Title | ||
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Galgo: A Bi-Objective Evolutionary Meta-Heuristic Identifies Robust Transcriptomic Classifiers Associated With Patient Outcome Across Multiple Cancer Types |
Abstract | ||
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Motivation: Statistical and machine-learning analyses of tumor transcriptomic profiles offer a powerful resource to gain deeper understanding of tumor subtypes and disease prognosis. Currently, prognostic gene-expression signatures do not exist for all cancer types, and most developed to date have been optimized for individual tumor types. In Galgo, we implement a bi-objective optimization approach that prioritizes gene signature cohesiveness and patient survival in parallel, which provides greater power to identify tumor transcriptomic phenotypes strongly associated with patient survival.Results: To compare the predictive power of the signatures obtained by Galgo with previously studied subtyping methods, we used a meta-analytic approach testing a total of 35 large population-based transcriptomic biobanks of four different cancer types. Galgo-generated colorectal and lung adenocarcinoma signatures were stronger predictors of patient survival compared to published molecular classification schemes. One Galgo-generated breast cancer signature outperformed PAM50, AIMS, SCMGENE and IntClust subtyping predictors. In high-grade serous ovarian cancer, Galgo signatures obtained similar predictive power to a consensus classification method. In all cases, Galgo subtypes reflected enrichment of gene sets related to the hallmarks of the disease, which highlights the biological relevance of the partitions found. |
Year | DOI | Venue |
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2020 | 10.1093/bioinformatics/btaa619 | BIOINFORMATICS |
DocType | Volume | Issue |
Journal | 36 | 20 |
ISSN | Citations | PageRank |
1367-4803 | 0 | 0.34 |
References | Authors | |
0 | 7 |
Name | Order | Citations | PageRank |
---|---|---|---|
M E Guerrero-Gimenez | 1 | 0 | 0.34 |
J M Fernandez-Muñoz | 2 | 0 | 0.34 |
B J Lang | 3 | 0 | 0.34 |
K M Holton | 4 | 0 | 0.34 |
D R Ciocca | 5 | 0 | 0.34 |
C A Catania | 6 | 0 | 0.34 |
F C M Zoppino | 7 | 0 | 0.34 |