Browsing by Subject "Pancreatic cancer"
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Item 65 Gene-specific risk of syndrome-associated cancers in first-degree relatives of pancreatic cancer patients with pathogenic/likely pathogenic variants(Cambridge University Press, 2023-04-24) Chen, Xuan; Rabe, Kari G.; Meyer, Margaret A.; Kemppainen, Jennifer L.; Horibe, Masayasu; Chandra, Shruti; Majumder, Shounak; Peterson, Gloria M.; Medical and Molecular Genetics, School of MedicineThis abstract is based on unpublished data. OBJECTIVES/GOALS: The estimates of unbiased first-degree relatives (FDRs) risk of cancers would enhance genetic counseling of at-risk FDRs in families where the pancreatic cancer (PC) proband carrying a germline variant. This study aims at quantifying gene-specific risks of six cancers among FDRs of PC patients with germline variants in cancer-associated genes. METHODS/STUDY POPULATION: In the prospective, clinic-based Mayo Clinic Biospecimen Resource for Pancreas Research registry, 4,562 PC patients had previously undergone germline genetic testing for pancreatic cancer-associated genes through either research studies or clinical testing. Of these, 234 PC probands were found to carry germline pathogenic/likely pathogenic variants (PLPV) among 9 genes of interest and had provided detailed demographic and cancer data on their FDRs by questionnaire. We focused on six cancer types (ovary, breast, uterus, pancreas, colon, and malignant melanoma) in FDRs as reported by the probands. Standardized incidence ratios were calculated to estimate risk of six cancers among FDRs of PC patients carrying PLPV by gene. RESULTS/ANTICIPATED RESULTS: 1,670 FDRs (mean age 58.1+17.8SD; 48.9% female) were included in the study. We found significantly increased risk of ovarian cancer in female FDRs of PC probands who carry PLPV in BRCA1 (SIR 9.49, 95%CI:3.06-22.14) or BRCA2 (3.72, 95%CI:1.36-8.11), and breast cancer risks were higher with BRCA2 (2.62, 95%CI:1.89-3.54). Uterine cancer risk was increased in FDRs of PC probands who carry PLPV for Lynch Syndrome mismatch repair (MMR) (6.53, 95%CI:2.81-12.86). PC risk was also increased (ATM 4.53, 95% CI:2.69-7.16; BRCA2 3.45, 95%CI:1.72-6.17; CDKN2A 7.38, 95%CI:3.18-14.54; PALB2 5.39, 95%CI:1.45-13.79). Increased colon cancer risk was observed in FDRs of probands who carried MMR PLPV (5.83, 95%CI:3.70-8.75), while melanoma risk was elevated for FDRs of probands with CDKN2A PLPV (7.47, 95%CI:3.97-12.77). DISCUSSION/SIGNIFICANCE: PLPV in nine syndrome-associated genes in PC probands are associated with increased risk of six cancers in FDRs. The findings underscore the importance of risk estimation of various other cancers in PC families for screening, early detection, intervention, and cascade genetic testing.Item A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging(Wolters Kluwer, 2023) Yao, Lanhong; Zhang, Zheyuan; Keles, Elif; Yazici, Cemal; Tirkes, Temel; Bagco, Ulas; Radiology and Imaging Sciences, School of MedicinePurpose of review: Early and accurate diagnosis of pancreatic cancer is crucial for improving patient outcomes, and artificial intelligence (AI) algorithms have the potential to play a vital role in computer-aided diagnosis of pancreatic cancer. In this review, we aim to provide the latest and relevant advances in AI, specifically deep learning (DL) and radiomics approaches, for pancreatic cancer diagnosis using cross-sectional imaging examinations such as computed tomography (CT) and magnetic resonance imaging (MRI). Recent findings: This review highlights the recent developments in DL techniques applied to medical imaging, including convolutional neural networks (CNNs), transformer-based models, and novel deep learning architectures that focus on multitype pancreatic lesions, multiorgan and multitumor segmentation, as well as incorporating auxiliary information. We also discuss advancements in radiomics, such as improved imaging feature extraction, optimized machine learning classifiers and integration with clinical data. Furthermore, we explore implementing AI-based clinical decision support systems for pancreatic cancer diagnosis using medical imaging in practical settings. Summary: Deep learning and radiomics with medical imaging have demonstrated strong potential to improve diagnostic accuracy of pancreatic cancer, facilitate personalized treatment planning, and identify prognostic and predictive biomarkers. However, challenges remain in translating research findings into clinical practice. More studies are required focusing on refining these methods, addressing significant limitations, and developing integrative approaches for data analysis to further advance the field of pancreatic cancer diagnosis.Item A TGF-β/KLF10 signaling axis regulates atrophy-associated genes to induce muscle wasting in pancreatic cancer(National Academy of Science, 2023) Dasgupta, Aneesha; Gibbard, Daniel F.; Schmitt, Rebecca E.; Arneson-Wissink, Paige C.; Ducharme, Alexandra M.; Bruinsma, Elizabeth S.; Hawse, John R.; Jatoi, Aminah; Doles, Jason D.; Anatomy, Cell Biology and Physiology, School of MedicineCancer cachexia, and its associated complications, represent a large and currently untreatable roadblock to effective cancer management. Many potential therapies have been proposed and tested-including appetite stimulants, targeted cytokine blockers, and nutritional supplementation-yet highly effective therapies are lacking. Innovative approaches to treating cancer cachexia are needed. Members of the Kruppel-like factor (KLF) family play wide-ranging and important roles in the development, maintenance, and metabolism of skeletal muscle. Within the KLF family, we identified KLF10 upregulation in a multitude of wasting contexts-including in pancreatic, lung, and colon cancer mouse models as well as in human patients. We subsequently interrogated loss-of-function of KLF10 as a potential strategy to mitigate cancer associated muscle wasting. In vivo studies leveraging orthotopic implantation of pancreas cancer cells into wild-type and KLF10 KO mice revealed significant preservation of lean mass and robust suppression of pro-atrophy muscle-specific ubiquitin ligases Trim63 and Fbxo32, as well as other factors implicated in atrophy, calcium signaling, and autophagy. Bioinformatics analyses identified Transforming growth factor beta (TGF-β), a known inducer of KLF10 and cachexia promoting factor, as a key upstream regulator of KLF10. We provide direct in vivo evidence that KLF10 KO mice are resistant to the atrophic effects of TGF-β. ChIP-based binding studies demonstrated direct binding to Trim63, a known wasting-associated atrogene. Taken together, we report a critical role for the TGF-β/KLF10 axis in the etiology of pancreatic cancer-associated muscle wasting and highlight the utility of targeting KLF10 as a strategy to prevent muscle wasting and limit cancer-associated cachexia.Item Abrogating cholesterol esterification suppresses growth and metastasis of pancreatic cancer(SpringerNature, 2016-12-15) Li, J.; Gu, D.; Lee, SS-Y.; Song, B.; Bandyopadhyay, S.; Chen, S.; Konieczny, SF.; Ratliff, TL.; Liu, X.; Xie, J.; Cheng, J-X.; Department of Pediatrics, IU School of MedicineCancer cells are known to execute reprogramed metabolism of glucose, amino acids and lipids. Here, we report a significant role of cholesterol metabolism in cancer metastasis. By using label-free Raman spectromicroscopy, we found an aberrant accumulation of cholesteryl ester in human pancreatic cancer specimens and cell lines, mediated by acyl-CoA cholesterol acyltransferase-1 (ACAT-1) enzyme. Expression of ACAT-1 showed a correlation with poor patient survival. Abrogation of cholesterol esterification, either by an ACAT-1 inhibitor or by shRNA knockdown, significantly suppressed tumor growth and metastasis in an orthotopic mouse model of pancreatic cancer. Mechanically, ACAT-1 inhibition increased intracellular free cholesterol level, which was associated with elevated endoplasmic reticulum stress and caused apoptosis. Collectively, our results demonstrate a new strategy for treating metastatic pancreatic cancer by inhibiting cholesterol esterification.Item Addressing unmet needs for people with cancer cachexia: recommendations from a multistakeholder workshop(Wiley, 2022-04) Garcia, Jose M.; Dunne, Richard F.; Santiago, Kristen; Martin, Lisa; Birnbaum, Morris J.; Crawford, Jeffrey; Hendifar, Andrew E.; Kochanczyk, Martin; Moravek, Cassadie; Piccinin, Doris; Picozzi, Vincent; Roeland, Eric J.; Selig, Wendy K.D.; Zimmers, Teresa A.; Surgery, School of MedicineItem Adipocytes enhance murine pancreatic cancer growth via a hepatocyte growth factor (HGF)-mediated mechanism(Elsevier, 2016-04) Ziegler, Kathryn M.; Considine, Robert V.; True, Eben; Swartz-Basile, Deborah A.; Pitt, Henry A.; Zyromski, Nicholas J.; Department of Surgery, IU School of MedicineINTRODUCTION: Obesity accelerates the development and progression of pancreatic cancer, though the mechanisms underlying this association are unclear. Adipocytes are biologically active, producing factors such as hepatocyte growth factor (HGF) that may influence tumor progression. We therefore sought to test the hypothesis that adipocyte-secreted factors including HGF accelerate pancreatic cancer cell proliferation. MATERIAL AND METHODS: Murine pancreatic cancer cells (Pan02 and TGP-47) were grown in a) conditioned medium (CM) from murine F442A preadipocytes, b) HGF-knockdown preadipocyte CM, c) recombinant murine HGF at increasing doses, and d) CM plus HGF-receptor (c-met) inhibitor. Cell proliferation was measured using the MTT assay. ANOVA and t-test were applied; p < 0.05 considered significant. RESULTS: Wild-type preadipocyte CM accelerated Pan02 and TGP-47 cell proliferation relative to control (59 ± 12% and 34 ± 12%, p < 0.01, respectively). Knockdown of preadipocyte HGF resulted in attenuated proliferation vs. wild type CM in Pan02 cells (35 ± 5% vs. 68 ± 14% greater than control; p < 0.05), but proliferation in TGP-47 cells remained unchanged. Recombinant HGF dose-dependently increased Pan02, but not TGP-47, proliferation (p < 0.05). Inhibition of HGF receptor, c-met, resulted in attenuated proliferation versus control in Pan02 cells, but not TGP-47 cells. CONCLUSIONS: These experiments demonstrate that adipocyte-derived factors accelerate murine pancreatic cancer proliferation. In the case of Pan02 cells, HGF is responsible, in part, for this proliferation.Item Advanced natural language processing and temporal mining for clinical discovery(2015-08-17) Mehrabi, Saeed; Jones, Josette F.; Palakal, Mathew J.; Chien, Stanley Yung-Ping; Liu, Xiaowen; Schmidt, C. MaxThere has been vast and growing amount of healthcare data especially with the rapid adoption of electronic health records (EHRs) as a result of the HITECH act of 2009. It is estimated that around 80% of the clinical information resides in the unstructured narrative of an EHR. Recently, natural language processing (NLP) techniques have offered opportunities to extract information from unstructured clinical texts needed for various clinical applications. A popular method for enabling secondary uses of EHRs is information or concept extraction, a subtask of NLP that seeks to locate and classify elements within text based on the context. Extraction of clinical concepts without considering the context has many complications, including inaccurate diagnosis of patients and contamination of study cohorts. Identifying the negation status and whether a clinical concept belongs to patients or his family members are two of the challenges faced in context detection. A negation algorithm called Dependency Parser Negation (DEEPEN) has been developed in this research study by taking into account the dependency relationship between negation words and concepts within a sentence using the Stanford Dependency Parser. The study results demonstrate that DEEPEN, can reduce the number of incorrect negation assignment for patients with positive findings, and therefore improve the identification of patients with the target clinical findings in EHRs. Additionally, an NLP system consisting of section segmentation and relation discovery was developed to identify patients' family history. To assess the generalizability of the negation and family history algorithm, data from a different clinical institution was used in both algorithm evaluations.Item Angiogenic gene signature in human pancreatic cancer correlates with TGF-beta and inflammatory transcriptomes(2016-04-11) Craven, Kelly E.; Korc, Murray; Liu, Yunlong; Mosley, Amber L.; Quilliam, Lawrence A.Pancreatic ductal adenocarcinoma (PDAC), which comprises 85% of pancreatic cancers, is the 4th leading cause of cancer death in the United States with a 5-year survival rate of 8%. While human PDACs (hPDACs) are hypovascular, they also overexpress a number of angiogenic growth factors and receptors. Additionally, the use of anti-angiogenic agents in murine models of PDAC leads to reduced tumor volume, tumor spread, and microvessel density (MVD), and improved survival. Nonetheless, clinical trials using anti-angiogenic therapy have been overwhelmingly unsuccessful in hPDAC. On the other hand, pancreatic neuroendocrine tumors (PNETs) account for only 2% of pancreatic tumors, yet they are very vascular and classically angiogenic, respond to anti-angiogenic therapy, and confer a better prognosis than PDAC even in the metastatic setting. In an effort to compare and contrast the angiogenic transcriptomes of these two tumor types, we analyzed RNA-Sequencing (RNA-Seq) data from The Cancer Genome Atlas (TCGA) and found that a pro-angiogenic gene signature is present in 35% of PDACs and that it is mostly distinct from the angiogenic signature present in PNETs. The pro-angiogenic PDAC subgroup also exhibits a transcriptome that reflects active TGF-β signaling, less frequent SMAD4 inactivation than PDACs without the signature, and up-regulation of several pro-inflammatory genes, including members of JAK signaling pathways. Consequently, targeting the TGF-β receptor type-1 kinase with SB505124 and JAK1/2 with ruxolitinib blocks proliferative crosstalk between human pancreatic cancer cells (PCCs) and human endothelial cells (ECs). Additionally, treatment of the KRC (oncogenic Kras, homozygous deletion of Rb1) and KPC (oncogenic Kras, mutated Trp53) genetically engineered PDAC mouse models with ruxolitinib suppresses murine PDAC (mPDAC) progression only in the KRC model, which shows superior enrichment and differential expression of the human pro-angiogenic gene signature as compared to KPC tumors. These findings suggest that targeting both TGF-β and JAK signaling in the 35% of PDAC patients whose cancers exhibit an pro-angiogenic gene signature should be explored in a clinical trial.Item Antidiabetic thiazolidinediones induce ductal differentiation but not apoptosis in pancreatic cancer cells(Elsevier, 2005-02-28) Ceni, Elisabetta; Mello, Tommaso; Tarocchi, Mirko; Crabb, David W.; Caldini, Anna; Invernizzi, Pietro; Surrenti, Calogero; Milani, Stefano; Galli, Andrea; Department of Biochemistry and Molecular Biology, IU School of MedicineAIM: Thiazolidinediones (TZD) are a new class of oral antidiabetic drugs that have been shown to inhibit growth of same epithelial cancer cells. Although TZD were found to be ligands for peroxisome proliferator-activated receptor gamma (PPARgamma), the mechanism by which TZD exert their anticancer effect is presently unclear. In this study, we analyzed the mechanism by which TZD inhibit growth of human pancreatic carcinoma cell lines in order to evaluate the potential therapeutic use of these drugs in pancreatic adenocarcinoma. METHODS: The effects of TZD in pancreatic cancer cells were assessed in anchorage-independent growth assay. Expression of PPARgamma was measured by reverse-transcription polymerase chain reaction and confirmed by Western blot analysis. PPARgamma activity was evaluated by transient reporter gene assay. Flow cytometry and DNA fragmentation assay were used to determine the effect of TZD on cell cycle progression and apoptosis respectively. The effect of TZD on ductal differentiation markers was performed by Western blot. RESULTS: Exposure to TZD inhibited colony formation in a PPARgamma-dependent manner. Growth inhibition was linked to G1 phase cell cycle arrest through induction of the ductal differentiation program without any increase of the apoptotic rate. CONCLUSION: TZD treatment in pancreItem Artificial Intelligence in Endoscopic Ultrasound for Pancreatic Cancer: Where Are We Now and What Does the Future Entail?(MDPI, 2022-12-16) Dahiya, Dushyant Singh; Al-Haddad, Mohammad; Chandan, Saurabh; Gangwani, Manesh Kumar; Aziz, Muhammad; Mohan, Babu P.; Ramai, Daryl; Canakis, Andrew; Bapaye, Jay; Sharma, Neil; Medicine, School of MedicinePancreatic cancer is a highly lethal disease associated with significant morbidity and mortality. In the United States (US), the overall 5-year relative survival rate for pancreatic cancer during the 2012–2018 period was 11.5%. However, the cancer stage at diagnosis strongly influences relative survival in these patients. Per the National Cancer Institute (NCI) statistics for 2012–2018, the 5-year relative survival rate for patients with localized disease was 43.9%, while it was 3.1% for patients with distant metastasis. The poor survival rates are primarily due to the late development of clinical signs and symptoms. Hence, early diagnosis is critical in improving treatment outcomes. In recent years, artificial intelligence (AI) has gained immense popularity in gastroenterology. AI-assisted endoscopic ultrasound (EUS) models have been touted as a breakthrough in the early detection of pancreatic cancer. These models may also accurately differentiate pancreatic cancer from chronic pancreatitis and autoimmune pancreatitis, which mimics pancreatic cancer on radiological imaging. In this review, we detail the application of AI-assisted EUS models for pancreatic cancer detection. We also highlight the utility of AI-assisted EUS models in differentiating pancreatic cancer from radiological mimickers. Furthermore, we discuss the current limitations and future applications of AI technology in EUS for pancreatic cancers.