Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA.
Division of Pediatric Hematology/Oncology, Department of Pediatrics, UCSF Benioff Children's Hospital, San Francisco, CA, USA.
Nat Biotechnol. 2021 Feb;39(2):215-224. doi: 10.1038/s41587-020-0652-7. Epub 2020 Sep 14.
Tumor-specific elucidation of physical and functional oncoprotein interactions could improve tumorigenic mechanism characterization and therapeutic response prediction. Current interaction models and pathways, however, lack context specificity and are not oncoprotein specific. We introduce SigMaps as context-specific networks, comprising modulators, effectors and cognate binding-partners of a specific oncoprotein. SigMaps are reconstructed de novo by integrating diverse evidence sources-including protein structure, gene expression and mutational profiles-via the OncoSig machine learning framework. We first generated a KRAS-specific SigMap for lung adenocarcinoma, which recapitulated published KRAS biology, identified novel synthetic lethal proteins that were experimentally validated in three-dimensional spheroid models and established uncharacterized crosstalk with RAB/RHO. To show that OncoSig is generalizable, we first inferred SigMaps for the ten most mutated human oncoproteins and then for the full repertoire of 715 proteins in the COSMIC Cancer Gene Census. Taken together, these SigMaps show that the cell's regulatory and signaling architecture is highly tissue specific.
肿瘤特异性阐明物理和功能致癌蛋白相互作用可以提高肿瘤发生机制的特征描述和治疗反应预测。然而，当前的相互作用模型和途径缺乏上下文特异性，并且不是致癌蛋白特异性的。我们引入 SigMaps 作为具有上下文特异性的网络，包括特定致癌蛋白的调节剂、效应物和同源结合伙伴。SigMaps 通过 OncoSig 机器学习框架整合多种证据来源（包括蛋白质结构、基因表达和突变谱）从头重建。我们首先为肺腺癌生成了一个 KRAS 特异性 SigMap，该图谱重现了已发表的 KRAS 生物学，鉴定了新的合成致死蛋白，并在三维球体模型中进行了实验验证，并与 RAB/RHO 建立了未表征的串扰。为了表明 OncoSig 具有通用性，我们首先推断了十个最常见突变的人类致癌蛋白的 SigMap，然后推断了 COSMIC 癌症基因目录中 715 个蛋白质的全部谱。总的来说，这些 SigMaps 表明细胞的调节和信号转导架构具有高度的组织特异性。
