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A Bioinformatics Approach for Understanding Genotype–Phenotype Correlation in Breast Cancer

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Big Data Analytics in Genomics

Abstract

Breast cancer (BC) patients can be clinically classified into three types, called ER+, PR+, and HER2+, indicating the name of biomarkers and linking treatments. The serious problem is that the patients, called “triple negative” (TN), who cannot be fallen into any of these three categories, have no clear treatment options. Thus linking TN patients to the main three phenotypes clinically is very important. Usually BC patients are profiled by gene expression, while their patient class sets (such as PAM50) are inconsistent with clinical phenotypes. On the other hand, location-specific sequence variants are expected to be more predictive to detect BC patient subgroups, since a variety of somatic, single mutations are well-demonstrated to be linked to the resultant tumors. However those mutations have not been necessarily evaluated well as patterns to predict BC phenotypes. We thus detect patterns, which can assign known phenotypes to BC TN patients, focusing more on paired or more complicated nucleotide/gene mutational patterns, by using three machine learning methods: limitless arity multiple procedure (LAMP), decision trees, and hierarchical disjoint clustering. Association rules obtained through LAMP reveal a patient classification scheme through combinatorial mutations in PIK3CA and TP53, consistent with the obtained decision tree and three major clusters (occupied 182/208 samples), revealing the validity of results from diverse approaches. The final clusters, containing TN patients, present sub-population features in the TN patient pool that assign clinical phenotypes to TN patients.This paper is an extended and detailed version on a pilot study conducted in Yotsukura et al. (Brief Bioinform, to appear).

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Acknowledgements

We acknowledge Dr. Ajit Bharti (Boston University, USA) on his innovative conception that a better apprehension of breast cancer subtypes is needed. We would like to thank the TCGA Data Access Committee (DAC) for providing us the opportunity to work with the data for this study.

S.Y. is supported by Grant-in-Aid for JSPS Fellows and JSPS KAKENHI #26-381. M.K. is supported by JSPS KAKENHI #26730120. I.T. is funded by Collaborative Research Program of Institute for Chemical Research, Kyoto University (Grant# 2014-27, #2015-33). H.M. is partially supported by JSPS KAKENHI #24300054.

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Authors and Affiliations

  1. Bioinformatics Center, Institute of Chemical Research, Kyoto University, Uji, Kyoto, Japan

    Sohiya Yotsukura & Hiroshi Mamitsuka

  2. Department of Computer Science, Nagoya Institute of Technology, Showa-ku, Nagoya, Japan

    Masayuki Karasuyama

  3. Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Hokkaido, Japan

    Ichigaku Takigawa

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  1. Sohiya Yotsukura
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  2. Masayuki Karasuyama
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  1. Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong

    Ka-Chun Wong

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Yotsukura, S., Karasuyama, M., Takigawa, I., Mamitsuka, H. (2016). A Bioinformatics Approach for Understanding Genotype–Phenotype Correlation in Breast Cancer. In: Wong, KC. (eds) Big Data Analytics in Genomics. Springer, Cham. https://doi.org/10.1007/978-3-319-41279-5_13

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