Braxton Alicia M, Kiemen Ashley L, Grahn Mia P, Forjaz André, Babu Jaanvi Mahesh, Zheng Lily, Jiang Liping, Cheng Haixia, Song Qianqian, Reichel Rebecca, Graham Sarah, Damanakis Alexander I, Fischer Catherine G, Mou Stephanie, Metz Cameron, Granger Julie, Liu Xiao-Ding, Bachmann Niklas, Almagro-Pérez Cristina, Jiang Ann Chenyu, Yoo Jeonghyun, Kim Bridgette, Du Scott, Foster Eli, Hsu Jocelyn Y, Rivera Paula Andreu, Chu Linda C, Liu Fengze, Niknafs Noushin, Fishman Elliot K, Yuille Alan, Roberts Nicholas J, Thompson Elizabeth D, Scharpf Robert B, Cornish Toby C, Jiao Yuchen, Karchin Rachel, Hruban Ralph H, Wu Pei-Hsun, Wirtz Denis, Wood Laura D
Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD.
Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD.
bioRxiv. 2023 Jan 28:2023.01.27.525553. doi: 10.1101/2023.01.27.525553.
Pancreatic intraepithelial neoplasia (PanIN) is a precursor to pancreatic cancer and represents a critical opportunity for cancer interception. However, the number, size, shape, and connectivity of PanINs in human pancreatic tissue samples are largely unknown. In this study, we quantitatively assessed human PanINs using CODA, a novel machine-learning pipeline for 3D image analysis that generates quantifiable models of large pieces of human pancreas with single-cell resolution. Using a cohort of 38 large slabs of grossly normal human pancreas from surgical resection specimens, we identified striking multifocality of PanINs, with a mean burden of 13 spatially separate PanINs per cm of sampled tissue. Extrapolating this burden to the entire pancreas suggested a median of approximately 1000 PanINs in an entire pancreas. In order to better understand the clonal relationships within and between PanINs, we developed a pipeline for CODA-guided multi-region genomic analysis of PanINs, including targeted and whole exome sequencing. Multi-region assessment of 37 PanINs from eight additional human pancreatic tissue slabs revealed that almost all PanINs contained hotspot mutations in the oncogene , but no gene other than was altered in more than 20% of the analyzed PanINs. PanINs contained a mean of 13 somatic mutations per region when analyzed by whole exome sequencing. The majority of analyzed PanINs originated from independent clonal events, with distinct somatic mutation profiles between PanINs in the same tissue slab. A subset of the analyzed PanINs contained multiple mutations, suggesting a polyclonal origin even in PanINs that are contiguous by rigorous 3D assessment. This study leverages a novel 3D genomic mapping approach to describe, for the first time, the spatial and genetic multifocality of human PanINs, providing important insights into the initiation and progression of pancreatic neoplasia.
胰腺上皮内瘤变(PanIN)是胰腺癌的前体,是癌症拦截的关键时机。然而,人类胰腺组织样本中PanIN的数量、大小、形状和连通性在很大程度上尚不清楚。在本研究中,我们使用CODA对人类PanIN进行了定量评估,CODA是一种用于3D图像分析的新型机器学习管道,可生成具有单细胞分辨率的大块人类胰腺的可量化模型。使用来自手术切除标本的38个大体正常人类胰腺大切片队列,我们发现PanIN具有显著的多灶性,每厘米采样组织中平均有13个空间上分离的PanIN。将此负担外推至整个胰腺表明,整个胰腺中PanIN的中位数约为1000个。为了更好地理解PanIN内部和之间的克隆关系,我们开发了一种用于PanIN的CODA引导多区域基因组分析的管道,包括靶向和全外显子组测序。对来自另外8个人类胰腺组织切片的37个PanIN进行多区域评估发现,几乎所有PanIN都在癌基因中含有热点突变,但在超过20%的分析PanIN中,除了该基因外没有其他基因发生改变。通过全外显子组测序分析,PanIN每个区域平均含有13个体细胞突变。大多数分析的PanIN起源于独立的克隆事件,同一组织切片中的PanIN之间具有不同的体细胞突变谱。一部分分析的PanIN包含多个该基因的突变,这表明即使在通过严格的3D评估相邻的PanIN中也存在多克隆起源。本研究利用一种新型的3D基因组图谱方法首次描述了人类PanIN的空间和遗传多灶性,为胰腺肿瘤的发生和发展提供了重要见解。