Abstract 3414: Identifying dysregulated lncRNAs and miRNAs in low grade glioma: Uncovering signaling pathways and novel therapeutic targets

Author(s):  
Stephen Carney ◽  
Felipe Nuñez ◽  
Pedro R. Lowenstein ◽  
Maria G. Castro
2019 ◽  
Vol 21 (Supplement_3) ◽  
pp. iii11-iii11
Author(s):  
A van de Stolpe ◽  
W Verhaegh ◽  
L Holtzer

Abstract BACKGROUND Diffuse Intrinsic Pontine Glioma (DIPG) is a pediatric brain tumor (glioma), resistant to chemotherapy, with only a temporary response to radiotherapy and an extremely bad prognosis. Genomic abnormalities have been found, indicating abnormal activation of certain growth factor signaling pathways, while expression analysis suggests involvement of developmental signaling pathways.10–15 signal transduction pathways can drive cancer growth and metastasis. We have developed, and biologically validated, a method which enables quantitative measurements of functional activity of signal transduction pathways in individual cell/tissue samples, based on Bayesian computational model inference of pathway activity from measurements of mRNA levels of target genes of the transcription factor associated with the respective signalling pathway. A major envisioned clinical utility is prediction of therapy response. MATERIAL AND METHODS For signaling pathway analysis, Affymetrix expression microarray data were available (GEO dataset GSE26576) from 2 normal brain stem samples and from 6 low grade glioma and 26 DIPG samples (post-mortem after therapy). Of one DIPG patient samples were available before and after therapy. Signaling pathway activity scores were calculated for estrogen and androgen receptor, PI3K-FOXO, MAPK-AP1, JAK-STAT, NFκB, Hedgehog (HH), TGFβ, NOTCH and Wnt pathways. PI3K pathway activity is the reverse of FOXO activity, in the absence of oxidative stress (measured by SOD2 expression). Pathway activity scores were compared between normal tissue and low grade glioma samples and DIPG, and k-means cluster analysis was performed on the DIPG pathway activity scores. RESULTS After treatment, HH pathway activity was increased in DIPG compared to low grade glioma (p=0.0003), PI3K pathway activity scores showed large variations in activity in the DIPG group. Tumors with cell cycle (CDK4/6, CCND1-3) or Receptor Tyrosine Kinase-related gene amplifications had higher PI3K and HH pathway activity compared to tumors without identified amplifications (p<0.05) which, in contrast, had higher MAPK-AP1 pathway activity (p<0.002). Pathway-based clustering analysis revealed two DIPG clusters, C1: high TGFβ/MAPK-AP1 and low PI3K/HH pathway activity; C2: low TGFβ/MAPK-AP1, high PI3K/HH pathway activity. C1 best resembled low grade glioma. In the patient with pre/post treatment samples, a C1 pathway profile switched to a C2 profile after treatment. CONCLUSION Using our quantitative analysis of signaling pathway activity in post-treatment DIPG, two pathway activity subtypes were identified, of which the HH/PI3K high, TGFβ low activity subtype was associated with defined gene amplifications, and may have been induced by chemoradiation therapy. Clusters are supported by a clear biological rationale. Identified signaling pathways are potentially drug targetable.


Author(s):  
Giuseppe Sgroi ◽  
Giulia Russo ◽  
Francesco Pappalardo

Abstract Summary Although several bioinformatics tools have been developed to examine signaling pathways, little attention has been given to ever long-distance crosstalk mechanisms. Here, we developed PETAL, a Python tool that automatically explores and detects the most relevant nodes within a KEGG pathway, scanning and performing an in-depth search. PETAL can contribute to discovering novel therapeutic targets or biomarkers that are potentially hidden and not considered in the network under study. Availability PETAL is a freely available open-source software. It runs on all platforms that support Python3. The user manual and source code are accessible from https://github.com/Pex2892/PETAL.


2015 ◽  
Vol 89 (9) ◽  
pp. 1401-1438 ◽  
Author(s):  
Yow Keat Tham ◽  
Bianca C. Bernardo ◽  
Jenny Y. Y. Ooi ◽  
Kate L. Weeks ◽  
Julie R. McMullen

2021 ◽  
Vol 11 ◽  
Author(s):  
Md Tipu Khan ◽  
Bharat Prajapati ◽  
Simran Lakhina ◽  
Mridula Sharma ◽  
Sachin Prajapati ◽  
...  

Differences in the incidence and outcome of glioma between males and females are well known, being more striking for glioblastoma (GB) than low-grade glioma (LGG). The extensive and well-annotated data in publicly available databases enable us to analyze the molecular basis of these differences at a global level. Here, we have analyzed The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases to identify molecular indicators for these gender-based differences by different methods. Based on the nature of data available/accessible, the transcriptomic profile was studied in TCGA by using DeSeq2 and in CGGA by T-test, after correction based. Only IDH1 wild-type tumors were studied in CGGA. Using weighted gene co-expression network analysis (WGCNA), network analysis was done, followed by the assessment of modular differential connectivity. Differentially affected signaling pathways were identified. The gender-based effects of differentially expressed genes on survival were determined. DNA methylation was studied as an indicator of gender-based epigenetic differences. The results clearly showed gender-based differences in both GB and LGG, whatever method or database was used. While there were differences in the results obtained between databases and methods used, some major signaling pathways such as Wnt signaling and pathways involved in immune processes and the adaptive immune response were common to different assessments. There was also a differential gender-based influence of several genes on survival. Also, the autosomal genes NOX, FRG1BP, and AL354714.2 and X-linked genes such as PUDP, KDM6A, DDX3X, and SYAP1 had differential DNA methylation and expression profile in male and female GB, while for LGG, these included autosomal genes such as CNIH3 and ANKRD11 and X-linked genes such as KDM6A, MAOB, and EIF2S3. Some, such as FGF13 and DDX3X, have earlier been shown to have a role in tumor behavior, though their dimorphic effects in males and females have not been identified. Our study thus identifies several crucial differences between male and female glioma, which could be validated further. It also highlights that molecular studies without consideration of gender can obscure critical elements of biology and emphasizes the importance of parallel but separate analyses of male and female glioma.


2020 ◽  
Author(s):  
Raunak Shrestha ◽  
Marta Llaurado Fernandez ◽  
Amy Dawson ◽  
Joshua Hoenisch ◽  
Stanislav Volik ◽  
...  

AbstractBackgroundLow-grade serous ovarian carcinoma (LGSOC) is a rare tumor subtype with high case fatality rates. As such, there is a pressing need to develop more effective treatments using newly available preclinical models for therapeutic discovery and drug evaluation. Here, we use a multiomics approach to interrogate a collection of LGSOC patient-derived cell lines to elucidate novel biomarkers and therapeutic vulnerabilities.MethodsFourteen LGSOC cell lines were interrogated using whole exome sequencing, RNA sequencing, and mass spectrometry-based proteomics. Somatic mutation, copy-number aberrations, gene and protein expression were analyzed and integrated using different computational approaches. LGSOC cell line data was compared to publicly available LGSOC tumor data (AACR GENIE cohort), and also used for predictive biomarker identification of MEK inhibitor (MEKi) efficacy. Protein interaction databases were evaluated to identify novel therapeutic targets.ResultsKRAS mutations were exclusively found in MEKi-sensitive and NRAS mutations mostly in MEKi-resistant cell lines. Analysis of COSMIC mutational signatures revealed distinct patterns of nucleotide substitution mutations in MEKi-sensitive and MEKi-resistant cell lines. Deletions of CDKN2A/B and MTAP genes (chromosome 9p21) were much more frequent in cell lines than tumor samples and possibly represent key driver events in the absence of KRAS/NRAS/BRAF mutations. For in-vitro MEKi efficacy prediction, proteomic data provided better discrimination than gene expression data. Condensin, MCM, and RFC protein complexes were identified as potential treatment targets in MEKi-resistant cell lines.ConclusionsOur LGSOC cell lines are representative models of the most common molecular aberrations found in LGSOC tumors. This study highlights the importance of using proteomic data in multiomics assessment of drug prediction and identification of potential therapeutic targets. CDKN2A/B and MTAP deficiency offer an opportunity to find synthetically lethal candidates for novel treatments. Multiomics approaches are crucial to improving our understanding of the molecular aberrations in LGSOC, establishing effective drug prediction programs and identifying novel therapeutic targets in LGSOC.


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