scholarly journals OTEH-6. Algorithmic approach to characterize post-treatment recurrent glioma using RNA sequencing and quantitative histopathology

2021 ◽  
Vol 3 (Supplement_2) ◽  
pp. ii11-ii11
Author(s):  
Michael Argenziano ◽  
Akshay Save ◽  
Deborah Boyett ◽  
Jack Grinband ◽  
Hyunsoo Yoon ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Treatment Effect ◽  
Post Treatment ◽  
Recurrent Glioma ◽  
Specific Gene ◽  
Recurrent Tumor ◽  
Algorithmic Approach ◽  
Tissue Specific ◽  
Tissue Specific Gene ◽  
Gene Sets

Abstract Introduction Distinguishing between tumor and treatment effect in post-treatment glioma, although crucial for clinical management, is difficult because contrast-enhancing regions are mixtures of recurrent tumor and reactive tissue, and definitive histopathological criteria do not exist. This study disentangles the marked intra-tumoral heterogeneity in the treatment-recurrent setting by developing an unsupervised framework to algorithmically categorize intraoperative MRI-localized biopsies into three clinically-relevant tissue clusters based on joint analysis of RNA sequencing and histopathological data. Methods A retrospective cohort of 84 MRI-localized biopsies from 37 patients with post-treatment recurrent glioblastoma underwent mRNA extraction and quantification via PLATEseq protocol. For 48 of 84 biopsies, a neighboring piece of tissue underwent quantitative histopathology based on labeling index (LI) for SOX2, CD68, NeuN, Ki67, and H&E. Correlation between LIs and gene expression for these 48 samples was performed. Genes significantly correlated (p<0.05) with ≥1 marker were used for hierarchical clustering of correlation matrix, identifying three mutually-exclusive tissue-specific gene sets. These sets were then used to perform ssGSEA to categorize each of 84 biopsies into one of three tissue types. Results Correlation analysis identified 7779 genes significantly correlated with ≥1 histopathological marker. Clustering revealed three gene sets associated with specific markers: SetA-3688 genes associated with SOX2/Ki67/H&E; SetB-2418 genes associated with CD68; SetC-1673 genes associated with NeuN. ssGSEA using these sets categorized each biopsy into one of three tissue types: 27 biopsies enriched in SetA, 28 in SetB, and 29 in SetC. Conclusions Using MRI-localized biopsies with both RNAseq and histopathological data, this algorithmic approach allows development of three orthogonal tissue-specific gene sets that can be applied to characterize the heterogeneity in post-treatment recurrent glioma: SetA: genes correlated with SOX2/Ki67/H&E, representing recurrent tumor; SetB: genes correlated with CD68 (microglia) representing reactive tissue consistent with treatment effect; SetC: genes correlated with NeuN (neurons), representing infiltrated brain.

scholarly journals TMOD-14. RADIOGRAPHIC, STIMULATED RAMAN HISTOLOGIC, AND MULTIPLEXED RNA-SEQUENCING ANALYSIS OF POST-TREATMENT RECURRENT HIGH-GRADE GLIOMAS

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi265-vi265
Author(s):  
Akshay Save ◽  
Todd Hollon ◽  
Zia Farooq ◽  
Deborah Boyett ◽  
Andrea Hawkins-Daarud ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Treatment Effect ◽  
Post Treatment ◽  
Response To Therapy ◽  
High Grade ◽  
Recurrent Tumor ◽  
Molecular Features ◽  
Reactive Changes ◽  
High Grade Gliomas ◽  
Stimulated Raman

Abstract High-grade gliomas (HGGs) nearly always recur after standard initial treatment, and the resulting mixture of recurrent tumor and treatment-induced reactive changes presents major diagnostic challenges. Anatomical imaging, such as MRI, cannot adequately distinguish progressive disease from treatment effect (pseudo-progression). Furthermore, there is marked intra-tumoral heterogeneity, such that some areas of a tumor may demonstrate necrotic treatment effect and others frank recurrence. Due to this difficulty reliably differentiating between these two clinical findings, analytic methods using multiple modalities are necessary to further our understanding of this disease process. To this end, we sought to correlate radiographic, histopathologic and molecular features of surgically sampled post-treatment suspected recurrence to identify markers distinguishing tumor growth from treatment effect. We performed Stimulated Raman Histology (SRH) imaging and highly multiplexed RNA-sequencing (PLATE-seq) on 84 MRI-localized biopsies from 39 patients with clinically suspected recurrent HGG. The SRH images were classified as recurrent tumor or gliotic/reactive tissue using a convolutional neural network trained on an independent cohort including a large set of recurrent HGG, and an automated cell-counting algorithm was used to quantify cellularity from the SRH image of each sample. Differential gene expression analysis of the PLATE-seq data was used to identify gene sets that distinguish recurrent tumor from treatment effect, and single sample gene set variation analysis (GSVA) was used to further assess the molecular and cellular composition of each MRI-localized sample. The histopathologic and molecular features of each sample were also correlated with the MRI features of the corresponding biopsy sites, and this data is currently being used to train machine learning models that predict the distribution of recurrent tumor and treatment-induced reactive changes within a patient’s radiographic lesion. These predictive radiomic models will help to guide neurosurgical sampling, and improve our ability to monitor glioma progression and response to therapy.

scholarly journals RNA-sequencing across three matched tissues reveals shared and tissue-specific gene expression and pathway signatures of COPD

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Jarrett D. Morrow ◽  
Robert P. Chase ◽  
Margaret M. Parker ◽  
Kimberly Glass ◽  
Minseok Seo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Specific Gene ◽  
Tissue Specific ◽  
Tissue Specific Gene ◽  
Specific Gene Expression

scholarly journals PEGS: An efficient tool for gene set enrichment within defined sets of genomic intervals

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 570
Author(s):  
Peter Briggs ◽  
A. Louise Hunter ◽  
Shen-hsi Yang ◽  
Andrew D. Sharrocks ◽  
Mudassar Iqbal
Keyword(s):  
Gene Expression ◽  
Gene Transcription ◽  
Expression Profiles ◽  
Specific Gene ◽  
Transcription Start ◽  
Tissue Specific ◽  
Tissue Specific Gene ◽  
Gene Sets ◽  
Biological Studies ◽  
Genomic Regions

Many biological studies of transcriptional control mechanisms produce lists of genes and non-coding genomic intervals from corresponding gene expression and epigenomic assays. In higher organisms, such as eukaryotes, genes may be regulated by distal elements, with these elements lying 10s–100s of kilobases away from a gene transcription start site. To gain insight into these distal regulatory mechanisms, it is important to determine comparative enrichment of genes of interest in relation to genomic regions of interest, and to be able to do so at a range of distances. Existing bioinformatics tools can annotate genomic regions to nearest known genes, or look for transcription factor binding sites in relation to gene transcription start sites. Here, we present PEGS (Peak set Enrichment in Gene Sets). This tool efficiently provides an exploratory analysis by calculating enrichment of multiple gene sets, associated with multiple non-coding elements (peak sets), at multiple genomic distances, and within topologically associated domains. We apply PEGS to gene sets derived from gene expression studies, and genomic intervals from corresponding ChIP-seq and ATAC-seq experiments to derive biologically meaningful results. We also demonstrate an extended application to tissue-specific gene sets and publicly available GWAS data, to find enrichment of sleep trait associated SNPs in relation to tissue-specific gene expression profiles.

scholarly journals A unique histone 3 lysine 14 chromatin signature underlies tissue-specific gene regulation

2021 ◽  
Author(s):  
Isabel Regadas ◽  
Olle Dahlberg ◽  
Roshan Vaid ◽  
Oanh Ho ◽  
Sergey Belikov ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Specific Gene ◽  
Chromatin Signature ◽  
Tissue Specific ◽  
Tissue Specific Gene ◽  
Histone 3

Histones: genetic diversity and tissue-specific gene expression

1997 ◽  
Vol 107 (1) ◽  
pp. 1-10 ◽  
Author(s):  
D. Doenecke ◽  
W. Albig ◽  
C. Bode ◽  
B. Drabent ◽  
K. Franke ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Diversity ◽  
Specific Gene ◽  
Tissue Specific ◽  
Tissue Specific Gene ◽  
Specific Gene Expression

scholarly journals Blood—Brain Barrier Genomics

2001 ◽  
Vol 21 (1) ◽  
pp. 61-68 ◽  
Author(s):  
Jian Yi Li ◽  
Ruben J. Boado ◽  
William M. Pardridge
Keyword(s):  
Gene Expression ◽  
Blood Brain Barrier ◽  
Brain Barrier ◽  
Specific Gene ◽  
Microvascular Endothelium ◽  
Tissue Specific ◽  
Tissue Specific Gene ◽  
Specific Gene Expression ◽  
Blood Brain ◽  
Tester Cdna

The blood–brain barrier (BBB) is formed by the brain microvascular endothelium, and the unique transport properties of the BBB are derived from tissue-specific gene expression within this cell. The current studies developed a gene microarray approach specific for the BBB by purifying the initial mRNA from isolated rat brain capillaries to generate tester cDNA. A polymerase chain reaction–based subtraction cloning method, suppression subtractive hybridization (SSH), was used, and the BBB cDNA was subtracted with driver cDNA produced from mRNA isolated from rat liver and kidney. Screening 5% of the subtracted tester cDNA resulted in identification of 50 gene products and more than 80% of those were selectively expressed at the BBB; these included novel gene sequences not found in existing databases, ESTs, and known genes that were not known to be selectively expressed at the BBB. Genes in the latter category include tissue plasminogen activator, insulin-like growth factor-2, PC-3 gene product, myelin basic protein, regulator of G protein signaling 5, utrophin, IκB, connexin-45, the class I major histocompatibility complex, the rat homologue of the transcription factors hbrm or EZH1, and organic anion transporting polypeptide type 2. Knowledge of tissue-specific gene expression at the BBB could lead to new targets for brain drug delivery and could elucidate mechanisms of brain pathology at the microvascular level.

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