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Mechanistically derived patient-level framework for precision medicine identifies a personalized immune prognostic signature in high-grade serous ovarian cancer.
Briefings in Bioinformatics ( IF 9.5 ) Pub Date : 2020-05-20 , DOI: 10.1093/bib/bbaa069
Hengqiang Zhao , Shanshan Gu , Siqi Bao , Congcong Yan , Zicheng Zhang , Ping Hou , Meng Zhou , Jie Sun

An accurate prognosis assessment for cancer patients could aid in guiding clinical decision-making. Reliance on traditional clinical features alone in a complex clinical environment is challenging and unsatisfactory in the era of precision medicine; thus, reliable prognostic biomarkers are urgently required to improve a patient staging system. In this study, we proposed a patient-level computational framework from mechanistic and translational perspectives to establish a personalized prognostic signature (named PLPPS) in high-grade serous ovarian carcinoma (HGSOC). The PLPPS composed of 68 immune genes achieved accurate prognostic risk stratification for 1190 patients in the meta-training cohort and was rigorously validated in multiple cross-platform independent cohorts comprising 792 HGSOC patients. Furthermore, the PLPPS was shown to be the better prognostic factor compared with clinical parameters in the univariate analysis and retained a significant independent association with prognosis after adjusting for clinical parameters in the multivariate analysis. In benchmark comparisons, the performance of PLPPS (hazard ratio (HR), 1.371; concordance index (C-index), 0.604 and area under the curve (AUC), 0.637) is comparable to or better than other published gene signatures (HR, 0.972 to 1.340; C-index, 0.495 to 0.592 and AUC, 0.48-0.624). With further validation in prospective clinical trials, we hope that the PLPPS might become a promising genomic tool to guide personalized management and decision-making of HGSOC in clinical practice.

中文翻译:

用于精准医学的机械衍生的患者级框架确定了高级别浆液性卵巢癌的个性化免疫预后特征。

对癌症患者进行准确的预后评估有助于指导临床决策。在精准医疗时代,在复杂的临床环境中仅仅依赖传统的临床特征是具有挑战性和不尽人意的;因此,迫切需要可靠的预后生物标志物来改进患者分期系统。在这项研究中,我们从机械和转化的角度提出了一个患者级别的计算框架,以在高级别浆液性卵巢癌 (HGSOC) 中建立个性化的预后特征(命名为 PLPPS)。由 68 个免疫基因组成的 PLPPS 为元训练队列中的 1190 名患者实现了准确的预后风险分层,并在包括 792 名 HGSOC 患者的多个跨平台独立队列中得到了严格验证。此外,与单变量分析中的临床参数相比,PLPPS 被证明是更好的预后因素,并在多变量分析中调整临床参数后与预后保持显着的独立关联。在基准比较中,PLPPS 的性能(风险比 (HR),1.371;一致性指数 (C-index),0.604 和曲线下面积 (AUC),0.637)与其他已公布的基因特征(HR, 0.972 至 1.340;C 指数,0.495 至 0.592 和 AUC,0.48-0.624)。随着前瞻性临床试验的进一步验证,我们希望 PLPPS 可能成为一种有前途的基因组工具,以指导临床实践中 HGSOC 的个性化管理和决策。
更新日期:2020-05-20
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