Phaedra Agius

Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites.

AbstractmirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.

Sequence and chromatin determinants of cell-type–specific transcription factor binding

Gene regulatory programs in distinct cell types are maintained in large part through the cell-type-specific binding of transcription factors (TFs). The determinants of TF binding include direct DNA sequence preferences, DNA sequence preferences of cofactors, and the local cell-dependent chromatin context. To explore the contribution of DNA sequence signal, histone modifications, and DNase accessibility to cell-type-specific binding, we analyzed 286 ChIP-seq experiments performed by the ENCODE Consortium. This analysis included experiments for 67 transcriptional regulators, 15 of which were profiled in both the GM12878 (lymphoblastoid) and K562 (erythroleukemic) human hematopoietic cell lines. To model TF-bound regions, we trained support vector machines (SVMs) that use flexible k-mer patterns to capture DNA sequence signals more accurately than traditional motif approaches. In addition, we trained SVM spatial chromatin signatures to model local histone modifications and DNase accessibility, obtaining significantly more accurate TF occupancy predictions than simpler approaches. Consistent with previous studies, we find that DNase accessibility can explain cell-line-specific binding for many factors. However, we also find that of the 10 factors with prominent cell-type-specific binding patterns, four display distinct cell-type-specific DNA sequence preferences according to our models. Moreover, for two factors we identify cell-specific binding sites that are accessible in both cell types but bound only in one. For these sites, cell-type-specific sequence models, rather than DNase accessibility, are better able to explain differential binding. Our results suggest that using a single motif for each TF and filtering for chromatin accessible loci is not always sufficient to accurately account for cell-type-specific binding profiles.

Computational and experimental identification of mirtrons in Drosophila melanogaster and Caenorhabditis elegans

Mirtrons are intronic hairpin substrates of the dicing machinery that generate functional microRNAs. In this study, we describe experimental assays that defined the essential requirements for entry of introns into the mirtron pathway. These data informed a bioinformatic screen that effectively identified functional mirtrons from the Drosophila melanogaster transcriptome. These included 17 known and six confident novel mirtrons among the top 51 candidates, and additional candidates had limited read evidence in available small RNA data. Our computational model also proved effective on Caenorhabditis elegans, for which the identification of 14 cloned mirtrons among the top 22 candidates more than tripled the number of validated mirtrons in this species. A few low-scoring introns generated mirtron-like read patterns from atypical RNA structures, but their paucity suggests that relatively few such loci were not captured by our model. Unexpectedly, we uncovered examples of clustered mirtrons in both fly and worm genomes, including a <8-kb region in C. elegans harboring eight distinct mirtrons. Altogether, we demonstrate that discovery of functional mirtrons, unlike canonical miRNAs, is amenable to computational methods independent of evolutionary constraint.

Inferring transcriptional and microRNA-mediated regulatory programs in glioblastoma

Large‐scale cancer genomics projects are profiling hundreds of tumors at multiple molecular layers, including copy number, mRNA and miRNA expression, but the mechanistic relationships between these layers are often excluded from computational models. We developed a supervised learning framework for integrating molecular profiles with regulatory sequence information to reveal regulatory programs in cancer, including miRNA‐mediated regulation. We applied our approach to 320 glioblastoma profiles and identified key miRNAs and transcription factors as common or subtype‐specific drivers of expression changes. We confirmed that predicted gene expression signatures for proneural subtype regulators were consistent with in vivo expression changes in a PDGF‐driven mouse model. We tested two predicted proneural drivers, miR‐124 and miR‐132, both underexpressed in proneural tumors, by overexpression in neurospheres and observed a partial reversal of corresponding tumor expression changes. Computationally dissecting the role of miRNAs in cancer may ultimately lead to small RNA therapeutics tailored to subtype or individual.

High Resolution Models of Transcription Factor-DNA Affinities Improve In Vitro and In Vivo Binding Predictions

Accurately modeling the DNA sequence preferences of transcription factors (TFs), and using these models to predict in vivo genomic binding sites for TFs, are key pieces in deciphering the regulatory code. These efforts have been frustrated by the limited availability and accuracy of TF binding site motifs, usually represented as position-specific scoring matrices (PSSMs), which may match large numbers of sites and produce an unreliable list of target genes. Recently, protein binding microarray (PBM) experiments have emerged as a new source of high resolution data on in vitro TF binding specificities. PBM data has been analyzed either by estimating PSSMs or via rank statistics on probe intensities, so that individual sequence patterns are assigned enrichment scores (E-scores). This representation is informative but unwieldy because every TF is assigned a list of thousands of scored sequence patterns. Meanwhile, high-resolution in vivo TF occupancy data from ChIP-seq experiments is also increasingly available. We have developed a flexible discriminative framework for learning TF binding preferences from high resolution in vitro and in vivo data. We first trained support vector regression (SVR) models on PBM data to learn the mapping from probe sequences to binding intensities. We used a novel -mer based string kernel called the di-mismatch kernel to represent probe sequence similarities. The SVR models are more compact than E-scores, more expressive than PSSMs, and can be readily used to scan genomics regions to predict in vivo occupancy. Using a large data set of yeast and mouse TFs, we found that our SVR models can better predict probe intensity than the E-score method or PBM-derived PSSMs. Moreover, by using SVRs to score yeast, mouse, and human genomic regions, we were better able to predict genomic occupancy as measured by ChIP-chip and ChIP-seq experiments. Finally, we found that by training kernel-based models directly on ChIP-seq data, we greatly improved in vivo occupancy prediction, and by comparing a TFs in vitro and in vivo models, we could identify cofactors and disambiguate direct and indirect binding.

Near universal detection of alterations in CTNNB1 and Wnt pathway regulators in desmoid-type fibromatosis by whole-exome sequencing and genomic analysis

CTNNB1 mutations or APC abnormalities have been observed in ∼85% of desmoids examined by Sanger sequencing and are associated with Wnt/β‐catenin activation. We sought to identify molecular aberrations in “wild‐type” tumors (those without CTNNB1 or APC alteration) and to determine their prognostic relevance. CTNNB1 was examined by Sanger sequencing in 117 desmoids; a mutation was observed in 101 (86%) and 16 were wild type. Wild‐type status did not associate with tumor recurrence. Moreover, in unsupervised clustering based on U133A‐derived gene expression profiles, wild‐type and mutated tumors clustered together. Whole‐exome sequencing of eight of the wild‐type desmoids revealed that three had a CTNNB1 mutation that had been undetected by Sanger sequencing. The mutation was found in a mean 16% of reads (vs. 37% for mutations identified by Sanger). Of the other five wild‐type tumors sequenced, two had APC loss, two had chromosome 6 loss, and one had mutation of BMI1. The finding of low‐frequency CTNNB1 mutation or APC loss in wild‐type desmoids was validated in the remaining eight wild‐type desmoids; directed miSeq identified low‐frequency CTNNB1 mutation in four and comparative genomic hybridization identified APC loss in one. These results demonstrate that mutations affecting CTNNB1 or APC occur more frequently in desmoids than previously recognized (111 of 117; 95%), and designation of wild‐type genotype is largely determined by sensitivity of detection methods. Even true CTNNB1 wild‐type tumors (determined by next‐generation sequencing) may have genomic alterations associated with Wnt activation (chromosome 6 loss/BMI1 mutation), supporting Wnt/β‐catenin activation as the common pathway governing desmoid initiation.

Integrin-α10 Dependency Identifies RAC and RICTOR as Therapeutic Targets in High-Grade Myxofibrosarcoma

Myxofibrosarcoma is a common mesenchymal malignancy with complex genomics and heterogeneous clinical outcomes. Through gene-expression profiling of 64 primary high-grade myxofibrosarcomas, we defined an expression signature associated with clinical outcome. The gene most significantly associated with disease-specific death and distant metastasis was ITGA10 (integrin-α10). Functional studies revealed that myxofibrosarcoma cells strongly depended on integrin-α10, whereas normal mesenchymal cells did not. Integrin-α10 transmitted its tumor-specific signal via TRIO and RICTOR, two oncoproteins that are frequently co-overexpressed through gene amplification on chromosome 5p. TRIO and RICTOR activated RAC/PAK and AKT/mTOR to promote sarcoma cell survival. Inhibition of these proteins with EHop-016 (RAC inhibitor) and INK128 (mTOR inhibitor) had antitumor effects in tumor-derived cell lines and mouse xenografts, and combining the drugs enhanced the effects. Our results demonstrate the importance of integrin-α10/TRIO/RICTOR signaling for driving myxofibrosarcoma progression and provide the basis for promising targeted treatment strategies for patients with high-risk disease. SIGNIFICANCE Identifying the molecular pathogenesis for myxofibrosarcoma progression has proven challenging given the highly complex genomic alterations in this tumor type. We found that integrin-α10 promotes tumor cell survival through activation of TRIO-RAC-RICTOR-mTOR signaling, and that inhibitors of RAC and mTOR have antitumor effects in vivo, thus identifying a potential treatment strategy for patients with high-risk myxofibrosarcoma. Cancer Discov; 6(10); 1148-65. ©2016 AACR.This article is highlighted in the In This Issue feature, p. 1069.

Machine learning integration of rheumatoid arthritis synovial histology and RNAseq data identifies three disease subtypes

In this study, we sought to refine histologic scoring of rheumatoid arthritis (RA) synovial tissue by training with gene expression data and machine learning.

miR-193b-regulated signaling networks serve as tumor suppressors in liposarcoma and promote adipogenesis in adipose-derived stem cells

Well-differentiated and dedifferentiated liposarcomas (WDLS/DDLS) account for approximately 13% of all soft tissue sarcoma in adults and cause substantial morbidity or mortality in the majority of patients. In this study, we evaluated the functions of miRNA (miR-193b) in liposarcoma in vitro and in vivo Deep RNA sequencing on 93 WDLS, 145 DDLS, and 12 normal fat samples demonstrated that miR-193b was significantly underexpressed in DDLS compared with normal fat. Reintroduction of miR-193b induced apoptosis in liposarcoma cells and promoted adipogenesis in human adipose-derived stem cells (ASC). Integrative transcriptomic and proteomic analysis of miR-193b-target networks identified novel direct targets, including CRK-like proto-oncogene (CRKL) and focal adhesion kinase (FAK). miR-193b was found to regulate FAK-SRC-CRKL signaling through CRKL and FAK. miR-193b also stimulated reactive oxygen species signaling by targeting the antioxidant methionine sulfoxide reductase A to modulate liposarcoma cell survival and ASC differentiation state. Expression of miR-193b in liposarcoma cells was downregulated by promoter methylation, resulting at least in part from increased expression of the DNA methyltransferase DNMT1 in WDLS/DDLS. In vivo, miR-193b mimetics and FAK inhibitor (PF-562271) each inhibited liposarcoma xenograft growth. In summary, miR-193b not only functions as a tumor suppressor in liposarcoma but also promotes adipogenesis in ASC. Furthermore, this study reveals key tyrosine kinase and DNA methylation pathways in liposarcoma, some with immediate implications for therapeutic exploration. Cancer Res; 77(21); 5728-40. ©2017 AACR.