scholarly journals Gene expression information improves reliability of receptor status in breast cancer patients

Oncotarget ◽  
2017 ◽  
Vol 8 (44) ◽  
pp. 77341-77359 ◽  
Author(s):  
Michael Kenn ◽  
Karin Schlangen ◽  
Dan Cacsire Castillo-Tong ◽  
Christian F. Singer ◽  
Michael Cibena ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
Breast Cancer Patients ◽  
Receptor Status ◽  
Gene Expression Information

scholarly journals Hormone Receptor-Status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape

Cancers ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1165
Author(s):  
Seokhyun Yoon ◽  
Hye Sung Won ◽  
Keunsoo Kang ◽  
Kexin Qiu ◽  
Woong June Park ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
Hormone Receptor ◽  
Expression Profiles ◽  
Receptor Expression ◽  
Luminal Breast Cancer ◽  
Breast Cancer Patients ◽  
Intrinsic Subtypes ◽  
Receptor Status

The cost of next-generation sequencing technologies is rapidly declining, making RNA-seq-based gene expression profiling (GEP) an affordable technique for predicting receptor expression status and intrinsic subtypes in breast cancer patients. Based on the expression levels of co-expressed genes, GEP-based receptor-status prediction can classify clinical subtypes more accurately than can immunohistochemistry (IHC). Using data from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA BRCA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets, we identified common predictor genes found in both datasets and performed receptor-status prediction based on these genes. By assessing the survival outcomes of patients classified using GEP- or IHC-based receptor status, we compared the prognostic value of the two methods. We found that GEP-based HR prediction provided higher concordance with the intrinsic subtypes and a stronger association with treatment outcomes than did IHC-based hormone receptor (HR) status. GEP-based prediction improved the identification of patients who could benefit from hormone therapy, even in patients with non-luminal breast cancer. We also confirmed that non-matching subgroup classification affected the survival of breast cancer patients and that this could be largely overcome by GEP-based receptor-status prediction. In conclusion, GEP-based prediction provides more reliable classification of HR status, improving therapeutic decision making for breast cancer patients.

scholarly journals Role of Metastasis in Hypertabastic Survival Analysis of Breast Cancer: Interaction with Clinical and Gene Expression Variables

2012 ◽  
Vol 5 ◽  
pp. CGM.S8821 ◽  
Author(s):  
Mohammad A. Tabatabai ◽  
Wayne M. Eby ◽  
Nadim Nimeh ◽  
Karan P. Singh
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Estrogen Receptor ◽  
Cancer Patients ◽  
Estrogen Receptor Status ◽  
The Other ◽  
Primary Concern ◽  
Breast Cancer Patients ◽  
Receptor Status

This paper analyzes the survival of breast cancer patients, exploring the role of a metastasis variable in combination with clinical and gene expression variables. We use the hypertabastic model in a detailed analysis of 295 breast cancer patients from the Netherlands Cancer Institute given in. 1 In comparison to Cox regression the increase in accuracy is complemented by the ability to analyze the time course of the disease progression using the explicitly described hazard and survival curves. We also demonstrate the ability to compute deciles for survival and probability of survival to a given time. Our primary concern in this article is the introduction of a variable representing the existence of metastasis and the effects on the other clinical and gene expression variables. In addition to making a quantitative assessment of the impact of metastasis on the prospects for survival, we are able to look at its interactions with the other prognostic variables. The estrogen receptor status increase in importance, while the significance of the gene expression variables used in the combined model diminishes. When considering only the subgroup of patients who experienced metastasis, the covariates in the model are only the clinical variables for estrogen receptor status and tumor grade.

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.

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.

Relation between steroid receptor status and body weight in breast cancer patients

1992 ◽  
Vol 28 (1) ◽  
pp. 112-115 ◽  
Author(s):  
Dario Giuffrida ◽  
Lorenzo Lupo ◽  
Gianfranco A. La Porta ◽  
Giacomo L. La Rosa ◽  
Giuseppa Padova ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Body Weight ◽  
Cancer Patients ◽  
Steroid Receptor ◽  
Breast Cancer Patients ◽  
Receptor Status
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