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RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-Seq and snRNA-Seq, scnRNA-Seq for short), can help characterize the composition of tissues and reveal cells that influence key functions in both healthy and disease tissues. However, the use of these technologies is operationally challenging because of high costs and stringent sample-collection requirements. Computational deconvolution methods that infer the composition of bulk-profiled samples using scnRNA-Seq-characterized cell types can broaden scnRNA-Seq applications, but their effectiveness remains controversial. We produced the first systematic evaluation of deconvolution methods on datasets with either known or scnRNA-Seq-estimated compositions. Our analyses revealed biases that are common to scnRNA-Seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-Seq and scnRNA-Seq profiles can help improve the accuracy of both scnRNA-Seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), combined RNA-Seq transformation and dampened weighted least-squares deconvolution approaches to consistently outperform other methods in predicting the composition of cell mixtures and tissue samples. Furthermore, our analysis suggested that only SQUID could identify outcomes-predictive cancer cell subclones in pediatric acute myeloid leukemia and neuroblastoma datasets, suggesting that deconvolution accuracy improvements are vital to enabling its applications in the life sciences.

Purpose To show that intrinsic radiosensitivity varies greatly for protons and carbon (C) ions in addition to photons, and that DNA repair capacity remains important in governing this variability. Methods We measured or obtained from the literature clonogenic survival data for a number of human cancer cell lines exposed to photons, protons (9.9 keV/μm), and C-ions (13.3-77.1 keV/μm). We characterized their intrinsic radiosensitivity by the dose for 10% or 50% survival (D10% or D50% ), and quantified the variability at each radiation quality by the coefficient of variation (COV) in D10% and D50% . We also treated cells with DNA repair inhibitors prior to irradiation to assess how DNA repair capacity affects their variability. Results We found no statistically significant differences in the COVs of D10% or D50% between any of the radiation qualities investigated. The same was true regardless of whether the cells were treated with DNA repair inhibitors, or whether they were stratified into histologic subsets. Even within histologic subsets, we found remarkable differences in radiosensitivity for high LET C-ions that were often greater than the variations in RBE, with brain cancer cells varying in D10% (D50% ) up to 100% (131%) for 77.1 keV/μm C-ions, and non-small cell lung cancer and pancreatic cancer cell lines varying up to 55% (76%) and 51% (78%), respectively, for 60.5 keV/μm C-ions. The cell lines with modulated DNA repair capacity had greater variability in intrinsic radiosensitivity across all radiation qualities. Conclusions Even for cell lines of the same histologic type, there are remarkable variations in intrinsic radiosensitivity, and these variations do not differ significantly between photon, proton or C-ion radiation. The importance of DNA repair capacity in governing the variability in intrinsic radiosensitivity is not significantly diminished for higher LET radiation.

Most of the transcribed human genome codes for noncoding RNAs (ncRNAs), and long noncoding RNAs (lncRNAs) make for the lion's share of the human ncRNA space. Despite growing interest in lncRNAs, because there are so many of them, and because of their tissue specialization and, often, lower abundance, their catalog remains incomplete and there are multiple ongoing efforts to improve it. Consequently, the number of human lncRNA genes may be lower than 10,000 or higher than 200,000. A key open challenge for lncRNA research, now that so many lncRNA species have been identified, is the characterization of lncRNA function and the interpretation of the roles of genetic and epigenetic alterations at their loci. After all, the most important human genes to catalog and study are those that contribute to important cellular functions-that affect development or cell differentiation and whose dysregulation may play a role in the genesis and progression of human diseases. Multiple efforts have used screens based on RNA-mediated interference (RNAi), antisense oligonucleotide (ASO), and CRISPR screens to identify the consequences of lncRNA dysregulation and predict lncRNA function in select contexts, but these approaches have unresolved scalability and accuracy challenges. Instead-as was the case for better-studied ncRNAs in the past-researchers often focus on characterizing lncRNA interactions and investigating their effects on genes and pathways with known functions. Here, we focus most of our review on computational methods to identify lncRNA interactions and to predict the effects of their alterations and dysregulation on human disease pathways.

Background Racial disparities in cancer outcomes are increasingly recognized, but comprehensive analyses, including molecular studies, are limited. The objective of the current study was to perform a pan-cancer clinical and epigenetic molecular analysis of outcomes in African American (AA) and European American (EA) patients. Methods Cross-platform analyses using cancer databases (the Surveillance, Epidemiology, and End Results program database and the National Cancer Data Base) and a molecular database (The Cancer Genome Ancestry Atlas) were performed to evaluate clinical and epigenetic molecular differences between AA and EA patients based on genetic ancestry. Results In the primary pan-cancer survival analysis using the Surveillance, Epidemiology, and End Results database (2,045,839 patients; 87.5% EA and 12.5% AA), AA patients had higher mortality rates for 28 of 42 cancer types analyzed (hazard ratio, >1.0). AAs continued to have higher mortality in 13 cancer types after adjustment for socioeconomic variables using the National Cancer Database (5,150,023 patients; 11.6% AA and 88.4% EA). Then, molecular features of 5,283 tumors were analyzed in patients who had genetic ancestry data available (87.2% EA and 12.8% AA). Genes were identified with altered DNA methylation along with increased microRNA expression levels unique to AA patients that are associated with cancer drug resistance. Increased miRNAs (miR-15a, miR-17, miR-130-3p, miR-181a) were noted in common among AAs with breast, kidney, thyroid, or prostate carcinomas. Conclusions The current results identified epigenetic features in AA patients who have cancer that may contribute to higher mortality rates compared with EA patients who have cancer. Therefore, a focus on molecular signatures unique to AAs may identify actionable molecular abnormalities.

Existing compendia of non-coding RNA (ncRNA) are incomplete, in part because they are derived almost exclusively from small and polyadenylated RNAs. Here we present a more comprehensive atlas of the human transcriptome, which includes small and polyA RNA as well as total RNA from 300 human tissues and cell lines. We report thousands of previously uncharacterized RNAs, increasing the number of documented ncRNAs by approximately 8%. To infer functional regulation by known and newly characterized ncRNAs, we exploited pre-mRNA abundance estimates from total RNA sequencing, revealing 316 microRNAs and 3,310 long non-coding RNAs with multiple lines of evidence for roles in regulating protein-coding genes and pathways. Our study both refines and expands the current catalog of human ncRNAs and their regulatory interactions. All data, analyses and results are available for download and interrogation in the R2 web portal, serving as a basis for future exploration of RNA biology and function.

Most of the transcribed human genome codes for noncoding RNAs (ncRNAs), and long noncoding RNAs (lncRNAs) make for the lion's share of the human ncRNA space. Despite growing interest in lncRNAs, because there are so many of them, and because of their tissue specialization and, often, lower abundance, their catalog remains incomplete and there are multiple ongoing efforts to improve it. Consequently, the number of human lncRNA genes may be lower than 10,000 or higher than 200,000. A key open challenge for lncRNA research, now that so many lncRNA species have been identified, is the characterization of lncRNA function and the interpretation of the roles of genetic and epigenetic alterations at their loci. After all, the most important human genes to catalog and study are those that contribute to important cellular functions-that affect development or cell differentiation and whose dysregulation may play a role in the genesis and progression of human diseases. Multiple efforts have used screens based on RNA-mediated interference (RNAi), antisense oligonucleotide (ASO), and CRISPR screens to identify the consequences of lncRNA dysregulation and predict lncRNA function in select contexts, but these approaches have unresolved scalability and accuracy challenges. Instead-as was the case for better-studied ncRNAs in the past-researchers often focus on characterizing lncRNA interactions and investigating their effects on genes and pathways with known functions. Here, we focus most of our review on computational methods to identify lncRNA interactions and to predict the effects of their alterations and dysregulation on human disease pathways.

PurposeTo show that radiation response across cancer cell lines of the same anatomic site and histologic type varies remarkably for protons and carbon (C) ions.Materials and MethodsWe measured and obtained from the literature clonogenic survival of human cancer cell lines of the lung (n=18), brain (n=10) and pancreas (n=10) exposed to photons, protons, and C-ions to assess their variability in response. We also treated cancer cell lines with DNA repair inhibitors prior to irradiation to assess how DNA repair capacity affects their variability in response. We quantified the variability in response by calculating the relative range (range/mean) and the coefficient of variation (COV) of the dose at 10% survival fraction (D10%) and relative biological effectiveness (RBE10%).ResultsThe relative range of D10% for lung cancer cell lines varied from 55-92% for photons, protons, and C-ions, with the relative range in RBE varying from 16-45% for protons and C-ions. For brain and pancreatic cancer cell lines, the relative range of D10% varied from 95-112%, and 39-75%, respectively, with the relative range in RBE varying from 27-33% and 25-50%, respectively. However, the COVs in D10% were approximately equal across radiation qualities, varying from 0.24±0.07–0.35±0.10, 0.35±0.09–0.69±0.62 and 0.13±0.03– 0.21±0.04 for lung, brain and pancreatic cancer cell lines, respectively. Greater relative ranges in D10% were observed in the cell lines with inhibited DNA repair, varying from 108%-157% for photons, protons, and C-ions, with relative ranges in RBE varying from 29-67%. The COVs in the D10% were also greater for the cell lines treated with inhibitors of DNA repair, varying from 0.34±0.09–0.41±0.06.ConclusionCell lines of the same anatomic site and histologic type have a remarkable variability in response, not only to photons but also to protons and C-ions. We attributed this variability to differences in DNA repair capacity.CategoryBiological Physics and Response Prediction

The human transcriptome consists of various RNA biotypes including multiple types of non-coding RNAs (ncRNAs). Current ncRNA compendia remain incomplete partially because they are almost exclusively derived from the interrogation of small- and polyadenylated RNAs. Here, we present a more comprehensive atlas of the human transcriptome that is derived from matching polyA-, total-, and small-RNA profiles of a heterogeneous collection of nearly 300 human tissues and cell lines. We report on thousands of novel RNA species across all major RNA biotypes, including a hitherto poorly-cataloged class of non-polyadenylated single-exon long non-coding RNAs. In addition, we exploit intron abundance estimates from total RNA-sequencing to test and verify functional regulation by novel non-coding RNAs. Our study represents a substantial expansion of the current catalogue of human ncRNAs and their regulatory interactions. All data, analyses, and results are available in the R2 web portal and serve as a basis to further explore RNA biology and function.

Long intergenic non-coding RNAs (lincRNAs) are emerging as integral components of signaling pathways in various cancer types. In neuroblastoma, only a handful of lincRNAs are known as upstream regulators or downstream effectors of oncogenes. Here, we exploit RNA sequencing data of primary neuroblastoma tumors, neuroblast precursor cells, neuroblastoma cell lines and various cellular perturbation model systems to define the neuroblastoma lincRNome and map lincRNAs up- and downstream of neuroblastoma driver genes MYCN, ALK and PHOX2B. Each of these driver genes controls the expression of a particular subset of lincRNAs, several of which are associated with poor survival and are differentially expressed in neuroblastoma tumors compared to neuroblasts. By integrating RNA sequencing data from both primary tumor tissue and cancer cell lines, we demonstrate that several of these lincRNAs are expressed in stromal cells. Deconvolution of primary tumor gene expression data revealed a strong association between stromal cell composition and driver gene status, resulting in differential expression of these lincRNAs. We also explored lincRNAs that putatively act upstream of neuroblastoma driver genes, either as presumed modulators of driver gene activity, or as modulators of effectors regulating driver gene expression. This analysis revealed strong associations between the neuroblastoma lincRNAs MIAT and MEG3 and MYCN and PHOX2B activity or expression. Together, our results provide a comprehensive catalogue of the neuroblastoma lincRNome, highlighting lincRNAs up- and downstream of key neuroblastoma driver genes. This catalogue forms a solid basis for further functional validation of candidate neuroblastoma lincRNAs.

Long intergenic non-coding RNAs (lincRNAs) are emerging as integral components of signaling pathways in various cancer types. In neuroblastoma, only a handful of lincRNAs are known as upstream regulators or downstream effectors of oncogenes. Here, we exploit RNA sequencing data of primary neuroblastoma tumors, neuroblast precursor cells, neuroblastoma cell lines and various cellular perturbation model systems to define the neuroblastoma lincRNome and map lincRNAs up- and downstream of neuroblastoma driver genes MYCN, ALK and PHOX2B. Each of these driver genes controls the expression of a particular subset of lincRNAs, several of which are associated with poor survival and are differentially expressed in neuroblastoma tumors compared to neuroblasts. By integrating RNA sequencing data from both primary tumor tissue and cancer cell lines, we demonstrate that several of these lincRNAs are expressed in stromal cells. Deconvolution of primary tumor gene expression data revealed a strong association between stromal cell composition and driver gene status, resulting in differential expression of these lincRNAs. We also explored lincRNAs that putatively act upstream of neuroblastoma driver genes, either as presumed modulators of driver gene activity, or as modulators of effectors regulating driver gene expression. This analysis revealed strong associations between the neuroblastoma lincRNAs MIAT and MEG3 and MYCN and PHOX2B activity or expression. Together, our results provide a comprehensive catalogue of the neuroblastoma lincRNome, highlighting lincRNAs up- and downstream of key neuroblastoma driver genes. This catalogue forms a solid basis for further functional validation of candidate neuroblastoma lincRNAs., SCOPUS: ar.j, info:eu-repo/semantics/published

microRNAs (miRNAs) play key roles in cancer, but their propensity to couple their targets as competing endogenous RNAs (ceRNAs) has only recently emerged. Multiple models have studied ceRNA regulation, but these models did not account for the effects of co-regulation by miRNAs with many targets. We modeled ceRNA and simulated its effects using established parameters for miRNA/mRNA interaction kinetics while accounting for co-regulation by multiple miRNAs with many targets. Our simulations suggested that co-regulation by many miRNA species is more likely to produce physiologically relevant context-independent couplings. To test this, we studied the overlap of inferred ceRNA networks from four tumor contexts—our proposed pan-cancer ceRNA interactome (PCI). PCI was composed of interactions between genes that were co-regulated by nearly three-times as many miRNAs as other inferred ceRNA interactions. Evidence from expression-profiling datasets suggested that PCI interactions are predictive of gene expression in 12 independent tumor- and non-tumor contexts. Biochemical assays confirmed ceRNA couplings for two PCI subnetworks, including oncogenes CCND1, HIF1A and HMGA2, and tumor suppressors PTEN, RB1 and TP53. Our results suggest that PCI is enriched for context-independent interactions that are coupled by many miRNA species and are more likely to be context independent.

LIN28 is a bipartite RNA-binding protein that post-transcriptionally inhibits let-7 microRNAs to regulate development and influence disease states. However, the mechanisms of let-7 suppression remains poorly understood, because LIN28 recognition depends on coordinated targeting by both the zinc knuckle domain (ZKD)—which binds a GGAG-like element in the precursor—and the cold shock domain (CSD), whose binding sites have not been systematically characterized. By leveraging single-nucleotide-resolution mapping of LIN28 binding sites in vivo, we determined that the CSD recognizes a (U)GAU motif. This motif partitions the let-7 family into Class I precursors with both CSD and ZKD binding sites and Class II precursors with ZKD but no CSD binding sites. LIN28 in vivo recognition—and subsequent 3′ uridylation and degradation—of Class I precursors is more efficient, leading to their stronger suppression in LIN28-activated cells and cancers. Thus, CSD binding sites amplify the effects of the LIN28 activation with potential implication in development and cancer.

We introduce a method for simultaneous prediction of microRNA–target interactions and their mediated competitive endogenous RNA (ceRNA) interactions. Using high-throughput validation assays in breast cancer cell lines, we show that our integrative approach significantly improves on microRNA–target prediction accuracy as assessed by both mRNA and protein level measurements. Our biochemical assays support nearly 500 microRNA–target interactions with evidence for regulation in breast cancer tumors. Moreover, these assays constitute the most extensive validation platform for computationally inferred networks of microRNA–target interactions in breast cancer tumors, providing a useful benchmark to ascertain future improvements.

Prediction of protein subcellular localization (PSL) is important for genome annotation, protein function prediction, and drug discovery. Many computational approaches for PSL prediction based on protein sequences have been proposed in recent years for Gram-negative bacteria. We present PSLDoc, a method based on gapped-dipeptides and probabilistic latent semantic analysis (PLSA) to solve this problem. A protein is considered as a term string composed by gapped-dipeptides, which are defined as any two residues separated by one or more positions. The weighting scheme of gapped-dipeptides is calculated according to a position specific score matrix, which includes sequence evolutionary information. Then, PLSA is applied for feature reduction, and reduced vectors are input to five one-versus-rest support vector machine classifiers. The localization site with the highest probability is assigned as the final prediction. It has been reported that there is a strong correlation between sequence homology and subcellular localization (Nair and Rost, Protein Sci 2002;11:2836-2847; Yu et al., Proteins 2006;64:643-651). To properly evaluate the performance of PSLDoc, a target protein can be classified into low- or high-homology data sets. PSLDoc's overall accuracy of low- and high-homology data sets reaches 86.84% and 98.21%, respectively, and it compares favorably with that of CELLO II (Yu et al., Proteins 2006;64:643-651). In addition, we set a confidence threshold to achieve a high precision at specified levels of recall rates. When the confidence threshold is set at 0.7, PSLDoc achieves 97.89% in precision which is considerably better than that of PSORTb v.2.0 (Gardy et al., Bioinformatics 2005;21:617-623). Our approach demonstrates that the specific feature representation for proteins can be successfully applied to the prediction of protein subcellular localization and improves prediction accuracy. Besides, because of the generality of the representation, our method can be extended to eukaryotic proteomes in the future. The web server of PSLDoc is publicly available at http://bio-cluster.iis.sinica.edu.tw/~ bioapp/PSLDoc/.

The prediction of transmembrane (TM) helix and topology provides important information about the structure and function of a membrane protein. Due to the experimental difficulties in obtaining a high-resolution model, computational methods are highly desirable. In this paper, we present a hierarchical classification method using support vector machines (SVMs) that integrates selected features by capturing the sequence-to-structure relationship and developing a new scoring function based on membrane protein folding. The proposed approach is evaluated on low- and high-resolution data sets with cross-validation, and the topology (sidedness) prediction accuracy reaches as high as 90%. Our method is also found to correctly predict both the location of TM helices and the topology for 69% of the low-resolution benchmark set. We also test our method for discrimination between soluble and membrane proteins and achieve very low overall false positive (0.5%) and false negative rates (0 to approximately 1.2%). Lastly, the analysis of the scoring function suggests that the topogeneses of single-spanning and multispanning TM proteins have different levels of complexity, and the consideration of interloop topogenic interactions for the latter is the key to achieving better predictions. This method can facilitate the annotation of membrane proteomes to extract useful structural and functional information. It is publicly available at http://bio-cluster.iis.sinica.edu.tw/~bioapp/SVMtop.

microRNAs play key roles in cancer etiology, and recent evidence suggests that targets of the same microRNA programs are stoichiometrically coupled via a competitive endogenous RNA (ceRNA) mechanism. Consequently, aberrant expression of multiple targets of the same microRNA program dysregulate coupled ceRNA- oncogenes and tumor suppressors. We computationally inferred and experimentally validated that coordinated copy number and methylation alterations contribute to physiologically significant in-trans dysregulation of hundreds of coupled oncogenes and tumor suppressors in multiple tumor contexts. Both high-throughput perturbation assays and low-throughput mutational assays confirmed significant ceRNA-mediated regulation of cancer genes in eight tumor contexts, including ESR1 and APC in breast and colon cancer, respectively. Our analysis infers roles for previously uncharacterized genomic alterations and it suggests that ceRNA-mediated interactions account for a substantial fraction of cancer's missing genomic variability. Citation Format: Hua-Sheng Chiu, Xuerui Yang, Andrea Califano, Pavel Sumazin. Genomic alterations dysregulate cancer genes by modulating microRNA activity. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr IA14.

β-turns play an important role in protein structures not only because of their sheer abundance, which is estimated to be approximately 25% of all protein residues, but also because of their significance in high-order structures of proteins. In this study, we introduce a new method of β-turn prediction that uses a two-stage classification scheme and an integrated framework for input features. Ten-fold cross validation based on a benchmark dataset of 426 non-homologue protein chains is used to evaluate our method's performance. The experimental results demon- strate that it achieves substantial improvements over BetaTurn, the current best method. The prediction accuracy, Qtotal, and the Matthews correlation coefficient (MCC) of our approach are 79% and 0.47 respectively, compared to 77% and 0.45 respec- tively for BetaTurn.

MicroRNAs (miRs) have been repeatedly linked to tumorigenesis and tumor progression, suggesting their potential value as biomarkers and targets for therapeutic intervention. Recent evidence suggests that mRNAs compete for binding and regulation by a finite pool of miRs, thus regulating each other as competitive endogenous RNAs (ceRNAs). The extent of this new regulatory layer and its potential to alter the expression of cancer genes has been shown in several tumor types, but the actual impact of ceRNA interactions on disease remains unclear. In this study, using TCGA (The Cancer Genome Atlas) tumor datasets, we provide evidence that ceRNA play an important role in tumor etiology. Leveraging millions of shRNA-mediated perturbations from LINCS (Library of Integrated Network-based Cellular Signatures), we extensively validated ceRNA interactions in breast and prostate cancer. Moreover, we identified over 160,000 ceRNA interactions that are conserved across tumor types despite being implemented by miR programs with tumor type-specific expression. We present evidence that these ultraconserved interactions are mechanistically responsible for the dysregulation of the majority of oncogenes and tumor suppressors through TCGA-profiled genomic alteration of their cognate ceRNA regulators. Using targeted biochemical validation in cell lines, we verified the potential of ceRNA regulators to target ESR1, an established oncogene and drug target in breast cancer, and APC, an established tumor suppressor in colon adenocarcinoma. Our analysis suggests that ceRNA interactions are implemented through competition for multiple miRNAs, rendering them virtually independent of single miRNA kinetics and availability, and more likely to be conserved across contexts. Using TCGA samples, we demonstrated that conserved ceRNA interactions enable systematic identification of genomic alterations that contribute to aberrant expression of hundreds of cancer genes in each of eight distinct tumor contexts. We submit this network as a resource for analyzing ceRNA regulation in datasets where reverse engineering context-specific networks is infeasible due to low sample number or lack of miRNA expression profiles. Citation Format: Hua-Sheng Chiu, Xuerui Yang, María Rodríguez Martínez, Archana Iyer, Pavel Sumazin, Andrea Califano. Highly conserved ceRNA regulatory interactions cooperate with genomic variability to modulate drivers of tumorigenesis. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5231. doi:10.1158/1538-7445.AM2013-5231

In this paper, Petri net models for flexible manufacturing systems (FMS) are constructed. A firing sequence of the Petri net from an initial marking to a final one can be seen as a schedule of the modeled FMS. By using the branch-and-bound algorithm, an optimal schedule of the FMS can be obtained. Timed marked graphs, a special class of timed Petri nets, are then used to analyze the long-term performance of the job-shop systems. For a job shop system, a procedure is proposed to derive the marked graph from the initial Petri net model after the schedule is determined. The branch and bound algorithm and proposed procedure are illustrated through a job-shop system.

By analyzing gene expression data in gliobastoma in combination with matched microRNA profiles, we have uncovered a post-transcriptional regulation layer of surprising magnitude, comprising over 248,000 microRNA -mediated interactions. These include ∼7,000 genes whose transcripts act as microRNA ‘sponges’ and 148 genes that act through alternative, non-sponge interactions. Biochemical analyses in cell lines confirmed that this network regulates established drivers of tumor initiation and subtype, including PTEN, PDGFRA, RB1, VEGFA, STAT3, and RUNX1, suggesting that these interactions mediate crosstalk between canonical oncogenic pathways. RNA silencing of 13 microRNA-mediated PTEN regulators, whose locus deletions are predictive of PTEN expression variability, was sufficient to downregulate PTEN in a 3′ UTR-dependent manner and to increase tumor-cell growth rates. Thus, this microRNA -mediated network provides a mechanistic, experimentally validated rationale for the loss of PTEN expression in a large number of glioma samples with an intact PTEN locus. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4186. doi:1538-7445.AM2012-4186

By analyzing gene expression data in gliobastoma in combination with matched microRNA profiles, we have uncovered a post-transcriptional regulation layer of surprising magnitude, comprising hundreds of thousands of microRNA -mediated interactions. These include ~7,000 genes whose transcripts act as microRNA ‘sponges’ and 148 genes that act through alternative, non-sponge interactions. Biochemical analyses in cell lines confirmed that this network regulates established drivers of tumor initiation and subtype, including PTEN, PDGFRA, RB1, VEGFA, STAT3, and RUNX1, suggesting that these interactions mediate crosstalk between canonical oncogenic pathways. RNA silencing of 13 microRNA-mediated PTEN regulators, whose locus deletions are predictive of PTEN expression variability, was sufficient to downregulate PTEN in a 3′ UTR-dependent manner and to increase tumor-cell growth rates. Thus, this microRNA-mediated network provides a mechanistic, experimentally validated rationale for the loss of PTEN expression in a large number of glioma samples with an intact PTEN locus. Recently, we extended this work to three other tumor types, showing that microRNA-mediated interactions propagate the effects of genetic alterations to regulate distal oncogenes and tumor suppressors in multiple cancers. Our results suggest that, although implemented by context-specific factors, hundreds of thousands of these interactions are context-independent. Note: This abstract was not presented at the conference because the presenter was unable to attend. Citation Format: Pavel Sumazin, Hua-Sheng Chiu, Andrea Califano. microRNA-mediated interactions regulate established oncongenic pathways in cancer [abstract]. In: Proceedings of the AACR Special Conference on Noncoding RNAs and Cancer; 2012 Jan 8-11; Miami Beach, FL. Philadelphia (PA): AACR; Cancer Res 2012;72(2 Suppl):Abstract nr A14.

SummaryBy analyzing gene expression data in glioblastoma in combination with matched microRNA profiles, we have uncovered a posttranscriptional regulation layer of surprising magnitude, comprising more than 248,000 microRNA (miR)-mediated interactions. These include ∼7,000 genes whose transcripts act as miR “sponges” and 148 genes that act through alternative, nonsponge interactions. Biochemical analyses in cell lines confirmed that this network regulates established drivers of tumor initiation and subtype implementation, including PTEN, PDGFRA, RB1, VEGFA, STAT3, and RUNX1, suggesting that these interactions mediate crosstalk between canonical oncogenic pathways. siRNA silencing of 13 miR-mediated PTEN regulators, whose locus deletions are predictive of PTEN expression variability, was sufficient to downregulate PTEN in a 3′UTR-dependent manner and to increase tumor cell growth rates. Thus, miR-mediated interactions provide a mechanistic, experimentally validated rationale for the loss of PTEN expression in a large number of glioma samples with an intact PTEN locus.

LIN28 is a bipartite RNA-binding protein that post-transcriptionally inhibits the biogenesis of let-7 microRNAs to regulate development and influence disease states. However, the mechanisms of let-7 suppression remains poorly understood, because LIN28 recognition depends on coordinated targeting by both the zinc knuckle domain (ZKD) —which binds a GGAG-like element in the precursor—and the cold shock domain (CSD), whose binding sites have not been systematically characterized. By leveraging single-nucleotide-resolution mapping of LIN28 binding sites in vivo, we determined that the CSD recognizes a (U)GAU motif. This motif partitions the let-7 microRNAs into two subclasses, precursors with both CSD and ZKD binding sites (CSD+) and precursors with ZKD but no CSD binding sites (CSD-). LIN28 in vivo recognition—and subsequent 3ʹ uridylation and degradation—of CSD+ precursors is more efficient, leading to their stronger suppression in LIN28-activated cells and cancers. Thus, CSD binding sites amplify the effects of the LIN28 activation.