胰腺癌的计算模型揭示了转移的动力学，提示了最佳的治疗策略。
Computational modeling of pancreatic cancer reveals kinetics of metastasis suggesting optimum treatment strategies.
机构信息
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, and Harvard School of Public Health, Boston, MA 02115, USA.
出版信息
Cell. 2012 Jan 20;148(1-2):362-75. doi: 10.1016/j.cell.2011.11.060.
Pancreatic cancer is a leading cause of cancer-related death, largely due to metastatic dissemination. We investigated pancreatic cancer progression by utilizing a mathematical framework of metastasis formation together with comprehensive data of 228 patients, 101 of whom had autopsies. We found that pancreatic cancer growth is initially exponential. After estimating the rates of pancreatic cancer growth and dissemination, we determined that patients likely harbor metastases at diagnosis and predicted the number and size distribution of metastases as well as patient survival. These findings were validated in an independent database. Finally, we analyzed the effects of different treatment modalities, finding that therapies that efficiently reduce the growth rate of cells earlier in the course of treatment appear to be superior to upfront tumor resection. These predictions can be validated in the clinic. Our interdisciplinary approach provides insights into the dynamics of pancreatic cancer metastasis and identifies optimum therapeutic interventions.
胰腺癌是癌症相关死亡的主要原因，主要是由于转移性扩散。我们通过利用转移形成的数学框架和 228 名患者的综合数据（其中 101 名患者进行了尸检）来研究胰腺癌的进展。我们发现胰腺癌的生长最初是指数级的。在估计了胰腺癌的生长和扩散速度后，我们确定患者在诊断时可能已经有了转移，并预测了转移的数量和大小分布以及患者的生存情况。这些发现得到了一个独立数据库的验证。最后，我们分析了不同治疗方式的效果，发现治疗方法在治疗早期能够有效地降低细胞的生长速度，似乎优于肿瘤切除术。这些预测可以在临床上得到验证。我们的跨学科方法提供了对胰腺癌转移动态的深入了解，并确定了最佳的治疗干预措施。
Zotero 插件