Publications by authors named "Ramana V Davuluri"

90 Publications

ExTraMapper: Exon- and Transcript-level mappings for orthologous gene pairs.

Bioinformatics 2021 May 20. Epub 2021 May 20.

Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, 11794, USA.

Motivation: Access to large-scale genomics and transcriptomics data from various tissues and cell lines allowed the discovery of wide-spread alternative splicing events and alternative promoter usage in mammalians. Between human and mouse, gene-level orthology is currently present for nearly 16k protein-coding genes spanning a diverse repertoire of over 200k total transcript isoforms.

Results: Here, we describe a novel method, ExTraMapper, which leverages sequence conservation between exons of a pair of organisms and identifies a fine-scale orthology mapping at the exon and then transcript level. ExTraMapper identifies more than 350k exon mappings, as well as 30k transcript mappings between human and mouse using only sequence and gene annotation information. We demonstrate that ExTraMapper identifies a larger number of exon and transcript mappings compared to previous methods. Further, it identifies exon fusions, splits, and losses due to splice site mutations, and finds mappings between microexons that are previously missed. By reanalysis of RNA-seq data from 13 matched human and mouse tissues, we show that ExTraMapper improves the correlation of transcript-specific expression levels suggesting a more accurate mapping of human and mouse transcripts. We also applied the method to detect conserved exon and transcript pairs between human and rhesus macaque genomes to highlight the point that ExTraMapper is applicable to any pair of organisms that have orthologous gene pairs.

Availability: The source code and the results are available at https://github.com/ay-lab/ExTraMapper and http://ay-lab-tools.lji.org/extramapper.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btab393DOI Listing
May 2021

RP58 Represses Transcriptional Programs Linked to Nonneuronal Cell Identity and Glioblastoma Subtypes in Developing Neurons.

Mol Cell Biol 2021 06 23;41(7):e0052620. Epub 2021 Jun 23.

Weill Cornell Medical College, Department of Neurological Surgery, New York, New York, USA.

How mammalian neuronal identity is progressively acquired and reinforced during development is not understood. We have previously shown that loss of RP58 (ZNF238 or ZBTB18), a BTB/POZ-zinc finger-containing transcription factor, in the mouse brain leads to microcephaly, corpus callosum agenesis, and cerebellum hypoplasia and that it is required for normal neuronal differentiation. The transcriptional programs regulated by RP58 during this process are not known. Here, we report for the first time that in embryonic mouse neocortical neurons a complex set of genes normally expressed in other cell types, such as those from mesoderm derivatives, must be actively repressed and that RP58 is a critical regulator of these repressed transcriptional programs. Importantly, gene set enrichment analysis (GSEA) analyses of these transcriptional programs indicate that repressed genes include distinct sets of genes significantly associated with glioma progression and/or pluripotency. We also demonstrate that reintroducing RP58 in glioma stem cells leads not only to aspects of neuronal differentiation but also to loss of stem cell characteristics, including loss of stem cell markers and decrease in stem cell self-renewal capacities. Thus, RP58 acts as an master guardian of the neuronal identity transcriptome, and its function may be required to prevent brain disease development, including glioma progression.
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http://dx.doi.org/10.1128/MCB.00526-20DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8315738PMC
June 2021

A first-in-human phase 0 clinical study of RNA interference-based spherical nucleic acids in patients with recurrent glioblastoma.

Sci Transl Med 2021 03;13(584)

Broom Street Associates, Wilmington, DE 19806, USA.

Glioblastoma (GBM) is one of the most difficult cancers to effectively treat, in part because of the lack of precision therapies and limited therapeutic access to intracranial tumor sites due to the presence of the blood-brain and blood-tumor barriers. We have developed a precision medicine approach for GBM treatment that involves the use of brain-penetrant RNA interference-based spherical nucleic acids (SNAs), which consist of gold nanoparticle cores covalently conjugated with radially oriented and densely packed small interfering RNA (siRNA) oligonucleotides. On the basis of previous preclinical evaluation, we conducted toxicology and toxicokinetic studies in nonhuman primates and a single-arm, open-label phase 0 first-in-human trial (NCT03020017) to determine safety, pharmacokinetics, intratumoral accumulation and gene-suppressive activity of systemically administered SNAs carrying siRNA specific for the GBM oncogene Bcl2Like12 (Bcl2L12). Patients with recurrent GBM were treated with intravenous administration of siBcl2L12-SNAs (drug moniker: NU-0129), at a dose corresponding to 1/50th of the no-observed-adverse-event level, followed by tumor resection. Safety assessment revealed no grade 4 or 5 treatment-related toxicities. Inductively coupled plasma mass spectrometry, x-ray fluorescence microscopy, and silver staining of resected GBM tissue demonstrated that intravenously administered SNAs reached patient tumors, with gold enrichment observed in the tumor-associated endothelium, macrophages, and tumor cells. NU-0129 uptake into glioma cells correlated with a reduction in tumor-associated Bcl2L12 protein expression, as indicated by comparison of matched primary tumor and NU-0129-treated recurrent tumor. Our results establish SNA nanoconjugates as a potential brain-penetrant precision medicine approach for the systemic treatment of GBM.
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http://dx.doi.org/10.1126/scitranslmed.abb3945DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8272521PMC
March 2021

DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome.

Bioinformatics 2021 Feb 4. Epub 2021 Feb 4.

Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA.

Motivation: Deciphering the language of non-coding DNA is one of the fundamental problems in genome research. Gene regulatory code is highly complex due to the existence of polysemy and distant semantic relationship, which previous informatics methods often fail to capture especially in data-scarce scenarios.

Results: To address this challenge, we developed a novel pre-trained bidirectional encoder represen-tation, named DNABERT, to capture global and transferrable understanding of genomic DNA sequences based on up and downstream nucleotide contexts. We compared DNABERT to the most widely used programs for genome-wide regulatory elements prediction and demonstrate its ease of use, accuracy, and efficiency. We show that the single pre-trained transformers model can simultaneously achieve state-of-the-art performance on prediction of promoters, splice sites, and transcription factor binding sites, after easy fine-tuning using small task-specific labelled data. Further, DNABERT enables direct visualization of nucleotide-level importance and semantic relationship within input sequences for better interpretability and accurate identification of conserved sequence motifs and functional genetic variant candidates. Finally, we demonstrate that pre-trained DNABERT with human genome can even be readily applied to other organisms with exceptional performance. We anticipate that the pre-trained DNABERT model can be fined tuned to many other sequence analyses tasks.

Availability: The source code, pretrained and finetuned model for DNABERT are available at GitHub https://github.com/jerryji1993/DNABERT.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btab083DOI Listing
February 2021

Distinct mechanisms control genome recognition by p53 at its target genes linked to different cell fates.

Nat Commun 2021 01 20;12(1):484. Epub 2021 Jan 20.

Department of Biochemistry and Molecular Biology, Thomas Jefferson University, Philadelphia, PA, USA.

The tumor suppressor p53 integrates stress response pathways by selectively engaging one of several potential transcriptomes, thereby triggering cell fate decisions (e.g., cell cycle arrest, apoptosis). Foundational to this process is the binding of tetrameric p53 to 20-bp response elements (REs) in the genome (RRRCWWGYYYNRRRCWWGYYY). In general, REs at cell cycle arrest targets (e.g. p21) are of higher affinity than those at apoptosis targets (e.g., BAX). However, the RE sequence code underlying selectivity remains undeciphered. Here, we identify molecular mechanisms mediating p53 binding to high- and low-affinity REs by showing that key determinants of the code are embedded in the DNA shape. We further demonstrate that differences in minor/major groove widths, encoded by G/C or A/T bp content at positions 3, 8, 13, and 18 in the RE, determine distinct p53 DNA-binding modes by inducing different Arg248 and Lys120 conformations and interactions. The predictive capacity of this code was confirmed in vivo using genome editing at the BAX RE to interconvert the DNA-binding modes, transcription pattern, and cell fate outcome.
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http://dx.doi.org/10.1038/s41467-020-20783-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7817693PMC
January 2021

Frizzled-7 Identifies Platinum-Tolerant Ovarian Cancer Cells Susceptible to Ferroptosis.

Cancer Res 2021 01 10;81(2):384-399. Epub 2020 Nov 10.

Department of Obstetrics and Gynecology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois.

Defining traits of platinum-tolerant cancer cells could expose new treatment vulnerabilities. Here, new markers associated with platinum-tolerant cells and tumors were identified using and ovarian cancer models treated repetitively with carboplatin and validated in human specimens. Platinum-tolerant cells and tumors were enriched in ALDH cells, formed more spheroids, and expressed increased levels of stemness-related transcription factors compared with parental cells. Additionally, platinum-tolerant cells and tumors exhibited expression of the Wnt receptor (). Knockdown of FZD7 improved sensitivity to platinum, decreased spheroid formation, and delayed tumor initiation. The molecular signature distinguishing FZD7 from FZD7 cells included epithelial-to-mesenchymal (EMT), stemness, and oxidative phosphorylation-enriched gene sets. Overexpression of FZD7 activated the oncogenic factor , driving upregulation of glutathione metabolism pathways, including glutathione peroxidase 4 (GPX4), which protected cells from chemotherapy-induced oxidative stress. FZD7 platinum-tolerant ovarian cancer cells were more sensitive and underwent ferroptosis after treatment with GPX4 inhibitors. , and glutathione metabolism gene sets were strongly correlated in the ovarian cancer Tumor Cancer Genome Atlas (TCGA) database and in residual human ovarian cancer specimens after chemotherapy. These results support the existence of a platinum-tolerant cell population with partial cancer stem cell features, characterized by FZD7 expression and dependent on the FZD7-β-catenin-Tp63-GPX4 pathway for survival. The findings reveal a novel therapeutic vulnerability of platinum-tolerant cancer cells and provide new insight into a potential "persister cancer cell" phenotype. SIGNIFICANCE: Frizzled-7 marks platinum-tolerant cancer cells harboring stemness features and altered glutathione metabolism that depend on GPX4 for survival and are highly susceptible to ferroptosis.
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http://dx.doi.org/10.1158/0008-5472.CAN-20-1488DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7855035PMC
January 2021

Identification of Geographic Specific SARS-Cov-2 Mutations by Random Forest Classification and Variable Selection Methods.

Stat Appl 2020 Jul 30;18(1):253-268. Epub 2020 Jun 30.

Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

RNA viral genomes have very high mutations rates. As infection spreads in the host populations, different viral lineages emerge acquiring independent mutations that can lead to varied infection and death rates in different parts of the world. By application of Random Forest classification and feature selection methods, we developed an analysis pipeline for identification of geographic specific mutations and classification of different viral lineages, focusing on the missense-variants that alter the function of the encoded proteins. We applied the pipeline on publicly available SARS-CoV-2 datasets and demonstrated that the analysis pipeline accurately identified country or region-specific viral lineages and specific mutations that discriminate different lineages. The results presented here can help designing country-specific diagnostic strategies and prioritizing the mutations for functional interpretation and experimental validations.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514111PMC
July 2020

FTO-Dependent -Methyladenosine Modifications Inhibit Ovarian Cancer Stem Cell Self-Renewal by Blocking cAMP Signaling.

Cancer Res 2020 08 30;80(16):3200-3214. Epub 2020 Jun 30.

Department of Obstetrics and Gynecology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois.

-Methyladenosine (mA) is the most abundant modification of mammalian mRNAs. RNA methylation fine tunes RNA stability and translation, altering cell fate. The fat mass- and obesity-associated protein (FTO) is an mA demethylase with oncogenic properties in leukemia. Here, we show that expression is suppressed in ovarian tumors and cancer stem cells (CSC). FTO inhibited the self-renewal of ovarian CSC and suppressed tumorigenesis , both of which required FTO demethylase activity. Integrative RNA sequencing and mA mapping analysis revealed significant transcriptomic changes associated with overexpression and mA loss involving stem cell signaling, RNA transcription, and mRNA splicing pathways. By reducing mA levels at the 3'UTR and the mRNA stability of two phosphodiesterase genes ( and ), FTO augmented second messenger 3', 5'-cyclic adenosine monophosphate (cAMP) signaling and suppressed stemness features of ovarian cancer cells. Our results reveal a previously unappreciated tumor suppressor function of FTO in ovarian CSC mediated through inhibition of cAMP signaling. SIGNIFICANCE: A new tumor suppressor function of the RNA demethylase FTO implicates mA RNA modifications in the regulation of cyclic AMP signaling involved in stemness and tumor initiation.
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http://dx.doi.org/10.1158/0008-5472.CAN-19-4044DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442742PMC
August 2020

In silico analysis of alternative splicing on drug-target gene interactions.

Sci Rep 2020 01 10;10(1):134. Epub 2020 Jan 10.

Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Identifying and evaluating the right target are the most important factors in early drug discovery phase. Most studies focus on one protein ignoring the multiple splice-variant or protein-isoforms, which might contribute to unexpected therapeutic activity or adverse side effects. Here, we present computational analysis of cancer drug-target interactions affected by alternative splicing. By integrating information from publicly available databases, we curated 883 FDA approved or investigational stage small molecule cancer drugs that target 1,434 different genes, with an average of 5.22 protein isoforms per gene. Of these, 618 genes have ≥5 annotated protein-isoforms. By analyzing the interactions with binding pocket information, we found that 76% of drugs either miss a potential target isoform or target other isoforms with varied expression in multiple normal tissues. We present sequence and structure level alignments at isoform-level and make this information publicly available for all the curated drugs. Structure-level analysis showed ligand binding pocket architectures differences in size, shape and electrostatic parameters between isoforms. Our results emphasize how potentially important isoform-level interactions could be missed by solely focusing on the canonical isoform, and suggest that on- and off-target effects at isoform-level should be investigated to enhance the productivity of drug-discovery research.
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http://dx.doi.org/10.1038/s41598-019-56894-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6954184PMC
January 2020

Multivariate Analysis of Preoperative Magnetic Resonance Imaging Reveals Transcriptomic Classification of Glioblastoma Patients.

Front Comput Neurosci 2019 12;13:81. Epub 2019 Dec 12.

Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.

Glioblastoma, the most frequent primary malignant brain neoplasm, is genetically diverse and classified into four transcriptomic subtypes, i. e., classical, mesenchymal, proneural, and neural. Currently, detection of transcriptomic subtype is based on analysis of tissue that does not capture the spatial tumor heterogeneity. In view of accumulative evidence of imaging signatures summarizing molecular features of cancer, this study seeks robust non-invasive radiographic markers of transcriptomic classification of glioblastoma, based solely on routine clinically-acquired imaging sequences. A pre-operative retrospective cohort of 112 pathology-proven glioblastoma patients, having multi-parametric MRI (T1, T1-Gd, T2, T2-FLAIR), collected from the Hospital of the University of Pennsylvania were included. Following tumor segmentation into distinct radiographic sub-regions, diverse imaging features were extracted and support vector machines were employed to multivariately integrate these features and derive an imaging signature of transcriptomic subtype. Extracted features included intensity distributions, volume, morphology, statistics, tumors' anatomical location, and texture descriptors for each tumor sub-region. The derived signature was evaluated against the transcriptomic subtype of surgically-resected tissue specimens, using a 5-fold cross-validation method and a receiver-operating-characteristics analysis. The proposed model was 71% accurate in distinguishing among the four transcriptomic subtypes. The accuracy (sensitivity/specificity) for distinguishing each subtype (classical, mesenchymal, proneural, neural) from the rest was equal to 88.4% (71.4/92.3), 75.9% (83.9/72.8), 82.1% (73.1/84.9), and 75.9% (79.4/74.4), respectively. The findings were also replicated in The Cancer Genomic Atlas glioblastoma dataset. The obtained imaging signature for the classical subtype was dominated by associations with features related to edge sharpness, whereas for the mesenchymal subtype had more pronounced presence of higher T2 and T2-FLAIR signal in edema, and higher volume of enhancing tumor and edema. The proneural and neural subtypes were characterized by the lower T1-Gd signal in enhancing tumor and higher T2-FLAIR signal in edema, respectively. Our results indicate that quantitative multivariate analysis of features extracted from clinically-acquired MRI may provide a radiographic biomarker of the transcriptomic profile of glioblastoma. Importantly our findings can be influential in surgical decision-making, treatment planning, and assessment of inoperable tumors.
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http://dx.doi.org/10.3389/fncom.2019.00081DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6923885PMC
December 2019

Immune landscapes associated with different glioblastoma molecular subtypes.

Acta Neuropathol Commun 2019 11 29;7(1):203. Epub 2019 Nov 29.

Department of Neurosurgery, University of Pennsylvania School of Medicine, Philadelphia, PA, 19104, USA.

Recent work has highlighted the tumor microenvironment as a central player in cancer. In particular, interactions between tumor and immune cells may help drive the development of brain tumors such as glioblastoma multiforme (GBM). Despite significant research into the molecular classification of glioblastoma, few studies have characterized in a comprehensive manner the immune infiltrate in situ and within different GBM subtypes.In this study, we use an unbiased, automated immunohistochemistry-based approach to determine the immune phenotype of the four GBM subtypes (classical, mesenchymal, neural and proneural) in a cohort of 98 patients. Tissue Micro Arrays (TMA) were stained for CD20 (B lymphocytes), CD5, CD3, CD4, CD8 (T lymphocytes), CD68 (microglia), and CD163 (bone marrow derived macrophages) antibodies. Using automated image analysis, the percentage of each immune population was calculated with respect to the total tumor cells. Mesenchymal GBMs displayed the highest percentage of microglia, macrophage, and lymphocyte infiltration. CD68 and CD163 cells were the most abundant cell populations in all four GBM subtypes, and a higher percentage of CD163 cells was associated with a worse prognosis. We also compared our results to the relative composition of immune cell type infiltration (using RNA-seq data) across TCGA GBM tumors and validated our results obtained with immunohistochemistry with an external cohort and a different method. The results of this study offer a comprehensive analysis of the distribution and the infiltration of the immune components across the four commonly described GBM subgroups, setting the basis for a more detailed patient classification and new insights that may be used to better apply or design immunotherapies for GBM.
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http://dx.doi.org/10.1186/s40478-019-0803-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6902522PMC
November 2019

Transcriptional consequences of impaired immune cell responses induced by cystic fibrosis plasma characterized via dual RNA sequencing.

BMC Med Genomics 2019 05 22;12(1):66. Epub 2019 May 22.

Division of Pulmonary Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, 60611, USA.

Background: In cystic fibrosis (CF), impaired immune cell responses, driven by the dysfunctional CF transmembrane conductance regulator (CFTR) gene, may determine the disease severity but clinical heterogeneity remains a major therapeutic challenge. The characterization of molecular mechanisms underlying impaired immune responses in CF may reveal novel targets with therapeutic potential. Therefore, we utilized simultaneous RNA sequencing targeted at identifying differentially expressed genes, transcripts, and miRNAs that characterize impaired immune responses triggered by CF and its phenotypes.

Methods: Peripheral blood mononuclear cells (PBMCs) extracted from a healthy donor were stimulated with plasma from CF patients (n = 9) and healthy controls (n = 3). The PBMCs were cultured (1 × 10 cells/well) for 9 h at 37 ° C in 5% CO. After culture, total RNA was extracted from each sample and used for simultaneous total RNA and miRNA sequencing.

Results: Analysis of expression signatures from peripheral blood mononuclear cells induced by plasma of CF patients and healthy controls identified 151 genes, 154 individual transcripts, and 41 miRNAs differentially expressed in CF compared to HC while the expression signatures of 285 genes, 241 individual transcripts, and seven miRNAs differed due to CF phenotypes. Top immune pathways influenced by CF included agranulocyte adhesion, diapedesis signaling, and IL17 signaling, while those influenced by CF phenotypes included natural killer cell signaling and PI3K signaling in B lymphocytes. Upstream regulator analysis indicated dysregulation of CCL5, NF-κB and IL1A due to CF while dysregulation of TREM1 and TP53 regulators were associated with CF phenotype. Five miRNAs showed inverse expression patterns with three target genes relevant in CF-associated impaired immune pathways while two miRNAs showed inverse expression patterns with two target genes relevant to a dysregulated immune pathway associated with CF phenotypes.

Conclusions: Our results indicate that miRNAs and individual transcript variants are relevant molecular targets contributing to impaired immune cell responses in CF.
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http://dx.doi.org/10.1186/s12920-019-0529-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6532208PMC
May 2019

Platform-Independent Classification System to Predict Molecular Subtypes of High-Grade Serous Ovarian Carcinoma.

JCO Clin Cancer Inform 2019 04;3:1-9

Northwestern University, Chicago, IL.

Purpose: Molecular cancer subtyping is an important tool in predicting prognosis and developing novel precision medicine approaches. We developed a novel platform-independent gene expression-based classification system for molecular subtyping of patients with high-grade serous ovarian carcinoma (HGSOC).

Methods: Unprocessed exon array (569 tumor and nine normal) and RNA sequencing (RNA-seq; 376 tumor) HGSOC data sets, with clinical annotations, were downloaded from the Genomic Data Commons portal. Sample clustering was performed by non-negative matrix factorization by using isoform-level expression estimates. The association between the subtypes and overall survival was evaluated by Cox proportional hazards regression model after adjusting for the covariates. A novel classification system was developed for HGSOC molecular subtyping. Robustness and generalizability of the gene signatures were validated using independent microarray and RNA-seq data sets.

Results: Sample clustering recaptured the four known The Cancer Genome Atlas molecular subtypes but switched the subtype for 22% of the cases, which resulted in significant ( = .006) survival differences among the refined subgroups. After adjusting for covariate effects, the mesenchymal subgroup was found to be at an increased hazard for death compared with the immunoreactive subgroup. Both gene- and isoform-level signatures achieved more than 92% prediction accuracy when tested on independent samples profiled on the exon array platform. When the classifier was applied to RNA-seq data, the subtyping calls agreed with the predictions made from exon array data for 95% of the 279 samples profiled by both platforms.

Conclusion: Isoform-level expression analysis successfully stratifies patients with HGSOC into groups with differing prognosis and has led to the development of robust, platform-independent gene signatures for HGSOC molecular subtyping. The association of the refined The Cancer Genome Atlas HGSOC subtypes with overall survival, independent of covariates, enhances the clinical annotation of the HGSOC cohort.
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http://dx.doi.org/10.1200/CCI.18.00096DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6873993PMC
April 2019

IDH3α regulates one-carbon metabolism in glioblastoma.

Sci Adv 2019 01 2;5(1):eaat0456. Epub 2019 Jan 2.

Ken and Ruth Davee Department of Neurology, The Northwestern Brain Tumor Institute, The Robert H. Lurie Comprehensive Cancer Center, Northwestern University, 303 East Superior, Chicago, IL 60611, USA.

Mutation or transcriptional up-regulation of isocitrate dehydrogenases 1 and 2 ( and ) promotes cancer progression through metabolic reprogramming and epigenetic deregulation of gene expression. Here, we demonstrate that IDH3α, a subunit of the IDH3 heterotetramer, is elevated in glioblastoma (GBM) patient samples compared to normal brain tissue and promotes GBM progression in orthotopic glioma mouse models. IDH3α loss of function reduces tricarboxylic acid (TCA) cycle turnover and inhibits oxidative phosphorylation. In addition to its impact on mitochondrial energy metabolism, IDH3α binds to cytosolic serine hydroxymethyltransferase (cSHMT). This interaction enhances nucleotide availability during DNA replication, while the absence of IDH3α promotes methionine cycle activity, -adenosyl methionine generation, and DNA methylation. Thus, the regulation of one-carbon metabolism via an IDH3α-cSHMT signaling axis represents a novel mechanism of metabolic adaptation in GBM.
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http://dx.doi.org/10.1126/sciadv.aat0456DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6314828PMC
January 2019

Metastatic cancers promote cachexia through ZIP14 upregulation in skeletal muscle.

Nat Med 2018 06 6;24(6):770-781. Epub 2018 Jun 6.

Institute for Cancer Genetics, Columbia University, New York, NY, USA.

Patients with metastatic cancer experience a severe loss of skeletal muscle mass and function known as cachexia. Cachexia is associated with poor prognosis and accelerated death in patients with cancer, yet its underlying mechanisms remain poorly understood. Here, we identify the metal-ion transporter ZRT- and IRT-like protein 14 (ZIP14) as a critical mediator of cancer-induced cachexia. ZIP14 is upregulated in cachectic muscles of mice and in patients with metastatic cancer and can be induced by TNF-α and TGF-β cytokines. Strikingly, germline ablation or muscle-specific depletion of Zip14 markedly reduces muscle atrophy in metastatic cancer models. We find that ZIP14-mediated zinc uptake in muscle progenitor cells represses the expression of MyoD and Mef2c and blocks muscle-cell differentiation. Importantly, ZIP14-mediated zinc accumulation in differentiated muscle cells induces myosin heavy chain loss. These results highlight a previously unrecognized role for altered zinc homeostasis in metastatic cancer-induced muscle wasting and implicate ZIP14 as a therapeutic target for its treatment.
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http://dx.doi.org/10.1038/s41591-018-0054-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6015555PMC
June 2018

Radiomic MRI signature reveals three distinct subtypes of glioblastoma with different clinical and molecular characteristics, offering prognostic value beyond IDH1.

Sci Rep 2018 03 23;8(1):5087. Epub 2018 Mar 23.

Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

The remarkable heterogeneity of glioblastoma, across patients and over time, is one of the main challenges in precision diagnostics and treatment planning. Non-invasive in vivo characterization of this heterogeneity using imaging could assist in understanding disease subtypes, as well as in risk-stratification and treatment planning of glioblastoma. The current study leveraged advanced imaging analytics and radiomic approaches applied to multi-parametric MRI of de novo glioblastoma patients (n = 208 discovery, n = 53 replication), and discovered three distinct and reproducible imaging subtypes of glioblastoma, with differential clinical outcome and underlying molecular characteristics, including isocitrate dehydrogenase-1 (IDH1), O-methylguanine-DNA methyltransferase, epidermal growth factor receptor variant III (EGFRvIII), and transcriptomic subtype composition. The subtypes provided risk-stratification substantially beyond that provided by WHO classifications. Within IDH1-wildtype tumors, our subtypes revealed different survival (p < 0.001), thereby highlighting the synergistic consideration of molecular and imaging measures for prognostication. Moreover, the imaging characteristics suggest that subtype-specific treatment of peritumoral infiltrated brain tissue might be more effective than current uniform standard-of-care. Finally, our analysis found subtype-specific radiogenomic signatures of EGFRvIII-mutated tumors. The identified subtypes and their clinical and molecular correlates provide an in vivo portrait of phenotypic heterogeneity in glioblastoma, which points to the need for precision diagnostics and personalized treatment.
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http://dx.doi.org/10.1038/s41598-018-22739-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5865162PMC
March 2018

Characterization of brain tumor initiating cells isolated from an animal model of CNS primitive neuroectodermal tumors.

Oncotarget 2018 Mar 9;9(17):13733-13747. Epub 2018 Feb 9.

Department of Cancer Biology & Pharmacology, University of Illinois College of Medicine, Peoria, Illinois, United States of America.

CNS Primitive Neuroectodermal tumors (CNS-PNETs) are members of the embryonal family of malignant childhood brain tumors, which remain refractory to current therapeutic treatments. Current paradigm of brain tumorigenesis implicates brain tumor-initiating cells (BTIC) in the onset of tumorigenesis and tumor maintenance. However, despite their significance, there is currently no comprehensive characterization of CNS-PNETs BTICs. Recently, we described an animal model of CNS-PNET generated by orthotopic transplantation of human Radial Glial (RG) cells - the progenitor cells for adult neural stem cells (NSC) - into NOD-SCID mice brain and proposed that BTICs may play a role in the maintenance of these tumors. Here we report the characterization of BTIC lines derived from this CNS-PNET animal model. BTIC's orthotopic transplantation generated highly aggressive tumors also characterized as CNS-PNETs. The BTICs have the hallmarks of NSCs as they demonstrate self-renewing capacity and have the ability to differentiate into astrocytes and early migrating neurons. Moreover, the cells demonstrate aberrant accumulation of wild type tumor-suppressor protein p53, indicating its functional inactivation, highly up-regulated levels of onco-protein cMYC and the BTIC marker OCT3/4, along with metabolic switch to glycolysis - suggesting that these changes occurred in the early stages of tumorigenesis. Furthermore, based on RNA- and DNA-seq data, the BTICs did not acquire any transcriptome-changing genomic alterations indicating that the onset of tumorigenesis may be epigenetically driven. The study of these BTIC self-renewing cells in our model may enable uncovering the molecular alterations that are responsible for the onset and maintenance of the malignant PNET phenotype.
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http://dx.doi.org/10.18632/oncotarget.24460DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862611PMC
March 2018

Publisher Correction: Transcribed ultraconserved region 339 promotes carcinogenesis by modulating tumor suppressor microRNAs.

Nat Commun 2018 01 8;9(1):160. Epub 2018 Jan 8.

Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.

In the originally published version of this Article, the positions of the final two authors in the author list were inadvertently inverted during the production process. This error has now been corrected in both the PDF and HTML versions of the Article.
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http://dx.doi.org/10.1038/s41467-017-02485-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5758615PMC
January 2018

Transcribed ultraconserved region 339 promotes carcinogenesis by modulating tumor suppressor microRNAs.

Nat Commun 2017 11 27;8(1):1801. Epub 2017 Nov 27.

Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.

The transcribed ultraconserved regions (T-UCRs) encode long non-coding RNAs implicated in human carcinogenesis. Their mechanisms of action and the factors regulating their expression in cancers are poorly understood. Here we show that high expression of uc.339 correlates with lower survival in 210 non-small cell lung cancer (NSCLC) patients. We provide evidence from cell lines and primary samples that TP53 directly regulates uc.339. We find that transcribed uc.339 is upregulated in archival NSCLC samples, functioning as a decoy RNA for miR-339-3p, -663b-3p, and -95-5p. As a result, Cyclin E2, a direct target of all these microRNAs is upregulated, promoting cancer growth and migration. Finally, we find that modulation of uc.339 affects microRNA expression. However, overexpression or downregulation of these microRNAs causes no significant variations in uc.339 levels, suggesting a type of interaction for uc.339 that we call "entrapping". Our results support a key role for uc.339 in lung cancer.
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http://dx.doi.org/10.1038/s41467-017-01562-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5703849PMC
November 2017

Cancer-Associated IDH1 Promotes Growth and Resistance to Targeted Therapies in the Absence of Mutation.

Cell Rep 2017 05;19(9):1858-1873

Ken and Ruth Davee Department of Neurology, The Northwestern Brain Tumor Institute, The Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, International Institute for Nanotechnology, Northwestern University, Chicago, IL 60611, USA. Electronic address:

Oncogenic mutations in two isocitrate dehydrogenase (IDH)-encoding genes (IDH1 and IDH2) have been identified in acute myelogenous leukemia, low-grade glioma, and secondary glioblastoma (GBM). Our in silico and wet-bench analyses indicate that non-mutated IDH1 mRNA and protein are commonly overexpressed in primary GBMs. We show that genetic and pharmacologic inactivation of IDH1 decreases GBM cell growth, promotes a more differentiated tumor cell state, increases apoptosis in response to targeted therapies, and prolongs the survival of animal subjects bearing patient-derived xenografts (PDXs). On a molecular level, diminished IDH1 activity results in reduced α-ketoglutarate (αKG) and NADPH production, paralleled by deficient carbon flux from glucose or acetate into lipids, exhaustion of reduced glutathione, increased levels of reactive oxygen species (ROS), and enhanced histone methylation and differentiation marker expression. These findings suggest that IDH1 upregulation represents a common metabolic adaptation by GBMs to support macromolecular synthesis, aggressive growth, and therapy resistance.
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http://dx.doi.org/10.1016/j.celrep.2017.05.014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5564207PMC
May 2017

Stabilization of HIF-1α and HIF-2α, up-regulation of MYCC and accumulation of stabilized p53 constitute hallmarks of CNS-PNET animal model.

PLoS One 2017 1;12(3):e0173106. Epub 2017 Mar 1.

Department of Cancer Biology & Pharmacology, University of Illinois College of Medicine, Peoria, Illinois, United States of America.

Recently, we described a new animal model of CNS primitive neuroectodermal tumors (CNS-PNET), which was generated by orthotopic transplantation of human Radial Glial (RG) cells into NOD-SCID mice's brain sub-ventricular zone. In the current study we conducted comprehensive RNA-Seq analyses to gain insights on the mechanisms underlying tumorigenesis in this mouse model of CNS-PNET. Here we show that the RNA-Seq profiles derived from these tumors cluster with those reported for patients' PNETs. Moreover, we found that (i) stabilization of HIF-1α and HIF-2α, which are involved in mediation of the hypoxic responses in the majority of cell types, (ii) up-regulation of MYCC, a key onco-protein whose dysregulation occurs in ~70% of human tumors, and (iii) accumulation of stabilized p53, which is commonly altered in human cancers, constitute hallmarks of our tumor model, and might represent the basis for CNS-PNET tumorigenesis in this model. We discuss the possibility that these three events might be interconnected. These results indicate that our model may prove invaluable to uncover the molecular events leading to MYCC and TP53 alterations, which would be of broader interest considering their relevance to many human malignancies. Lastly, this mouse model might prove useful for drug screening targeting MYCC and related members of its protein interaction network.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0173106PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5332108PMC
August 2017

Orthogonal ubiquitin transfer identifies ubiquitination substrates under differential control by the two ubiquitin activating enzymes.

Nat Commun 2017 01 30;8:14286. Epub 2017 Jan 30.

Department of Pharmacology, Northwestern University, Chicago, Illinois 60611, USA.

Protein ubiquitination is mediated sequentially by ubiquitin activating enzyme E1, ubiquitin conjugating enzyme E2 and ubiquitin ligase E3. Uba1 was thought to be the only E1 until the recent identification of Uba6. To differentiate the biological functions of Uba1 and Uba6, we applied an orthogonal ubiquitin transfer (OUT) technology to profile their ubiquitination targets in mammalian cells. By expressing pairs of an engineered ubiquitin and engineered Uba1 or Uba6 that were generated for exclusive interactions, we identified 697 potential Uba6 targets and 527 potential Uba1 targets with 258 overlaps. Bioinformatics analysis reveals substantial differences in pathways involving Uba1- and Uba6-specific targets. We demonstrate that polyubiquitination and proteasomal degradation of ezrin and CUGBP1 require Uba6, but not Uba1, and that Uba6 is involved in the control of ezrin localization and epithelial morphogenesis. These data suggest that distinctive substrate pools exist for Uba1 and Uba6 that reflect non-redundant biological roles for Uba6.
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http://dx.doi.org/10.1038/ncomms14286DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5290280PMC
January 2017

Identification of Genetic and Epigenetic Variants Associated with Breast Cancer Prognosis by Integrative Bioinformatics Analysis.

Cancer Inform 2017 9;16:1-13. Epub 2017 Jan 9.

Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Introduction: Breast cancer being a multifaceted disease constitutes a wide spectrum of histological and molecular variability in tumors. However, the task for the identification of these variances is complicated by the interplay between inherited genetic and epigenetic aberrations. Therefore, this study provides an extrapolate outlook to the sinister partnership between DNA methylation and single-nucleotide polymorphisms (SNPs) in relevance to the identification of prognostic markers in breast cancer. The effect of these SNPs on methylation is defined as methylation quantitative trait loci (meQTL).

Materials And Methods: We developed a novel method to identify prognostic gene signatures for breast cancer by integrating genomic and epigenomic data. This is based on the hypothesis that multiple sources of evidence pointing to the same gene or pathway are likely to lead to reduced false positives. We also apply random resampling to reduce overfitting noise by dividing samples into training and testing data sets. Specifically, the common samples between Illumina 450 DNA methylation, Affymetrix SNP array, and clinical data sets obtained from the Cancer Genome Atlas (TCGA) for breast invasive carcinoma (BRCA) were randomly divided into training and test models. An intensive statistical analysis based on log-rank test and Cox proportional hazard model has established a significant association between differential methylation and the stratification of breast cancer patients into high- and low-risk groups, respectively.

Results: The comprehensive assessment based on the conjoint effect of CpG-SNP pair has guided in delaminating the breast cancer patients into the high- and low-risk groups. In particular, the most significant association was found with respect to cg05370838-rs2230576, cg00956490-rs940453, and cg11340537-rs2640785 CpG-SNP pairs. These CpG-SNP pairs were strongly associated with differential expression of , , and genes, respectively. Besides, the exclusive effect of SNPs such as rs10101376, rs140679, and rs1538146 also hold significant prognostic determinant.

Conclusions: Thus, the analysis based on DNA methylation and SNPs have resulted in the identification of novel susceptible loci that hold prognostic relevance in breast cancer.
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http://dx.doi.org/10.4137/CIN.S39783DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5224237PMC
January 2017

Combining Anti-Mir-155 with Chemotherapy for the Treatment of Lung Cancers.

Clin Cancer Res 2017 06 30;23(11):2891-2904. Epub 2016 Nov 30.

Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

The oncogenic miR-155 is upregulated in many human cancers, and its expression is increased in more aggressive and therapy-resistant tumors, but the molecular mechanisms underlying miR-155-induced therapy resistance are not fully understood. The main objectives of this study were to determine the role of miR-155 in resistance to chemotherapy and to evaluate anti-miR-155 treatment to chemosensitize tumors. We performed studies on cell lines to investigate the role of miR-155 in therapy resistance. To assess the effects of miR-155 inhibition on chemoresistance, we used an orthotopic lung cancer model of athymic nude mice, which we treated with anti-miR-155 alone or in combination with chemotherapy. To analyze the association of miR-155 expression and the combination of miR-155 and expression with cancer survival, we studied 956 patients with lung cancer, chronic lymphocytic leukemia, and acute lymphoblastic leukemia. We demonstrate that miR-155 induces resistance to multiple chemotherapeutic agents , and that downregulation of miR-155 successfully resensitizes tumors to chemotherapy We show that anti-miR-155-DOPC can be considered non-toxic We further demonstrate that miR-155 and are linked in a negative feedback mechanism and that a combination of high expression of miR-155 and low expression of is significantly associated with shorter survival in lung cancer. Our findings support the existence of an miR-155/TP53 feedback loop, which is involved in resistance to chemotherapy and which can be specifically targeted to overcome drug resistance, an important cause of cancer-related death. .
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http://dx.doi.org/10.1158/1078-0432.CCR-16-1025DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5449263PMC
June 2017

Identification and validation of regulatory SNPs that modulate transcription factor chromatin binding and gene expression in prostate cancer.

Oncotarget 2016 08;7(34):54616-54626

Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.

Prostate cancer (PCa) is the second most common solid tumor for cancer related deaths in American men. Genome wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with the increased risk of PCa. Because most of the susceptibility SNPs are located in noncoding regions, little is known about their functional mechanisms. We hypothesize that functional SNPs reside in cell type-specific regulatory elements that mediate the binding of critical transcription factors (TFs), which in turn result in changes in target gene expression. Using PCa-specific functional genomics data, here we identify 38 regulatory candidate SNPs and their target genes in PCa. Through risk analysis by incorporating gene expression and clinical data, we identify 6 target genes (ZG16B, ANKRD5, RERE, FAM96B, NAALADL2 and GTPBP10) as significant predictors of PCa biochemical recurrence. In addition, 5 SNPs (rs2659051, rs10936845, rs9925556, rs6057110 and rs2742624) are selected for experimental validation using Chromatin immunoprecipitation (ChIP), dual-luciferase reporter assay in LNCaP cells, showing allele-specific enhancer activity. Furthermore, we delete the rs2742624-containing region using CRISPR/Cas9 genome editing and observe the drastic downregulation of its target gene UPK3A. Taken together, our results illustrate that this new methodology can be applied to identify regulatory SNPs and their target genes that likely impact PCa risk. We suggest that similar studies can be performed to characterize regulatory variants in other diseases.
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http://dx.doi.org/10.18632/oncotarget.10520DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5338917PMC
August 2016

MNK Inhibition Disrupts Mesenchymal Glioma Stem Cells and Prolongs Survival in a Mouse Model of Glioblastoma.

Mol Cancer Res 2016 10 30;14(10):984-993. Epub 2016 Jun 30.

Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, Illinois. Division of Hematology/Oncology, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois. Department of Medicine, Jesse Brown VA Medical Center, Chicago, Illinois.

Glioblastoma multiforme remains the deadliest malignant brain tumor, with glioma stem cells (GSC) contributing to treatment resistance and tumor recurrence. We have identified MAPK-interacting kinases (MNK) as potential targets for the GSC population in glioblastoma multiforme. Isoform-level subtyping using The Cancer Genome Atlas revealed that both MNK genes (MKNK1 and MKNK2) are upregulated in mesenchymal glioblastoma multiforme as compared with other subtypes. Expression of MKNK1 is associated with increased glioma grade and correlated with the mesenchymal GSC marker, CD44, and coexpression of MKNK1 and CD44 predicts poor survival in glioblastoma multiforme. In established and patient-derived cell lines, pharmacologic MNK inhibition using LY2801653 (merestinib) inhibited phosphorylation of the eukaryotic translation initiation factor 4E, a crucial effector for MNK-induced mRNA translation in cancer cells and a marker of transformation. Importantly, merestinib inhibited growth of GSCs grown as neurospheres as determined by extreme limiting dilution analysis. When the effects of merestinib were assessed in vivo using an intracranial xenograft mouse model, improved overall survival was observed in merestinib-treated mice. Taken together, these data provide strong preclinical evidence that pharmacologic MNK inhibition targets mesenchymal glioblastoma multiforme and its GSC population.

Implications: These findings raise the possibility of MNK inhibition as a viable therapeutic approach to target the mesenchymal subtype of glioblastoma multiforme. Mol Cancer Res; 14(10); 984-93. ©2016 AACR.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5082426PMC
http://dx.doi.org/10.1158/1541-7786.MCR-16-0172DOI Listing
October 2016

Comparative evaluation of isoform-level gene expression estimation algorithms for RNA-seq and exon-array platforms.

Brief Bioinform 2017 03;18(2):260-269

Center for Systems and Computational Biology, Molecular and Cellular Oncogenesis Program, The Wistar Institute, 19104 Philadelphia, PA, USA.

Given that the majority of multi-exon genes generate diverse functional products, it is important to evaluate expression at the isoform level. Previous studies have demonstrated strong gene-level correlations between RNA sequencing (RNA-seq) and microarray platforms, but have not studied their concordance at the isoform level. We performed transcript abundance estimation on raw RNA-seq and exon-array expression profiles available for common glioblastoma multiforme samples from The Cancer Genome Atlas using different analysis pipelines, and compared both the isoform- and gene-level expression estimates between programs and platforms. The results showed better concordance between RNA-seq/exon-array and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) platforms for fold change estimates than for raw abundance estimates, suggesting that fold change normalization against a control is an important step for integrating expression data across platforms. Based on RT-qPCR validations, eXpress and Multi-Mapping Bayesian Gene eXpression (MMBGX) programs achieved the best performance for RNA-seq and exon-array platforms, respectively, for deriving the isoform-level fold change values. While eXpress achieved the highest correlation with the RT-qPCR and exon-array (MMBGX) results overall, RSEM was more highly correlated with MMBGX for the subset of transcripts that are highly variable across the samples. eXpress appears to be most successful in discriminating lowly expressed transcripts, but IsoformEx and RSEM correlate more strongly with MMBGX for highly expressed transcripts. The results also reinforce how potentially important isoform-level expression changes can be masked by gene-level estimates, and demonstrate that exon arrays yield comparable results to RNA-seq for evaluating isoform-level expression changes.
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http://dx.doi.org/10.1093/bib/bbw016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5444266PMC
March 2017

Evaluation of data discretization methods to derive platform independent isoform expression signatures for multi-class tumor subtyping.

BMC Genomics 2015 10;16 Suppl 11:S3. Epub 2015 Nov 10.

Background: Many supervised learning algorithms have been applied in deriving gene signatures for patient stratification from gene expression data. However, transferring the multi-gene signatures from one analytical platform to another without loss of classification accuracy is a major challenge. Here, we compared three unsupervised data discretization methods--Equal-width binning, Equal-frequency binning, and k-means clustering--in accurately classifying the four known subtypes of glioblastoma multiforme (GBM) when the classification algorithms were trained on the isoform-level gene expression profiles from exon-array platform and tested on the corresponding profiles from RNA-seq data.

Results: We applied an integrated machine learning framework that involves three sequential steps; feature selection, data discretization, and classification. For models trained and tested on exon-array data, the addition of data discretization step led to robust and accurate predictive models with fewer number of variables in the final models. For models trained on exon-array data and tested on RNA-seq data, the addition of data discretization step dramatically improved the classification accuracies with Equal-frequency binning showing the highest improvement with more than 90% accuracies for all the models with features chosen by Random Forest based feature selection. Overall, SVM classifier coupled with Equal-frequency binning achieved the best accuracy (> 95%). Without data discretization, however, only 73.6% accuracy was achieved at most.

Conclusions: The classification algorithms, trained and tested on data from the same platform, yielded similar accuracies in predicting the four GBM subgroups. However, when dealing with cross-platform data, from exon-array to RNA-seq, the classifiers yielded stable models with highest classification accuracies on data transformed by Equal frequency binning. The approach presented here is generally applicable to other cancer types for classification and identification of molecular subgroups by integrating data across different gene expression platforms.
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http://dx.doi.org/10.1186/1471-2164-16-S11-S3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4652565PMC
September 2016

Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques.

Neuro Oncol 2016 Mar 16;18(3):417-25. Epub 2015 Jul 16.

Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania (L.M., J.M.P., M.A., L.R., N.D., D.M.O.); Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania (H.A., X.D., R.L.W., M.B., C.D.); Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania (H.A., X.D., V.P., M.B., C.D.); Department of Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, Illinois (Y.B., S.P., R.V.D.); Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas (G.B.); Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania (M.M.-L., D.M.O.).

Background: MRI characteristics of brain gliomas have been used to predict clinical outcome and molecular tumor characteristics. However, previously reported imaging biomarkers have not been sufficiently accurate or reproducible to enter routine clinical practice and often rely on relatively simple MRI measures. The current study leverages advanced image analysis and machine learning algorithms to identify complex and reproducible imaging patterns predictive of overall survival and molecular subtype in glioblastoma (GB).

Methods: One hundred five patients with GB were first used to extract approximately 60 diverse features from preoperative multiparametric MRIs. These imaging features were used by a machine learning algorithm to derive imaging predictors of patient survival and molecular subtype. Cross-validation ensured generalizability of these predictors to new patients. Subsequently, the predictors were evaluated in a prospective cohort of 29 new patients.

Results: Survival curves yielded a hazard ratio of 10.64 for predicted long versus short survivors. The overall, 3-way (long/medium/short survival) accuracy in the prospective cohort approached 80%. Classification of patients into the 4 molecular subtypes of GB achieved 76% accuracy.

Conclusions: By employing machine learning techniques, we were able to demonstrate that imaging patterns are highly predictive of patient survival. Additionally, we found that GB subtypes have distinctive imaging phenotypes. These results reveal that when imaging markers related to infiltration, cell density, microvascularity, and blood-brain barrier compromise are integrated via advanced pattern analysis methods, they form very accurate predictive biomarkers. These predictive markers used solely preoperative images, hence they can significantly augment diagnosis and treatment of GB patients.
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http://dx.doi.org/10.1093/neuonc/nov127DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4767233PMC
March 2016
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