Li Guangyuan, Bhattacharjee Anukana, Salomonis Nathan
Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA.
Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH, 45267, USA.
bioRxiv. 2023 Mar 6:2023.03.03.530994. doi: 10.1101/2023.03.03.530994.
In diseases such as cancer, the design of new therapeutic strategies requires extensive, costly, and unfortunately sometimes deadly testing to reveal life threatening "off target" effects. A crucial first step in predicting toxicity are analyses of normal RNA and protein tissue expression, which are now possible using comprehensive molecular tissue atlases. However, no standardized approaches exist for target prioritization, which instead rely on ad-hoc thresholds and manual inspection. Such issues are compounded, given that genomic and proteomic data detection sensitivity and accuracy are often problematic. Thus, quantifiable probabilistic scores for tumor specificity that address these challenges could enable the creation of new predictive models for combinatorial drug design and correlative analyses. Here, we propose a Bayesian Tumor Specificity (BayesTS) score that can naturally account for multiple independent forms of molecular evidence derving from both RNA-Seq and protein expression while preserving the uncertainty of the inference. We applied BayesTS to 24,905 human protein-coding genes across 3,644 normal samples (GTEx and TCGA) spanning 63 tissues. These analyses demonstrate the ability of BayesTS to accurately incorporate protein, RNA and tissue distribution evidence, while effectively capturing the uncertainty of these inferences. This approach prioritized well-established drug targets, while deemphasizing those which were later found to induce toxicity. BayesTS allows for the adjustment of tissue importance weights for tissues of interest, such as reproductive and physiologically dispensable tissues (e.g., tonsil, appendix), enabling clinically translatable prioritizations. Our results show that BayesTS can facilitate novel drug target discovery and can be easily generalized to unconventional molecular targets, such as splicing neoantigens. We provide the code and inferred tumor specificity predictions as a database available online (https://github.com/frankligy/BayesTS). We envision that the widespread adoption of BayesTS will facilitate improved target prioritization for oncology drug development, ultimately leading to the discovery of more effective and safer drugs.
在癌症等疾病中，设计新的治疗策略需要进行广泛、昂贵且有时不幸会致命的测试，以揭示危及生命的“脱靶”效应。预测毒性的关键第一步是分析正常RNA和蛋白质组织表达，而现在使用全面的分子组织图谱就可以做到这一点。然而，目前不存在用于靶点优先级排序的标准化方法，而是依赖临时阈值和人工检查。鉴于基因组和蛋白质组数据检测的灵敏度和准确性往往存在问题，这些问题更加复杂。因此，能够解决这些挑战的可量化肿瘤特异性概率评分可以为组合药物设计和相关分析创建新的预测模型。在这里，我们提出了一种贝叶斯肿瘤特异性（BayesTS）评分，它可以自然地考虑来自RNA测序和蛋白质表达的多种独立形式的分子证据，同时保留推理的不确定性。我们将BayesTS应用于跨越63个组织的3644个正常样本（GTEx和TCGA）中的24905个人类蛋白质编码基因。这些分析证明了BayesTS能够准确整合蛋白质、RNA和组织分布证据，同时有效捕捉这些推理的不确定性。这种方法对已确立的药物靶点进行了优先级排序，同时淡化了那些后来被发现会诱导毒性的靶点。BayesTS允许针对感兴趣的组织调整组织重要性权重，例如生殖和生理上可舍弃的组织（如扁桃体、阑尾），从而实现临床可转化的优先级排序。我们的结果表明，BayesTS可以促进新型药物靶点的发现，并且可以很容易地推广到非常规分子靶点，如剪接新抗原。我们提供了代码和推断的肿瘤特异性预测结果作为在线数据库（https://github.com/frankligy/BayesTS）。我们设想，BayesTS的广泛应用将有助于改善肿瘤学药物开发中的靶点优先级排序，最终导致发现更有效、更安全的药物。
