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 contribution This 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.

中文翻译：

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精准肿瘤学中错义突变分析的计算工作流程


每年有超过 1900 万癌症病例被诊断出来，并且这个数字每年都在持续增加。由于标准治疗方案对于不同类型的癌症有不同的成功率，因此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。使用下一代测序进行个性化高通量分析，可以对活检标本进行全面检查。此外，该技术的广泛使用产生了有关癌症特异性基因改变的大量信息。然而，已识别的改变与其已证实的对蛋白质功能的影响之间存在显着差距。在这里，我们提出了一个生物信息学管道，可以快速分析错义突变对已知致癌蛋白稳定性和功能的影响。该管道与预测器相结合，该预测器总结了整个管道中使用的不同工具的输出，提供单一概率得分，实现了 86% 以上的平衡准确度。该管道采用虚拟筛选方法来建议潜在的 FDA/EMA 批准的药物考虑用于治疗。我们展示了三个案例研究来证明该管道的及时效用。为了方便访问和分析与癌症相关的突变，我们将管道打包为网络服务器，可以在 https://loschmidt.chemi.muni.cz/predictonco/ 上免费获取。科学贡献这项工作提出了一种新颖的生物信息学管道，它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。 该管道独特地将快速蛋白质建模、稳定性预测和进化分析与虚拟药物筛选相结合，同时为精准肿瘤学提供可行的见解。这种综合方法通过自动解释突变并提出潜在的治疗建议，超越了现有工具，从而努力弥合测序数据和临床应用之间的差距。