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.

物理和功能性癌蛋白相互作用的肿瘤特异性阐明可以改善致瘤机制表征和治疗反应预测。然而，当前的相互作用模型和途径缺乏背景特异性，并且不是癌蛋白特异性的。我们将 SigMaps 引入为上下文特定网络，包括特定癌蛋白的调节器、效应器和同源结合伙伴。 SigMap 通过 OncoSig 机器学习框架整合不同的证据来源（包括蛋白质结构、基因表达和突变谱）从头重建。我们首先生成了肺腺癌的 KRAS 特异性 SigMap，它概括了已发表的 KRAS 生物学，鉴定了新型合成致死蛋白，这些蛋白在三维球体模型中经过实验验证，并与 RAB/RHO 建立了未表征的串扰。为了证明 OncoSig 具有普适性，我们首先推断了 10 种突变最严重的人类癌蛋白的 SigMap，然后推断了 COSMIC 癌症基因普查中 715 种蛋白的完整库。总而言之，这些 SigMap 显示细胞的调控和信号传导结构具有高度的组织特异性。