Exploring the Functional Disorder and Corresponding Key Transcription Factors in Intraductal Papillary Mucinous Neoplasms Progression

The lysosomal aminopeptidase tripeptidyl peptidase 1 displays increased activity in malignant pancreatic cysts

Incidental detection of pancreatic cysts has increased dramatically over the last decade, but risk stratification and clinical management remain a challenge. Mucinous cysts are precursor lesions to pancreatic cancer, however, the majority are indolent. Current diagnostics cannot identify mucinous cysts that harbor cancer or reliably differentiate these lesions from nonmucinous cysts, which present minimal risk of malignant progression. We previously determined that activity of two aspartyl proteases was increased in mucinous cysts. Using a global protease activity profiling technology, termed multiplex substrate profiling by mass spectrometry (MSP-MS), we now show that aminopeptidase activity is also elevated in mucinous cysts. The serine aminopeptidase, tripeptidyl peptidase 1 (TPP1), was detected by proteomic analysis of cyst fluid samples and quantitation using targeted MS demonstrated that this protease was significantly more abundant in mucinous cysts. In a cohort of 110 cyst fluid samples, TPP1 activity was increased more than 3-fold in mucinous cysts relative to nonmucinous cysts. Moreover, TPP1 activity is primarily associated with mucinous cysts that harbor high-grade dysplasia or invasive carcinoma. Although only 59% accurate for differentiating these lesions, measurement of TPP1 activity may improve early detection and treatment of high-risk pancreatic cysts when used in conjunction with other promising biomarkers.

Downregulation of Friend Leukemia Integration 1 (FLI1) follows the stepwise progression to gastric adenocarcinoma

Gastric adenocarcinoma (GC) is a leading cause of cancer-related deaths worldwide. The transcription factor gene Friend Leukemia Integration 1 (FLI1) is methylated and downregulated in human GC tissues. Using human GC samples, we determined which cells downregulate FLI1, when FLI1 downregulation occurs, if FLI1 downregulation correlates with clinical-pathologic characteristics, and whether FLI1 plays a role in invasion and/or proliferation of cultured cells. We analyzed stomach tissues from 98 patients [8 normal mucosa, 8 intestinal metaplasia (IM), 7 dysplasia, 91 GC] by immunohistochemistry for FLI1. Epithelial cells from normal, IM, and low-grade dysplasia (LGD) showed strong nuclear FLI1 staining. GC epithelial cells showed significantly less nuclear FLI1 staining as compared to normal epithelium, IM and LGD (P=1.2×10^-5, P=1.4×10^-6 and P=0.006, respectively). FLI1 expression did not correlate with tumor stage or differentiation, but was associated with patient survival, depending on tumor differentiation. We tested the functional role of FLI1 by assaying proliferation and invasion in cultured GC cells. Lentiviral-transduced FLI1 overexpression in GC AGS cells inhibited invasion by 73.5% (P = 0.001) and proliferation by 31.5% (P = 0.002), as compared to controls. Our results support a combined role for FLI1 as a suppressor of invasiveness and proliferation in gastric adenocarcinoma, specifically in the transition from pre-cancer lesions and dysplasia to invasive adenocarcinoma, and suggest that FLI1 may be a prognostic biomarker of survival in gastric cancers.

Exact association test for small size sequencing data

Background

Recent statistical methods for next generation sequencing (NGS) data have been successfully applied to identifying rare genetic variants associated with certain diseases. However, most commonly used methods (e.g., burden tests and variance-component tests) rely on large sample sizes. Notwithstanding, due to its-still high cost, NGS data is generally restricted to small sample sizes, that cannot be analyzed by most existing methods.

Methods

In this work, we propose a new exact association test for sequencing data that does not require a large sample approximation, which is applicable to both common and rare variants. Our method, based on the Generalized Cochran-Mantel-Haenszel (GCMH) statistic, was applied to NGS datasets from intraductal papillary mucinous neoplasm (IPMN) patients. IPMN is a unique pancreatic cancer subtype that can turn into an invasive and hard-to-treat metastatic disease.

Results

Application of our method to IPMN data successfully identified susceptible genes associated with progression of IPMN to pancreatic cancer.

Conclusions

Our method is expected to identify disease-associated genetic variants more successfully, and corresponding signal pathways, improving our understanding of specific disease’s etiology and prognosis.