Center for Systems and Computational Biology, Rutgers Cancer Institute of New Jersey, Rutgers University, 195 Albany St., New Brunswick, NJ 08901, USA.
Department of Surgery, University of Rochester Medical Center, 601 Elmwood Avenue, Box SURG, Rochester, NY 14642, USA.
Cancer Cell. 2022 Oct 10;40(10):1240-1253.e5. doi: 10.1016/j.ccell.2022.09.009.
Microorganisms are detected in multiple cancer types, including in putatively sterile organs, but the contexts in which they influence oncogenesis or anti-tumor responses in humans remain unclear. We recently developed single-cell analysis of host-microbiome interactions (SAHMI), a computational pipeline to recover and denoise microbial signals from single-cell sequencing of host tissues. Here we use SAHMI to interrogate tumor-microbiome interactions in two human pancreatic cancer cohorts. We identify somatic-cell-associated bacteria in a subset of tumors and their near absence in nonmalignant tissues. These bacteria predominantly pair with tumor cells, and their presence is associated with cell-type-specific gene expression and pathway activities, including cell motility and immune signaling. Modeling results indicate that tumor-infiltrating lymphocytes closely resemble T cells from infected tissue. Finally, using multiple independent datasets, a signature of cell-associated bacteria predicts clinical prognosis. Tumor-microbiome crosstalk may modulate tumorigenesis in pancreatic cancer with implications for clinical management.
微生物在多种癌症类型中被检测到，包括在据称无菌的器官中，但它们在人类中影响肿瘤发生或抗肿瘤反应的情况仍不清楚。我们最近开发了一种宿主-微生物组相互作用的单细胞分析（SAHMI）方法，这是一种从宿主组织的单细胞测序中恢复和去噪微生物信号的计算管道。在这里，我们使用 SAHMI 来研究两个人类胰腺癌队列中的肿瘤-微生物组相互作用。我们在一部分肿瘤中发现了与体细胞相关的细菌，而在非恶性组织中几乎不存在这些细菌。这些细菌主要与肿瘤细胞配对，它们的存在与细胞类型特异性基因表达和途径活性相关，包括细胞迁移和免疫信号。模型结果表明，肿瘤浸润淋巴细胞与来自感染组织的 T 细胞非常相似。最后，使用多个独立的数据集，细胞相关细菌的特征可预测临床预后。肿瘤-微生物组的串扰可能会调节胰腺癌的肿瘤发生，这对临床管理具有重要意义。
