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Computational Approaches for the Investigation of Intra-tumor Heterogeneity and Clonal Evolution from Bulk Sequencing Data in Precision Oncology Applications

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Computational Methods for Precision Oncology

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1361))

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Abstract

While the clonal model of cancer evolution was first proposed over 40 years ago, only recently next-generation sequencing has allowed a more precise and quantitative assessment of tumor clonal and subclonal landscape. Consequently, a plethora of computational approaches and tools have been developed to analyze this data with the goal of inferring the clonal landscape of a tumor and characterize its temporal or spatial evolution. This chapter introduces intra-tumor heterogeneity (ITH) in the context of precision oncology applications and provides an overview of the basic concepts, algorithms, and tools for the dissection, analysis, and visualization of ITH from bulk DNA sequencing.

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Authors and Affiliations

  1. Department of Genetics and Genomic Sciences, Department of Oncological Sciences, Mount Sinai Icahn School of Medicine, New York, NY, USA

    Alessandro Laganà

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  1. Alessandro Laganà
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Correspondence to Alessandro Laganà .

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  1. Department of Genetics and Genomic Sciences, Department of Oncological Sciences, Mount Sinai Icahn School of Medicine, New York, NY, USA

    Alessandro Laganà

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Laganà, A. (2022). Computational Approaches for the Investigation of Intra-tumor Heterogeneity and Clonal Evolution from Bulk Sequencing Data in Precision Oncology Applications. In: Laganà, A. (eds) Computational Methods for Precision Oncology. Advances in Experimental Medicine and Biology, vol 1361. Springer, Cham. https://doi.org/10.1007/978-3-030-91836-1_6

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