Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.
Curr Opin Biotechnol. 2020 Jun;63:177-189. doi: 10.1016/j.copbio.2020.02.005. Epub 2020 Mar 18.
Genetic variants are often not predictive of the phenotypic outcome. Individuals carrying the same pathogenic variant, associated with Mendelian or complex disease, can manifest to different extents, from severe-to-mild to no disease. Improving the accuracy of predicted clinical manifestations of genetic variants has emerged as one of the biggest challenges in precision medicine, which can only be addressed by understanding the mechanisms underlying genotype-phenotype relationships. Efforts to understand the molecular basis of these relationships have identified complex systems of interacting biomolecules that underlie cellular function. Here, we review recent advances in how modeling cellular systems as networks of interacting proteins has fueled identification of disease-associated processes, delineation of underlying molecular mechanisms, and prediction of the pathogenicity of variants. This review is intended to be inspiring for clinicians, geneticists, and network biologists alike who aim to jointly advance our understanding of human disease and accelerate progress toward precision medicine.
遗传变异通常不能预测表型结果。携带相同致病变异的个体，与孟德尔或复杂疾病相关，可以表现出不同的程度，从严重到轻度到没有疾病。提高遗传变异预测临床表型的准确性已成为精准医学面临的最大挑战之一，只有通过了解基因型-表型关系的机制才能解决这一问题。为了理解这些关系的分子基础，人们已经确定了复杂的相互作用生物分子系统，这些系统是细胞功能的基础。在这里，我们回顾了最近在将细胞系统建模为相互作用蛋白网络方面的进展，这些进展推动了对疾病相关过程的识别、对潜在分子机制的描述以及对变异致病性的预测。这篇综述旨在为临床医生、遗传学家和网络生物学家提供灵感，他们的目标是共同提高对人类疾病的认识，并加速迈向精准医学的进程。
