as expression quantitative trait loci (eQTLs) of transcripts including non-coding RNA. Long non-coding RNAs (lncRNAs) are implicated in various human cancers. However, the pathological and clinical impacts of the genetic determinants of lncRNAs in cancers remain largely unknown. In this study, we performed eQTL mapping of lncRNA expression (elncRNA) in 11 TCGA cancer types and characterized the biological processes of elncRNAs in the setting of genomic location, cancer treatment responses, and immune microenvironment. As a result, 10.86% of the cis-eQTLs and 1.67% of the trans-eQTLs of lncRNA were related to known genome-wide association studies (GWAS) cancer risk loci. The elncRNAs are significantly enriched for those which are previously annotated as predictive of drug sensitivities in cancer cell lines. We further revealed the downstream transcriptomic effectors of eQTL-elncRNA pairs. Our data specifically suggested that the genes affected by eQTL-elncRNA associations are enriched in the immune system processes and eQTL-elncRNA associations influence the constitution of tumor infiltrating lymphocytes. In ovarian cancer, the "rs34631313-AC092580.4" pair was associated with increased fraction of CD8+ T cells and M1 Macrophage; whereas in KIRC, the "rs9546285-LINC00426" pair was associated with increased fraction of CD8+ T cells and a decreased fraction of M2 macrophages. Our findings provide a systematic view of the transcriptomic impacts of the eQTL landscape of lncRNA in human cancers and suggest its strong potential relevance to cancer immunity and treatment.

Pharmacogenomics to Predict Tumor Therapy Response: A Focus on ATP-Binding Cassette Transporters and Cytochromes P450

Pharmacogenomics is an evolving tool of precision medicine. Recently, due to the introduction of next-generation sequencing and projects generating "Big Data", a plethora of new genetic variants in pharmacogenes have been discovered. Cancer resistance is a major complication often preventing successful anticancer treatments. Pharmacogenomics of both somatic mutations in tumor cells and germline variants may help optimize targeted treatments and improve the response to conventional oncological therapy. In addition, integrative approaches combining copy number variations and long noncoding RNA profiling with germline and somatic variations seem to be a promising approach as well. In pharmacology, expression and enzyme activity are traditionally the more studied aspects of ATP-binding cassette transporters and cytochromes P450. In this review, we briefly introduce the field of pharmacogenomics and the advancements driven by next-generation sequencing and outline the possible roles of genetic variation in the two large pharmacogene superfamilies. Although the evidence needs further substantiation, somatic and copy number variants as well as rare variants and common polymorphisms in these genes could all affect response to cancer therapy. Regulation by long noncoding RNAs has also been shown to play a role. However, in all these areas, more comprehensive studies on larger sets of patients are needed.

Mechanisms of drug resistance mediated by long non-coding RNAs in non-small-cell lung cancer

Non-small-cell lung cancer (NSCLC) is the most prevalent form of lung cancer and has a poor five-year survival rate of 15%. Chemotherapy and targeted therapies have significantly improved patients' prognosis. Nevertheless, after a successful initial response, some patients relapse when cancer cells become resistant to drug treatments, representing an important clinical limitation. Therefore, investigating the mechanisms of drug resistance is of significant importance. Recently, considerable attention has been given to long non-coding RNAs (lncRNAs), a heterogeneous class of regulatory molecules that play essential roles in tumorigenesis by modulating genes and signalling pathways involved in cell growth, metastasis and drug response. In this article, we review recent research findings on the role of lncRNAs in drug resistance in NSCLC, highlighting their mechanisms of action.

The Role of Long Noncoding RNAs in Human Papillomavirus-associated Pathogenesis

Infections with high-risk human papillomaviruses cause ~5% of all human cancers. E6 and E7 are the only viral genes that are consistently expressed in cancers, and they are necessary for tumor initiation, progression, and maintenance. E6 and E7 encode small proteins that lack intrinsic enzymatic activities and they function by binding to cellular regulatory molecules, thereby subverting normal cellular homeostasis. Much effort has focused on identifying protein targets of the E6 and E7 proteins, but it has been estimated that ~98% of the human transcriptome does not encode proteins. There is a growing interest in studying noncoding RNAs as biochemical targets and biological mediators of human papillomavirus (HPV) E6/E7 oncogenic activities. This review focuses on HPV E6/E7 targeting cellular long noncoding RNAs, a class of biologically versatile molecules that regulate almost every known biological process and how this may contribute to viral oncogenesis.

Inferences of Individual Drug Response-Related Long Non-coding RNAs Based on Integrating Multi-omics Data in Breast Cancer

Differences in individual drug responses are obstacles in breast cancer (BRCA) treatment, so predicting responses would help to plan treatment strategies. The accumulation of cancer molecular profiling and drug response data provide opportunities and challenges to identify novel molecular signatures and mechanisms of tumor responsiveness to drugs in BRCA. This study evaluated drug responses with a multi-omics integrated system that depended on long non-coding RNAs (lncRNAs). We identified drug response-related lncRNAs (DRlncs) by combining expression data of lncRNA, microRNA, messenger RNA, methylation levels, somatic mutations, and the survival data of cancer patients treated with drugs. We constructed an integrated and computational multi-omics approach to identify DRlncs for diverse chemotherapeutic drugs in BRCA. Some DRlncs were identified with Adriamycin, Cytoxan, Tamoxifen, and all samples for BRCA patients. These DRlncs showed specific features regarding both expression and computational accuracies. The DRlnc-gene co-expression networks were constructed and analyzed. Key DRlncs, such as HOXA-AS2 (Ensembl: ENSG00000253552), in the drug Adriamycin were characterized. The experimental analysis also suggested that HOXA-AS2 (Ensembl: ENSG00000253552) was a key DRlnc in Adriamycin drug resistance in BRCA patients. Some DRlncs were associated with survival and some specific functions. A possible mechanism of DRlnc HOXA-AS2 (Ensembl: ENSG00000253552) in the Adriamycin drug response for BRCA resistance was inferred. In summary, this study provides a framework for lncRNA-based evaluation of clinical drug responses in BRCA. Understanding the underlying molecular mechanisms of drug responses will facilitate improved responses to chemotherapy and outcomes of BRCA treatment.

Emerging role of long non-coding RNAs in cancer precision medicine

A vast majority of the human genome encodes long non-coding RNAs (lncRNAs) as compared to protein-coding genes (PCGs). But most efforts to determine biomarkers of anticancer drug response have focused entirely on PCGs. Comprehensive investigation of lncRNAs and drug response demonstrates that lncRNAs are indeed crucial biomarkers of drug response.