Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA.
Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Cleveland Clinic Lerner College of Medicine, Cleveland, OH 44195, USA.
Am J Hum Genet. 2020 Jun 4;106(6):818-829. doi: 10.1016/j.ajhg.2020.04.014. Epub 2020 May 21.
Germline variation in PTEN results in variable clinical presentations, including benign and malignant neoplasia and neurodevelopmental disorders. Despite decades of research, it remains unclear how the PTEN genotype is related to clinical outcomes. In this study, we combined two recent deep mutational scanning (DMS) datasets probing the effects of single amino acid variation on enzyme activity and steady-state cellular abundance with a large, well-curated clinical cohort of PTEN-variant carriers. We sought to connect variant-specific molecular phenotypes to the clinical outcomes of individuals with PTEN variants. We found that DMS data partially explain quantitative clinical traits, including head circumference and Cleveland Clinic (CC) score, which is a semiquantitative surrogate of disease burden. We built logistic regression models that use DMS and CADD scores to separate clinical PTEN variation from gnomAD control-only variation with high accuracy. By using a survival-like analysis, we identified molecular phenotype groups with differential risk of early cancer onset as well as lifetime risk of cancer. Finally, we identified classes of DMS-defined variants with significantly different risk levels for classical hamartoma-related features (odds ratio [OR] range of 4.1-102.9). In stark contrast, the risk for developing autism or developmental delay does not significantly change across variant classes (OR range of 5.4-12.4). Together, these findings highlight the potential impact of combining DMS datasets with rich clinical data and provide new insights that might guide personalized clinical decisions for PTEN-variant carriers.
PTEN 种系变异导致不同的临床表现，包括良性和恶性肿瘤以及神经发育障碍。尽管已经进行了几十年的研究，但仍不清楚 PTEN 基因型与临床结果的关系。在这项研究中，我们将两个最近的深度突变扫描 (DMS) 数据集结合在一起，这些数据集探究了单个氨基酸变异对酶活性和细胞稳态丰度的影响，同时还结合了一个大型、精心编辑的 PTEN 变异携带者临床队列。我们试图将特定变异的分子表型与 PTEN 变异个体的临床结果联系起来。我们发现，DMS 数据部分解释了定量临床特征，包括头围和克利夫兰诊所 (CC) 评分，这是疾病负担的半定量替代指标。我们构建了逻辑回归模型，这些模型使用 DMS 和 CADD 评分来准确地区分临床 PTEN 变异和 gnomAD 仅对照变异。通过使用类似于生存分析的方法，我们确定了分子表型组，这些组具有不同的早期癌症发病风险和终生癌症风险。最后，我们确定了 DMS 定义的具有显著不同风险水平的变异类别的特征，包括经典错构瘤相关特征（优势比 [OR] 范围为 4.1-102.9）。相比之下，变异类别之间发展自闭症或发育迟缓的风险并没有显著变化（OR 范围为 5.4-12.4）。总之，这些发现强调了将 DMS 数据集与丰富的临床数据相结合的潜力，并提供了新的见解，这些见解可能为 PTEN 变异携带者的个性化临床决策提供指导。
