Filipp Fabian V
Systems Biology and Cancer Metabolism, Program for Quantitative Systems Biology, University of California Merced, 2500 North Lake Road, Merced, CA, 95343, USA.
Cancer Metastasis Rev. 2017 Mar;36(1):91-108. doi: 10.1007/s10555-017-9662-4.
Molecular insights from genome and systems biology are influencing how cancer is diagnosed and treated. We critically evaluate big data challenges in precision medicine. The melanoma research community has identified distinct subtypes involving chronic sun-induced damage and the mitogen-activated protein kinase driver pathway. In addition, despite low mutation burden, non-genomic mitogen-activated protein kinase melanoma drivers are found in membrane receptors, metabolism, or epigenetic signaling with the ability to bypass central mitogen-activated protein kinase molecules and activating a similar program of mitogenic effectors. Mutation hotspots, structural modeling, UV signature, and genomic as well as non-genomic mechanisms of disease initiation and progression are taken into consideration to identify resistance mutations and novel drug targets. A comprehensive precision medicine profile of a malignant melanoma patient illustrates future rational drug targeting strategies. Network analysis emphasizes an important role of epigenetic and metabolic master regulators in oncogenesis. Co-occurrence of driver mutations in signaling, metabolic, and epigenetic factors highlights how cumulative alterations of our genomes and epigenomes progressively lead to uncontrolled cell proliferation. Precision insights have the ability to identify independent molecular pathways suitable for drug targeting. Synergistic treatment combinations of orthogonal modalities including immunotherapy, mitogen-activated protein kinase inhibitors, epigenetic inhibitors, and metabolic inhibitors have the potential to overcome immune evasion, side effects, and drug resistance.
来自基因组学和系统生物学的分子见解正在影响癌症的诊断和治疗方式。我们批判性地评估了精准医学中的大数据挑战。黑色素瘤研究界已经确定了不同的亚型,涉及慢性阳光诱导的损伤和丝裂原活化蛋白激酶驱动途径。此外,尽管突变负担较低,但在膜受体、代谢或表观遗传信号中发现了非基因组丝裂原活化蛋白激酶黑色素瘤驱动因子,它们能够绕过核心丝裂原活化蛋白激酶分子,并激活类似的促有丝分裂效应器程序。考虑到突变热点、结构建模、紫外线特征以及疾病发生和发展的基因组和非基因组机制,以识别耐药突变和新的药物靶点。恶性黑色素瘤患者的全面精准医学概况说明了未来合理的药物靶向策略。网络分析强调了表观遗传和代谢主调节因子在肿瘤发生中的重要作用。信号传导、代谢和表观遗传因素中驱动突变的共现突出了我们基因组和表观基因组的累积改变如何逐渐导致不受控制的细胞增殖。精准见解有能力识别适合药物靶向的独立分子途径。包括免疫疗法、丝裂原活化蛋白激酶抑制剂、表观遗传抑制剂和代谢抑制剂在内的正交模式的协同治疗组合有可能克服免疫逃逸、副作用和耐药性。