Abstract
Recent advances in the methods for reconstruction of cancer evolutionary trajectories opened up the prospects of deciphering the subclonal populations and their evolutionary architectures within cancer ecosystems. An important challenge of the cancer evolution studies is how to connect genetic aberrations in subclones to a clinically interpretable and actionable target in the subclones for individual patients. In this study, our aim is to develop a novel method for constructing a model of tumor subclonal progression in terms of cancer hallmark acquisition using multiregional sequencing data. We prepare a subclonal evolutionary tree inferred from variant allele frequencies and estimate pathway alteration probabilities from large-scale cohort genomic data. We then construct an evolutionary tree of pathway alterations that takes into account selectivity of pathway alterations via selectivity score. We show the effectiveness of our method on a dataset of clear cell renal cell carcinomas.
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Acknowledgments
This work was supported by 16K16146, 16H01572, 15H05707, 18K18151, 18H04899 from The Japan Society for the Promotion of Science (http://www.jsps.go.jp/english/e-grants/grants01.html).
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Matsui, Y., Miyano, S., Shimamura, T. (2019). Tumor Subclonal Progression Model for Cancer Hallmark Acquisition. In: Bartoletti, M., et al. Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2017. Lecture Notes in Computer Science(), vol 10834. Springer, Cham. https://doi.org/10.1007/978-3-030-14160-8_12
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DOI: https://doi.org/10.1007/978-3-030-14160-8_12
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