Molecular biology in breast cancer: Should molecular classifiers be assessed by conventional tools or by gene expression arrays?

2012 ◽  
Vol 84 ◽  
pp. e58-e69 ◽  
Author(s):  
Debora Fumagalli ◽  
Fabrice Andre ◽  
Martine J. Piccart-Gebhart ◽  
Christos Sotiriou ◽  
Christine Desmedt
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Molecular Biology ◽  
Expression Arrays ◽  
Gene Expression Arrays

scholarly journals Microarray Normalization Revisited for Reproducible Breast Cancer Biomarkers

2020 ◽  
Vol 2020 ◽  
pp. 1-27
Author(s):  
Michael Kenn ◽  
Dan Cacsire Castillo-Tong ◽  
Christian F. Singer ◽  
Michael Cibena ◽  
Heinz Kölbl ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Breast Cancer Subtype ◽  
Good Deal ◽  
Cancer Biomarkers ◽  
Breast Cancer Patients ◽  
Expression Arrays ◽  
Gene Expression Arrays ◽  
Normalization Methods ◽  
Golden Standard

Precision medicine for breast cancer relies on biomarkers to select therapies. However, the reliability of biomarkers drawn from gene expression arrays has been questioned and calls for reassessment, in particular for large datasets. We revisit widely used data-normalization procedures and evaluate differences in outcome in order to pinpoint the most reliable reprocessing methods biomarkers can be based upon. We generated a database of 3753 breast cancer patients out of 38 studies by downloading and curating patient samples from NCBI-GEO. As gene-expression biomarkers, we select the assessment of receptor status and breast cancer subtype classification. Each normalization procedure is applied separately, and biomarkers are then evaluated for each patient. Differences between normalization pipelines are quantified as percentages of patients having outcomes different for each pipeline. Some normalization procedures lead to quite consistent biomarkers, differing only in 1-2% of patients. Other normalization procedures—some of them have been used in many clinical studies—end up with distrusting discrepancies (10% and more). A good deal of doubt regarding the reliability of microarrays may root in the haphazard application of inadequate preprocessing pipelines. Several modes of batch corrections are evaluated regarding a possible improvement of receptor prediction from gene expression versus the golden standard of immunohistochemistry. Finally, we nominate those normalization methods yielding consistent and trustable results. Adequate bioinformatics data preprocessing is key and crucial for any subsequent statistics to arrive at trustable results. We conclude with a suggestion for future bioinformatics development to further increase the reliability of cancer biomarkers.

scholarly journals Immune Effects of Transtuzumab in HER2 Positive Breast Cancer

2019 ◽  
Vol 70 (8) ◽  
pp. 2791-2794
Author(s):  
Anca Zgura ◽  
Laurentia Gales ◽  
Bogdan Haineala ◽  
Elvira Bratila ◽  
Claudia Mehedintu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
B Lymphocytes ◽  
Immunogenic Cell Death ◽  
Immune Markers ◽  
Her2 Positive ◽  
Expression Arrays ◽  
Her2 Positive Breast Cancer ◽  
Gene Expression Arrays ◽  
Positive Breast Cancer

The immune system could mediate the antitumor activity of several anticancer treatments. Several chemotherapy compounds, including anthracyclines and oxaliplatin, induce immunogenic cell death that in turn activates the antitumor immune response. Trastuzumab induces antibody-dependant cell-mediated cytotoxicity. On the basis of this background, immune markers have recently been the focus of intense translational research to predict and monitor the efficacy of treatments. Gene expression arrays and immunohistochemistry have assessed immune activation and infiltration by macrophages, natural killer, and T and B lymphocytes. In this paper we present the results of a study that included 22 patients diagnosed with Her2 positive breast cancer undergoing treatment with Transtuzumab.

Topological analysis of gene expression arrays identifies high risk molecular subtypes in breast cancer

2012 ◽  
Vol 23 (1-2) ◽  
pp. 3-15 ◽  
Author(s):  
Javier Arsuaga ◽  
Nils A. Baas ◽  
Daniel DeWoskin ◽  
Hideaki Mizuno ◽  
Aleksandr Pankov ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
High Risk ◽  
Molecular Subtypes ◽  
Topological Analysis ◽  
Expression Arrays ◽  
Gene Expression Arrays

scholarly journals Immune modulators in disease: integrating knowledge from the biomedical literature and gene expression

2015 ◽  
Vol 23 (3) ◽  
pp. 617-626 ◽  
Author(s):  
Nophar Geifman ◽  
Sanchita Bhattacharya ◽  
Atul J Butte
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Biomedical Literature ◽  
Cytokine Gene Expression ◽  
Future Research ◽  
Cytokine Gene ◽  
Medical Subject Headings ◽  
Expression Arrays ◽  
Gene Expression Arrays ◽  
Subject Headings

Abstract Objective Cytokines play a central role in both health and disease, modulating immune responses and acting as diagnostic markers and therapeutic targets. This work takes a systems-level approach for integration and examination of immune patterns, such as cytokine gene expression with information from biomedical literature, and applies it in the context of disease, with the objective of identifying potentially useful relationships and areas for future research. Results We present herein the integration and analysis of immune-related knowledge, namely, information derived from biomedical literature and gene expression arrays. Cytokine-disease associations were captured from over 2.4 million PubMed records, in the form of Medical Subject Headings descriptor co-occurrences, as well as from gene expression arrays. Clustering of cytokine-disease co-occurrences from biomedical literature is shown to reflect current medical knowledge as well as potentially novel relationships between diseases. A correlation analysis of cytokine gene expression in a variety of diseases revealed compelling relationships. Finally, a novel analysis comparing cytokine gene expression in different diseases to parallel associations captured from the biomedical literature was used to examine which associations are interesting for further investigation. Discussion We demonstrate the usefulness of capturing Medical Subject Headings descriptor co-occurrences from biomedical publications in the generation of valid and potentially useful hypotheses. Furthermore, integrating and comparing descriptor co-occurrences with gene expression data was shown to be useful in detecting new, potentially fruitful, and unaddressed areas of research. Conclusion Using integrated large-scale data captured from the scientific literature and experimental data, a better understanding of the immune mechanisms underlying disease can be achieved and applied to research.

scholarly journals RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays

2008 ◽  
Vol 18 (9) ◽  
pp. 1509-1517 ◽  
Author(s):  
J. C. Marioni ◽  
C. E. Mason ◽  
S. M. Mane ◽  
M. Stephens ◽  
Y. Gilad
Keyword(s):  
Gene Expression ◽  
Rna Seq ◽  
Expression Arrays ◽  
Gene Expression Arrays ◽  
Technical Reproducibility

scholarly journals Analysis of the Early Immune Response to Infection by Infectious Bursal Disease Virus in Chickens Differing in Their Resistance to the Disease

2014 ◽  
Vol 89 (5) ◽  
pp. 2469-2482 ◽  
Author(s):  
Jacqueline Smith ◽  
Jean-Remy Sadeyen ◽  
Colin Butter ◽  
Pete Kaiser ◽  
David W. Burt
Keyword(s):  
Gene Expression ◽  
Disease Virus ◽  
Infectious Bursal Disease Virus ◽  
Host Response ◽  
Infectious Bursal Disease ◽  
Expression Arrays ◽  
Gene Expression Arrays ◽  
Antiviral Genes ◽  
Bursal Disease ◽  
Comprehensive Study

ABSTRACTChicken whole-genome gene expression arrays were used to analyze the host response to infection by infectious bursal disease virus (IBDV). Spleen and bursal tissue were examined from control and infected birds at 2, 3, and 4 days postinfection from two lines that differ in their resistance to IBDV infection. The host response was evaluated over this period, and differences between susceptible and resistant chicken lines were examined. Antiviral genes, includingIFNA,IFNG,MX1,IFITM1,IFITM3, andIFITM5, were upregulated in response to infection. Evaluation of this gene expression data allowed us to predict several genes as candidates for involvement in resistance to IBDV.IMPORTANCEInfectious bursal disease (IBD) is of economic importance to the poultry industry and thus is also important for food security. Vaccines are available, but field strains of the virus are of increasing virulence. There is thus an urgent need to explore new control solutions, one of which would be to breed birds with greater resistance to IBD. This goal is perhaps uniquely achievable with poultry, of all farm animal species, since the genetics of 85% of the 60 billion chickens produced worldwide each year is under the control of essentially two breeding companies. In a comprehensive study, we attempt here to identify global transcriptomic differences in the target organ of the virus between chicken lines that differ in resistance and to predict candidate resistance genes.

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