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In Bioinformatics (Oxford, England)

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 4 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.

AVAILABILTY : The open-source R package is available on

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Guerrero-Gimenez M E, Fernandez-Muñoz J M, Lang B J, Holton K M, Ciocca D R, Catania C A, Zoppino F C M