Ghaddar Bassel C, Blaser Martin J, De Subhajyoti
Center for Systems and Computational Biology, Rutgers Cancer Institute, Rutgers University; 195 Albany St., New Brunswick, New Jersey 08901.
Center for Advanced Biotechnology and Medicine, Rutgers University; 679 Hoes Lane West, Piscataway, New Jersey 08854.
bioRxiv. 2025 Jan 24:2025.01.21.634087. doi: 10.1101/2025.01.21.634087.
Recent controversy around the cancer microbiome highlights the need for improved microbial analysis methods for human genomics data. We developed PRISM, a computational approach for precise microorganism identification and decontamination from low-biomass sequencing data. PRISM removes spurious signals and achieves excellent performance when benchmarked on a curated dataset of 62,006 known true- and false-positive taxa. We then use PRISM to detect microbes in 8 cancer types from the CPTAC and TCGA datasets. We identify rich microbiomes in gastrointestinal tract tumors in CPTAC and identify bacteria in a subset of pancreatic tumors that are associated with altered glycoproteomes, more extensive smoking histories, and higher tumor recurrence risk. We find relatively sparse microbes in other cancer types and in TCGA, which we demonstrate may reflect differing sequencing parameters. Overall, PRISM does not replace gold-standard controls, but it enables higher-confidence analyses and reveals tumor-associated microorganisms with potential molecular and clinical significance.
近期围绕癌症微生物组的争议凸显了改进人类基因组数据微生物分析方法的必要性。我们开发了PRISM，这是一种用于从低生物量测序数据中精确识别微生物并进行净化的计算方法。PRISM去除了虚假信号，在一个由62,006个已知真阳性和假阳性分类群组成的精选数据集上进行基准测试时表现出色。然后，我们使用PRISM检测来自CPTAC和TCGA数据集的8种癌症类型中的微生物。我们在CPTAC的胃肠道肿瘤中识别出丰富的微生物组，并在一部分胰腺肿瘤中识别出与糖蛋白组改变、更广泛的吸烟史和更高的肿瘤复发风险相关的细菌。我们在其他癌症类型和TCGA中发现微生物相对较少，我们证明这可能反映了不同的测序参数。总体而言，PRISM并不能取代金标准对照，但它能够进行更高可信度的分析，并揭示具有潜在分子和临床意义的肿瘤相关微生物。
