Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany.
Int J Radiat Oncol Biol Phys. 2022 Jul 1;113(3):635-647. doi: 10.1016/j.ijrobp.2022.03.006. Epub 2022 Mar 12.
Radiation therapy (RT) is a mainstay of cancer care, and accumulating evidence suggests the potential for synergism with components of the immune response. However, few data describe the tumor immune contexture in relation to RT sensitivity. To address this challenge, we used the radiation sensitivity index (RSI) gene signature to estimate the RT sensitivity of >10,000 primary tumors and characterized their immune microenvironments in relation to the RSI.
We analyzed gene expression profiles of 10,469 primary tumors (31 types) within a prospective tissue collection protocol. The RT sensitivity of each tumor was estimated by the RSI and respective distributions were characterized. The tumor biology measured by the RSI was evaluated by differentially expressed genes combined with single sample gene set enrichment analysis. Differences in the expression of immune regulatory molecules were assessed and deconvolution algorithms were used to estimate immune cell infiltrates in relation to the RSI. A subset (n = 2368) of tumors underwent DNA sequencing for mutational frequency characterization.
We identified a wide range of RSI values within and across various tumor types, with several demonstrating nonunimodal distributions (eg, colon, renal, lung, prostate, esophagus, pancreas, and PAM50 breast subtypes; P < .05). Across all tumor types, stratifying RSI at a tumor type-specific median identified 7148 differentially expressed genes, of which 146 were coordinate in direction. Network topology analysis demonstrates RSI measures a coordinated STAT1, IRF1, and CCL4/MIP-1β transcriptional network. Tumors with an estimated high sensitivity to RT demonstrated distinct enrichment of interferon-associated signaling pathways and immune cell infiltrates (eg, CD8 T cells, activated natural killer cells, M1-macrophages; q < 0.05), which was in the context of diverse expression patterns of various immunoregulatory molecules.
This analysis describes the immune microenvironments of patient tumors in relation to the RSI gene expression signature.
放射治疗（RT）是癌症治疗的主要手段，越来越多的证据表明其与免疫反应的各个组成部分具有协同作用的潜力。然而，很少有数据描述与 RT 敏感性相关的肿瘤免疫结构。为了解决这一挑战，我们使用放射敏感性指数（RSI）基因特征来估计超过 10000 个原发性肿瘤的 RT 敏感性，并描述它们的免疫微环境与 RSI 的关系。
我们分析了一个前瞻性组织采集方案中 10469 个原发性肿瘤（31 种类型）的基因表达谱。每个肿瘤的 RT 敏感性由 RSI 估计，并对各自的分布进行了特征描述。通过差异表达基因与单样本基因集富集分析相结合，对由 RSI 衡量的肿瘤生物学进行了评估。评估了免疫调节分子的表达差异，并使用去卷积算法来估计与 RSI 相关的免疫细胞浸润。一部分（n=2368）肿瘤进行了 DNA 测序，以进行突变频率特征分析。
我们在各种肿瘤类型中发现了广泛的 RSI 值范围，其中一些表现出非单峰分布（例如，结肠、肾、肺、前列腺、食管、胰腺和 PAM50 乳腺癌亚型；P<0.05）。在所有肿瘤类型中，在肿瘤类型特异性中位数处对 RSI 进行分层可确定 7148 个差异表达基因，其中 146 个在方向上协调一致。网络拓扑分析表明，RSI 衡量的是一个协调的 STAT1、IRF1 和 CCL4/MIP-1β 转录网络。估计对 RT 高度敏感的肿瘤表现出丰富的干扰素相关信号通路和免疫细胞浸润（例如，CD8 T 细胞、激活的自然杀伤细胞、M1 巨噬细胞；q<0.05），这是在各种免疫调节分子的不同表达模式的背景下发生的。
该分析描述了与 RSI 基因表达特征相关的患者肿瘤的免疫微环境。
