Ralph Hruban

Pancreatic ductal adenocarcinoma (PDAC) is a deadly cancer that can develop from pancreatic intraepithelial neoplasia (PanIN), a microscopic lesion in the pancreatic ductal system. PanIN and PDAC are conventionally studied in 2D via histological tissue sections. As such, their true structure is poorly understood due to the inability to image them in 3D. CODA, a recently developed technique for reconstruction of tissues at cellular resolution, is used to study the 3D morphology of the pancreas. Here, CODA is extended through 3D printing of normal pancreatic ducts, PanIN, and PDAC at cm‐scale and µm resolution. A methodology is presented to create 3D printable files from anatomical maps generated from serial histological images and to show detailed validation of the accuracy of this method. Existing 3D printing workflows utilizing medical images derived from computerized tomography (CT), X‐ray, and …

Infiltrating ductal adenocarcinoma of the pancreas, referred to here as “pancreatic cancer,” is one of the deadliest of all of the solid malignancies. The five-year survival rate in the United States for individuals diagnosed today with pancreatic cancer is a dismal 12%. Many invasive cancers, including pancreatic cancer, however, arise from histologically and genetically well-characterized precursor lesions, and these precancers are curable. Precursor lesions therefore are an attractive target for early detection and treatment. This is particularly true for individuals with an increased risk of developing invasive cancer, such as individuals with a strong family history of pancreatic cancer, and individuals with a germline variant known to increase the risk of developing pancreatic cancer. There is therefore a need to understand the precursor lesions that can give rise to invasive pancreatic cancer in these individuals.

Introduction Metastatic cancer affects millions of people worldwide annually and is the leading cause of cancer-related deaths. Most patients with metastatic disease are not eligible for surgical resection, and current therapeutic regimens have varying success rates, some with 5-year survival rates below 5%. Here we test the hypothesis that metastatic cancer can be genetically targeted by exploiting single base substitution mutations unique to individual cells that occur as part of normal aging prior to transformation. These mutations are targetable because ~10% of them form novel tumor-specific “NGG” protospacer adjacent motif (PAM) sites targetable by CRISPR-Cas9. Methods Whole genome sequencing was performed on five rapid autopsy cases of patient-matched primary tumor, normal and metastatic tissue from pancreatic ductal adenocarcinoma decedents. CRISPR-Cas9 PAM targets were determined by bioinformatic tumor-normal subtraction for each patient and verified in metastatic samples by high-depth capture-based sequencing. Results We found that 90% of PAM targets were maintained between primary carcinomas and metastases overall. We identified rules that predict PAM loss or retention, where PAMs located in heterozygous regions in the primary tumor can be lost in metastases (private LOH), but PAMs occurring in regions of loss of heterozygosity (LOH) in the primary tumor were universally conserved in metastases. Conclusions Regions of truncal LOH are strongly retained in the presence of genetic instability, and therefore represent genetic vulnerabilities in pancreatic adenocarcinomas. A CRISPR-based gene therapy …

ObjectiveThe aim of the study is to assess the relationship between magnetic resonance imaging (MRI)-based estimation of pancreatic fat and histology-based measurement of pancreatic composition.Materials and MethodsIn this retrospective study, MRI was used to noninvasively estimate pancreatic fat content in preoperative images from high-risk individuals and disease controls having normal pancreata. A deep learning algorithm was used to label 11 tissue components at micron resolution in subsequent pancreatectomy histology. A linear model was used to determine correlation between histologic tissue composition and MRI fat estimation.ResultsTwenty-seven patients (mean age 64.0±12.0 years [standard deviation], 15 women) were evaluated. The fat content measured by MRI ranged from 0% to 36.9%. Intrapancreatic histologic tissue fat content ranged from 0.8% to 38.3%. MRI pancreatic fat estimation …

Pancreatic intraepithelial neoplasias (PanINs) are the most common precursors of pancreatic cancer, but their small size and inaccessibility in humans make them challenging to study. Critically, the number, dimensions and connectivity of human PanINs remain largely unknown, precluding important insights into early cancer development. Here, we provide a microanatomical survey of human PanINs by analysing 46 large samples of grossly normal human pancreas with a machine-learning pipeline for quantitative 3D histological reconstruction at single-cell resolution. To elucidate genetic relationships between and within PanINs, we developed a workflow in which 3D modelling guides multi-region microdissection and targeted and whole-exome sequencing. From these samples, we calculated a mean burden of 13 PanINs per cm3 and extrapolated that the normal intact adult pancreas harbours hundreds of …

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancer types, partly because it is frequently identified at an advanced stage, when surgery is no longer feasible. Therefore, early detection using minimally invasive methods such as blood tests may improve outcomes. However, studies to discover molecular signatures for the early detection of PDAC using blood tests have only been marginally successful. In the current study, a quantitative glycoproteomic approach via data-independent acquisition mass spectrometry was utilized to detect glycoproteins in 29 patient-matched PDAC tissues and sera. A total of 892 N-linked glycopeptides originating from 141 glycoproteins had PDAC-associated changes beyond normal variation. We further evaluated the specificity of these serum-detectable glycoproteins by comparing their abundance in 53 independent PDAC patient sera and 65 cancer-free …

PurposeDelay in diagnosis can contribute to poor outcomes in pancreatic ductal adenocarcinoma (PDAC), and new tools for early detection are required. Recent application of artificial intelligence to cancer imaging has demonstrated great potential in detecting subtle early lesions. The aim of the study was to evaluate global and local accuracies of deep neural network (DNN) segmentation of normal and abnormal pancreas with pancreatic mass.MethodsOur previously developed and reported residual deep supervision network for segmentation of PDAC was applied to segment pancreas using CT images of potential renal donors (normal pancreas) and patients with suspected PDAC (abnormal pancreas). Accuracy of DNN pancreas segmentation was assessed using DICE simulation coefficient (DSC), average symmetric surface distance (ASSD), and Hausdorff distance 95% percentile (HD95) as compared to …

Immunotherapies are generally ineffective in pancreatic ductal adenocarcinoma (PDAC). Paradoxically, inflammation is believed to play a key role in PDAC development and invasion: patients suffering from chronic pancreatitis have a 13-fold increase in risk of developing PDAC. PDAC cells are surrounded by a dense network of fibrotic tissue containing immunosuppressive cells such as regulatory T cells, tumor-associated macrophages, and cancer-associated fibroblasts. Better understanding of the role of inflammation in PDAC could lead to the design of effective immunotherapies. Pancreatic intraepithelial neoplasia (PanIN) is a precursor to PDAC. While most of us will develop PanINs, few of these lesions will progress to invasive cancer. Efforts to determine which PanINs are likely to progress have shown that the size, incidence, and genetic variation of these lesions is high. Here, we add to these efforts …