Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Via Claudio 21, 80128, Naples, Italy.
Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), Via Adamello 16, 20139, Milan, Italy.
J Transl Med. 2023 Jan 30;21(1):55. doi: 10.1186/s12967-023-03907-z.
Somatic alterations in cancer cause dysregulation of signaling pathways that control cell-cycle progression, apoptosis, and cell growth. The effect of individual alterations in these pathways differs between individual tumors and tumor types. Recognizing driver events is a complex task requiring integrating multiple molecular data, including genomics, epigenomics, and functional genomics. A common hypothesis is that these driver events share similar effects on the hallmarks of cancer. The availability of large-scale multi-omics studies allows for inferring these common effects from data. Once these effects are known, one can then deconvolve in every individual patient whether a given genomics alteration is a driver event.
Here, we develop a novel data-driven approach to identify shared oncogenic expression signatures among tumors. We aim to identify gene onco-signature for classifying tumor patients in homogeneous subclasses with distinct prognoses and specific genomic alterations. We derive expression pan-cancer onco-signatures from TCGA gene expression data using a discovery set of 9107 primary pan-tumor samples together with respective matched mutational data and a list of known cancer-related genes from COSMIC database.
We use the derived ono-signatures to state their prognostic significance and apply them to the TCGA breast cancer dataset as proof of principle of our approach. We uncover a "mitochondrial" sub-group of Luminal patients characterized by its biological features and regulated by specific genetic modulators. Collectively, our results demonstrate the effectiveness of onco-signatures-based methodologies, and they also contribute to a comprehensive understanding of the metabolic heterogeneity of Luminal tumors.
These findings provide novel genomics evidence for developing personalized breast cancer patient treatments. The onco-signature approach, demonstrated here on breast cancer, is general and can be applied to other cancer types.
癌症中的体细胞改变导致控制细胞周期进程、细胞凋亡和细胞生长的信号通路失调。这些通路中的个体改变的影响在不同肿瘤和肿瘤类型之间有所不同。识别驱动事件是一项复杂的任务，需要整合多种分子数据，包括基因组学、表观基因组学和功能基因组学。一个常见的假设是，这些驱动事件对癌症的标志性特征具有相似的影响。大规模多组学研究的可用性允许从数据中推断这些共同的影响。一旦这些影响已知，就可以在每个个体患者中推断给定的基因组改变是否是驱动事件。
在这里，我们开发了一种新的数据驱动方法来识别肿瘤之间共享的致癌表达特征。我们旨在确定基因致癌签名，以将肿瘤患者分类为具有不同预后和特定基因组改变的同质亚类。我们使用来自 TCGA 基因表达数据的发现集（共 9107 个原发性泛肿瘤样本）以及来自 COSMIC 数据库的已知癌症相关基因列表，使用一种发现集来推导泛癌症的表达致癌签名。
我们使用推导的致癌签名来确定其预后意义，并将其应用于 TCGA 乳腺癌数据集，作为我们方法的原理验证。我们揭示了一种 Luminal 患者的“线粒体”亚组，其特征在于其生物学特征，并受特定遗传调节剂的调控。总的来说，我们的结果证明了基于致癌签名的方法的有效性，并且它们也有助于全面理解 Luminal 肿瘤的代谢异质性。
这些发现为开发针对乳腺癌患者的个性化治疗提供了新的基因组学证据。这里展示的致癌签名方法是通用的，可以应用于其他癌症类型。
