Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, United States of America.
Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America.
PLoS Comput Biol. 2021 Feb 25;17(2):e1008720. doi: 10.1371/journal.pcbi.1008720. eCollection 2021 Feb.
Increased availability of drug response and genomics data for many tumor cell lines has accelerated the development of pan-cancer prediction models of drug response. However, it is unclear how much between-tissue differences in drug response and molecular characteristics may contribute to pan-cancer predictions. Also unknown is whether the performance of pan-cancer models could vary by cancer type. Here, we built a series of pan-cancer models using two datasets containing 346 and 504 cell lines, each with MEK inhibitor (MEKi) response and mRNA expression, point mutation, and copy number variation data, and found that, while the tissue-level drug responses are accurately predicted (between-tissue ρ = 0.88-0.98), only 5 of 10 cancer types showed successful within-tissue prediction performance (within-tissue ρ = 0.11-0.64). Between-tissue differences make substantial contributions to the performance of pan-cancer MEKi response predictions, as exclusion of between-tissue signals leads to a decrease in Spearman's ρ from a range of 0.43-0.62 to 0.30-0.51. In practice, joint analysis of multiple cancer types usually has a larger sample size, hence greater power, than for one cancer type; and we observe that higher accuracy of pan-cancer prediction of MEKi response is almost entirely due to the sample size advantage. Success of pan-cancer prediction reveals how drug response in different cancers may invoke shared regulatory mechanisms despite tissue-specific routes of oncogenesis, yet predictions in different cancer types require flexible incorporation of between-cancer and within-cancer signals. As most datasets in genome sciences contain multiple levels of heterogeneity, careful parsing of group characteristics and within-group, individual variation is essential when making robust inference.
越来越多的肿瘤细胞系的药物反应和基因组学数据的可用性加速了药物反应的泛癌预测模型的发展。然而，目前尚不清楚药物反应和分子特征的组织间差异在多大程度上可能对泛癌预测产生影响。也不知道泛癌模型的性能是否会因癌症类型而有所不同。在这里，我们使用包含 346 和 504 个细胞系的两个数据集构建了一系列泛癌模型，每个数据集都包含 MEK 抑制剂 (MEKi) 反应和 mRNA 表达、点突变和拷贝数变异数据，结果发现，尽管组织水平的药物反应可以被准确预测（组织间 ρ=0.88-0.98），但只有 10 种癌症类型中的 5 种表现出成功的组织内预测性能（组织内 ρ=0.11-0.64）。组织间差异对泛癌 MEKi 反应预测的性能有很大贡献，因为排除组织间信号会导致 Spearman's ρ 值从 0.43-0.62 范围下降到 0.30-0.51。实际上，多种癌症类型的联合分析通常比一种癌症类型具有更大的样本量，因此具有更大的效力；我们观察到，泛癌预测 MEKi 反应的准确性更高几乎完全是由于样本量的优势。泛癌预测的成功揭示了不同癌症中的药物反应如何在尽管存在组织特异性致癌途径的情况下调用共享的调节机制，但在不同的癌症类型中进行预测需要灵活地纳入癌症间和癌症内的信号。由于基因组科学中的大多数数据集都包含多个层次的异质性，因此在进行稳健推断时，仔细解析组特征和组内个体变异是至关重要的。
