The Beginning of a Molecular Description of Human Cancer: Chromatin 3D-Structure, DNA Supercoil, H1 Histone, Microtubules and the Modulation of Unbalanced Gene Expression

1991 ◽  
pp. 73-99
Author(s):  
C. Nicolini
Keyword(s):  
Gene Expression ◽  
Human Cancer ◽  
3D Structure ◽  
H1 Histone ◽  
Molecular Description

scholarly journals Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yuanyuan Li ◽  
David M. Umbach ◽  
Juno M. Krahn ◽  
Igor Shats ◽  
Xiaoling Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Predictive Models ◽  
Drug Sensitivity ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
The Cancer Genome Atlas ◽  
Differential Sensitivity ◽  
Tumor Type

Abstract Background Human cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients’ care. Tremendous progress has been made. Results In this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC50) from cancer cell lines. We identified many known drug-gene interactions and uncovered several potentially novel drug-gene associations. Importantly, we further applied these predictive models to ~ 17,000 bulk RNA-seq samples from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to predict drug sensitivity for both normal and tumor tissues. We created a web site for users to visualize and download our predicted data (https://manticore.niehs.nih.gov/cancerRxTissue). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug. Conclusions We demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our prediction could have relevance for preclinical drug testing and in phase I clinical design.

Regulation of DNA repair gene expression in human cancer cell lines

2006 ◽  
Vol 97 (5) ◽  
pp. 1121-1136 ◽  
Author(s):  
Claire J. McGurk ◽  
Michele Cummings ◽  
Beate Köberle ◽  
John A. Hartley ◽  
R. Timothy Oliver ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Repair ◽  
Cell Lines ◽  
Cancer Cell ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
Human Cancer Cell ◽  
Repair Gene ◽  
Human Cancer Cell Lines ◽  
Dna Repair Gene

Do wine polyphenols modulate p53 gene expression in human cancer cell lines?

2001 ◽  
Vol 34 (5) ◽  
pp. 415-420 ◽  
Author(s):  
George J Soleas ◽  
David M Goldberg ◽  
Linda Grass ◽  
Michael Levesque ◽  
Eleftherios P Diamandis
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Human Cancer ◽  
P53 Gene ◽  
Cancer Cell Lines ◽  
Human Cancer Cell ◽  
Human Cancer Cell Lines ◽  
Wine Polyphenols

scholarly journals BIOM-16. IMMUNOMIC ANALYSIS OF GLIOBLASTOMA (GBM) USING GENE EXPRESSION PROFILING

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii5-ii5
Author(s):  
Michael Castro ◽  
Nilofar Badra-Azar ◽  
Thomas Kessler ◽  
Moritz Schütte ◽  
Bodo Lange ◽  
...  
Keyword(s):  
Gene Expression ◽  
Antigen Presentation ◽  
Hierarchical Clustering ◽  
Immune Evasion ◽  
Human Cancer ◽  
Progression Free Survival ◽  
Immune Checkpoints ◽  
Successful Implementation ◽  
Cell Trafficking ◽  
Box Plots

Abstract BACKGROUND Despite the success of immunotherapy across the spectrum of human cancer, a successful strategy has not emerged for GBM. While PD-L1 IHC and TMB have demonstrated some utility as predictors of immunotherapy benefit, responsiveness is complexly determined by factors affecting T cell trafficking, antigen presentation, other immune checkpoints, and mediators of immune exhaustion. Thus, we set out to to characterize mediators of immune resistance and their diversity in a population of GBM patients utilizing quantitative gene expression. METHODS A set of 54 immunotherapy and checkpoint relevant genes and seven genes related to immune failure were selected from the literature. RNA gene counts for TCGA glioblastoma multiforme samples (N=163) were downloaded from https://portal.gdc.cancer.gov/. Annotation on subtypes and PFS values were obtained from PMID: 24120142. Gene expression normalization as FPKM, hierarchical clustering and box-plots were performed using R-3.6.0. Statistical differences of gene expression between subtypes were quantified using a TurkeyHSD test. RESULTS A heatmap with hierarchical clustering for immune related genes for the TCGA GBM cohort was generated including colored annotation for the subtype and progression free survival. The graph shows a rough separation into two groups, where one group of the genes is tentatively associated with mesenchymal subtype and shorter survival and showing higher expression for most immune evasion genes. However, a heterogeneity of immune evasion signatures was identified within and across subtypes. Transcripts related to antigen presentation, EZH2, and LDHA varied significantly between GBM subtypes (p < 0.05). CONCLUSION Gene expression analysis has utility to identify specific mediators of immune evasion and to inform the selection of combination therapies for discrete subsets of patients. A Bayesian approach to patient selection for specific immunotherapy strategies may enhance the likelihood of successful implementation of immunotherapy in the clinic.

scholarly journals A Mechanism of Cyclin D1 Action Encoded in the Patterns of Gene Expression in Human Cancer

Cell ◽  
2003 ◽  
Vol 114 (3) ◽  
pp. 323-334 ◽  
Author(s):  
Justin Lamb ◽  
Sridhar Ramaswamy ◽  
Heide L. Ford ◽  
Bernardo Contreras ◽  
Robert V. Martinez ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cyclin D1 ◽  
Human Cancer

scholarly journals Universal attenuators and their interactions with feedback loops in gene regulatory networks

2016 ◽  
Author(s):  
Dianbo Liu ◽  
Luca Albergante ◽  
Timothy J Newman
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Human Cancer ◽  
Cancer Cell Line ◽  
Feedback Loops ◽  
Human Cancer Cell ◽  
E Coli ◽  
Gene Regulatory

AbstractUsing a combination of mathematical modelling, statistical simulation and large-scale data analysis we study the properties of linear regulatory chains (LRCs) within gene regulatory networks (GRNs). Our modelling indicates that downstream genes embedded within LRCs are highly insulated from the variation in expression of upstream genes, and thus LRCs act as attenuators. This observation implies a progressively weaker functionality of LRCs as their length increases. When analysing the preponderance of LRCs in the GRNs of E. coli K12 and several other organisms, we find that very long LRCs are essentially absent. In both E. coli and M. tuberculosis we find that four-gene LRCs are intimately linked to identical feedback loops that are involved in potentially chaotic stress response, indicating that the dynamics of these potentially destabilising motifs are strongly restrained under homeostatic conditions. The same relationship is observed in a human cancer cell line (K562), and we postulate that four-gene LRCs act as “universal attenuators”. These findings suggest a role for long LRCs in dampening variation in gene expression, thereby protecting cell identity, and in controlling dramatic shifts in cell-wide gene expression through inhibiting chaos-generating motifs.In briefWe present a general principle that linear regulatory chains exponentially attenuate the range of expression in gene regulatory networks. The discovery of a universal interplay between linear regulatory chains and genetic feedback loops in microorganisms and a human cancer cell line is analysed and discussed.HighlightsWithin gene networks, linear regulatory chains act as exponentially strong attenuators of upstream variationBecause of their exponential behaviour, linear regulatory chains beyond a few genes provide no additional functionality and are rarely observed in gene networks across a range of different organismsNovel interactions between four-gene linear regulatory chains and feedback loops were discovered in E. coli, M. tuberculosis and human cancer cells, suggesting a universal mechanism of control.

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