A prognostic gene expression index in ovarian cancer-validation across different independent data sets

2009 ◽  
Vol 218 (2) ◽  
pp. 273-280 ◽  
Author(s):  
Carsten Denkert ◽  
Jan Budczies ◽  
Silvia Darb-Esfahani ◽  
Balazs Györffy ◽  
Jalid Sehouli ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Data Sets ◽  
Independent Data ◽  
Expression Index ◽  
Prognostic Gene

scholarly journals Prognostic gene expression signature for high-grade serous ovarian cancer

2020 ◽  
Vol 31 (9) ◽  
pp. 1240-1250 ◽  
Author(s):  
J. Millstein ◽  
T. Budden ◽  
E.L. Goode ◽  
M.S. Anglesio ◽  
A. Talhouk ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Gene Expression Signature ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Expression Signature ◽  
Prognostic Gene

scholarly journals The impact of stroma on the discovery of molecular subtypes and prognostic gene signatures in serous ovarian cancer

2018 ◽  
Author(s):  
Matthew Schwede ◽  
Levi Waldron ◽  
Samuel C. Mok ◽  
Wei Wei ◽  
Azfar Basunia ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Stromal Cells ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Gene Signatures ◽  
Prognostic Gene ◽  
Bulk Tissue

AbstractPurposeRecent efforts to improve outcomes for high-grade serous ovarian cancer, a leading cause of cancer death in women, have focused on identifying molecular subtypes and prognostic gene signatures, but existing subtypes have poor cross-study robustness. We tested the contribution of cell admixture in published ovarian cancer molecular subtypes and prognostic gene signatures.Experimental DesignPublic gene expression data, two molecular subtype classifications, and 61 published gene signatures of ovarian cancer were examined. Using microdissected data, we developed gene signatures of ovarian tumor and stroma. Computational simulations of increasing stromal cell proportion were performed by mixing gene expression profiles of paired microdissected ovarian tumor and stroma.ResultsEstablished ovarian cancer molecular subtypes are strongly associated with the cell admixture. Tumors were classified as different molecular subtypes in simulations, when the percentage of stromal cells increased. Stromal gene expression in bulk tumor was weakly prognostic, and in one dataset, increased stroma was associated with anatomic sampling location. Five published prognostic gene signatures were no longer prognostic in a multivariate model that adjusted for stromal content alone.ConclusionsThe discovery that molecular subtypes of high grade serous ovarian cancer is influenced by cell admixture, and stromal cell gene expression is crucial for interpretation and reproduction of ovarian cancer molecular subtypes and gene signatures derived from bulk tissue. Single cell analysis may be required to refine the molecular subtypes of high grade serous ovarian cancer. Because stroma proportion was weakly prognostic, elucidating the role of the tumor microenvironment’s components will be important.Translational relevanceOvarian cancer is a leading cause of cancer death in women in the United States. Although the tumor responds to standard therapy for the majority of patients, it frequently recurs and becomes drug-resistant. Recent efforts have focused on identifying molecular subtypes and prognostic gene signatures of ovarian cancer in order to tailor therapy and improve outcomes. This study demonstrates that molecular subtype identification depends on the ratio of tumor to stroma within the specimen. We show that the specific anatomic location of the biopsy may influence the proportion of stromal involvement and potentially the resulting gene expression pattern. It will be crucial for these factors to be taken into consideration when interpreting and reproducing ovarian cancer molecular subtypes and gene signatures derived using bulk tissue and single cells. Furthermore, it will be important to define the relative proportions of stromal cells and model their prognostic importance in the tumor microenvironment.

scholarly journals Unsupervised Machine Learning for Data Encoding applied to Ovarian Cancer Transcriptomes

2019 ◽  
Author(s):  
Tom M George ◽  
Pietro Lio
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Ovarian Cancer ◽  
Gene Expression Data ◽  
Clustering Algorithm ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Dimensional Manifold ◽  
Expression Data ◽  
Wide Range

AbstractMachine learning algorithms are revolutionising how information can be extracted from complex and high-dimensional data sets via intelligent compression. For example, unsupervised Autoen-coders train a deep neural network with a low-dimensional “bottlenecked” central layer to reconstruct input vectors. Variational Autoencoders (VAEs) have shown promise at learning meaningful latent spaces for text, image and more recently, gene-expression data. In the latter case they have been shown capable of capturing biologically relevant features such as a patients sex or tumour type. Here we train a VAE on ovarian cancer transcriptomes from The Cancer Genome Atlas and show that, in many cases, the latent spaces learns an encoding predictive of cisplatin chemotherapy resistance. We analyse the effectiveness of such an architecture to a wide range of hyperparameters as well as use a state-of-the-art clustering algorithm, t-SNE, to embed the data in a two-dimensional manifold and visualise the predictive power of the trained latent spaces. By correlating genes to resistance-predictive encodings we are able to extract biological processes likely responsible for platinum resistance. Finally we demonstrate that variational autoencoders can reliably encode gene expression data contaminated with significant amounts of Gaussian and dropout noise, a necessary feature if this technique is to be applicable to other data sets, including those in non-medical fields.

PAX3/FOXO1 Fusion Gene Status Is the Key Prognostic Molecular Marker in Rhabdomyosarcoma and Significantly Improves Current Risk Stratification

2012 ◽  
Vol 30 (14) ◽  
pp. 1670-1677 ◽  
Author(s):  
Edoardo Missiaglia ◽  
Dan Williamson ◽  
Julia Chisholm ◽  
Pratyaksha Wirapati ◽  
Gaëlle Pierron ◽  
...  
Keyword(s):  
Gene Expression ◽  
Risk Stratification ◽  
Risk Score ◽  
Fusion Gene ◽  
Gene Expression Signature ◽  
Independent Data ◽  
Gene Signatures ◽  
Prognostic Gene ◽  
Gene Status ◽  
Current Risk

Purpose To improve the risk stratification of patients with rhabdomyosarcoma (RMS) through the use of clinical and molecular biologic data. Patients and Methods Two independent data sets of gene-expression profiling for 124 and 101 patients with RMS were used to derive prognostic gene signatures by using a meta-analysis. These and a previously published metagene signature were evaluated by using cross validation analyses. A combined clinical and molecular risk-stratification scheme that incorporated the PAX3/FOXO1 fusion gene status was derived from 287 patients with RMS and evaluated. Results We showed that our prognostic gene-expression signature and the one previously published performed well with reproducible and significant effects. However, their effect was reduced when cross validated or tested in independent data and did not add new prognostic information over the fusion gene status, which is simpler to assay. Among nonmetastatic patients, patients who were PAX3/FOXO1 positive had a significantly poorer outcome compared with both alveolar-negative and PAX7/FOXO1-positive patients. Furthermore, a new clinicomolecular risk score that incorporated fusion gene status (negative and PAX3/FOXO1 and PAX7/FOXO1 positive), Intergroup Rhabdomyosarcoma Study TNM stage, and age showed a significant increase in performance over the current risk-stratification scheme. Conclusion Gene signatures can improve current stratification of patients with RMS but will require complex assays to be developed and extensive validation before clinical application. A significant majority of their prognostic value was encapsulated by the fusion gene status. A continuous risk score derived from the combination of clinical parameters with the presence or absence of PAX3/FOXO1 represents a robust approach to improving current risk-adapted therapy for RMS.

scholarly journals A gene-expression signature to predict survival in breast cancer across independent data sets

Oncogene ◽  
2006 ◽  
Vol 26 (10) ◽  
pp. 1507-1516 ◽  
Author(s):  
A Naderi ◽  
A E Teschendorff ◽  
N L Barbosa-Morais ◽  
S E Pinder ◽  
A R Green ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Signature ◽  
Data Sets ◽  
Independent Data ◽  
Expression Signature

The visual arrays task…visual storage capacity or attention control?

2019 ◽  
Author(s):  
Jessie Martin ◽  
Jason S. Tsukahara ◽  
Christopher Draheim ◽  
Zach Shipstead ◽  
Cody Mashburn ◽  
...  
Keyword(s):  
Working Memory ◽  
Storage Capacity ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Attention Control ◽  
Data Sets ◽  
Independent Data ◽  
Visual Storage ◽  
Design Selection ◽  
The Relationship

**The uploaded manuscript is still in preparation** In this study, we tested the relationship between visual arrays tasks and working memory capacity and attention control. Specifically, we tested whether task design (selection or non-selection demands) impacted the relationship between visual arrays measures and constructs of working memory capacity and attention control. Using analyses from 4 independent data sets we showed that the degree to which visual arrays measures rely on selection influences the degree to which they reflect domain-general attention control.

scholarly journals Multi-view feature selection for identifying gene markers: a diversified biological data driven approach

2020 ◽  
Vol 21 (S18) ◽  
Author(s):  
Sudipta Acharya ◽  
Laizhong Cui ◽  
Yi Pan
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Selection ◽  
Marker Gene ◽  
Biological Data ◽  
Protein Interaction Data ◽  
Marker Genes ◽  
Data Sets ◽  
Gene Markers ◽  
Multi Objective

Abstract Background In recent years, to investigate challenging bioinformatics problems, the utilization of multiple genomic and proteomic sources has become immensely popular among researchers. One such issue is feature or gene selection and identifying relevant and non-redundant marker genes from high dimensional gene expression data sets. In that context, designing an efficient feature selection algorithm exploiting knowledge from multiple potential biological resources may be an effective way to understand the spectrum of cancer or other diseases with applications in specific epidemiology for a particular population. Results In the current article, we design the feature selection and marker gene detection as a multi-view multi-objective clustering problem. Regarding that, we propose an Unsupervised Multi-View Multi-Objective clustering-based gene selection approach called UMVMO-select. Three important resources of biological data (gene ontology, protein interaction data, protein sequence) along with gene expression values are collectively utilized to design two different views. UMVMO-select aims to reduce gene space without/minimally compromising the sample classification efficiency and determines relevant and non-redundant gene markers from three cancer gene expression benchmark data sets. Conclusion A thorough comparative analysis has been performed with five clustering and nine existing feature selection methods with respect to several internal and external validity metrics. Obtained results reveal the supremacy of the proposed method. Reported results are also validated through a proper biological significance test and heatmap plotting.

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