Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China; Department of Radiation Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China.
Radiother Oncol. 2024 Jul;196:110261. doi: 10.1016/j.radonc.2024.110261. Epub 2024 Mar 26.
Radiation pneumonitis (RP) is the major dose-limiting toxicity of thoracic radiotherapy. This study aimed to developed a dual-omics (single nucleotide polymorphisms, SNP and dosiomics) prediction model for symptomatic RP.
The potential SNPs, which are of significant difference between the RP grade ≥ 3 group and the RP grade ≤ 1 group, were selected from the whole exome sequencing SNPs using the Fisher's exact test. Patients with lung cancer who received thoracic radiotherapy at our institution from 2009 to 2016 were enrolled for SNP selection and model construction. The factorization machine (FM) method was used to model the SNP epistasis effect, and to construct the RP prediction model (SNP-FM). The dosiomics features were extracted, and further selected using the minimum redundancy maximum relevance (mRMR) method. The selected dosiomics features were added to the SNP-FM model to construct the dual-omics model.
For SNP screening, peripheral blood samples of 28 patients with RP grade ≥ 3 and the matched 28 patients with RP grade ≤ 1 were sequenced. 81 SNPs were of significant difference (P < 0.015) and considered as potential SNPs. In addition, 21 radiation toxicity related SNPs were also included. For model construction, 400 eligible patients (including 108 RP grade ≥ 2) were enrolled. Single SNP showed no strong correlation with RP. On the other hand, the SNP-SNP interaction (epistasis effect) of 19 SNPs were modeled by the FM method, and achieved an area under the curve (AUC) of 0.76 in the testing group. In addition, 4 dosiomics features were selected and added to the model, and increased the AUC to 0.81.
A novel dual-omics model by synergizing the SNP epistasis effect with dosiomics features was developed. The enhanced the RP prediction suggested its promising clinical utility in identifying the patients with severe RP during thoracic radiotherapy.
放射性肺炎（RP）是胸部放疗的主要剂量限制毒性。本研究旨在开发一种用于预测症状性 RP 的双重组学（单核苷酸多态性，SNP 和 dosiomics）预测模型。
使用 Fisher 精确检验从全外显子测序 SNP 中选择 RP 分级≥3 组和 RP 分级≤1 组之间存在显著差异的潜在 SNP。从 2009 年至 2016 年在我们机构接受胸部放疗的肺癌患者被纳入 SNP 选择和模型构建。使用因子分解机（FM）方法对 SNP 上位效应进行建模，并构建 RP 预测模型（SNP-FM）。提取 dosiomics 特征，并使用最小冗余最大相关性（mRMR）方法进一步选择。将选定的 dosiomics 特征添加到 SNP-FM 模型中，构建双重组学模型。
对于 SNP 筛选，对 28 例 RP 分级≥3 的患者和 28 例匹配的 RP 分级≤1 的患者进行外周血样本测序。有 81 个 SNP 存在显著差异（P<0.015），被认为是潜在的 SNP。此外，还包括 21 个与放射毒性相关的 SNP。对于模型构建，纳入了 400 名符合条件的患者（包括 108 名 RP 分级≥2）。单个 SNP 与 RP 无明显相关性。另一方面，使用 FM 方法对 19 个 SNP 的 SNP-SNP 相互作用（上位效应）进行建模，在测试组中获得了 0.76 的 AUC。此外，选择了 4 个 dosiomics 特征并添加到模型中，将 AUC 提高到 0.81。
通过协同 SNP 上位效应和 dosiomics 特征开发了一种新的双重组学模型。该模型提高了对 RP 的预测能力，有望在胸部放疗期间识别出严重 RP 的患者。
