Clinico-pathological characteristics and clinical outcome of different histological types of pancreatic cancer in a large Middle European series

Diagnostic dilemma: a collision of pancreatic neuroendocrine tumor G3 and adenocarcinoma with extensive fibrosis

An 82-year-old man presented with intermittent abdominal pain and elevated liver enzymes. Blood tests showed normal levels of tumor markers (CEA, CA19-9, NSE). Contrast-enhanced computed tomography (CE-CT) revealed a 20 mm hypovascular mass in the pancreatic head. Magnetic resonance imaging indicated low intensity on both T1- and T2-weighted images and high intensity on diffusion-weighted images. Endoscopic ultrasonography visualized an irregular hypoechoic mass. Initially, it was diagnosed as pancreatic ductal adenocarcinoma (PDAC) based on imaging. Subsequent histopathological analysis via endoscopic ultrasound-guided fine-needle aspiration revealed a neuroendocrine tumor (NET). The preoperative diagnosis was changed to a pancreatic NET grade1. Consequently, a pancreaticoduodenectomy was performed. Histopathological examination of the resected specimen unveiled a mixed tumor-NET-Grade1/Grade3 and invasive PDAC. No clear transition between the NETs and PDAC was observed. The high grade of NET with significant fibrosis contributed to decreased enhancement of CE-CT. Finally, we diagnosed this case as a pancreatic collision tumor involving both NET and PDAC components, with lymph node metastases attributed to the NET components. In this case, achieving an accurate preoperative diagnosis was challenging despite utilizing both imaging and biopsy diagnostics. This unique case underscores the difficulties encountered in the preoperative assessment of mixed tumors.

Prognostic Value of Somatostatin Receptor-Derived Volumetric Parameters from a Hybrid Standardized Uptake Value Thresholding Method in Patients with 68Ga-DOTATATE-Avid Stage IV Neuroendocrine Neoplasms: A Preliminary Study

INTRODUCTION

The ability of PET/CT imaging to delineate neuroendocrine neoplasms (NENs) and predict prognosis in affected patients is often compromised by substantial uptake heterogeneity. We hereby proposed a hybrid standardized uptake value (SUV) thresholding algorithm to extract volumetric parameters from somatostatin receptor (SSTR) PET/CT imaging and investigate their prognostic performance in patients with 68Ga-DOTATATE-avid stage IV NENs.

METHODS

For 38 retrospectively enrolled patients, we used either fixed SUV thresholding of normal liver parenchyma (method A), 41% of the SUVmax for each lesion (method B), or a hybrid method (method A for liver metastases; fixed SUV threshold of normal bone for bone metastases; method B for primary tumors and other metastases) to quantify the whole-body SSTR-expressing tumor volume (SRETVwb) and total lesion SSTR expression (TLSREwb). Patient survival was also recorded and analyzed.

RESULTS

PET/CT images revealed heterogeneous uptake of 68Ga-DOTATATE at primary and metastatic sites. Progression-free survival (PFS) and overall survival (OS) were negatively correlated with the extent of liver or bone metastases (p < 0.05), but not significantly correlated with tumor grade or 18F-FDG PET/CT positivity. By the hybrid method, PFS was significantly shorter in patients with high SRETVwb, and OS was significantly shorter in those with high SRETVwb and TLSREwb (p < 0.05). However, when derived from method A or method B, neither SRETVwb nor TLSREwb could predict patient outcomes.

CONCLUSION

Compared with other methods used in 68Ga-DOTATATE-avid stage IV NENs, our hybrid SUV thresholding method demonstrated robustness, with greater precision, reliability, and prognostic power.

A population-based nomogram to individualize treatment modality for pancreatic cancer patients underlying surgery

As the most aggressive tumor, TNM staging does not accurately identify patients with pancreatic cancer who are sensitive to therapy. This study aimed to identify associated risk factors and develop a nomogram to predict survival in pancreatic cancer surgery patients and to select the most appropriate comprehensive treatment regimen. First, the survival difference between radiotherapy and no radiotherapy was calculated based on propensity score matching (PSM). Cox regression was conducted to select the predictors of overall survival (OS). The model was constructed using seven variables: histologic type, grade, T stage, N stage, stage, chemotherapy and radiotherapy. All patients were classified into high- or low-risk groups based on the nomogram. The nomogram model for OS was established and showed good calibration and acceptable discrimination (C-index 0.721). Receiver operating characteristic curve (ROC) and DCA curves showed that nomograms had better predictive performance than TNM stage. Patients were divided into low-risk and high-risk groups according to nomogram scores. Radiotherapy is recommended for high-risk patients but not for low-risk patients. We have established a well-performing nomogram to effectively predict the prognosis of pancreatic cancer patients underlying surgery. The web version of the nomogram https://rockeric.shinyapps.io/DynNomapp/ may contribute to treatment optimization in clinical practice.

The Development of a Prediction Model Based on Random Survival Forest for the Postoperative Prognosis of Pancreatic Cancer: A SEER-Based Study

Simple Summary

Surgery is the main treatment to cure pancreatic cancer (PC). However, the 5-year survival rate of surgical resection is only 10–20%. The aim of our study was to develop a prediction model with the novel machine learning algorithm random survival forest (RSF) and to offer easy-to-use prediction tools, including risk stratification and individual prognosis. The study would benefit patients and physicians in postoperative management and facilitate personalized medicine.

Abstract

Accurate prediction for the prognosis of patients with pancreatic cancer (PC) is a emerge task nowadays. We aimed to develop survival models for postoperative PC patients, based on a novel algorithm, random survival forest (RSF), traditional Cox regression and neural networks (Deepsurv), using the Surveillance, Epidemiology, and End Results Program (SEER) database. A total of 3988 patients were included in this study. Eight clinicopathological features were selected using least absolute shrinkage and selection operator (LASSO) regression analysis and were utilized to develop the RSF model. The model was evaluated based on three dimensions: discrimination, calibration, and clinical benefit. It found that the RSF model predicted the cancer-specific survival (CSS) of the postoperative PC patients with a c-index of 0.723, which was higher than the models built by Cox regression (0.670) and Deepsurv (0.700). The Brier scores at 1, 3, and 5 years (0.188, 0.177, and 0.131) of the RSF model demonstrated the model’s favorable calibration and the decision curve analysis illustrated the model’s value of clinical implement. Moreover, the roles of the key variables were visualized in the Shapley Additive Explanations plotting. Lastly, the prediction model demonstrates value in risk stratification and individual prognosis. In this study, a high-performance prediction model for PC postoperative prognosis was developed, based on RSF The model presented significant strengths in the risk stratification and individual prognosis prediction.

Expression and Role of Dickkopf-1 (Dkk1) in Tumors: From the Cells to the Patients

Dickkopf-1 (Dkk1) is a secretory antagonist of the classical Wnt signaling pathway. Many studies have reported that Dkk1 is abnormally expressed in tumor cells, and abnormal expression of Dkk1 can inhibit cell proliferation or induce apoptosis through pro-apoptotic factors, However, due to the differences in tumor environment and the complex regulatory mechanisms in different tumors, Dkk1 has different effects on the progression of different tumors. In many tumors, high expression of Dkk1 may promote tumor metastasis. However, Dkk1, which is highly expressed in other tumors, can inhibit tumor invasion and metastasis. More and more evidence shows that Dkk1 plays a complex and different role in tumor occurrence, development and metastasis in different tumor environments and through a variety of complex regulatory mechanisms. Therefore, Dkk1 may not only be a useful biomarker of metastasis, but also a target for studying the metabolic mechanism of tumor cells and treating tumors in many tumor types. Therefore, this article reviews the research progress on the expression, mechanism and function of Dkk1 in different tumors, and at the same time, based on the public database data, we made a further analysis of the expression of Dkk1 in different tumors.

The Lipase/Amylase Ratio (LAR) in Peripheral Blood Might Represent a Novel Prognostic Marker in Patients with Surgically Resectable Pancreatic Cancer

Pancreatic enzymes might play a pivotal role in the pathophysiology and prognosis of pancreatic cancer. The aim of this study is to investigate the lipase/amylase ratio (LAR), representing a marker previously used in the differentiation of pancreatitis, as a potential prognostic marker in pancreatic cancer. Data from 157 surgically treated patients with ductal pancreatic adenocarcinoma and 351 patients with metastatic disease were evaluated retrospectively. Cancer-specific survival (CSS) was considered the endpoint of the study. After applying Kaplan-Meier curve analysis, uni- and multivariate Cox regression models were calculated to evaluate the prognostic relevance of LAR. An elevated LAR at diagnosis of localized pancreatic cancer was significantly associated with higher CA19-9 levels (p < 0.05). In univariate analysis, we observed an increased LAR as a significant factor for lower CSS in localized pancreatic cancer patients (HR = 1.63; 95% CI = 1.12-2.36; p = 0.01), but not in metastatic patients (HR = 1.12; 95% CI = 0.87-1.43; p = 0.363). In multivariate analysis, including age, gender, tumor stage, Karnofsky Performance Status, tumor grade, administration of chemotherapy and the LAR, an increased LAR was confirmed to represent an independent prognostic factor regarding CSS (HR = 1.81; 95% CI = 1.17-2.77; p = 0.007) in localized pancreatic cancer patients. In conclusion, our study identified the LAR as an independent prognostic factor in surgically treated pancreatic cancer patients.

Sweetened Beverages Consumption and Pancreatic Cancer: A Meta-Analysis

No study has summarized earlier findings on the association of sweetened beverages (SBs) consumption and risk of Pancreatic Cancer (PC). This systematic review and meta-analysis was conducted to systematically review available observational studies that examined the association of SB consumption with risk of PC. Relevant papers published up to December 2017 were searched through PubMed, MEDLINE, SCOPUS, EMBASE, and Google Scholar databases, using relevant keywords. Cohort and case-control studies that examined the association of SB with PC in adults were included. Overall, 5 cohort studies with 2,041,689 participants (58.68% female, 41.32% male) and 4 case-control studies [enrolled 1,496 cases with PC (55.82% male, 44.18% female) and 3,179 controls (53.32% male, 46.68% female)] were included. Combining effect sizes from cohort studies, we found no significant association between SB consumption and risk of PC (RR: 1.06; 95% CI: 0.87-1.29). Although, stratification by the study location, follow-up duration, exposure and outcome assessment method, and adjustment for physical activity and race/ethnicity removed between-study heterogeneity, it did not affect the association. We found that 50 g/d increment in SB consumption was not linearly associated with risk of PC (RR: 1.00, 95% CI: 0.96, 1.04). No significant nonlinear association was also reached (P-nonlinearity = 0.13). In addition, pooling effect sizes from case-control studies, we did not find significant association between SB consumption and risk of PC (RR: 1.11; 95% CI: 0.92-1.35). We did not find any significant association between SB consumption and risk of PC. Additional studies are required to shed light on this issue.

Early Loss of Forkhead Transcription Factor, O Subgroup, Member 1 Protein in the Development of Pancreatic Ductal Adenocarcinoma

OBJECTIVES

Forkhead transcription factor, O subgroup, member 1 (FOXO1) is a regulatory protein that plays an essential role in cellular homeostasis. A biological function as a tumor suppressor has been proposed. Here, we examined FOXO1 expression in human pancreatic ductal adenocarcinoma (PDAC) and its precursor lesions.

METHODS

We immunohistochemically labeled tissue samples from 47 patients with PDAC for FOXO1 protein. In addition, we extracted data from the Cancer Genome Atlas and the Cancer Cell Line Encyclopedia and studied a potential association with well-established genetic variants. A publicly available microarray dataset of 102 PDAC samples was used to explore the influence of FOXO1 expression on patients' clinical outcome.

RESULTS

Normal ductal epithelium universally expressed nuclear and cytoplasmic FOXO1. Reduced expression was observed in PanIN lesions and PDAC of all cases. Analysis of several datasets showed that the FOXO1 gene transcript levels do not correlate with KRAS, TP53, SMAD4, or CDKN2A mutation status, but positively correlate with patients' outcomes.

CONCLUSIONS

Loss of FOXO1 protein is identified as an early event during PDAC development and may be independent of the top 4 mutated cancer genes. Because of its strong expression in normal ductal cells, immunohistochemical detection of FOXO1 can function as a valuable test to establish the diagnosis of transformation and malignancy in pancreatic tissues.

Elevated amylase in plasma represents an adverse prognostic marker in patients with metastatic pancreatic cancer : A retrospective analysis

Background and aim

The aim of this study was to investigate the prognostic relevance of plasma amylase and lipase concerning survival of patients suffering from metastatic pancreatic cancer (PC).

Method

This retrospective study included 351 patients with metastatic PC, who were treated in a single academic institution. Cancer-specific survival (CSS) was analyzed using the Kaplan-Meier method. To further evaluate the prognostic significance of lipase and amylase, univariate and multivariate values were calculated using Cox proportional models.

Results

In univariate analysis, an increased amylase level was associated with shorter CSS in PC patients (hazard ratio HR = 1.258; 95% confidence interval CI = 1.011–1.566; p = 0.039). In multivariate analysis, including gender, age, CA19-9 and administration of chemotherapy, increased amylase levels prevailed as an independent prognostic factor for CSS (HR = 1.373; 95%CI = 1.004–1.878; p = 0.047).

Conclusions

Plasma amylase was found to be an independent prognostic factor in patients with metastatic PC. The results indicate that amylase might represent a novel and useful marker for better patient stratification in PC management.

Genome-scale analysis to identify prognostic microRNA biomarkers in patients with early stage pancreatic ductal adenocarcinoma after pancreaticoduodenectomy

Background

The aim of the study was to investigate potential prognostic microRNA (miRNA) biomarkers for patients with early stage pancreatic ductal adenocarcinoma (PDAC) after pancreaticoduodenectomy using a miRNA-sequencing (miRNA-seq) data set from The Cancer Genome Atlas (TCGA). A miRNA expression-based prognostic signature was generated, and the potential role of target genes in overall survival (OS) in patients with PDAC was examined.

Methods

A miRNA-seq data set of 112 PDAC patients who underwent pancreaticoduodenectomy was obtained from TCGA. Survival analysis was performed to identify potential prognostic biomarkers.

Results

Eleven miRNAs (hsa-mir-501, hsa-mir-4521, hsa-mir-5091, hsa-mir-24-1, hsa-mir-126, hsa-mir-30e, hsa-mir-3157, hsa-let-7a-3, hsa-mir-133a-1, hsa-mir-4709, and hsa-mir-421) were used to construct a prognostic signature using the step function. The 11-miRNA prognostic signature showed good performance for prognosis prediction (adjusted P<0.0001, adjusted hazard ratio =4.285, 95% confidence interval =2.146–8.554), and the time-dependent receiver operating characteristic analysis showed an area under the curve of 0.864, 0.877, and 0.787 for 1-, 2-, and 3-year PDAC OS predictions, respectively. Comprehensive survival analysis suggested that the prognostic signature could serve as an independent prognostic factor for PDAC OS and performs better in prognosis prediction than other traditional clinical indicators. Functional assessment of the target genes of the miRNAs indicated that they were significantly enriched in multiple biological processes and pathways, including cell proliferation, cell cycle biological processes, the forkhead box O, mitogen-activated protein kinase, Janus kinase/signal transducers and activators of transcription signaling pathways, pathways in cancer, and the ErbB signaling pathway. Several target genes of these miRNAs were also associated with PDAC OS.

Conclusion

The present study identified a novel miRNA expression signature that showed potential as a prognostic biomarker for PDAC after pancreaticoduodenectomy.

Construction and analysis of dysregulated lncRNA-associated ceRNA network identified novel lncRNA biomarkers for early diagnosis of human pancreatic cancer

It is increasing evidence that ceRNA activity of long non-coding RNAs (lncRNAs) played critical roles in both normal physiology and tumorigenesis. However, functional roles and regulatory mechanisms of lncRNAs as ceRNAs in pancreatic ductal adenocarcinoma (PDAC), and their potential implications for early diagnosis remain unclear. In this study, we performed a genome-wide analysis to investigate potential lncRNA-mediated ceRNA interplay based on "ceRNA hypothesis". A dysregulated lncRNA-associated ceRNA network (DLCN) was constructed by utilizing sample-matched miRNA, lncRNA and mRNA expression profiles in PDAC and normal samples in combination with miRNA regulatory network. The results of network analysis uncovered seven novel lncRNAs as functional ceRNAs whose aberrant expression will result in the extensive variation in tumorigenic or tumor-suppressive gene expression through DLCN at the post-transcriptional level contributing to PDAC. Therefore, we developed a 7-lncRNA signature (termed LncRisk-7) based on the expression data of seven lncRNAs and SVM algorithm as a novel diagnostic tool to improve early diagnosis of PDAC. The LncRisk-7 achieved high performance in distinguishing PDAC patients from nonmalignant pancreas samples in the discovery cohort and was further confirmed in another two independent validation cohorts. Functional analysis demonstrated that seven lncRNA biomarkers act as ceRNAs involving the regulation of cell death, cell adhesion and cell cycle. This study will help to improve our understanding of the lncRNA-mediated ceRNA regulatory mechanisms in the pathogenesis of PDAC and provide novel lncRNAs as candidate diagnostic biomarkers or potential therapeutic targets.

The role of Dickkopf-1 as a potential prognostic marker in pancreatic ductal adenocarcinoma

Dickkopf-1(DKK-1), the downstream target of β-catenin/T-cell factor, participates in a negative feedback loop in the Wnt signaling and reported as an important biomarker in many tumors. In this study, we analyzed the expression of DKK-1 in pancreatic ductal adenocarcinoma (PDAC) patients at both mRNA and protein levels. We used real-time PCR to detect the expression of DKK-1 in 32 PDAC and paired adjacent non-tumor tissues, results suggested that the expression of DKK-1 was increased in PDAC tissues. We found the similar results in the analysis of 3 independent microarray data sets. Immunohistochemical staining of 311 pairs of PDAC tissues suggested that DKK-1 expression was significantly associated with T classification (P = 0.039) and lymph node metastasis (P = 0.035). Furthermore, Kaplan-Meier analysis for DKK-1 expression demonstrated that patients with higher DKK-1 level had shorter overall survival (OS) and relapse-free survival (RFS) time in Ren Ji cohort and online PDAC database at both mRNA and protein levels. Univariable and multivariable Cox regression analysis confirmed that DKK-1 as well as lymph node metastasis and histology were independent predictors of OS in patients with PDAC. This study demonstrated that DKK-1 may be a predictor for prognosis in PDAC patients.

Comprehensive immunohistochemical analysis of histone deacetylases in pancreatic neuroendocrine tumors: HDAC5 as a predictor of poor clinical outcome

Epigenetic factors contribute to carcinogenesis, tumor promotion, and chemoresistance. Histone deacetylases (HDACs) are epigenetic regulators that primarily cause chromatin compaction, leading to inaccessibility of promoter regions and eventually gene silencing. Many cancer entities feature overexpression of HDACs. Currently, the role of HDACs in pancreatic neuroendocrine tumors (pNETs) is unclear. We analyzed the expression patterns of all HDAC classes (classes I, IIA, IIB, III, and IV) in 5 human tissue microarrays representing 57 pNETs resected between 1997 and 2013 and corresponding control tissue. All pNET cases were characterized clinically and pathologically according to recent staging guidelines. The investigated cases included 32 (56.1%) female and 25 (43.9%) male pNET patients (total n=57, 47.4% immunohistochemically endocrine positive). Immunohistochemical profiling revealed a significant up-regulation of all HDAC classes in pNET versus control, with different levels of intensity and extensity ranging from 1.5- to >7-fold up-regulation. In addition, expression of several HDACs (HDAC1, HDAC2, HDAC5, HDAC11, and Sirt1) was significantly increased in G3 tumors. Correlation analysis showed a significant association between the protein expression of HDAC classes I, III, and IV and rate of the pHH3/Ki-67-associated mitotic and proliferation index. Furthermore, especially HDAC5 proved as a negative predictor of disease-free and overall survival in pNET patients. Overall, we demonstrate that specific members of all 4 HDAC classes are heterogeneously expressed in pNET. Moreover, expression of HDACs was associated with tumor grading, proliferation markers, and patient survival, therefore representing interesting new targets in pNET treatment.

Combination of tumour markers CEA and CA19-9 improves the prognostic prediction in patients with pancreatic cancer

AIMS

Tumour markers including carcinoembryonic antigen (CEA) or carbohydrate antigen 19-9 (CA19-9) are frequently determined at the time of diagnosis in patients with pancreatic cancer. Several studies indicate a prognostic relevance of these markers in pancreatic cancer, but space for improvement with regard to the predictive accuracy and ability is given. In this work, the main focus is on mathematical combinations of these two tumour markers in order to validate an improvement of prognostic test results in terms of sensitivity and specificity.

METHODS

This retrospective study includes 393 patients with pancreatic cancer, who were treated between the years 2005 and 2012 at the Division of Oncology, Medical University of Graz, Austria. The goal of this study was to explore whether an appropriate combination of two tumour markers leads to a statistically significant improvement of the prognostic prediction.

RESULTS

Receiver operating characteristic curves comparison analyses with the classification variable cancer-specific survival showed that the mathematical product of two tumour markers (TM(product)= (CEA×CA19-9); area under the curve (AUC)=0.727; 95% CI 0.680 to 0.770) is significantly better than CEA alone (AUC=0.644; 95% CI 0.594 to 0.691; p=0.003) but not significant compared with CA19-9 (AUC=0.710; 95% CI 0.662 to 0.754; p=0.1215). A linear combination of CEA and CA19-9 (TM(linear)=(85×CEA+CA19-9); AUC=0.748; 95% CI 0.702 to 0.790) is significantly better than CEA (p<0.0001) as well as CA19-9 alone (p=0.0304).

CONCLUSIONS

Mathematical combinations of pretherapeutic tumour markers CEA and CA19-9 are feasible and can significantly improve the prognostic prediction in patients with pancreatic cancer.

The lymphocyte to monocyte ratio in peripheral blood represents a novel prognostic marker in patients with pancreatic cancer

Intra-tumoral macrophages have been involved as important players in the pathogenesis and progression of cancer. Recently, inflammatory parameters of the systemic inflammatory response have also been proposed as usefully prognostic biomarkers. One of these, the lymphocyte to monocyte ratio (LMR) in peripheral blood has been shown as a prognostic factor in hematologic and some solid tumors. In this study we analyzed for the first time the prognostic value of LMR in a large middle European cohort of pancreatic cancer (PC) patients.

Data from 474 consecutive patients with ductal adenocarcinoma of the pancreas were evaluated retrospectively. Cancer-specific survival (CSS) was analyzed using the Kaplan-Meier method. To further evaluate the prognostic significance of the LMR, univariate and multivariate Cox regression models were calculated.

Increased LMR at diagnosis was significantly associated with well-established prognostic factors, including high tumor stage and tumor grade (p<0.05). In univariate analysis, we observed that an increased LMR was a significant factor for better CSS in PC patients (HR 0.70; 95% CI 0.57–0.85; p<0.001). In multivariate analysis including age, Karnofsky Index, tumor grade, tumor stage, administration of chemotherapy, LMR and surgical resection, we confirmed increased LMR as an independent prognostic factor for CSS (HR 0.81; 95% CI 0.66–0.99; p=0.04).

In conclusion, we identified LMR as an independent prognostic factor in PC patients. Our results indicate that the LMR might represent a novel and useful marker for patient stratification in PC management.

Evaluation of uric acid as a prognostic blood-based marker in a large cohort of pancreatic cancer patients

BACKGROUND

Recently, chemical blood parameters gain more attraction as potential prognostic parameters in pancreatic cancer (PC). In the present study we investigated the prognostic relevance of the uric acid (UA) level in blood plasma at the time of diagnosis for overall survival (OS) in a large cohort of patients with PC.

PATIENTS AND METHODS

Data from 466 consecutive patients with ductal adenocarcinoma of the pancreas were evaluated retrospectively. Overall survival (OS) was analysed using the Kaplan-Meier method. To further evaluate the prognostic significance of the UA level, univariate and multivariate Cox regression models were calculated.

RESULTS

None of the clinicopathological parameters (tumour grade, clinical stage, age, CA19-9 level, Karnofski Index (KI) or surgical resection) except gender was associated with UA level. In univariate analysis we observed the elevated UA level (<5.1 versus ≥5.1 mg/dl, p = 0.017) as poor prognostic factor for OS. In the multivariate analysis that included age, gender, tumour grade, tumour stage, surgical resection, CA19-9 level, the KI and UA level we confirmed the UA level as independent prognostic factor for OS (HR = 1.373%; CI = 1.077-1.751; p = 0.011).

CONCLUSION

In conclusion, we identified the UA level at time of diagnosis as an independent prognostic factor in PC patients. Our results indicate that the UA level might represent a novel and useful marker for patient stratification in PC management.

External validation of the derived neutrophil to lymphocyte ratio as a prognostic marker on a large cohort of pancreatic cancer patients

BACKGROUND

With growing evidence on the role of inflammation in cancer biology, the presence of a systemic inflammatory response has been postulated as having prognostic significance in a wide range of cancer types. The derived neutrophil to lymphocyte ratio (dNLR), which represents an easily determinable potential prognostic marker in daily practise and clinical trials, has never been externally validated in pancreatic cancer (PC) patients.

METHODS

Data from 474 consecutive PC patients, treated between 2004 and 2012 at a single centre, were evaluated retrospectively. Cancer-specific survival (CSS) was assessed using the Kaplan-Meier method. To evaluate the prognostic relevance of dNLR, univariate and multivariate Cox regression models were applied.

RESULTS

We calculated by ROC analysis a cut-off value of 2.3 for the dNLR to be ideal to discriminate between patients' survival in the whole cohort. Kaplan-Meier curve reveals a dNLR≥2.3 as a factor for decreased CSS in PC patients (p<0.001, log-rank test). An independent significant association between high dNLR≥2.3 and poor clinical outcome in multivariate analysis (HR = 1.24, CI95% = 1.01-1.51, p = 0.041) was identified.

CONCLUSION

In the present study we confirmed elevated pre-treatment dNLR as an independent prognostic factor for clinical outcome in PC patients. Our data encourage independent replication in other series and settings of this easily available parameter as well as stratified analysis according to tumor resectability.