Differentiation Of Pancreatic Ductal Adenocarcinoma Research Articles

Dual-layer spectral detector CT: A noninvasive preoperative tool for predicting histopathological differentiation in pancreatic ductal adenocarcinoma

PurposeTo predict histopathological differentiation grades in patients with pancreatic ductal adenocarcinoma (PDAC) before surgery with quantitative and qualitative variables obtained from dual-layer spectral detector CT (DLCT). MethodsTotally 128 patients with histopathologically confirmed PDAC and preoperative DLCT were retrospectively enrolled and categorized into the low-grade (LG) (well and moderately differentiated, n = 82) and high-grade (HG) (poorly differentiated, n = 46) subgroups. Both conventional and spectral variables for PDAC were measured. The ratio of iodine concentration (IC) values in arterial phase(AP) and venous phase (VP) was defined as iodine enhancement fraction_AP/VP (IEF_AP/VP). Necrosis was visually assessed on both conventional CT images (necrosis_con) and virtual mono-energetic images (VMIs) at 40 keV (necrosis_40keV). Forward stepwise logistic regression method was conducted to perform univariable and multivariable analysis. Receiver operating characteristic (ROC) curves and the DeLong method were used to evaluate and compare the efficiencies of variables in predicting tumor grade. ResultsNecrosis_con (odds ratio [OR] = 2.84, 95% confidence interval [CI]: 1.13–7.13; p < 0.001) was an independent predictor among conventional variables, and necrosis_40keV (OR = 5.82, 95% CI: 1.98–17.11; p = 0.001) and IEF_AP/VP (OR = 1.12, 95% CI:1.07–1.17; p < 0.001) were independent predictors among spectral variables for distinguishing LG PDAC from HG PDAC. IEF_AP/VP (AUC = 0.754, p = 0.016) and combination model (AUC = 0.812, p < 0.001) had better predictive performances than necrosis_con (AUC = 0.580). The combination model yielded the highest sensitivity (72%) and accuracy (79%), while IEF_AP/VP exhibited the highest specificity (89%). ConclusionVariables derived from DLCT have the potential to preoperatively evaluate PDAC tumor grade. Furthermore, spectral variables and their combination exhibited superior predictive performances than conventional CT variables.

CA 19-9 but Not IGF-1/IGFBP-2 Is a Useful Biomarker for Pancreatic Ductal Adenocarcinoma (PDAC) and Chronic Pancreatitis (CP) Differentiation.

There are still no effective diagnostic and prognostic biomarkers in pancreatic ductal adenocarcinoma (PDAC). The differentiation between PDAC and chronic pancreatitis (CP) is often challenging. The inflammatory mass in the course of CP causes diagnostic difficulties in differentiating them from neoplastic lesions and, thus, delays the initiation of radical treatment. Insulin-like growth factor 1 (IGF-1) and insulin-like growth factor-binding protein 2 (IGFBP-2) form a network involved in PDAC development. The role of IGFs in promoting pancreatic cancer cell proliferation, survival, and migration is well established, and their ability to stimulate tumor growth and metastasis is well documented. The aim of the study was to evaluate the usability of IGF-1, IGFBP-2, and IGF-1/IGFBP-2 ratio in PDAC and CP differentiation. The study included 137 patients: 89 patients with PDAC and 48 patients with CP. All subjects were tested for the levels of IGF-1 and IGFBP-2 using the ELISA method (Corgenix UK Ltd. R&D Systems), along with the level of CA 19-9 in serum. Additionally, the IGF-1/IGFBP-2 ratio was calculated. Further analyses used logit and probit models with varying determinants in order to discern between PDAC and CP patients. The models served as a basis for AUROC calculation. The mean IGF-1 serum level was equal to 52.12 ± 33.13 ng/mL in PDAC vs. 74.23 ± 48.98 ng/mL in CP (p = 0.0053). The mean level of IGFBP-2 was equal to 305.95 ± 194.58 ng/mL in PDAC vs. 485.43 ± 299 ng/mL in CP (p = 0.0002). The mean CA 19-9 serum concentration was 434.95 ± 419.98 U/mL in PDAC vs. 78.07 ± 182.36 U/mL in CP (p = 0.0000). The mean IGF-1/IGFBP-2 ratio was 0.213 ± 0.14 in PDAC vs. 0.277 ± 0.33 in CP (p = 0.1914). The diagnostic usefulness of indicators for the purpose of PDAC and CP differentiation was assessed by means of AUROC comparison. The AUROCs of IGF-1, IGFBP-2, and IGF-1/IGFBP-2 ratio ranged below 0.7, being lower than the AUROC of CA 19-9 (0.7953; 0.719 within 95% CI). Together, the CA 19-9 and IGFBP-2 AUROCs also ranged below 0.8. When age was included, the AUROC increased to 0.8632, and its 95% confidence interval held above the 0.8 limit. The sensitivity of the used markers was not correlated to the stage of pancreatic PDAC. The presented results indicate that CA 19-9 is a marker demonstrating high potential for PDAC and CP differentiation. The inclusion of additional variables into the model, such as the serum level of IGF-1 or IGFBP-2, slightly increased the sensitivity in differentiating CP from PDAC. The IGF-1/IGFBP-2 ratio turned out to be a good marker of pancreatic diseases, but insufficient for the purpose of CP and PDAC differentiation.

Integrated analysis revealed hypoxia signatures and LDHA related to tumor cell dedifferentiation and unfavorable prognosis in pancreatic adenocarcinoma: Hypoxia in PDAC

Pancreatic ductal adenocarcinoma (PDAC) is a highly heterogeneous cancer with limited understanding of its classification and tumor microenvironment. Here, by analyzing single-nucleus RNA sequencing of 43, 817 tumor cells from 15 PDAC tumors and non-tumor, we find that hypoxia signatures were heterogeneous across samples and were potential regulators for tumor progression and more aggressive phenotype. Hypoxia-high PDAC tends to present a basal/squamous-like phenotype and has significantly increased outgoing signaling, which enhances tumor cell stemness and promotes metastasis, angiogenesis, and fibroblast differentiation in PDAC. Hypoxia is related to an extracellular matrix enriched microenvironment, and increased possibility of TP53 mutation in PDAC. TP63 is a specific marker of squamous-like phenotype, and presents elevated transcriptome levels in most hypoxia PDAC tumors. In summary, our research highlights the potential linkage of hypoxia, tumor progression and genome alteration in PDAC, leading to further understand of the formation of inter-tumoral and intra-tumoral heterogenous in PDAC. Our study extends the understanding of the diversity and transition of tumor cells in PDAC, which provides insight into future PDAC management.

Abstract 787: RE1-silencing transcription factor (REST) controls neuroendocrine gene programs in pancreatic ductal adenocarcinoma

Abstract Neuroendocrine (NE) differentiation features contribute to intratumoral heterogeneity and aggressive biology in a subset of pancreatic ductal adenocarcinoma (PDAC) tumors associated with poor disease outcome. We recently showed that treating pancreatic cancer cells with gemcitabine, a standard-of-care chemotherapy for PDAC, increases expression of NE markers, suggesting that ductal to neuroendocrine lineage plasticity could play a role in drug resistance. NE features in prostate cancer have been previously associated with loss of the neuronal gene repressor RE1-Silencing Transcription Factor (REST); in the present study, we explore a potential role of REST in regulating NE differentiation in PDAC. Our experiments show that loss of REST in PDAC cells increases NE gene expression, gemcitabine resistance, and colony formation. RNA sequencing data from gemcitabine-treated PDAC cells were enriched for REST target genes, suggesting gemcitabine relieves REST-mediated repression. Chromatin immunoprecipitation experiments corroborated this by demonstrating that gemcitabine reduces REST binding to the NE genes SYP and SNAP25. In addition, single cell ATAC sequencing also uncovered a heterogeneous response to gemcitabine treatment, revealing two gemcitabine-driven cell states with differing REST motif accessibility. Our study indicates that REST controls NE gene programs in PDAC and that loss of REST promotes gemcitabine resistance and oncogenic growth. Mirroring the heterogeneity of PDAC tumors, a subset of PDAC cells treated with gemcitabine have reduced REST motif accessibility, which may reflect a therapy resistant NE-like subpopulation. Citation Format: Kevin Alexander MacPherson, Meghan M. Joly, Brittany Allen-Petersen, Carl Pelz, Mary C. Thoma, Kristof Torkenczy, Andrew Adey, Daniel Liefwalker, Patrick J. Worth, Rosalie C. Sears. RE1-silencing transcription factor (REST) controls neuroendocrine gene programs in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 787.

Intertumoral CD4+ T-cells instruct monocyte differentiation in pancreatic ductal adenocarcinoma

Abstract Pancreatic ductal adenocarcinoma (PDA) is a lethal malignancy that is currently the third leading cause of cancer related deaths. PDA creates a suppressive fibroinflammatory tumor microenvironment (TME) composed of stromal and immune cells that inhibit anti-tumor immune responses. Tumor associated macrophages (TAMs) account for a large percentage of the TME and have remarkably heterogeneous functions, with some subsets promoting anti-tumor responses while others suppress tumor specific T-cells. While many studies have highlighted macrophage heterogeneity in PDA, it remains unclear mechanisms regulating monocyte differentiation into pro vs anti-tumor populations. To understand monocyte differentiation in PDA, we performed trajectory analysis on scRNA-seq data from mouse and human tumors and identified predicted monocyte differentiation pathways toward either MHCII-hi anti-tumor TAMs or Trem2-hi pro-tumor TAMs. Using a newly designed monocyte tracking mouse (CCR2CreER x R26tdTomato) implanted with orthotopic tumors that express the click beetle luciferase (CB) neoantigen, we temporally tracked monocyte differentiation within tumors into TAM populations. Furthermore, using antibody depletion, we found that CD4 depletion led to increased monocyte differentiation into pro-tumor macrophages and a dramatic downregulation of PDL1 on monocyte derived TAMs. Finally, using Ifngr1−/− mice implanted with CB+ tumors, we identify that polarization toward anti-tumor macrophages was driven by IFNy, but TAM PDL1 expression was IFNy independent. Together these data are the first to address monocyte differentiation within PDA and identify previously unexpected role for CD4 T cells governing TAM differentiation program.

Tumor-Specific CD4 T Cells Instruct Monocyte Differentiation in Pancreatic Ductal Adenocarcinoma

Tumor-Specific CD4 T Cells Instruct Monocyte Differentiation in Pancreatic Ductal Adenocarcinoma

Differentiation of pancreatic ductal adenocarcinoma and chronic pancreatitis using graph neural networks on histopathology and collagen fiber features

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal human cancers. However, the symptoms and radiographic appearance of chronic pancreatitis (CP) mimics that of PDAC, and sometimes the 2 entities can also be difficult to differentiate microscopically. The need for accurate differentiation of PDAC and CP has become a major topic in pancreatic pathology. These 2 diseases can present similar histomorphological features, such as excessive deposition of fibrotic stroma in the tissue microenvironment and inflammatory cell infiltration. In this paper, we present a quantitative analysis pipeline empowered by graph neural networks (GNN) capable of automatic detection and differentiation of PDAC and CP in human histological specimens. Modeling histological images as graphs and deploying graph convolutions can enable the capture of histomorphological features at different scales, ranging from nuclear size to the organization of ducts. The analysis pipeline combines image features computed from co-registered hematoxylin and eosin (H&E) images and Second-Harmonic Generation (SHG) microscopy images, with the SHG images enabling the extraction of collagen fiber morphological features. Evaluating the analysis pipeline on a human tissue micro-array dataset consisting of 786 cores and a tissue region dataset consisting of 268 images, it attained 86.4% accuracy with an average area under the curve (AUC) of 0.954 and 88.9% accuracy with an average AUC of 0.957, respectively. Moreover, incorporating topological features of collagen fibers computed from SHG images into the model further increases the classification accuracy on the tissue region dataset to 91.3% with an average AUC of 0.962, suggesting that collagen characteristics are diagnostic features in PDAC and CP detection and differentiation.

Establishment of a Machine Learning Model for Early and Differential Diagnosis of Pancreatic Ductal Adenocarcinoma Using Laboratory Routine Data

Early diagnosis and clear differentiation of pancreatic ductal adenocarcinoma (PDAC) from chronic pancreatitis (CP) is clinically challenging. A machine learning model is developed for the diagnosis of PDAC. The model is induced using a dataset of 13 987 participants, of which 12 402 are used for training the model and the remaining 1585 for testing purposes. One thousand sixty‐six laboratory variables are reduced to 18 measures using standard filtering and feature importance methods. Then, five machine learning classifiers are evaluated for the study. Hyperparameter optimization for each classifier is carried out, and the optimal algorithm is established using a tenfold cross validation on the training data. Finally, gradient boosting decision tree‐based ternary classifier composed of 18 routine laboratory variables (GBDT‐TC18) is established. In the test cohort, GBDT‐TC18 differentiates PDAC from CP and healthy control (HC) with an accuracy better than carbohydrate antigen 19‐9 (CA19‐9)‐based diagnosis. It also maintains a high diagnostic accuracy for stages I, IIA, and IIB PDAC, small‐sized PDAC, body and tail adenocarcinoma, CA19‐9‐negative PDAC, and nonjaundice PDAC. What's more, GBDT‐TC18 shows a higher accuracy than CA19‐9 in distinguishing PDAC from CP. GBDT‐TC18 can be used to augment the capability of doctors for early and differential diagnosis of PDAC.

Abstract 2480: RE1-silencing transcription factor (REST) controls neuroendocrine gene programs in pancreatic ductal adenocarcinoma

Abstract Neuroendocrine (NE) differentiation features contribute to intratumoral heterogeneity and aggressive biology in a subset of pancreatic ductal adenocarcinoma (PDAC) tumors associated with poor disease outcome. We recently showed that treating pancreatic cancer cells with gemcitabine, a standard-of-care chemotherapy for PDAC, increases expression of NE markers, suggesting that ductal to neuroendocrine lineage plasticity could play a role in drug resistance. NE features in prostate cancer have been previously associated with loss of the neuronal gene repressor RE1-Silencing Transcription Factor (REST); in the present study, we explore a potential role of REST in regulating NE differentiation in PDAC. Our experiments show that loss of REST in PDAC cells increases NE gene expression, gemcitabine resistance, and colony formation. RNA sequencing data from gemcitabine-treated PDAC cells were enriched for REST target genes, suggesting gemcitabine relieves REST-mediated repression. Chromatin immunoprecipitation experiments corroborated this by demonstrating that gemcitabine reduces REST binding to the NE genes SYP and SNAP25. In addition, single cell ATAC sequencing also uncovered a heterogeneous response to gemcitabine treatment, revealing two gemcitabine-driven cell states with differing REST motif accessibility. Our study indicates that REST controls NE gene programs in PDAC and that loss of REST promotes gemcitabine resistance and oncogenic growth. Mirroring the heterogeneity of PDAC tumors, a subset of PDAC cells treated with gemcitabine have reduced REST motif accessibility, which may reflect a therapy resistant NE-like subpopulation. Citation Format: Kevin Alexander MacPherson, Meghan M. Joly, Brittany Allen-Petersen, Carl Pelz, Mary C. Thoma, Kristof Torkenczy, Andrew Adey, Daniel Liefwalker, Patrick J. Worth, Rosalie C. Sears. RE1-silencing transcription factor (REST) controls neuroendocrine gene programs in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2480.

Pancreatic cancer stem cells may define tumor stroma characteristics and recurrence patterns in pancreatic ductal adenocarcinoma

BackgroundHerein, we investigate the relationship between pancreatic stem cell markers (PCSC markers), CD44, and epithelial-specific antigen (ESA), tumor stroma, and the impact on recurrence outcomes in pancreatic ductal adenocarcinoma (PDAC) patients.MethodsPDAC patients who underwent surgical resection between 01/2012–06/2014 were identified. CD44 and ESA expression was assessed by immunohistochemistry. Stroma was classified as loose, moderate, and dense based on fibroblast content. Overall survival (OS) and relapse-free survival (RFS) were estimated using the Kaplan-Meier method and compared between subgroups by log-rank test. The association between PCSC markers and stroma type was assessed by Fisher’s exact test.ResultsN = 93 PDAC patients were identified. The number of PDAC patients with dense, moderate density, and loose stroma was 11 (12%), 51 (54%), and 31 (33%) respectively. PDAC with CD44+/ESA− had highest rate of loose stroma (63%) followed by PDAC CD44+/ESA+ (50%), PDAC CD44−/ESA+ (35%), CD44−/ESA− (9%) (p = 0.0033). Conversely, lack of CD44 and ESA expression was associated with the highest rate of moderate and dense stroma (91% p = 0.0033). No local recurrence was observed in patients with dense stroma and 9 had distant recurrence. The highest rate of cumulative local recurrence was observed in patients with loose stroma. No statistically significant difference in RFS and OS was observed among subgroups (P = 0.089).ConclusionsThese data indicate PCSCs may have an important role in stroma differentiation in PDAC. Our results further suggest that tumor stroma may influence the recurrence pattern in PDAC patients.

Influence of the low-dose CE-MDCT scanning protocol parameters on the results of pancreatic ductal adenocarcinoma radiomic analysis

To investigate the dependence of textural parameters of pancreatic ductal adenocarcinoma on using standard and low-dose CT protocols.Materials and methods. The study included 52 consecutive patients with histologically confirmed pancreatic ductal adenocarcinoma who underwent contrast enhanced computed tomography using standard (120 kV) and low-dose (100 kV) scanning protocols. We compared radiomics features of the identical histological tumors in all scanning phases.Results. We calculated 53 radiomics features in all types of pancreatic ductal adenocarcinoma grade differentiation for all scanning phases. We identified that out of 53 features of texture analysis, less than half was statistically different for each scan phase (11 parameters (20.8%) for the unenhanced phase; 18 parameters (34%) for the arterial; 19 parameters (35.8%) for the venous and delayed scanning phases), in all types of tumor differentiation (GLCM_ Contrast, GLCM_Correlation, GLCM_Dissimilarity, GLRLM_SRHGE etc., p &lt; 0.05).The diagnostic accuracy of more than 50% of the radiomics features is preserved when changing kV in the CT scan protocol.Conclusion. The use of a low-dose CT protocol doesn’t affect the diagnostic accuracy of the features of texture analysis in the preoperative assessment of the degree of differentiation of pancreatic ductal adenocarcinoma.

Distinguishing pancreatic cancer and autoimmune pancreatitis with in vivo tomoelastography.

To prospectively investigate the stiffness and fluidity of pancreatic ductal adenocarcinoma (PDAC) and autoimmune pancreatitis (AIP) with tomoelastography, and to evaluate its diagnostic performance in distinguishing the two entities. Tomoelastography provided high-resolution maps of shear wave speed (c in m/s) and phase angle (φ in rad), allowing mechanical characterization of the stiffness and fluidity properties of the pancreas. Forty patients with untreated PDAC and 33 patients with untreated AIP who underwent diagnostic pancreatic MRI at 3-T together with multifrequency MR elastography and tomoelastography data processing were prospectively enrolled. Ten healthy volunteers served as controls. Two radiologists and a technician measured pancreatic stiffness and fluidity independently. The two radiologists also independently evaluated the patients' conventional MR sequences using the following diagnostic score: 1, definitely PDAC; 2, probably PDAC; 3, indeterminate; 4, probably AIP; and 5, definitely AIP. Interobserver agreement was assessed. Stiffness and fluidity of PDAC, AIP, and healthy pancreas, as well as diagnostic performance of tomoelastography and conventional MRI, were compared. AIP showed significantly lower stiffness and fluidity than PDAC and significantly higher stiffness and fluidity than healthy pancreas. Pancreatic fluidity was not influenced by secondary obstructive changes. The intraclass correlation coefficient for pancreatic stiffness and fluidity by the 3 readers was near-perfect (0.951-0.979, all p < 0.001). Both stiffness and fluidity allowed distinguishing PDAC from AIP. AUCs were 0.906 for stiffness, 0.872 for fluidity, and 0.842 for conventional MRI. Pancreatic stiffness and fluidity both allow differentiation of PDAC and AIP with high accuracy. • AIP showed significantly lower stiffness and fluidity than PDAC and significantly higher stiffness and fluidity than healthy pancreas. • Both stiffness and fluidity allowed distinguishing PDAC from AIP. • Pancreatic fluidity could distinguish malignancy from non-malignant secondary obstructive changes.

Cholesterol Pathway Inhibition Induces TGF-β Signaling to Promote Basal Differentiation in Pancreatic Cancer

Oncogenic transformation alters lipid metabolism to sustain tumor growth. We define a mechanism by which cholesterol metabolism controls the development and differentiation of pancreatic ductal adenocarcinoma (PDAC). Disruption of distal cholesterol biosynthesis by conditional inactivation of the rate-limiting enzyme Nsdhl or treatment with cholesterol-lowering statins switches glandular pancreatic carcinomas to a basal (mesenchymal) phenotype in mouse models driven by KrasG12D expression and homozygous Trp53 loss. Consistently, PDACs in patients receiving statins show enhanced mesenchymal features. Mechanistically, statins and NSDHL loss induce SREBP1 activation, which promotes the expression of Tgfb1, enabling epithelial-mesenchymal transition. Evidence from patient samples in this study suggests that activation of transforming growth factor β signaling and epithelial-mesenchymal transition by cholesterol-lowering statins may promote the basal type of PDAC, conferring poor outcomes in patients.

Complementary role of computed tomography texture analysis for differentiation of pancreatic ductal adenocarcinoma from pancreatic neuroendocrine tumors in the portal-venous enhancement phase

PurposeTo assess the role of CT-texture analysis (CTTA) for differentiation of pancreatic ductal adenocarcinoma (PDAC) from pancreatic neuroendocrine neoplasm (PNEN) in the portal-venous phase as compared with visual assessment and tumor-to-pancreas attenuation ratios.Methods53 patients (66.1 ± 8.6y) with PDAC and 42 patients (65.5 ± 12.2y) with PNEN who underwent contrast-enhanced CT for primary staging were evaluated. Volumes of interests (VOIs) were set in the tumor tissue at the portal-venous phase excluding adjacent structures. Based on pyradiomics library, 92 textural features were extracted including 1st, 2nd, and higher order features, and then compared between PNEN and PDAC. The visual assessment classified tumors into hypo-, iso-, or hyperdense to pancreas parenchyma or into homogeneous/heterogeneous. Additionally, attenuation ratios between the tumors and the non-involved pancreas were calculated.Results8/92 (8.6%) highly significant (p < 0.005) discriminatory textural features between PDAC and PNEN were identified including the 1st order features “median,” “total energy,” “energy,” “10th percentile,” “90th percentile,” “minimum,” “maximum,” and the 2nd order feature “Gray-Level co-occurrence Matrix (GLCM) Informational Measure of Correlation (Imc2).” In PNEN, the higher order feature “GLSZM Small Area High Gray-Level Emphasis” proved significantly higher in G1 compared to G2/3 tumors (p < 0.05). The tumor/parenchyma ratios as well as the visual assessment into hypo-/iso-/hyperdense or homogeneous/heterogeneous did not significantly differ between PDAC and PNEN.ConclusionsOur data indicate that CTTA is a feasible tool for differentiation of PNEN from PDAC and also of G1 from G2/3 PNEN in the portal-venous phase. Visual assessment and tumor-to-parenchyma ratios were not useful for discrimination.

Abstract C06: Cholesterol deprivation induces TGFβ signaling to promote basal differentiation in pancreatic cancer

Abstract Oncogenic transformation alters the metabolism of cellular nutrients to sustain tumor growth. We here define a mechanism by which modifications in cholesterol metabolism control the formation of pancreatic ductal adenocarcinoma (PDAC). Disruption of distal cholesterol biosynthesis by means of conditional inactivation of Nsdhl in mice bearing a tumor-inducing Kras mutation (KrasG12D) prevented PDAC formation in the context of a heterozygous Trp53f/+ genotype without impairing normal pancreatic development. In mice with pancreatic Nsdhl ablation and homozygous loss of Trp53, the emerging tumors presented with the aggressive basal (mesenchymal) phenotype as opposed to the classic (glandular) PDAC. This paralleled significantly reduced expression of cholesterol metabolic pathway genes in human basal PDAC subtype. Mechanistically, we demonstrate that genetic or metabolic reduction in cellular cholesterol induces the expression of transforming growth factor beta (TGF) and activates pro-mesenchymal effectors in human and murine PDAC, providing a direct transcriptional mechanism by which cholesterol metabolism can condition tumor differentiation. Activity of TGF beta pathway estimated via pSMAD2 expression in surgical PDAC samples inversely correlated with patients’ blood cholesterol levels, especially in patients taking cholesterol-lowering statins. Conclusions: Low levels of cellular cholesterol induce autocrine TGF beta signaling in PDAC. Malnutrition or cholesterol-lowering statins may be mechanistically linked to basal PDAC differentiation. Citation Format: Linara Gabitova, Suraj Peri, Ralph Francescone, Janusz Franco-Barraza, Neelima Shah, Emmanuelle Nicolas, Elizabeth A. Handorf, Kathy Q. Cai, Ido Sloma, Rachel Chiaverelli, Richard Moffitt, Erica A. Golemis, Carolyn Fang, Shannon Lynch, Edna Cukierman, Igor Astsaturov. Cholesterol deprivation induces TGFβ signaling to promote basal differentiation in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2019 Sept 6-9; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2019;79(24 Suppl):Abstract nr C06.

Association of pancreatic cancer stem cells with tumor stroma type.

e15771 Background: Pancreatic ductal adenocarcinoma (PDA) is a heterogeneous disease with distinct stroma features. Cancer stem cells (CSC) in PDA may express CD44 (C) +/- Epithelial Specific Antigen (E). We investigated the relationship of CSC markers with tumor stroma and clinical outcomes in PDA patients (pts) who had surgical resection. Methods: Pts who underwent PDA resection with IRB #00-032/#06-107 consent between 01/2012-06/2014 at Memorial Sloan Kettering were identified. C and E immunohistochemical (IHC) expression scored as follow: 0, none; 1, 1%–10%; 2, 11%–50%; 3, 51%–80%; 4, 81%–100%. Staining intensity was scored as 0, none; 1, weak; 2, moderate; 3, strong. The total scores (0-12) were averaged and was considered positive when average score &gt; median. Stroma was classified as loose, moderate and dense based on fibroblast content using H&amp;E stain. Overall survival (OS) and relapse-free survival (RFS) were estimated using the Kaplan-Meier and compared by log-rank test; association between CSC markers and stroma type was assessed by Fisher`s exact test. Results: N = 93 PDA pts identified. PDA with C(+) E(-) had significantly higher loose stroma and PDA with C(-) E(+) and C(-) E(-) had more moderate and dense stroma (p = 0.0033). The number of PDA pts with dense, moderate, and loose stroma was: 11, 31, and 51 respectively. No local recurrence in pts with dense stroma observed and 8/11 had either lung or liver recurrence. Six of 31pts with loose stroma had a local recurrence and 13/31 pts had either liver or lung recurrence. No statistically significant difference in OS and RFS were observed among subgroups (P = 0.089). Median time from relapse to death was: 2, 11.5, 10,5 and months 7 in C+/E-, C-/E+, C+/E+, and C-/E- groups respectively. Conclusions: PDA CSCs appears to have an association with PDA stroma type. We also observed different recurrence patterns among stroma subgroups. Respecting small sample size, these data indicate CSCs may have an important role in stroma differentiation in PDA. CSC markers do not predict OS and RFS, however, resected PDA with C(+) E(-) may have more aggressive behavior following recurrence. [Table: see text]

Plasma Chromogranin A as a Prognostic Marker in Pancreatic Ductal Adenocarcinoma.

Neuroendocrine differentiation in pancreatic ductal adenocarcinoma (PDAC) is known, but its clinical significance still remains uncertain. The clinical role of chromogranin A (CgA), a marker of neuroendocrine tumor, was evaluated in patients with PDAC. We retrospectively analyzed 350 patients with PDAC. All patients had plasma CgA levels at diagnosis. Patients were classified as normal and high CgA groups according to the upper limit of plasma CgA. There were 202 patients (57.7%) in the normal CgA group and 148 patients (42.3%) in the high CgA group. High CgA group presented higher rate of metastatic disease (61.5% vs 45.0%; P = 0.002) and shorter median overall survival (OS) (8.2 vs 11.6 months; P = 0.015). Upon grouping patients based on clinical stages, OS was significantly different between the CgA groups only in metastatic disease (6.6 vs 7.2 months; P = 0.022). Multivariate analysis showed no association between high CgA and OS (hazard ratio, 1.22; 95% confidence interval, 0.97-1.54; P = 0.090). However, high CgA was associated with poor OS in patients with metastatic disease (hazard ratio, 1.37; 95% confidence interval, 1.01-1.87; P = 0.047). High CgA levels may predict poor prognosis in patients with pancreatic cancer, especially during metastatic stages.

Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue.

OBJECTIVE. The objective of our study was to determine the utility of radiomics features in differentiating CT cases of pancreatic ductal adenocarcinoma (PDAC) from normal pancreas. MATERIALS AND METHODS. In this retrospective case-control study, 190 patients with PDAC (97 men, 93 women; mean age ± SD, 66 ± 9 years) from 2012 to 2017 and 190 healthy potential renal donors (96 men, 94 women; mean age ± SD, 52 ± 8 years) without known pancreatic disease from 2005 to 2009 were identified from radiology and pathology databases. The 3D volume of the pancreas was manually segmented from the preoperative CT scans by four trained researchers and verified by three abdominal radiologists. Four hundred seventy-eight radiomics features were extracted to express the phenotype of the pancreas. Forty features were selected for analysis because of redundancy of computed features. The dataset was divided into 255 training cases (125 normal control cases and 130 PDAC cases) and 125 validation cases (65 normal control cases and 60 PDAC cases). A random forest classifier was used for binary classification of PDAC versus normal pancreas of control cases. Accuracy, sensitivity, and specificity were calculated. RESULTS. Mean tumor size was 4.1 ± 1.7 (SD) cm. The overall accuracy of the random forest binary classification was 99.2% (124/125), and AUC was 99.9%. All PDAC cases (60/60) were correctly classified. One case from a renal donor was misclassified as PDAC (1/65). The sensitivity was 100%, and specificity was 98.5%. CONCLUSION. Radiomics features extracted from whole pancreas can be used to differentiate between CT cases from patients with PDAC and healthy control subjects with normal pancreas.

Differentiation of pancreatic ductal adenocarcinoma from inflammatory mass: added value of magnetic resonance elastography

To examine whether incorporating magnetic resonance elastography (MRE) with contrast-enhanced computed tomography (CE-CT) or dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can provide a benefit in differentiating pancreatic ductal adenocarcinoma (PDAC) from inflammatory mass (IM). Thirty patients with IM and 56 patients with PDAC confirmed by histopathology were identified retrospectively via a prospectively maintained database. All patients underwent CE-CT or DCE-MRI together with dual-frequency MRE at 40 and 60Hz. A five-point scale for likelihood of pancreatic malignancy was used by two experienced radiologists in consensus. Diagnostic accuracy of CE-CT/DCE-MRI, MRE, and their combination (modeled by logistic regression analysis) was estimated using receiver operating characteristic (ROC) analysis with a leave-one-out cross validation. Accuracies for determination of PDAC by 60-Hz MRE, 40-Hz MRE, CE-CT/DCE-MRI, and the combination of CE-CT or DCE-MRI and 60- and 40-Hz MRE, were 70.2%, 77.4%, 83.3%, 75%, and 92.9%, respectively. CE-CT or DCE-MRI combined with 40-Hz MRE significantly improved diagnostic performance versus CE-CT or DCE-MRI alone (area under the ROC curve [AUC]: 0.937 versus 0.783, p<0.01) by increasing specificity (96.9% versus 62.1%, p=0.002) without a significant loss of sensitivity (90.9% versus 94.6%, p=0.727), while combined CE-CT or DCE-MRI with 60-Hz MRE did not significantly change diagnostic performance versus CE-CT/DCE-MRI alone (AUC: 0.760 versus 0.783, p=0.697). Combined assessment by 40-Hz MRE with CE-CT/DCE-MRI may help to differentiate PDAC from IM in a relatively non-invasive fashion.

Non-invasively predicting differentiation of pancreatic cancer through comparative serum metabonomic profiling

BackgroundThe differentiation of pancreatic ductal adenocarcinoma (PDAC) could be associated with prognosis and may influence the choices of clinical management. No applicable methods could reliably predict the tumor differentiation preoperatively. Thus, the aim of this study was to compare the metabonomic profiling of pancreatic ductal adenocarcinoma with different differentiations and assess the feasibility of predicting tumor differentiations through metabonomic strategy based on nuclear magnetic resonance spectroscopy.MethodsBy implanting pancreatic cancer cell strains Panc-1, Bxpc-3 and SW1990 in nude mice in situ, we successfully established the orthotopic xenograft models of PDAC with different differentiations. The metabonomic profiling of serum from different PDAC was achieved and analyzed by using 1H nuclear magnetic resonance (NMR) spectroscopy combined with the multivariate statistical analysis. Then, the differential metabolites acquired were used for enrichment analysis of metabolic pathways to get a deep insight.ResultsAn obvious metabonomic difference was demonstrated between all groups and the pattern recognition models were established successfully. The higher concentrations of amino acids, glycolytic and glutaminolytic participators in SW1990 and choline-contain metabolites in Panc-1 relative to other PDAC cells were demonstrated, which may be served as potential indicators for tumor differentiation. The metabolic pathways and differential metabolites identified in current study may be associated with specific pathways such as serine-glycine-one-carbon and glutaminolytic pathways, which can regulate tumorous proliferation and epigenetic regulation.ConclusionThe NMR-based metabonomic strategy may be served as a non-invasive detection method for predicting tumor differentiation preoperatively.