scholarly journals Comprehensive Evaluation of Composite Gene Features in Cancer Outcome Prediction

2014 ◽  
Vol 13s3 ◽  
pp. CIN.S14028
Author(s):  
Dezhi Hou ◽  
Mehmet Koyutürk
Keyword(s):  
Outcome Prediction ◽  
Comprehensive Evaluation ◽  
Test Procedures ◽  
Individual Gene ◽  
Cancer Subtypes ◽  
Cancer Types ◽  
Cancer Outcome ◽  
Robust Prediction ◽  
Microarray Datasets ◽  
Gene Feature

Owing to the heterogeneous and continuously evolving nature of cancers, classifiers based on the expression of individual genes usually do not result in robust prediction of cancer outcome. As an alternative, composite gene features that combine functionally related genes have been proposed. It is expected that such features can be more robust and reproducible since they can capture the alterations in relevant biological processes as a whole and may be less sensitive to fluctuations in the expression of individual genes. Various algorithms have been developed for the identification of composite features and inference of composite gene feature activity, which all claim to improve the prediction accuracy. However, because of the limitations of test datasets incorporated by each individual study and inconsistent test procedures, the results of these studies are sometimes conflicting and unproducible. For this reason, it is difficult to have a comprehensive understanding of the prediction performance of composite gene features, particularly across different cancers, cancer subtypes, and cohorts. In this study, we implement various algorithms for the identification of composite gene features and their utilization in cancer outcome prediction, and perform extensive comparison and evaluation using seven microarray datasets covering two cancer types and three different phenotypes. Our results show that, while some algorithms outperform others for certain classification tasks, no single algorithm consistently outperforms other algorithms and individual gene features.

scholarly journals Pan-Cancer Molecular Patterns and Biological Implications Associated with a Tumor-Specific Molecular Signature

Cells ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 45
Author(s):  
Darío Rocha ◽  
Iris A. García ◽  
Aldana González Montoro ◽  
Andrea Llera ◽  
Laura Prato ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Cell Cycle Progression ◽  
Uncertainty Assessment ◽  
Cell Cycle Checkpoints ◽  
Molecular Signature ◽  
Cancer Subtypes ◽  
United States Food ◽  
Cancer Types ◽  
Molecular Patterns ◽  
Pan Cancer

Studying tissue-independent components of cancer and defining pan-cancer subtypes could be addressed using tissue-specific molecular signatures if classification errors are controlled. Since PAM50 is a well-known, United States Food and Drug Administration (FDA)-approved and commercially available breast cancer signature, we applied it with uncertainty assessment to classify tumor samples from over 33 cancer types, discarded unassigned samples, and studied the emerging tumor-agnostic molecular patterns. The percentage of unassigned samples ranged between 55.5% and 86.9% in non-breast tissues, and gene set analysis suggested that the remaining samples could be grouped into two classes (named C1 and C2) regardless of the tissue. The C2 class was more dedifferentiated, more proliferative, with higher centrosome amplification, and potentially more TP53 and RB1 mutations. We identified 28 gene sets and 95 genes mainly associated with cell-cycle progression, cell-cycle checkpoints, and DNA damage that were consistently exacerbated in the C2 class. In some cancer types, the C1/C2 classification was associated with survival and drug sensitivity, and modulated the prognostic meaning of the immune infiltrate. Our results suggest that PAM50 could be repurposed for a pan-cancer context when paired with uncertainty assessment, resulting in two classes with molecular, biological, and clinical implications.

scholarly journals Ensemble classifier based on context specific miRNA regulation modules: a new method for cancer outcome prediction

2013 ◽  
Vol 14 (Suppl 12) ◽  
pp. S6 ◽  
Author(s):  
Xionghui Zhou ◽  
Juan Liu ◽  
Xinghuo Ye ◽  
Wei Wang ◽  
Jianghui Xiong
Keyword(s):  
Outcome Prediction ◽  
Ensemble Classifier ◽  
New Method ◽  
Mirna Regulation ◽  
Cancer Outcome ◽  
Context Specific

scholarly journals High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes

2016 ◽  
Author(s):  
Marta R. Hidalgo ◽  
Cankut Cubuk ◽  
Alicia Amadoz ◽  
Francisco Salavert ◽  
José Carbonell-Caballero ◽  
...  
Keyword(s):  
High Throughput ◽  
Gene Mutations ◽  
Diagnostic Value ◽  
Therapeutic Interventions ◽  
Biological Knowledge ◽  
Individual Gene ◽  
Main Challenge ◽  
Activity Measurements ◽  
Cancer Types ◽  
Or Gene

AbstractUnderstanding the aspects of the cell functionality that account for disease or drug action mechanisms is a main challenge for precision medicine. Here we propose a new method that models cell signaling using biological knowledge on signal transduction. The method recodes individual gene expression values (and/or gene mutations) into accurate measurements of changes in the activity of signaling circuits, which ultimately constitute high-throughput estimations of cell functionalities caused by gene activity within the pathway. Moreover, such estimations can be obtained either at cohort-level, in case/control comparisons, or personalized for individual patients. The accuracy of the method is demonstrated in an extensive analysis involving 5640 patients from 12 different cancer types. Circuit activity measurements not only have a high diagnostic value but also can be related to relevant disease outcomes such as survival, and can be used to assess therapeutic interventions.

scholarly journals MBNL1 alternative splicing isoforms play opposing roles in cancer

2018 ◽  
Vol 1 (5) ◽  
pp. e201800157 ◽  
Author(s):  
Tommaso Tabaglio ◽  
Diana HP Low ◽  
Winnie Koon Lay Teo ◽  
Pierre Alexis Goy ◽  
Piotr Cywoniuk ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Cancer Cells ◽  
Cancer Progression ◽  
Gene Function ◽  
Dominant Negative ◽  
Individual Gene ◽  
Cancer Types ◽  
Splicing Isoforms ◽  
Isoform Switching ◽  
And Migration

The extent of and the oncogenic role played by alternative splicing (AS) in cancer are well documented. Nonetheless, only few studies have attempted to dissect individual gene function at an isoform level. Here, we focus on the AS of splicing factors during prostate cancer progression, as these factors are known to undergo extensive AS and have the potential to affect hundreds of downstream genes. We identified exon 7 (ex7) in the MBNL1 (Muscleblind-like 1) transcript as being the most differentially included exon in cancer, both in cell lines and in patients' samples. In contrast, MBNL1 overall expression was down-regulated, consistently with its described role as a tumor suppressor. This observation holds true in the majority of cancer types analyzed. We first identified components associated to the U2 splicing complex (SF3B1, SF3A1, and PHF5A) as required for efficient ex7 inclusion and we confirmed that this exon is fundamental for MBNL1 protein homodimerization. We next used splice-switching antisense oligonucleotides (AONs) or siRNAs to compare the effect of MBNL1 splicing isoform switching with knockdown. We report that whereas the absence of MBNL1 is tolerated in cancer cells, the expression of isoforms lacking ex7 (MBNL1 Δex7) induces DNA damage and inhibits cell viability and migration, acting as dominant negative proteins. Our data demonstrate the importance of studying gene function at the level of alternative spliced isoforms and support our conclusion that MBNL1 Δex7 proteins are antisurvival factors with a defined tumor suppressive role that cancer cells tend to down-regulate in favor of MBNL +ex7 isoforms.

scholarly journals A data-driven interactome of synergistic genes improves network-based cancer outcome prediction

2019 ◽  
Vol 15 (2) ◽  
pp. e1006657 ◽  
Author(s):  
Amin Allahyar ◽  
Joske Ubels ◽  
Jeroen de Ridder
Keyword(s):  
Outcome Prediction ◽  
Data Driven ◽  
Cancer Outcome

Deep learning with evolutionary and genomic profiles for identifying cancer subtypes

2019 ◽  
Vol 17 (03) ◽  
pp. 1940005 ◽  
Author(s):  
Chun-Yu Lin ◽  
Peiying Ruan ◽  
Ruiming Li ◽  
Jinn-Moon Yang ◽  
Simon See ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Evolutionary Conservation ◽  
Core Gene ◽  
Breast Cancer Subtypes ◽  
Gene Set ◽  
Cancer Subtypes ◽  
The Core ◽  
Cancer Types ◽  
Core Genes

Cancer subtype identification is an unmet need in precision diagnosis. Recently, evolutionary conservation has been indicated to contain informative signatures for functional significance in cancers. However, the importance of evolutionary conservation in distinguishing cancer subtypes remains largely unclear. Here, we identified the evolutionarily conserved genes (i.e. core genes) and observed that they are primarily involved in cellular pathways relevant to cell growth and metabolisms. By using these core genes, we developed two novel strategies, namely a feature-based strategy (FES) and an image-based strategy (IMS) by integrating their evolutionary and genomic profiles with the deep learning algorithm. In comparison with the FES using the random set and the strategy using the PAM50 classifier, the core gene set-based FES achieved a higher accuracy for identifying breast cancer subtypes. The IMS and FES using the core gene set yielded better performances than the other strategies, in terms of classifying both breast cancer subtypes and multiple cancer types. Moreover, the IMS is reproducible even using different gene expression data (i.e. RNA-seq and microarray). Comprehensive analysis of eight cancer types demonstrates that our evolutionary conservation-based models represent a valid and helpful approach for identifying cancer subtypes and the core gene set offers distinguishable clues of cancer subtypes.

Validation of a Rectal Cancer Outcome Prediction Model in Routine Chinese Patients

2014 ◽  
Vol 90 (1) ◽  
pp. S387-S388
Author(s):  
L. Shen ◽  
J. Van Soest ◽  
J. Yu ◽  
J. Wang ◽  
W. Hu ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Prediction Model ◽  
Outcome Prediction ◽  
Chinese Patients ◽  
Cancer Outcome

scholarly journals Network information improves cancer outcome prediction

2012 ◽  
Vol 15 (4) ◽  
pp. 612-625 ◽  
Author(s):  
J. Roy ◽  
C. Winter ◽  
Z. Isik ◽  
M. Schroeder
Keyword(s):  
Outcome Prediction ◽  
Network Information ◽  
Cancer Outcome
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