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.

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 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 Human Plasma Metabolomics for Biomarker Discovery: Targeting the Molecular Subtypes in Breast Cancer

Cancers ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 147
Author(s):  
Leticia Díaz-Beltrán ◽  
Carmen González-Olmedo ◽  
Natalia Luque-Caro ◽  
Caridad Díaz ◽  
Ariadna Martín-Blázquez ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Biomarker Discovery ◽  
Molecular Subtypes ◽  
Breast Cancer Subtype ◽  
P Value ◽  
Plasma Samples ◽  
Breast Cancer Patients ◽  
Clinical Value ◽  
Molecular Phenotypes

Purpose: The aim of this study is to identify differential metabolomic signatures in plasma samples of distinct subtypes of breast cancer patients that could be used in clinical practice as diagnostic biomarkers for these molecular phenotypes and to provide a more individualized and accurate therapeutic procedure. Methods: Untargeted LC-HRMS metabolomics approach in positive and negative electrospray ionization mode was used to analyze plasma samples from LA, LB, HER2+ and TN breast cancer patients and healthy controls in order to determine specific metabolomic profiles through univariate and multivariate statistical data analysis. Results: We tentatively identified altered metabolites displaying concentration variations among the four breast cancer molecular subtypes. We found a biomarker panel of 5 candidates in LA, 7 in LB, 5 in HER2 and 3 in TN that were able to discriminate each breast cancer subtype with a false discovery range corrected p-value < 0.05 and a fold-change cutoff value > 1.3. The model clinical value was evaluated with the AUROC, providing diagnostic capacities above 0.85. Conclusion: Our study identifies metabolic profiling differences in molecular phenotypes of breast cancer. This may represent a key step towards therapy improvement in personalized medicine and prioritization of tailored therapeutic intervention strategies.

scholarly journals Association between the nucleosome footprint of plasma DNA and neoadjuvant chemotherapy response for breast cancer

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Xu Yang ◽  
Geng-Xi Cai ◽  
Bo-Wei Han ◽  
Zhi-Wei Guo ◽  
Ying-Song Wu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Neoadjuvant Chemotherapy ◽  
Cancer Patients ◽  
Cell Receptor ◽  
Chemotherapy Response ◽  
Breast Cancer Patients ◽  
Plasma Dna ◽  
Transcription Start Sites ◽  
Response To Chemotherapy

AbstractGene expression signatures have been used to predict the outcome of chemotherapy for breast cancer. The nucleosome footprint of cell-free DNA (cfDNA) carries gene expression information of the original tissues and thus may be used to predict the response to chemotherapy. Here we carried out the nucleosome positioning on cfDNA from 85 breast cancer patients and 85 healthy individuals and two cancer cell lines T-47D and MDA-MB-231 using low-coverage whole-genome sequencing (LCWGS) method. The patients showed distinct nucleosome footprints at Transcription Start Sites (TSSs) compared with normal donors. In order to identify the footprints of cfDNA corresponding with the responses to neoadjuvant chemotherapy in patients, we mapped on nucleosome positions on cfDNA of patients with different responses: responders (pretreatment, n = 28; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 12) and nonresponders (pretreatment, n = 10; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 10). The coverage depth near TSSs in plasma cfDNA differed significantly between responders and nonresponders at pretreatment, and also after neoadjuvant chemotherapy treatment cycles. We identified 232 TSSs with differential footprints at pretreatment and 321 after treatment and found enrichment in Gene Ontology terms such as cell growth inhibition, tumor suppressor, necrotic cell death, acute inflammatory response, T cell receptor signaling pathway, and positive regulation of vascular endothelial growth factor production. These results suggest that cfDNA nucleosome footprints may be used to predict the efficacy of neoadjuvant chemotherapy for breast cancer patients and thus may provide help in decision making for individual patients.

Repression of protocadherin 17 is correlated with elevated angiogenesis and hypoxia markers in female patients with breast cancer

2021 ◽  
pp. 1-10
Author(s):  
Sanaa A. El-Benhawy ◽  
Samia A. Ebeid ◽  
Nadia A. Abd El Moneim ◽  
Rabie R. Abdel Wahed ◽  
Amal R.R. Arab
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Clinical Stage ◽  
Suppressor Gene ◽  
Normal Breast ◽  
Vital Role ◽  
Control Group ◽  
Breast Cancer Patients ◽  
Cd34 Cells ◽  
Breast Tissues

BACKGROUND: Altered cadherin expression plays a vital role in tumorigenesis, angiogenesis and tumor progression. However, the function of protocadherin 17 (PCDH17) in breast cancer remains unclear. OBJECTIVE: Our target is to explore PCDH17 gene expression in breast carcinoma tissues and its relation to serum angiopoietin-2 (Ang-2), carbonic anhydrase IX (CAIX) and % of circulating CD34+ cells in breast cancer patients (BCPs). METHODS: This study included Fifty female BCPs and 50 healthy females as control group. Cancerous and neighboring normal breast tissues were collected from BCPs as well as blood samples at diagnosis PCDH17 gene expression was evaluated by RT-PCR. Serum Ang-2, CAIX levels were measured by ELISA and % CD34+ cells were assessed by flow cytometry. RESULTS: PCDH17 was downregulated in cancerous breast tissues and its repression was significantly correlated with advanced stage and larger tumor size. Low PCDH17 was significantly correlated with serum Ang-2, % CD34+ cells and serum CAIX levels. Serum CAIX, Ang-2 and % CD34+ cells levels were highly elevated in BCPs and significantly correlated with clinical stage. CONCLUSIONS: PCDH17 downregulation correlated significantly with increased angiogenic and hypoxia biomarkers. These results explore the role of PCDH17 as a tumor suppressor gene inhibiting tumor growth and proliferation.

scholarly journals Decision theory for precision therapy of breast cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Michael Kenn ◽  
Dan Cacsire Castillo-Tong ◽  
Christian F. Singer ◽  
Rudolf Karch ◽  
Michael Cibena ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Decision Theory ◽  
Data Science ◽  
Gene Expression Omnibus ◽  
Hormone Receptor Status ◽  
Breast Cancer Patients ◽  
Receptor Status ◽  
Kaplan Meier ◽  
Precision Therapy

AbstractCorrectly estimating the hormone receptor status for estrogen (ER) and progesterone (PGR) is crucial for precision therapy of breast cancer. It is known that conventional diagnostics (immunohistochemistry, IHC) yields a significant rate of wrongly diagnosed receptor status. Here we demonstrate how Dempster Shafer decision Theory (DST) enhances diagnostic precision by adding information from gene expression. We downloaded data of 3753 breast cancer patients from Gene Expression Omnibus. Information from IHC and gene expression was fused according to DST, and the clinical criterion for receptor positivity was re-modelled along DST. Receptor status predicted according to DST was compared with conventional assessment via IHC and gene-expression, and deviations were flagged as questionable. The survival of questionable cases turned out significantly worse (Kaplan Meier p < 1%) than for patients with receptor status confirmed by DST, indicating a substantial enhancement of diagnostic precision via DST. This study is not only relevant for precision medicine but also paves the way for introducing decision theory into OMICS data science.

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