Three-dimensional genomic mapping of human pancreatic tissue reveals striking multifocality and genetic heterogeneity in precancerous lesions

Abstract

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 cm3 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 KRAS, but no gene other than KRAS 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 KRAS 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.

Magnetic Resonance Imaging–Based Assessment of Pancreatic Fat Strongly Correlates With Histology-Based Assessment of Pancreas Composition

“…Prior reports correlate pancreatic fat content measured by MRI and computed tomography with histology determined by visual inspection of a limited number of tissue sections, 5,14,18,19 which may limit the accuracy of the comparison. Recent deep learning–based tissue segmentation algorithms are capable of rapidly deconvolving histologic slides into their various microanatomical components 20–24 . These algorithms allow rapid, consistent calculation of tissue composition that can be quantitatively validated.…”

“…Recent deep learning-based tissue segmentation algorithms are capable of rapidly deconvolving histologic slides into their various microanatomical components. [20][21][22][23][24] These algorithms allow rapid, consistent calculation of tissue composition that can be quantitatively validated. In addition, deep learning algorithms can discern diverse pancreatic structures, allowing comparisons of fat content to a large number of other pancreatic tissue components.…”

Three-dimensional assessments are necessary to determine the true, spatially-resolved composition of tissues

“…For this calculation, we first utilized a previously reported cohort of 48 large 3D reconstructed samples of human pancreas tissue containing PanINs. 5 We defined PanIN content as the volume percent of PanIN within the pancreatic ductal system: Pcontent = volume of PanIN / (volume of PanIN + normal ductal epithelium). Next, we calculated Pcontent for all possible combinations of consecutive slides subsampled from the above 3D cohort and calculated the relative error of the subsampled region to the Pcontent of the full 3D sample (Fig 7A).…”

“…Recent developments in spatial profiling technologies have led to the construction of atlases to characterize cellular and tissue compositions, structure, and the "-omic" (genomic, epigenomic, transcriptomic, proteomic, and metabolomic) landscapes of tissues, organs, and whole organisms. [1][2][3][4][5][6][7][8][9] These techniques have led to important discoveries regarding changes in cellular composition during development, aging and the progression of diseases such as cancer and cardiovascular disease. However, due to technical and financial limitations, current spatial omic methods -CODEX, IMC, Visium, DBitseq, seqFISH, MERFISH, etc.…”

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

“…[6][7][8][9][10][11] Recent works utilizing a large cohort of 3D reconstructed human pancreata revealed that the pancreata of some individuals contain hundreds of PanIN lesions. 12,13 This number contrasts with the relative rarity of PDAC and suggests that most PanIN lesions will never progress to cancer in a person's lifetime. The mechanism governing this extensive PanIN initiation and growth in human tissues is poorly understood.…”