Department of Genetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
NPJ Syst Biol Appl. 2024 Oct 3;10(1):114. doi: 10.1038/s41540-024-00445-2.
Elucidating the molecular dependencies behind the cancer-type specificity of driver mutations may reveal new therapeutic opportunities. We hypothesized that developmental programs would impact the transduction of oncogenic signaling activated by a driver mutation and shape its cancer-type specificity. Therefore, we designed a computational analysis framework by combining single-cell gene expression profiles during fetal organ development, latent factor discovery, and information theory-based differential network analysis to systematically identify transcription factors that selectively respond to driver mutations under the influence of organ-specific developmental programs. After applying this approach to VHL mutations, which are highly specific to clear cell renal cell carcinoma (ccRCC), we revealed important regulators downstream of VHL mutations in ccRCC and used their activities to cluster patients with ccRCC into three subtypes. This classification revealed a more significant difference in prognosis than the previous mRNA profile-based method and was validated in an independent cohort. Moreover, we found that EP300, a key epigenetic factor maintaining the regulatory network of the subtype with the worst prognosis, can be targeted by a small inhibitor, suggesting a potential treatment option for a subset of patients with ccRCC. This work demonstrated an intimate relationship between organ development and oncogenesis from the perspective of systems biology, and the method can be generalized to study the influence of other biological processes on cancer driver mutations.
阐明驱动突变背后的癌症类型特异性的分子依赖性，可能揭示新的治疗机会。我们假设发育程序会影响驱动突变激活的致癌信号的转导，并塑造其癌症类型特异性。因此，我们设计了一个计算分析框架，通过结合胎儿器官发育过程中的单细胞基因表达谱、潜在因子发现和基于信息论的差异网络分析，系统地识别在器官特异性发育程序影响下选择性响应驱动突变的转录因子。将这种方法应用于 VHL 突变后，我们揭示了 ccRCC 中 VHL 突变下游的重要调节因子，并利用它们的活性将 ccRCC 患者聚类为三个亚型。与以前基于 mRNA 谱的方法相比，这种分类方法在预后方面的差异更为显著，并在独立队列中得到了验证。此外，我们发现 EP300 是维持预后最差亚型调节网络的关键表观遗传因子，可被一种小分子抑制剂靶向，这提示了 ccRCC 亚组患者的一种潜在治疗选择。这项工作从系统生物学的角度展示了器官发育和肿瘤发生之间的密切关系，并且该方法可以推广到研究其他生物过程对癌症驱动突变的影响。
