Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic findings

被引:0
作者
Kiemen, Ashley L. [1 ,2 ]
Wu, Pei-Hsun [3 ]
Braxton, Alicia M. [4 ]
Cornish, Toby C. [5 ]
Hruban, Ralph H. [1 ,2 ]
Wood, Laura D. [1 ,2 ]
Wirtz, Denis [1 ,2 ,3 ]
Zwicker, David [6 ]
机构
[1] Johns Hopkins Univ, Sch Med, Sol Goldman Pancreat Canc Res Ctr, Dept Pathol, Baltimore, MD 21218 USA
[2] Johns Hopkins Univ, Sol Goldman Pancreat Canc Res Ctr, Sch Med, Dept Oncol, Baltimore, MD 21218 USA
[3] Johns Hopkins Univ, Dept Chem & Biomol Engn, Baltimore, MD USA
[4] Med Univ South Carolina, Dept Comparat Med, Charleston, SC USA
[5] Univ Colorado, Sch Med, Dept Pathol, Aurora, CO USA
[6] Max Planck Inst Dynam & Selforg, Gottingen, Germany
关键词
SMOLUCHOWSKIS EQUATION; SOMATIC MUTATIONS; COAGULATION; EVOLUTION;
D O I
10.1126/sciadv.ado5103
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Pancreatic ductal adenocarcinoma is a rare but lethal cancer. Recent evidence suggests that pancreatic intraepithelial neoplasia (PanIN), a microscopic precursor lesion that gives rise to pancreatic cancer, is larger and more prevalent than previously believed. Better understanding of the growth-law dynamics of PanINs may improve our ability to understand how a miniscule fraction makes the transition to invasive cancer. Here, using three-dimensional tissue mapping, we analyzed >1000 PanINs and found that lesion size is distributed according to a power law. Our data suggest that in bulk, PanIN size can be predicted by general growth behavior without consideration for the heterogeneity of the pancreatic microenvironment or an individual's age, history, or lifestyle. Our models suggest that intraductal spread and fusing of lesions drive our observed size distribution. This analysis lays the groundwork for future mathematical modeling efforts integrating PanIN incidence, morphology, and molecular features to understand tumorigenesis and demonstrates the utility of combining experimental measurement with dynamic modeling in understanding tumorigenesis.
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页数:9
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