ProCan-DepMapSanger

The proteome provides unique insights into disease biology beyond the genome and transcriptome. A lack of large proteomic datasets has restricted the identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types are analyzed by mass spectrometry. Deploying a workflow to quantify 8,498 proteins, these data capture evidence of cell-type and post-transcriptional modifications. Integrating multi-omics, drug response, and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline reveals thousands of protein biomarkers of cancer vulnerabilities that are not significant at the transcript level. The power of the proteome to predict drug response is very similar to that of the transcriptome. Further, random downsampling to only 1,500 proteins has limited impact on predictive power, consistent with protein networks being highly connected and co-regulated.蛋白质组（proteome）能够提供基因组（genome）与转录组（transcriptome）无法覆盖的独特疾病生物学洞察。此前大型蛋白质组数据集的匮乏，制约了新型癌症生物标志物的发掘。本研究通过质谱技术（mass spectrometry），对覆盖28种组织类型的949株癌细胞系的蛋白质组开展分析。本研究采用标准化分析工作流完成了8498种蛋白质的定量表征，所获数据涵盖了细胞类型特异性特征与转录后修饰（post-transcriptional modification）的相关证据。将多组学（multi-omics）数据、药物响应数据与CRISPR-Cas9基因必需性筛选结果，结合基于深度学习（deep learning）的分析管线进行整合分析后，本研究挖掘出数千个在转录组层面无显著关联的癌症脆弱性相关蛋白质生物标志物。蛋白质组预测药物响应的效能与转录组的预测效能近乎一致。进一步分析表明，仅随机抽取1500种蛋白质进行分析，对模型预测性能的影响微乎其微，这与蛋白质组网络具有高度连通性与共调控特性的结论相符。

搜集汇总

背景与挑战

背景概述

该数据集通过质谱分析了949种癌细胞系的蛋白质组，量化了8,498种蛋白质，以捕捉细胞类型和转录后修饰信息。整合多组学、药物反应和CRISPR-Cas9筛选数据，揭示了蛋白质组在预测癌症脆弱性和药物反应方面的关键作用，其预测能力与转录组相似。