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Platform-Independent Gene-Expression Based Classification-System for Molecular Sub-typing of Cancer

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Personalized and Precision Medicine Informatics

Part of the book series: Health Informatics ((HI))

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Abstract

Molecular stratification of cancer patients is driving the development of precision medicine-targeted therapies. Clustering of tumor samples based on gene expression profiles from high-throughput platforms, such as microarrays or NextGen sequencing, has resulted in distinct tumor subtypes for numerous cancers. However, the majority of the derived classifiers or gene signatures have not reached clinical utility. Therefore, informatics methods to accurately translate the derived gene-signature from the high-throughput platform to a clinically adaptable low-dimensional platform are critical. In this chapter, we discuss a workflow to derive and then transfer gene signatures from one analytical platform to another for cancer patient stratification. We summarize the results of the workflow on two different cancers. Finally we discuss the importance of data-discretization in dealing with the cross-platform data and incorporating the splice-variant or isoform-level gene expression profiles in the statistical analyses.

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Acknowledgments

This work was supported by the National Library of Medicine of the National Institutes of Health [Award Number R01LM011297 to RD]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Authors and Affiliations

  1. Division of Health and Biomedical Informatics, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Evanston, IL, USA

    Yingtao Bi & Ramana V. Davuluri

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  1. Yingtao Bi
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  2. Ramana V. Davuluri
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Correspondence to Ramana V. Davuluri .

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Editors and Affiliations

  1. Pharmaceutical Care & Health Systems, University of Minnesota Pharmaceutical Care & Health Systems, Minneapolis, MN, USA

    Terrence Adam

  2. Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA

    Constantin Aliferis

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Bi, Y., Davuluri, R.V. (2020). Platform-Independent Gene-Expression Based Classification-System for Molecular Sub-typing of Cancer. In: Adam, T., Aliferis, C. (eds) Personalized and Precision Medicine Informatics. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-030-18626-5_10

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  • DOI: https://doi.org/10.1007/978-3-030-18626-5_10

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