Supplementary Data for "CHASMplus reveals the scope of somatic missense mutations driving human cancers"

Large-scale cancer sequencing studies of patient cohorts have statistically implicated many cancer driver genes, with a long-tail of infrequently mutated genes. Here we present CHASMplus, a computational method to predict driver missense mutations, which is uniquely powered to identify rare driver mutations within the long-tail. We show that it substantially outperforms comparable methods across a wide variety of benchmark sets. Applied to 8,657 samples across 32 cancer types, CHASMplus identifies over 4,000 unique driver mutations in 240 genes, further distinguished by their specific cancer types. Our results support a prominent emerging role for rare driver mutations, with substantial variability in the frequency spectrum of drivers across cancer types. The trajectory of driver discovery may already be effectively saturated for certain cancer types, a finding with policy implications for future sequencing. As a resource to handle newly observed rare driver mutations, we systematically score every possible missense mutation across the genome. With the ever-growing pace of DNA sequencing of human tumors, the total number of detected mutations in cancer continues to accelerate. However, only a few mutations in each tumor may actually “drive” the growth of cancer, some of which can have value for diagnostic, prognostic, or therapeutic purposes. Based on a new rigorous statistical analysis of The Cancer Genome Atlas (TCGA), we find a prominent emerging role for rare missense mutations predicted to be “drivers” of cancer, which may have potential implications for genome-driven precision oncology, since rare driver mutations that are putatively actionable could be newly observed in a patient, thus, requiring personalized modeling and assessment. To extend beyond the TCGA, we provide a systematic resource to assess such newly observed missense mutations as cancer drivers. Lastly, we assess the driver landscape of human cancers and find that discovery for some cancer types are already approaching saturation. Detailed results of the manuscript "CHASMplus reveals the scope of somatic missense mutations driving human cancers" are provided in the following Supplementary Tables. Supplementary Table 1. Features used by CHASMplus. Supplementary Table S2. Driver somatic missense mutation results from pan-cancer analysis. Supplementary Table 3. Cancer type specific driver somatic missense mutation results Supplementary Table 4. Subtype enrichment for driver missense mutations predicted by CHASMplus Supplementary Table 5. Comparison of CHASMplus to saturation mutagenesis experiments of PTEN Supplementary Table 6. CHASMplus results on 1,013 prostate adenocarcinoma samples (Armenia et al.) Supplementary Table 7. Cancer type-specific driver somatic missense mutation results for skin cutaneous melanoma

大规模癌症患者队列测序研究已通过统计学方法关联了诸多癌症驱动基因，其中存在大量携带低频突变的长尾基因（long-tail genes）。本文介绍CHASMplus——一种用于预测错义驱动突变（driver missense mutation）的计算方法，其独特优势在于能够识别长尾分布中的罕见驱动突变。

我们证明，在各类基准测试集上，该方法的性能显著优于同类工具。将其应用于32种癌症类型的8657个样本后，CHASMplus在240个基因中鉴定出超过4000种独特的驱动突变，并可按对应癌症类型进一步区分。我们的研究结果证实了罕见驱动突变日益凸显的重要作用，且不同癌症类型的驱动突变频率谱存在显著差异。部分癌症类型的驱动突变发现已接近饱和，这一发现对未来测序相关研究具有政策指导意义。

为了应对新发现的罕见驱动突变，我们系统性地对基因组中所有可能的错义突变（missense mutation）进行了评分。随着人类肿瘤DNA测序速度的不断提升，癌症中检测到的突变总数持续增长。然而，每个肿瘤中仅少数突变可真正“驱动”癌症进展，其中部分突变可用于诊断、预后评估或治疗靶点。

基于对癌症基因组图谱（The Cancer Genome Atlas, TCGA）的全新严谨统计分析，我们发现被预测为癌症驱动因子的罕见错义突变正发挥着愈发重要的作用，这可能对基因组导向的精准肿瘤学（precision oncology）具有潜在价值：因为患者体内新发现的可靶向罕见驱动突变，往往需要个性化的建模与评估。

为了将研究拓展至TCGA之外的场景，我们提供了一套系统性工具资源，用于评估新发现的错义突变是否为癌症驱动突变。

最后，我们对人类癌症的驱动突变图谱进行了分析，发现部分癌症类型的驱动突变发现已接近饱和。

本论文《CHASMplus揭示驱动人类癌症的体细胞错义突变范围》的详细结果见下述补充表格：
补充表1：CHASMplus使用的特征
补充表S2：泛癌（pan-cancer）分析得到的体细胞错义驱动突变（somatic missense driver mutation）结果
补充表3：癌症类型特异性体细胞错义驱动突变结果
补充表4：CHASMplus预测的驱动错义突变的亚型富集分析结果
补充表5：CHASMplus与PTEN饱和诱变实验的对比结果
补充表6：针对1013例前列腺腺癌样本的CHASMplus分析结果（引自Armenia等的研究）
补充表7：皮肤黑色素瘤的癌症类型特异性体细胞错义驱动突变结果