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A deep imputation and inference framework for estimating personalized and race-specific causal effects of genomic alterations on PSA
Journal of Bioinformatics and Computational Biology ( IF 1 ) Pub Date : 2021-07-02 , DOI: 10.1142/s0219720021500165
Zhong Chen 1 , Bo Cao 2 , Andrea Edwards 1 , Hongwen Deng 3 , Kun Zhang 1, 4
Affiliation  

Prostate Specific Antigen (PSA) level in the serum is one of the most widely used markers in monitoring prostate cancer (PCa) progression, treatment response, and disease relapse. Although significant efforts have been taken to analyze various socioeconomic and cultural factors that contribute to the racial disparities in PCa, limited research has been performed to quantitatively understand how and to what extent molecular alterations may impact differential PSA levels present at varied tumor status between African–American and European–American men. Moreover, missing values among patients add another layer of difficulty in precisely inferring their outcomes. In light of these issues, we propose a data-driven, deep learning-based imputation and inference framework (DIIF). DIIF seamlessly encapsulates two modules: an imputation module driven by a regularized deep autoencoder for imputing critical missing information and an inference module in which two deep variational autoencoders are coupled with a graphical inference model to quantify the personalized and race-specific causal effects. Large-scale empirical studies on the independent sub-cohorts of The Cancer Genome Atlas (TCGA) PCa patients demonstrate the effectiveness of DIIF. We further found that somatic mutations in TP53, ATM, PTEN, FOXA1, and PIK3CA are statistically significant genomic factors that may explain the racial disparities in different PCa features characterized by PSA.

中文翻译:

用于估计基因组改变对 PSA 的个性化和种族特异性因果影响的深度插补和推理框架

血清中的前列腺特异性抗原 (PSA) 水平是监测前列腺癌 (PCa) 进展、治疗反应和疾病复发的最广泛使用的标志物之一。尽管已经付出了巨大的努力来分析导致 PCa 种族差异的各种社会经济和文化因素,但在定量了解分子改变如何以及在多大程度上可能影响非洲 -美国和欧洲裔美国人。此外,患者中的缺失值增加了准确推断其结果的另一层困难。鉴于这些问题,我们提出了一个数据驱动、基于深度学习的插补和推理框架(DIIF)。DIIF无缝封装了两个模块:一个由正则化深度自动编码器驱动的插补模块,用于插补关键缺失信息和一个推理模块,其中两个深度变分自动编码器与图形推理模型相结合,以量化个性化和种族特定的因果效应。对癌症基因组图谱 (TCGA) PCa 患者独立亚组的大规模实证研究证明了 DIIF 的有效性。我们进一步发现 TP53、ATM、PTEN、FOXA1 和 PIK3CA 的体细胞突变是具有统计学意义的基因组因素,可以解释以 PSA 为特征的不同 PCa 特征的种族差异。
更新日期:2021-07-02
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