scholarly journals Modulating gene expression in breast cancer via DNA secondary structure and the CRISPR toolbox

NAR Cancer ◽  
2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Jessica A Kretzmann ◽  
Kelly L Irving ◽  
Nicole M Smith ◽  
Cameron W Evans
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Transcription Factors ◽  
Active Sites ◽  
Early Stage ◽  
Expression Patterns ◽  
Significant Risk ◽  
Metastatic Breast ◽  
Survival Prognosis ◽  
Disease Heterogeneity

Abstract Breast cancer is the most commonly diagnosed malignancy in women, and while the survival prognosis of patients with early-stage, non-metastatic disease is ∼75%, recurrence poses a significant risk and advanced and/or metastatic breast cancer is incurable. A distinctive feature of advanced breast cancer is an unstable genome and altered gene expression patterns that result in disease heterogeneity. Transcription factors represent a unique therapeutic opportunity in breast cancer, since they are known regulators of gene expression, including gene expression involved in differentiation and cell death, which are themselves often mutated or dysregulated in cancer. While transcription factors have traditionally been viewed as ‘undruggable’, progress has been made in the development of small-molecule therapeutics to target relevant protein–protein, protein–DNA and enzymatic active sites, with varying levels of success. However, non-traditional approaches such as epigenetic editing, transcriptional control via CRISPR/dCas9 systems, and gene regulation through non-canonical nucleic acid secondary structures represent new directions yet to be fully explored. Here, we discuss these new approaches and current limitations in light of new therapeutic opportunities for breast cancers.

Gene Expression Patterns and Profile Changes Pre- and Post-Erlotinib Treatment in Patients with Metastatic Breast Cancer

2005 ◽  
Vol 11 (17) ◽  
pp. 6226-6232 ◽  
Author(s):  
Sherry X. Yang ◽  
Richard M. Simon ◽  
Antoinette R. Tan ◽  
Diana Nguyen ◽  
Sandra M. Swain
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Metastatic Breast Cancer ◽  
Expression Patterns ◽  
Metastatic Breast ◽  
Gene Expression Patterns ◽  
Profile Changes

scholarly journals TuBA: Tunable Biclustering Algorithm Reveals Clinically Relevant Tumor Transcriptional Profiles in Breast Cancer

2018 ◽  
Author(s):  
Amartya Singh ◽  
Gyan Bhanot ◽  
Hossein Khiabanian
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Disease Heterogeneity ◽  
Transcriptional Profiles ◽  
Biologically Relevant ◽  
Tumor Subtypes ◽  
Stromal Tissue ◽  
Copy Number Changes

ABSTRACTBackgroundTraditional clustering approaches for gene expression data are not well adapted to address the complexity and heterogeneity of tumors, where small sets of genes may be aberrantly co-expressed in specific subsets of tumors. Biclustering algorithms that perform local clustering on subsets of genes and conditions help address this problem. We propose a graph-based Tunable Biclustering Algorithm (TuBA) based on a novel pairwise proximity measure, examining the relationship of samples at the extremes of genes’ expression profiles to identify similarly altered signatures.ResultsTuBA’s predictions are consistent in 3,940 Breast Invasive Carcinoma (BRCA) samples from three independent sources, employing different technologies for measuring gene expression (RNASeq and Microarray). Over 60% of biclusters identified independently in each dataset had significant agreement in their gene sets, as well as similar clinical implications. About 50% of biclusters were enriched in the ER-/HER2- (or basal-like) subtype, while more than 50% were associated with transcriptionally active copy number changes. Biclusters representing gene co-expression patterns in stromal tissue were also identified in tumor specimens.ConclusionTuBA offers a simple biclustering method that can identify biologically relevant gene co-expression signatures not captured by traditional unsupervised clustering approaches. It complements biclustering approaches that are designed to identify constant or coherent submatrices in gene expression datasets, and outperforms them in identifying a multitude of altered transcriptional profiles that are associated with observed genomic heterogeneity of diseased states in breast cancer, both within and across tumor subtypes, a promising step in understanding disease heterogeneity, and a necessary first step in individualized therapy.

scholarly journals Immune gene expression profiling reveals heterogeneity in luminal breast tumors

2019 ◽  
Author(s):  
Bin Zhu ◽  
Shelly Lap Ah Tse ◽  
Difei Wang ◽  
Hela Koka ◽  
Tongwu Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Profiling ◽  
T Cell Activation ◽  
Expression Profiling ◽  
Expression Patterns ◽  
Luminal Breast Cancer ◽  
Immune Gene ◽  
Disease Heterogeneity ◽  
Immune Gene Expression

AbstractDisease heterogeneity of immune gene expression patterns of luminal breast cancer (BC) has not been well studied. We performed immune gene expression profiling of tumor and adjacent normal tissue in 92 Asian luminal BC patients and identified three distinct immune subtypes. Tumors in one subtype exhibited signs of T-cell activation, lower ESR1/ESR2 expression ratio and higher expression of immune checkpoint genes, nonsynonymous mutation burden, APOBEC-signature mutations, and increasing body mass index compared to other luminal tumors. Tumors in a second subtype were characterized by increased expression of interferon-stimulated genes and enrichment for TP53 somatic mutations. The presence of three immune subtypes within luminal BC was replicated in cases drawn from The Cancer Genome Atlas and a Korean breast cancer study. Our findings suggest that immune gene expression and associated genomic features could be useful to further stratify luminal BC beyond the current luminal A/B classification.

scholarly journals Development and validation for research assessment of Oncotype DX® Breast Recurrence Score, EndoPredict® and Prosigna®

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Richard Buus ◽  
Zsolt Szijgyarto ◽  
Eugene F. Schuster ◽  
Hui Xiao ◽  
Ben P. Haynes ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Early Stage ◽  
Recurrence Score ◽  
Distant Recurrence ◽  
Research Assessment ◽  
Expression Data ◽  
Her2 Breast Cancer ◽  
Validation Set ◽  
Development And Validation

AbstractMulti-gene prognostic signatures including the Oncotype® DX Recurrence Score (RS), EndoPredict® (EP) and Prosigna® (Risk Of Recurrence, ROR) are widely used to predict the likelihood of distant recurrence in patients with oestrogen-receptor-positive (ER+), HER2-negative breast cancer. Here, we describe the development and validation of methods to recapitulate RS, EP and ROR scores from NanoString expression data. RNA was available from 107 tumours from postmenopausal women with early-stage, ER+, HER2− breast cancer from the translational Arimidex, Tamoxifen, Alone or in Combination study (TransATAC) where previously these signatures had been assessed with commercial methodology. Gene expression was measured using NanoString nCounter. For RS and EP, conversion factors to adjust for cross-platform variation were estimated using linear regression. For ROR, the steps to perform subgroup-specific normalisation of the gene expression data and calibration factors to calculate the 46-gene ROR score were assessed and verified. Training with bootstrapping (n = 59) was followed by validation (n = 48) using adjusted, research use only (RUO) NanoString-based algorithms. In the validation set, there was excellent concordance between the RUO scores and their commercial counterparts (rc(RS) = 0.96, 95% CI 0.93–0.97 with level of agreement (LoA) of −7.69 to 8.12; rc(EP) = 0.97, 95% CI 0.96–0.98 with LoA of −0.64 to 1.26 and rc(ROR) = 0.97 (95% CI 0.94–0.98) with LoA of −8.65 to 10.54). There was also a strong agreement in risk stratification: (RS: κ = 0.86, p < 0.0001; EP: κ = 0.87, p < 0.0001; ROR: κ = 0.92, p < 0.001). In conclusion, the calibrated algorithms recapitulate the commercial RS and EP scores on individual biopsies and ROR scores on samples based on subgroup-centreing method using NanoString expression data.

scholarly journals Integrative transcriptome data mining for identification of core lncRNAs in breast cancer

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7821 ◽  
Author(s):  
Xiaoming Zhang ◽  
Jing Zhuang ◽  
Lijuan Liu ◽  
Zhengguo He ◽  
Cun Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Prognostic Value ◽  
Early Stage ◽  
Expression Profiles ◽  
Diagnostic Value ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Data Set

Background Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. Methods First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). Results Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. Conclusion Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.

scholarly journals Alterations of Growth and Focal Adhesion Molecules in Human Breast Cancer Cells Exposed to the Random Positioning Machine

2021 ◽  
Vol 9 ◽  
Author(s):  
Jayashree Sahana ◽  
Thomas J. Corydon ◽  
Markus Wehland ◽  
Marcus Krüger ◽  
Sascha Kopp ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Target Genes ◽  
Interaction Analysis ◽  
Expression Patterns ◽  
Focal Adhesions ◽  
Multicellular Spheroids ◽  
Down Regulation ◽  
Random Positioning Machine ◽  
Mcf 7

In this study, we evaluated changes in focal adhesions (FAs) in two types of breast cancer cell (BCC) lines (differentiated MCF-7 and the triple-negative MDA-MB-231 cell line) exposed to simulated microgravity (s-μg) created by a random positioning machine (RPM) for 24 h. After exposure, the BCC changed their growth behavior and exhibited two phenotypes in RPM samples: one portion of the cells grew as a normal two-dimensional monolayer [adherent (AD) BCC], while the other portion formed three-dimensional (3D) multicellular spheroids (MCS). After 1 h and 30 min (MDA-MB-231) and 1 h 40 min (MCF-7), the MCS adhered completely to the slide flask bottom. After 2 h, MDA-MB-231 MCS cells started to migrate, and after 6 h, a large number of the cells had left the MCS and continued to grow in a scattered pattern, whereas MCF-7 cells were growing as a confluent monolayer after 6 h and 24 h. We investigated the genes associated with the cytoskeleton, the extracellular matrix and FAs. ACTB, TUBB, FN1, FAK1, and PXN gene expression patterns were not significantly changed in MDA-MB-231 cells, but we observed a down-regulation of LAMA3, ITGB1 mRNAs in AD cells and of ITGB1, TLN1 and VCL mRNAs in MDA-MB-231 MCS. RPM-exposed MCF-7 cells revealed a down-regulation in the gene expression of FAK1, PXN, TLN1, VCL and CDH1 in AD cells and PXN, TLN and CDH1 in MCS. An interaction analysis of the examined genes involved in 3D growth and adhesion indicated a central role of fibronectin, vinculin, and E-cadherin. Live cell imaging of eGFP-vinculin in MCF-7 cells confirmed these findings. β-catenin-transfected MCF-7 cells revealed a nuclear expression in 1g and RPM-AD cells. The target genes BCL9, MYC and JUN of the Wnt/β-catenin signaling pathway were differentially expressed in RPM-exposed MCF-7 cells. These findings suggest that vinculin and β-catenin are key mediators of BCC to form MCS during 24 h of RPM-exposure.

Development of blood based gene expression test to detect early stage breast cancer in an Indian population

2008 ◽  
Vol 6 (7) ◽  
pp. 98
Author(s):  
D. Tobin ◽  
T. Lindahl ◽  
K. Bardsen ◽  
M. Kauczynska ◽  
D.R. Punia ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Indian Population ◽  
Early Stage ◽  
Early Stage Breast Cancer
Sign in / Sign up

Export Citation Format

Share Document