scholarly journals Stable gene expression for normalisation and single-sample scoring

2020 ◽  
Vol 48 (19) ◽  
pp. e113-e113
Author(s):  
Dharmesh D Bhuva ◽  
Joseph Cursons ◽  
Melissa J Davis
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
Clinical Application ◽  
Gene Expression Signature ◽  
Molecular Signature ◽  
Stable Gene ◽  
Breast Cancer Patients ◽  
Gene Expression Signatures ◽  
Pam50 Subtypes

Abstract Gene expression signatures have been critical in defining the molecular phenotypes of cells, tissues, and patient samples. Their most notable and widespread clinical application is stratification of breast cancer patients into molecular (PAM50) subtypes. The cost and relatively large amounts of fresh starting material required for whole-transcriptome sequencing has limited clinical application of thousands of existing gene signatures captured in repositories such as the Molecular Signature Database. We identified genes with stable expression across a range of abundances, and with a preserved relative ordering across thousands of samples, allowing signature scoring and supporting general data normalisation for transcriptomic data. Our new method, stingscore, quantifies and summarises relative expression levels of signature genes from individual samples through the inclusion of these ‘stably-expressed genes’. We show that our list of stable genes has better stability across cancer and normal tissue data than previously proposed gene sets. Additionally, we show that signature scores computed from targeted transcript measurements using stingscore can predict docetaxel response in breast cancer patients. This new approach to gene expression signature analysis will facilitate the development of panel-type tests for gene expression signatures, thus supporting clinical translation of the powerful insights gained from cancer transcriptomic studies.

scholarly journals Stable gene expression for normalisation and single-sample scoring

2020 ◽  
Author(s):  
Dharmesh D. Bhuva ◽  
Joseph Cursons ◽  
Melissa J. Davis
Keyword(s):  
Gene Expression ◽  
Gene Expression Signature ◽  
Molecular Signature ◽  
Similar Degree ◽  
Stable Gene ◽  
Breast Cancer Patients ◽  
Signature Genes ◽  
Gene Expression Signatures ◽  
Molecular Phenotypes ◽  
Whole Transcriptome

AbstractBackgroundTranscriptomic signatures are useful in defining the molecular phenotypes of cells, tissues, and patient samples. Their most successful and widespread clinical application is the stratification of breast cancer patients into molecular (PAM50) subtypes. In most cases, gene expression signatures are developed using transcriptome-wide measurements, thus methods that match signatures to samples typically require a similar degree of measurements. The cost and relatively large amounts of fresh starting material required for whole-transcriptome sequencing has limited clinical applications, and accordingly thousands of existing gene signatures are unexplored in a clinical context.ResultsGenes in a molecular signature can provide information about molecular phenotypes and their underlying transcriptional programs from tissue samples, however determining the transcriptional state of these genes typically requires the measurement of all genes across multiple samples to allow for comparison. An efficient assay and scoring method should quantify the relative abundance of signature genes with a minimal number of additional measurements. We identified genes with stable expression across a range of abundances, and with a preserved relative ordering across large numbers (thousands) of samples, allowing signature scoring, and supporting general data normalisation for transcriptomic data. Based on singscore, we have developed a new method, stingscore, which quantifies and summarises relative expression levels of signature genes from individual samples through the inclusion of these “stably-expressed genes”.ConclusionWe show that our proposed list of stable genes has better stability across cancer and normal tissue data than previously proposed stable or housekeeping genes. Additionally, we show that signature scores computed from whole-transcriptome data are comparable to those calculated using only values for signature genes and our panel of stable genes. This new approach to gene expression signature analysis may facilitate the development of panel-type tests for gene expression signatures, thus supporting clinical translation of the powerful insights gained from cancer transcriptomic studies.

Hypoxia gene expression signature in predicting outcome in breast cancer patients

2005 ◽  
Vol 23 (16_suppl) ◽  
pp. 9503-9503
Author(s):  
D. S. A. Nuyten ◽  
J.-T. A. Chi ◽  
Z. Wang ◽  
L. van ’t Veer ◽  
H. G. M. Bartelink ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
Gene Expression Signature ◽  
Breast Cancer Patients ◽  
Expression Signature ◽  
Predicting Outcome

Breast cancer patients stratification by microarray-based gene expression profiling from FFPET samples

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e22041-e22041
Author(s):  
C. Lih ◽  
Y. Li ◽  
L. Trinh ◽  
S. Chien ◽  
X. Wu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Profiling ◽  
Cancer Patients ◽  
Expression Profiling ◽  
Gene Expression Signature ◽  
Biological Data ◽  
Clinical Samples ◽  
Breast Cancer Patients ◽  
Disease Stratification

e22041 Background: Microarrays have been used to monitor global genes expression and have aided the identification of novel biomarkers for patients stratification and drug response prediction . To date there has been limited application of microarray- based gene expression analysis to formalin fixed paraffin embedded tissues (FFPET). FFPE tissues are the most commonly available clinical samples with documented clinical information for retrospective clinical analysis. However, FFPET RNA has proven to be an obstacle for microarray analysis because of low yield and compromised RNA integrity. Methods: Using a novel RNA amplification method, Single Primer Isothermal Amplification (SPIA, NuGEN Inc, San Carlos, CA), we amplified FFPET RNA, hybridized amplified, and labeled cDNA onto Affymetrix HG U133plus2 GeneChips. Results: We found that SPIA amplification successfully overcomes the problems of poor quality of FFPET RNA, and produced informative biological data. Comparing the gene expression data from 5 different types of FFPET archival cancer samples (breast, lung, ovarian, colon, and melanoma), we demonstrated that gene expression signatures clearly distinguish the tissue of origin. Further, from an analysis of 91 FFPET samples comprised of ER+, HER2+, triple negative breast cancer patients, and normal breast tissue, we have identified a 103 gene signature that distinguishes the intrinsic sub-types of breast cancer. Finally, the accuracy of gene expression measured by microarray was verified by real time PCR quantitation of the ERBB2 gene, resulting in a significant correlation (R = 0.88). Conclusions: We have demonstrated the feasibility of global gene expression profiling using RNA extracted from FFPET and have shown that a gene expression signature can stratify patient samples into different subtypes of disease. This study paves the way to identify novel molecular biomarkers for disease stratification and therapy response from archival FFPET samples, leading to the goals of personalized medicine. No significant financial relationships to disclose.

scholarly journals PD-1-Associated Gene Expression Signature of Neoadjuvant Trastuzumab-Treated Tumors Correlates with Patient Survival in HER2-Positive Breast Cancer

Cancers ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1566 ◽  
Author(s):  
William P. D. Hendricks ◽  
Natalia Briones ◽  
Rebecca F. Halperin ◽  
Salvatore Facista ◽  
Paul R. Heaton ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
Gene Expression Signature ◽  
Predictive Biomarkers ◽  
Breast Cancer Patients ◽  
Her2 Positive ◽  
Expression Signature ◽  
Her2 Breast Cancer ◽  
Group 2

The therapeutic HER2-targeting antibody trastuzumab has been shown to elicit tumor immune response in a subset of HER2-positive (HER2+) breast cancer. We performed genomic and immunohistochemical profiling of tumors from eight patients who have completed multiple rounds of neoadjuvant trastuzumabb to identify predictive biomarkers for trastuzumab-elicited tumor immune responses. Immunohistochemistry showed that all tumors had an activated tumor immune microenvironment positive for nuclear NF-κB/p65RelA, CD4, and CD8 T cell markers, but only four out of eight tumors were positive for the PD-1 immune checkpoint molecule, which is indicative of an exhausted immune environment. Exome sequencing showed no specific driver mutations correlating with PD-1 positivity. Hierarchical clustering of the RNA sequencing data revealed two distinct groups, of which Group 2 represented the PD-1 positive tumors. A gene expression signature that was derived from this clustering composed of 89 genes stratified HER2+ breast cancer patients in the TCGA dataset and it was named PD-1-Associated Gene Expression Signature in HER2+ Breast Cancer (PAGES-HBC). Patients with the Group 2 PAGES-HBC composition had significantly more favorable survival outcomes with mortality reduced by 83% (hazard ratio 0.17; 95% CI, 0.05 to 0.60; p = 0.011). Analysis of three longitudinal samples from a single patient showed that PAGES-HBC might be transiently induced by trastuzumab, independent of clonal tumor expansion over time. We conclude that PAGES-HBC could be further developed as a prognostic predictor of trastuzumab response in HER2+ breast cancer patients and be potentially used as an alternative biomarker for anti-PD-1 therapy trials.

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