PDF(Pubmed)
Abstract：
Every year, more than 19 million cancer cases are diagnosed, and this number continues to increase annually. Since standard treatment options have varying success rates for different types of cancer, understanding the biology of an individual\'s tumour becomes crucial, especially for cases that are difficult to treat. Personalised high-throughput profiling, using next-generation sequencing, allows for a comprehensive examination of biopsy specimens. Furthermore, the widespread use of this technology has generated a wealth of information on cancer-specific gene alterations. However, there exists a significant gap between identified alterations and their proven impact on protein function. Here, we present a bioinformatics pipeline that enables fast analysis of a missense mutation\'s effect on stability and function in known oncogenic proteins. This pipeline is coupled with a predictor that summarises the outputs of different tools used throughout the pipeline, providing a single probability score, achieving a balanced accuracy above 86%. The pipeline incorporates a virtual screening method to suggest potential FDA/EMA-approved drugs to be considered for treatment. We showcase three case studies to demonstrate the timely utility of this pipeline. To facilitate access and analysis of cancer-related mutations, we have packaged the pipeline as a web server, which is freely available at https://loschmidt.chemi.muni.cz/predictonco/ .Scientific contributionThis work presents a novel bioinformatics pipeline that integrates multiple computational tools to predict the effects of missense mutations on proteins of oncological interest. The pipeline uniquely combines fast protein modelling, stability prediction, and evolutionary analysis with virtual drug screening, while offering actionable insights for precision oncology. This comprehensive approach surpasses existing tools by automating the interpretation of mutations and suggesting potential treatments, thereby striving to bridge the gap between sequencing data and clinical application.
摘要：
每年,超过1900万例癌症被诊断，这个数字每年都在增加。由于标准治疗方案对不同类型的癌症有不同的成功率，了解个体肿瘤的生物学变得至关重要，特别是对于难以治疗的病例。个性化的高通量分析，使用下一代测序，允许全面检查活检标本。此外，这项技术的广泛使用产生了关于癌症特异性基因改变的大量信息。然而,已确定的改变与已证实的对蛋白质功能的影响之间存在显著差距.这里,我们提出了一个生物信息学管道，能够快速分析错义突变对已知致癌蛋白的稳定性和功能的影响。该管道与一个预测器相结合，该预测器汇总了整个管道中使用的不同工具的输出，提供单个概率得分，达到86％以上的平衡精度。该管道采用了虚拟筛选方法，以建议考虑使用FDA/EMA批准的潜在药物进行治疗。我们展示了三个案例研究，以证明该管道的及时实用性。为了促进癌症相关突变的获取和分析，我们把管道打包成一个网络服务器，它可以在https://loschmidt上免费获得。Chemi.Muni.cz/prejectonco/。科学贡献这项工作提出了一种新颖的生物信息学管道，该管道集成了多种计算工具来预测错义突变对肿瘤学感兴趣的蛋白质的影响。管道独特地结合了快速蛋白质建模，稳定性预测，以及虚拟药物筛选的进化分析，同时为精准肿瘤学提供可操作的见解。这种全面的方法通过自动解释突变并建议潜在的治疗方法，超越了现有的工具。从而努力弥合测序数据与临床应用之间的差距。
