Abstract
Precision medicine, a highly disruptive paradigm shift in healthcare targeting the personalizing treatment, heavily relies on genomic data. However, the complexity of the biological interactions, the important number of genes as well as the lack of substantial patient’s clinical data consist a tremendous bottleneck on the clinical implementation of precision medicine. In this work, we introduce a generic, low dimensional gene signature that represents adequately the tumor type. Our gene signature is produced using LP-stability algorithm, a high dimensional center-based unsupervised clustering algorithm working in the dual domain, and is very versatile as it can consider any arbitrary distance metric between genes. The gene signature produced by LP-stability reports at least 10 times better statistical significance and \(35\%\) better biological significance than the ones produced by two referential unsupervised clustering methods. Moreover, our experiments demonstrate that our low dimensional biomarker (27 genes) surpass significantly existing state of the art methods both in terms of qualitative and quantitative assessment while providing better associations to tumor types than methods widely used in the literature that rely on several omics data.
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Acknowledgements
We would like to acknowledge the partial support of Amazon Web Services and Pr. Stefano Soatto for fruitful discussions. We also thank Y. Boursin, M. Azoulay and Gustave Roussy Cancer Campus DTNSI team for providing the infrastructure resources used in this work. This work was supported by the Fondation pour la Recherche Médicale (FRM; no. DIC20161236437).
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Battistella, E. et al. (2019). Gene Expression High-Dimensional Clustering Towards a Novel, Robust, Clinically Relevant and Highly Compact Cancer Signature. In: Rojas, I., Valenzuela, O., Rojas, F., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2019. Lecture Notes in Computer Science(), vol 11465. Springer, Cham. https://doi.org/10.1007/978-3-030-17938-0_41
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