3D genomic mapping reveals multifocality of human pancreatic precancers.

Pancreatic intraepithelial neoplasias (PanINs) are the most common precursors of pancreatic cancer, but their small size and inaccessibility in humans make them challenging to study1. 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 PanINs, almost all with oncogenic KRAS hotspot mutations. We found that most PanINs originate as independent clones with distinct somatic mutation profiles. Some spatially continuous PanINs were found to contain multiple KRAS mutations; computational and in situ analyses demonstrated that different KRAS mutations localize to distinct cell subpopulations within these neoplasms, indicating their polyclonal origins. The extensive multifocality and genetic heterogeneity of PanINs raises important questions about mechanisms that drive precancer initiation and confer differential progression risk in the human pancreas. This detailed 3D genomic mapping of molecular alterations in human PanINs provides an empirical foundation for early detection and rational interception of pancreatic cancer.

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

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.

30-Year Experience With 22 Cases of Malignant Transformation Arising From Ovarian Mature Cystic Teratoma: A Rare Disease.

Objective: To investigate the clinical characteristics and survival outcomes of patients with malignant transformation arising from ovarian mature cystic teratoma (MT-MCT). Methods: This retrospective study included patients with ovarian MCTs at Peking Union Medical College Hospital (PUMCH) during 1990.01-2020.12. When the pathologic histology was MT-MCT, detailed information was collected. Results: Overall, 7229 ovarian MCT patients and 22 patients with MT-MCT were enrolled. The rate of malignant transformation of all ovarian MCTs was 0.30%. Most patients with MT-MCT were 51 (21-75) years old, and the tumor mass size was 10 (3-30) cm. The typical clinical symptoms were mainly abdominal pain and distension. The levels of tumor markers were elevated on preoperative examination. Early diagnosis could be made by ultrasonic examination, pelvic enhanced MRI and CT. Most patients underwent debulking surgery and adjuvant chemotherapy. The most common histological type to exhibit malignant transformation was squamous cell carcinoma (59.1%), followed by adenocarcinoma (13.6%), carcinoid (9.1%), and borderline tumor (18.2%). The 5-year RFS and OS rates were 54.5% and 81.8%, respectively. Patients with FIGO stage I had the best RFS (P=0.047) and OS (P=0.018), followed by those with FIGO stage II-IV. Conclusion: MT-MCTs mainly occur in elderly females, are rare and have a poor prognosis. Advanced FIGO stage is a risk factor for survival. Although there is no standard treatment, cytoreductive debulking surgery and adjuvant chemotherapy could be considered. Perimenopausal and menopausal women with MCT should receive surgical treatment.

A new prognosis prediction model combining TNM stage with MAP2K4 and JNK in postoperative pancreatic cancer patients.

Mitogen-activated protein kinase kinase 4 (MAP2K4) is a tumor suppressor in many cancers. However, its roles and action mechanisms in pancreatic ductal adenocarcinoma (PDAC) remain unclear. Here, we analyzed MAP2K4 and its downstream kinases (c-Jun N-terminal kinase (JNK) and p38) using immunohistochemical staining and their prognostic significances using univariate and multivariate Cox proportional hazards regression analysis in our PDAC cohort. Then, we validated MAP2K4/JNK/p38 mRNA levels and prognostic significances using The Cancer Genome Atlas (TCGA) database. Finally, we evaluated the effects of MAP2K4 on the proliferation and invasion of PDAC cells. MAP2K4, JNK, and p38 proteins were expressed in 97.3 % (72/74), 95.6 % (65/68), and 88.6 % (62/70) of the samples, respectively, and their levels in tumor tissues were significantly higher than those in normal ducts. MAP2K4 protein expression was lower in male patients (p = 0.028). In our PDAC cohort, advanced TNM stage, low MAP2K4, and high JNK protein levels were significant prognostic factors for poor overall survival (OS) based on a univariate survival analysis (p = 0.006, p < 0.001, and p = 0.004, respectively). N stage and MAP2K4 and JNK protein levels were independent prognostic factors for OS based on multivariate analysis. We then built a prognosis prediction nomogram combining the standard TNM staging system with MAP2K4 and JNK expression that had a Harrell's C-index of 0.645. The new prognosis prediction model effectively stratified the resected patients with PDAC, from both our cohort and TCGA database, into low- and high-risk groups. Finally, MAP2K4 overexpression inhibited pancreatic cancer cell proliferation and migration in vitro. This study shows that reduced protein and mRNA levels of MAP2K4 found in PDAC patients, coupled to in vitro effects observed, support the tumor suppressor role of MAP2K4 in PDAC. Importantly, combining MAP2K4 and JNK expression with the TNM staging system results in a better prediction of postoperative survival of patients with PDAC.