Personalized Cancer Medicine, Biobank, Ioannina University, Ioannina, Greece.
Pharmacogenomics J. 2011 Apr;11(2):81-92. doi: 10.1038/tpj.2010.81. Epub 2010 Oct 26.
Life diversity can now be clearly explored with the next-generation DNA sequencing technology, allowing the discovery of genetic variants among individuals, patients and tumors. However, beyond causal mutations catalog completion, systems medicine is essential to link genotype to phenotypic cancer diversity towards personalized medicine. Despite advances with traditional single genes molecular research, including rare mutations in BRCA1/2 and CDH1 for primary prevention and trastuzumab for treating HER2-overexpressing breast and gastric tumors, overall, treatment failure and death rates are still alarmingly high. Revolution in sequencing reveals that, now both a huge number and widespread variability of driver mutations, including single-nucleotide polymorphisms, genomic rearrangements and copy-number changes involved in breast cancer development. All these genetic alterations result in a heterogeneous deregulation of signaling pathways, including EGFR, HER2, VEGF, Wnt/Notch, TGF and others.Cancer initiation, progression and metastases are driven by complex molecular networks rather than linear genotype-phenotype relationship. Therefore, clinical expectations by traditional molecular research strategies targeting single genes and single signaling pathways are likely minimal. This review discusses the necessity of molecular networks modeling to understand complex gene-gene, protein-protein and gene-environment interactions. Moreover, the potential of systems clinico-biological approaches to predict intracellular signaling pathways components networks and cancer heterogeneous cells within an individual tumor is described. A flowchart specific for three steps in cancer evolution separately tumorigenesis, early-stage and advanced-stage breast cancer is presented. Using reverse engineering starting with the integration of available established clinical, environmental, treatment and oncological outcomes (survival and death) data and then the still incomplete but progressively accumulating genotypic data into computational networks modeling may lead to bionetworks-based discovery of robust biomarkers and highly effective cancer drugs targets.
利用下一代 DNA 测序技术，可以清晰地探索生命多样性，从而发现个体、患者和肿瘤之间的遗传变异。然而，除了完成因果突变目录，系统医学对于将基因型与表型癌症多样性联系起来以实现个体化医学至关重要。尽管在传统的单基因分子研究方面取得了进展，包括 BRCA1/2 和 CDH1 基因的罕见突变用于一级预防和曲妥珠单抗治疗 HER2 过表达的乳腺癌和胃癌，但总体而言，治疗失败和死亡率仍然高得惊人。测序技术的革新表明，现在驱动突变的数量巨大且广泛存在，包括单核苷酸多态性、基因组重排和涉及乳腺癌发生的拷贝数变化。所有这些遗传改变导致信号通路的异质性失调，包括 EGFR、HER2、VEGF、Wnt/Notch、TGF 等。癌症的发生、进展和转移是由复杂的分子网络驱动的，而不是线性的基因型-表型关系。因此，传统的针对单个基因和单个信号通路的分子研究策略的临床预期可能微乎其微。本文讨论了对复杂的基因-基因、蛋白-蛋白和基因-环境相互作用进行分子网络建模的必要性。此外，还描述了系统临床生物学方法在预测个体肿瘤内细胞内信号通路成分网络和癌症异质性细胞方面的潜力。本文还提出了一个特定于癌症演化的三个步骤（肿瘤发生、早期和晚期乳腺癌）的流程图。使用反向工程，从整合现有的临床、环境、治疗和肿瘤学结果（生存和死亡）数据开始，然后将仍然不完整但逐渐积累的基因型数据集成到计算网络模型中，可能会导致基于生物网络的发现稳健的生物标志物和高效的癌症药物靶点。
