Validation of In Vitro Trained Transcriptomic Radiosensitivity Signatures in Clinical Cohorts.
Validation of In Vitro Trained Transcriptomic Radiosensitivity Signatures in Clinical Cohorts.
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临床队列中经过体外训练的转录组放射敏性特征的验证。
作者：
放射治疗（RT）是癌症的重要治疗方法。过去 100 年的技术进步有助于以越来越高的准确度向肿瘤（并避开正常组织）进行放射治疗。根据人与人之间的遗传差异来调整放射治疗方面取得的进展相对较小。提出的调整剂量的技术已在临床数据集中进行了评估，但研究尚未考虑技术方面如何影响辐射的预测或基因特征特异性。这项工作表明预处理对模型预测有很大影响，并证明现有基于基因表达的模型中缺乏辐射特异性的证据。近年来，放射治疗的转录组个性化引起了人们极大的兴趣。然而，体外数据的独立模型测试表现不佳。在这项工作中，我们评估了放射敏感性特征在临床应用中的再现性。使用不同的微阵列标准化方法评估了已发表的签名的放射敏感性预测之间的一致性。根据重新采样的体外数据开发的对照特征在临床队列中进行了基准测试。使用临床转录组数据中的每个基因进行生存分析，并使用基因集富集分析来确定与预测生存和复发的模型性能相关的途径。标准化方法影响计算的放射敏感性指数 (RSI) 值。事实上，不同标准化方法的一致性极限超过了 20%。没有公布的签名显着改善了用于预测临床结果的重采样对照。基因模型的功能注释表明，许多重叠的生物过程与接受放疗和未接受放疗的患者的癌症结果相关，包括增殖和免疫反应。总之，不同的归一化方法不应互换使用。鉴于大部分基因与癌症结果相关，已发表的签名的效用仍不清楚。对于接受或不接受放射治疗的患者来说，影响结果的生物过程是重叠的，这表明现有的特征可能缺乏特异性。
Radiation therapy (RT) is an important treatment for cancer. Advances in technology over the last 100 years have helped to deliver radiation to tumours (and avoid normal tissues) with increasing accuracy. Comparatively little progress has been made in adjusting radiation treatment based on genetic differences from one person to another. Techniques proposed for adjusting dose have been assessed in clinical datasets, but studies have not considered how technical aspects affect predictions or gene signature specificity to radiation. This work shows that preprocessing has a large influence on model predictions and demonstrates a lack of evidence for radiation specificity in existing gene expression-based models. Transcriptomic personalisation of radiation therapy has gained considerable interest in recent years. However, independent model testing on in vitro data has shown poor performance. In this work, we assess the reproducibility in clinical applications of radiosensitivity signatures. Agreement between radiosensitivity predictions from published signatures using different microarray normalization methods was assessed. Control signatures developed from resampled in vitro data were benchmarked in clinical cohorts. Survival analysis was performed using each gene in the clinical transcriptomic data, and gene set enrichment analysis was used to determine pathways related to model performance in predicting survival and recurrence. The normalisation approach impacted calculated radiosensitivity index (RSI) values. Indeed, the limits of agreement exceeded 20% with different normalisation approaches. No published signature significantly improved on the resampled controls for prediction of clinical outcomes. Functional annotation of gene models suggested that many overlapping biological processes are associated with cancer outcomes in RT treated and non-RT treated patients, including proliferation and immune responses. In summary, different normalisation methods should not be used interchangeably. The utility of published signatures remains unclear given the large proportion of genes relating to cancer outcome. Biological processes influencing outcome overlapped for patients treated with or without radiation suggest that existing signatures may lack specificity.
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肿瘤细胞和颈部癌的肿瘤细胞和成纤维细胞的体外放射敏性：相互关系以及与临床数据的相关性。
影响因子：
8.8
作者：
Stausbol-Gron, B;Bentzen, SM;Jorgensen, KE;Nielsen, OS;Bundgaard, T;Overgaard, J
通讯作者：
Overgaard, J
影响因子：
3.7
作者：
Hall JS;Iype R;Senra J;Taylor J;Armenoult L;Oguejiofor K;Li Y;Stratford I;Stern PL;O'Connor MJ;Miller CJ;West CM
通讯作者：
West CM
影响因子：
14.9
作者：
Colaprico A;Silva TC;Olsen C;Garofano L;Cava C;Garolini D;Sabedot TS;Malta TM;Pagnotta SM;Castiglioni I;Ceccarelli M;Bontempi G;Noushmehr H
通讯作者：
Noushmehr H
DOI：
10.1016/j.ijrobp.2022.03.006
发表时间：
2022-07-01
影响因子：
7
作者：
Grass, G. Daniel;Alfonso, Juan C. L.;Welsh, Eric;Ahmed, Kamran A.;Teer, Jamie K.;Pilon-Thomas, Shari;Harrison, Louis B.;Cleveland, John L.;Mule, James J.;Eschrich, Steven A.;Enderling, Heiko;Torres-Roca, Javier F.
通讯作者：
Torres-Roca, Javier F.
影响因子：
11.5
作者：
Eschrich, Steven A.;Fulp, William J.;Torres-Roca, Javier F.
通讯作者：
Torres-Roca, Javier F.