Gaffin Center for Neuro-Oncology, Sharett Institute for Oncology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel; The Wohl Institute for Translational Medicine, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel; Department of Microbiology and Molecular Genetics, IMRIC, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
Gaffin Center for Neuro-Oncology, Sharett Institute for Oncology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel; The Wohl Institute for Translational Medicine, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
Cell Rep Med. 2024 Jun 18;5(6):101608. doi: 10.1016/j.xcrm.2024.101608. Epub 2024 Jun 11.
While mutational signatures provide a plethora of prognostic and therapeutic insights, their application in clinical-setting, targeted gene panels is extremely limited. We develop a mutational representation model (which learns and embeds specific mutation signature connections) that enables prediction of dominant signatures with only a few mutations. We predict the dominant signatures across more than 60,000 tumors with gene panels, delineating their landscape across different cancers. Dominant signature predictions in gene panels are of clinical importance. These included UV, tobacco, and apolipoprotein B mRNA editing enzyme, catalytic polypeptide (APOBEC) signatures that are associated with better survival, independently from mutational burden. Further analyses reveal gene and mutation associations with signatures, such as SBS5 with TP53 and APOBEC with FGFR3. In a clinical use case, APOBEC signature is a robust and specific predictor for resistance to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs). Our model provides an easy-to-use way to detect signatures in clinical setting assays with many possible clinical implications for an unprecedented number of cancer patients.
虽然突变特征提供了大量的预后和治疗见解，但它们在临床靶向基因面板中的应用极其有限。我们开发了一种突变表示模型（它学习并嵌入特定的突变特征连接），该模型仅用几个突变就可以预测主要特征。我们通过基因面板预测了超过 60000 个肿瘤中的主要特征，描绘了它们在不同癌症中的分布情况。基因面板中的主要特征预测具有重要的临床意义。其中包括与更好的生存相关的 UV、烟草和载脂蛋白 B mRNA 编辑酶、催化多肽（APOBEC）特征，这些特征与突变负担无关。进一步的分析揭示了与特征相关的基因和突变关联，例如 SBS5 与 TP53 和 APOBEC 与 FGFR3。在一个临床应用案例中，APOBEC 特征是表皮生长因子受体酪氨酸激酶抑制剂（EGFR-TKIs）耐药的一个强大而特异的预测因子。我们的模型提供了一种在临床环境检测特征的简便方法，对数量空前的癌症患者具有许多潜在的临床意义。
