关键词: biomarker cancer biology computational biology human immunotherapy machine learning multi‐omic pancreatic cancer systems biology

Mesh : Humans Gene Expression Profiling / methods Consensus Artificial Intelligence Panobinostat Pancreatic Neoplasms / genetics pathology Machine Learning Biomarkers Pancreatic Neoplasms

来  源:   DOI:10.7554/eLife.80150   PDF(Pubmed)

Abstract:
As the most aggressive tumor, the outcome of pancreatic cancer (PACA) has not improved observably over the last decade. Anatomy-based TNM staging does not exactly identify treatment-sensitive patients, and an ideal biomarker is urgently needed for precision medicine. Based on expression files of 1280 patients from 10 multicenter cohorts, we screened 32 consensus prognostic genes. Ten machine-learning algorithms were transformed into 76 combinations, of which we selected the optimal algorithm to construct an artificial intelligence-derived prognostic signature (AIDPS) according to the average C-index in the nine testing cohorts. The results of the training cohort, nine testing cohorts, Meta-Cohort, and three external validation cohorts (290 patients) consistently indicated that AIDPS could accurately predict the prognosis of PACA. After incorporating several vital clinicopathological features and 86 published signatures, AIDPS exhibited robust and dramatically superior predictive capability. Moreover, in other prevalent digestive system tumors, the nine-gene AIDPS could still accurately stratify the prognosis. Of note, our AIDPS had important clinical implications for PACA, and patients with low AIDPS owned a dismal prognosis, higher genomic alterations, and denser immune cell infiltrates as well as were more sensitive to immunotherapy. Meanwhile, the high AIDPS group possessed observably prolonged survival, and panobinostat may be a potential agent for patients with high AIDPS. Overall, our study provides an attractive tool to further guide the clinical management and individualized treatment of PACA.
摘要:
作为最具侵袭性的肿瘤,胰腺癌(PACA)的预后在过去十年中没有显著改善.基于解剖学的TNM分期并不能准确识别治疗敏感的患者,精准医学迫切需要一种理想的生物标志物。基于来自10个多中心队列的1280名患者的表达文件,我们筛选了32个共识预后基因.十种机器学习算法被转化为76种组合,根据9个测试队列中的平均C指数,我们选择了最佳算法来构建人工智能衍生的预后特征(AIDPS)。培训队列的结果,九个测试小组,元队列,三个外部验证队列(290例患者)一致表明AIDPS可以准确预测PACA的预后。在结合了几个重要的临床病理特征和86个已发表的签名之后,AIDPS表现出强大而卓越的预测能力。此外,在其他常见的消化系统肿瘤中,9基因AIDPS仍然可以准确地对预后进行分层.值得注意的是,我们的AIDPS对PACA有重要的临床意义,低AIDPS患者预后不佳,更高的基因组改变,和更密集的免疫细胞浸润以及对免疫疗法更敏感。同时,高AIDPS组具有明显延长的生存期,帕比司他可能是高AIDPS患者的潜在药物。总的来说,我们的研究为进一步指导PACA的临床管理和个体化治疗提供了有吸引力的工具.
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