Supplementary Material for "A Bayesian hierarchical model uncovers targetable oncogenic hotspots in disordered regions of FGFRs" by Hommerich, Naveja, et al.

Table S1. Samples from UCT Mainz cancer patients with FGFR mutations.Table S2. Comparison of the area under the ROC curve for the different modelspredicting the oncogenic effect of 137 missense mutations as reported by Nakamura et al.Table S3. We extracted the clinical results from RAGNAR for 22 missense mutations includedthere and scored them using our model. For the response rate we compute the proportion ofresponders (either CR or PR). Our prediction is based on whether we find the residue positionto be a hotspot with at least 99% probability.Table S4. Mutations reported in the UCT dataset and whether they are included in RAGNAR orpredicted to be oncogenic by BRIGHT-FGFR.Table S5. Alpha estimates from the model and statistics of the posterior distribution. MAP:mean a posteriori. aa_pos: amino acid position. P: percentile of the posterior distribution. Table S6. Beta (BRIGHT-FGFR) estimates from the model and statistics of the posteriordistribution. MAP: mean a posteriori. aa_pos: amino acid position. P: percentile of the posteriordistribution.附表S1：来自美因茨大学癌症中心（UCT Mainz）携带FGFR（Fibroblast Growth Factor Receptor）突变的癌症患者样本。 附表S2：不同模型对137个错义突变致癌效应的预测性能对比，以ROC曲线（Receiver Operating Characteristic curve）下面积为评价指标，数据源自Nakamura等的研究报道。 附表S3：从RAGNAR数据库中提取纳入的22个错义突变的临床结果，并使用本研究模型对其进行评分。本研究中，客观缓解率通过应答者（完全缓解CR（Complete Response）或部分缓解PR（Partial Response））的占比计算得出；预测结果基于残基位点是否为概率≥99%的突变热点。 附表S4：UCT数据集内报道的突变信息，以及这些突变是否被纳入RAGNAR数据库，或被BRIGHT-FGFR模型预测为致癌突变。 附表S5：模型得到的α参数估计值及后验分布统计量。注：MAP为后验均值（mean a posteriori），aa_pos为氨基酸位点（amino acid position），P为后验分布百分位数（percentile of the posterior distribution）。 附表S6：模型得到的β（BRIGHT-FGFR）参数估计值及后验分布统计量。注：MAP为后验均值（mean a posteriori），aa_pos为氨基酸位点（amino acid position），P为后验分布百分位数（percentile of the posterior distribution）。