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Pathological imaging‐assisted cancer gene–environment interaction analysis

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  • Kuangnan Fang
  • Jingmao Li
  • Qingzhao Zhang
  • Yaqing Xu
  • Shuangge Ma

Abstract

Gene–environment (G–E) interactions have important implications for cancer outcomes and phenotypes beyond the main G and E effects. Compared to main‐effect‐only analysis, G–E interaction analysis more seriously suffers from a lack of information caused by higher dimensionality, weaker signals, and other factors. It is also uniquely challenged by the “main effects, interactions” variable selection hierarchy. Effort has been made to bring in additional information to assist cancer G–E interaction analysis. In this study, we take a strategy different from the existing literature and borrow information from pathological imaging data. Such data are a “byproduct” of biopsy, enjoys broad availability and low cost, and has been shown as informative for modeling prognosis and other cancer outcomes/phenotypes in recent studies. Building on penalization, we develop an assisted estimation and variable selection approach for G–E interaction analysis. The approach is intuitive, can be effectively realized, and has competitive performance in simulation. We further analyze The Cancer Genome Atlas (TCGA) data on lung adenocarcinoma (LUAD). The outcome of interest is overall survival, and for G variables, we analyze gene expressions. Assisted by pathological imaging data, our G–E interaction analysis leads to different findings with competitive prediction performance and stability.

Suggested Citation

  • Kuangnan Fang & Jingmao Li & Qingzhao Zhang & Yaqing Xu & Shuangge Ma, 2023. "Pathological imaging‐assisted cancer gene–environment interaction analysis," Biometrics, The International Biometric Society, vol. 79(4), pages 3883-3894, December.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:4:p:3883-3894
    DOI: 10.1111/biom.13873
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    References listed on IDEAS

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    1. Qianying Liu & Lin S. Chen & Dan L. Nicolae & Brandon L. Pierce, 2016. "A unified set-based test with adaptive filtering for gene–environment interaction analyses," Biometrics, The International Biometric Society, vol. 72(2), pages 629-638, June.
    2. Ni Zhao & Haoyu Zhang & Jennifer J. Clark & Arnab Maity & Michael C. Wu, 2019. "Composite kernel machine regression based on likelihood ratio test for joint testing of genetic and gene–environment interaction effect," Biometrics, The International Biometric Society, vol. 75(2), pages 625-637, June.
    3. Yunfeng Zhang & Irina Gaynanova, 2022. "Joint association and classification analysis of multi‐view data," Biometrics, The International Biometric Society, vol. 78(4), pages 1614-1625, December.
    4. Yaqing Xu & Mengyun Wu & Shuangge Ma, 2022. "Multidimensional molecular measurements–environment interaction analysis for disease outcomes," Biometrics, The International Biometric Society, vol. 78(4), pages 1542-1554, December.
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