Development and Validation of a Nomogram for Predicting Survival in Patients with Advanced Pancreatic Ductal Adenocarcinoma

Establishment of a nomogram based on Lasso Cox regression for albumin combined with systemic immune-inflammation index score to predict prognosis in advanced pancreatic carcinoma

Purpose

The study aims to establish a nomogram to predict advanced pancreatic carcinoma patients' overall survival (OS), incorporating albumin combined with systemic immune-inflammation index (A-SII) score and clinical characteristics.

Methods

A retrospective study analyzed the clinical data of 205 advanced pancreatic carcinoma patients without antitumor treatment from the Yancheng No.1 People's Hospital between October 2011 and June 2023, and the study divided patients into the training set and the validation set randomly at the proportion of three to one. The A-SII score was divided into scores of 0, 1, and 2 according to the different levels of albumin and SII. Receiver operating characteristic (ROC) curves and time-dependent area under the curve were used to evaluate the predictive ability of the A-SII score. The nomogram1 and nomogram2 were established by the multivariate Cox regression and Lasso Cox regression respectively. The study evaluated the discriminability of nomogram1 and nomogram2 based on C-index and ROC curves to obtain the optimal model. Subsequently, we plotted decision curve analyses (DCA) and calibration curves to estimate the clinical benefit and accuracy of nomogram2.

Results

Lasso Cox regression showed that A-SII score, number of organ metastases, tumor size, chemotherapy, targeted therapy, Neutrophil-to-albumin ratio, and lactate dehydrogenase were independent prognostic factors for the OS of advanced pancreatic carcinoma patients. The C-index and ROC curve of the nomogram2 are better than the nomogram1. Subsequently, the DCA and calibration curve of the nomogram2 demonstrate excellent performance.

Conclusion

The nomogram based on the A-SII score and other independent prognostic factors determined by Lasso Cox regression can accurately predict the OS of patients suffering from advanced pancreatic carcinoma.

A predictive risk-scoring model for survival prognosis of multiple myeloma based on gain/amplification of 1q21: Experience in a tertiary hospital in South-Western China

BACKGROUND

Chromosomal 1q gains and amplifications (+1q21) are frequently observed in patients with newly diagnosed multiple myeloma (NDMM). However, the interpretation of the high-risk (HR) prognostic implications stemming from 1q21 abnormalities remain challenging to implement effectively.

METHODS

In a comprehensive analysis of 367 consecutive patients with symptomatic MM, we assessed the prognostic significance of +1q21 using FISH with a threshold of 7.4%. The patient cohort was randomly divided into a training set (66.5%, n = 244) and a validation set (33.5%, n = 133). A multivariate Cox regression analysis was conducted to identify significant prognostic factors associated with PFS. Weight scores were assigned to each risk factor based on the β-value of the corresponding regression coefficient. A predictive risk-scoring model involving +1q21 was then developed, utilizing the total score derived from these weight scores. The model's discriminative ability was evaluated using the AUC in both the training and validation sets. Finally, we compared the performance of the +1q21-involved risk with the established R-ISS and R2-ISS models.

RESULTS

Upon initial diagnosis, 159 patients (43.32%) exhibited +1q21, with 94 (59.11%) having three copies, referred to as Gain(1q21), and 65 (40.89%) possessing four or more copies, referred to as Amp (1q21). Both were significantly linked to a reduced PFS in myeloma (p < 0.05), which could be effectively mitigated by ASCT. The +1q21-involved risk model, with an AUC of 0.697 in the training set and 0.725 in the validation set, was constructed including Gain(1q21), Amp(1q21), no-ASCT, and TP53 deletion. This model, termed the ultra-high-risk (UHR) model, demonstrated superior performance in predicting shorter PFS compared to the R-ISS stage 3 and R2-ISS stage 4.

CONCLUSION

The UHR model, which integrates the presence of +1q21 with no-ASCT and TP53 deletion, is designed to identify the early relapse subgroup among patients with +1q21 in NDMM.

Construction of a risk prediction model for detecting postintensive care syndrome-mental disorders

BACKGROUND

Postintensive care syndrome (PICS) has adverse multidimensional effects on nearly half of the patients discharged from ICU. Mental disorders such as anxiety, depression and post-traumatic stress disorder (PTSD) are the most common psychological problems for patients with PICS with harmful complications. However, developing prediction models for mental disorders in post-ICU patients is an understudied problem.

AIMS

To explore the risk factors of PICS mental disorders, establish the prediction model and verify its prediction efficiency.

STUDY DESIGN

In this cohort study, data were collected from 393 patients hospitalized in the ICU of a tertiary hospital from April to September 2022. Participants were randomly assigned to modelling and validation groups using a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was performed to select the predictors, multiple logistic regression analysis was used to establish the risk prediction model, and a dynamic nomogram was developed. The Hosmer-Lemeshow (HL) test was performed to determine the model's goodness of fit. The area under the receiver operating characteristic (ROC) curve was used to evaluate the model's prediction efficiency.

RESULTS

The risk factors of mental disorders were Sepsis-related organ failure assessment (SOFA) score, Charlson comorbidity index (CCI), delirium duration, ICU depression score and ICU sleep score. The HL test revealed that p = .249, the area under the ROC curve = 0.860, and the corresponding sensitivity and specificity were 84.8% and 71.0%, respectively. The area under the ROC curve of the verification group was 0.848. A mental disorders dynamic nomogram for post-ICU patients was developed based on the regression model.

CONCLUSIONS

The prediction model provides a reference for clinically screening patients at high risk of developing post-ICU mental disorders, to enable the implementation of timely preventive management measures.

RELEVANCE TO CLINICAL PRACTICE

The dynamic nomogram can be used to systematically monitor various factors associated with mental disorders. Furthermore, nurses need to develop and apply accurate nursing interventions that consider all relevant variables.

Serum aspartate aminotransferase is an adverse prognostic indicator for patients with resectable pancreatic ductal adenocarcinoma

OBJECTIVE

In this study, the association between preoperative levels of serum liver enzymes and overall survival (OS) was evaluated in patients with resectable pancreatic cancer.

METHODS

Preoperative serum levels of alanine aminotransferase (ALT), aspartate aminotransferases (AST), γ-glutamyltransferase, alkaline phosphatase, and lactate dehydrogenase of 101 patients with pancreatic ductal adenocarcinoma (PDAC) were collected. Univariate and multivariate Cox hazard models were used to identify independent variables associated with OS in this cohort.

RESULTS

Patients with elevated AST levels had significantly worse OS than patients with lower AST levels. A nomogram was created using TNM staging and AST levels and was shown to be more accurate in prediction than the American Joint Committee on Cancer 8th edition standard method.

CONCLUSION

Preoperative AST levels could be a novel independent prognostic biomarker for patients with PDAC. The incorporation of AST levels into a nomogram with TNM staging can be an accurate predictive model for OS in patients with resectable PDAC.

Scoring model with serum albumin and CA19-9 for metastatic pancreatic cancer in second-line treatment: results from the NAPOLEON study

BACKGROUND

Patients with metastatic pancreatic cancer refractory to first-line chemotherapy (CTx) have few treatment options. It is unclear what kind of patients could be brought about survival benefit by 2nd-line CTx after refractory to gemcitabine + nab-PTX (GnP) or FOLFIRINOX.

METHODS

This analysis was conducted as part of a multicenter retrospective study of GnP or FOLFIRINOX in patients with metastatic pancreatic cancer. Excluding censored cases, 156 and 77 patients, respectively, received second-line chemotherapy (CTx) and best supportive care (BSC). Using prognostic factors for post-discontinuation survivals (PDSs) at the first-line determination in multivariate analysis, we developed a scoring system to demonstrate the benefit of second-line CTx.

RESULTS

The second-line CTx group had a median PDS of 5.2 months, whereas the BSC group had a median PDS of 2.7 months (hazard ratio 0.42; 95% confidence interval [CI] 0.31-0.57; p < 0.01). According to the Cox regression model, serum albumin levels below 3.5 g/dL, and CA19-9 levels above 1000 U/mL were independent prognostic factors (p < 0.01). Serum albumin (≥ and < 3.5 g/dL allotted to scores 0 and 1) and CA19-9 (< and ≥ 1000 U/mL allotted to scores 0 and 1) at first-line determination were used to develop the scoring system. The PDSs of patients with scores of 0 and 1 were significantly better than those of the BSC group; however, there was no significant difference between the PDSs of patients with score 2 and the BSC group.

CONCLUSION

The survival advantage of second-line CTx, was observed in patients with scores of 0 and 1 but not in those with score 2.

Prognostic models to predict survival in patients with pancreatic cancer: a systematic review

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has poor survival. Current treatments offer little likelihood of cure or long-term survival. This systematic review evaluates prognostic models predicting overall survival in patients diagnosed with PDAC.

METHODS

We conducted a comprehensive search of eight electronic databases from their date of inception through to December 2019. Studies that published models predicting survival in patients with PDAC were identified.

RESULTS

3297 studies were identified; 187 full-text articles were retrieved and 54 studies of 49 unique prognostic models were included. Of these, 28 (57.1%) were conducted in patients with advanced disease, 17 (34.7%) with resectable disease, and four (8.2%) in all patients. 34 (69.4%) models were validated, and 35 (71.4%) reported model discrimination, with only five models reporting values >0.70 in both derivation and validation cohorts. Many (n = 27) had a moderate to high risk of bias and most (n = 33) were developed using retrospective data. No variables were unanimously found to be predictive of survival when included in more than one study.

CONCLUSION

Most prognostic models were developed using retrospective data and performed poorly. Future research should validate instruments performing well locally in international cohorts and investigate other potential predictors of survival.

Establishment and verification of a nomogram prediction model of hypertension risk in Xinjiang Kazakhs

Hypertension is the main risk factor for cardiovascular and renal diseases. It is of great importance to develop effective risk prediction models to identify high-risk groups of hypertension. This study is to establish and verify a nomogram model for predicting the risk of hypertension among Kazakh herders in Xinjiang, China.This is a prospective cohort study. Totally, 5327 Kazakh herders from the Nanshan pastoral area of Xinjiang were enrolled. They were randomly divided into the modeling set of 3729 cases (70%) and the validation set of 1598 cases (30%). In the modeling set, univariate analysis, least absolute shrinkage and selection operator regression and multivariate Logistic regression were used to analyze the influencing factors of hypertension, and a nomogram prediction model was constructed. We then validated the model in the validation set, and evaluated the accuracy of the model using receiver operating characteristic and calibration curve.Based on univariate analysis, least absolute shrinkage and selection operator regression and multivariate logistic regression analysis, we identified 14 independent predictors of hypertension in the modeling set, including age, smoking, alcohol consumption, baseline body mass index, baseline diastolic blood pressure, baseline systolic blood pressure, daily salt intake, yak-butter intake, daily oil intake, fruit and vegetable intake, low-density lipoprotein, cholesterol, abdominal circumference, and family history. The area under the receiver operating characteristic curve of the modeling set and the verification set was 0.803 and 0.809, respectively. Moreover, the calibration curve showed a higher agreement between the nomogram prediction and the actual observation of hypertension.The risk prediction nomogram model has good predictive ability and could be used as an effective tool for the risk prediction of hypertension among Kazakh herders in Xinjiang.

A Generic Nomogram Predicting the Stage of Liver Fibrosis Based on Serum Biochemical Indicators Among Chronic Hepatitis B Patients

Introduction: Liver fibrosis staging is of great importance for reducing unnecessary injuries and prompting treatment in chronic viral hepatitis B patients. Liver biopsy is not suitable to act a screening method although it is a gold standard because of various shortcomings. This study aimed to establish a predictive nomogram as a convenient tool to effectively identify potential patients with different stages of liver fibrosis for patients with chronic hepatitis B.

Methods: A nomogram for multinomial model was developed in a training set to calculate the probability for each stage of fibrosis and tested in a validation set. Fibrosis stages were subgrouped as followed: severe fibrosis/cirrhosis (F3–F4), moderate fibrosis (F2), and nil-mild fibrosis (F0–F1). The indicators were demographic characteristics and biochemical indicators of patients. Continuous indicators were divided into several groups according to the optimal candidate value generated by the decision tree.

Results: This study recruited 964 HBV patients undergoing percutaneous liver biopsy. The multinomial model with 10 indicators was transformed into the final nomogram. The calibration plot showed a good agreement between nomogram-predicted and observed probability of different fibrosis stages. Areas under the receiver operating characteristics (AUROCs) for severe fibrosis/cirrhosis were 0.809 for training set and 0.879 for validation set. For moderate fibrosis, the AUROCs were 0.75 and 0.781. For nil-mild fibrosis, the AUROCs were 0.792 and 0.843. All the results above showed great predictive performance in predicting the stage of fibrosis by our nomogram.

Conclusion: Our model demonstrated good discrimination and extensibility in internal and external validation. The proposed nomogram in this study resulted in great reliability and it can be widely used as a convenient and efficient way.

SOURCE-PANC: A Prediction Model for Patients With Metastatic Pancreatic Ductal Adenocarcinoma Based on Nationwide Population-Based Data

BACKGROUND

A prediction model for overall survival (OS) in metastatic pancreatic ductal adenocarcinoma (PDAC) including patient and treatment characteristics is currently not available, but it could be valuable for supporting clinicians in patient communication about expectations and prognosis. We aimed to develop a prediction model for OS in metastatic PDAC, called SOURCE-PANC, based on nationwide population-based data.

MATERIALS AND METHODS

Data on patients diagnosed with synchronous metastatic PDAC in 2015 through 2018 were retrieved from the Netherlands Cancer Registry. A multivariate Cox regression model was created to predict OS for various treatment strategies. Available patient, tumor, and treatment characteristics were used to compose the model. Treatment strategies were categorized as systemic treatment (subdivided into FOLFIRINOX, gemcitabine/nab-paclitaxel, and gemcitabine monotherapy), biliary drainage, and best supportive care only. Validation was performed according to a temporal internal-external cross-validation scheme. The predictive quality was assessed with the C-index and calibration.

RESULTS

Data for 4,739 patients were included in the model. Sixteen predictors were included: age, sex, performance status, laboratory values (albumin, bilirubin, CA19-9, lactate dehydrogenase), clinical tumor and nodal stage, tumor sublocation, presence of distant lymph node metastases, liver or peritoneal metastases, number of metastatic sites, and treatment strategy. The model demonstrated a C-index of 0.72 in the internal-external cross-validation and showed good calibration, with the intercept and slope 95% confidence intervals including the ideal values of 0 and 1, respectively.

CONCLUSIONS

A population-based prediction model for OS was developed for patients with metastatic PDAC and showed good performance. The predictors that were included in the model comprised both baseline patient and tumor characteristics and type of treatment. SOURCE-PANC will be incorporated in an electronic decision support tool to support shared decision-making in clinical practice.

Development and Validation of Multicenter Predictive Nomograms for Locally Advanced Pancreatic Cancer After Chemoradiotherapy

OBJECTIVE

Due to common practice of hypofractionated radiotherapy in pancreatic cancer and heterogeneous chemotherapy regimens in previous studies, modified nomograms are required. Therefore, we aim to develop and validate prognostic nomograms for locally advanced pancreatic cancer (LAPC) after stereotactic body radiation therapy (SBRT) and chemotherapy.

METHODS

The development cohort comprised 925 patients with LAPC receiving SBRT and gemcitabine-based chemotherapy in our center, while 297 patients from another two centers formed the validation cohort. Nomograms were created from COX models and internally validated by bootstrap. Model discriminations were evaluated by calibration plots and concordance index (C-index). A decision curve analysis (DCA) was performed to evaluate clinical benefits of nomograms. Additionally, recursive partitioning analysis (RPA) was used for stratifications of survival probability based on the total score of each patient calculated by nomograms.

RESULTS

Weight loss, tumor diameter, radiation dose, CA19-9 kinetics after treatment and surgical resection were included in the nomogram for overall survival (OS), while the five factors plus performance status formed the nomogram for progression free survival (PFS). The corrected C-indexes for estimated 1-year and 2-year OS of the development cohort were 0.88 (95% CI: 0.85-0.91) and 0.86 (95% CI: 0.83-0.90). For those of the validation cohort, it was 0.88 (95% CI: 0.82-0.94) and 0.83 (95% CI: 0.74-0.91). Additionally, the corrected C-index for predicted 1-year PFS in the development and validation cohort was 0.83 (95% CI: 0.81-0.86) and 0.82 (95% CI: 0.78-0.87), respectively. The calibration plots showed good agreement of 1- and 2-year OS and 1-year PFS between the estimations and actual observations. Potential clinical benefits were demonstrated with DCA. Additionally, for 1- and 2-year OS and 1-year PFS, patients were stratified into four groups with different survival probability by RPA.

CONCLUSION

The validated nomograms provided useful predictions of OS and PFS for LAPC with chemoradiotherapy.

Development and External Validation of Survival Prediction Model for Pancreatic Cancer Using Two Nationwide Database: Surveillance, Epidemiology and End Results (SEER) and Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP)

Background/Aims

Several prediction models for evaluating the prognosis of nonmetastatic resected pancreatic ductal adenocarcinoma (PDAC) have been developed, and their performances were reported to be superior to that of the 8th edition of the American Joint Committee on Cancer (AJCC) staging system. We developed a prediction model to evaluate the prognosis of resected PDAC and externally validated it with data from a nationwide Korean database.

Methods

Data from the Surveillance, Epidemiology and End Results (SEER) database were utilized for model development, and data from the Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP) database were used for external validation. Potential candidate variables for model development were age, sex, histologic differentiation, tumor location, adjuvant chemotherapy, and the AJCC 8th staging system T and N stages. For external validation, the concordance index (C-index) and time-dependent area under the receiver operating characteristic curve (AUC) were evaluated.

Results

Between 2004 and 2016, data from 9,624 patients were utilized for model development, and data from 3,282 patients were used for external validation. In the multivariate Cox proportional hazard model, age, sex, tumor location, T and N stages, histologic differentiation, and adjuvant chemotherapy were independent prognostic factors for resected PDAC. After an exhaustive search and 10-fold cross validation, the best model was finally developed, which included all prognostic variables. The C-index, 1-year, 2-year, 3-year, and 5-year time-dependent AUCs were 0.628, 0.650, 0.665, 0.675, and 0.686, respectively.

Conclusions

The survival prediction model for resected PDAC could provide quantitative survival probabilities with reliable performance. External validation studies with other nationwide databases are needed to evaluate the performance of this model.

Recent Discoveries of Diagnostic, Prognostic and Predictive Biomarkers for Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with a dismal prognosis that is frequently diagnosed at an advanced stage. Although less common than other malignant diseases, it currently ranks as the fourth most common cause of cancer-related death in the European Union with a five-year survival rate of below 9%. Surgical resection, followed by adjuvant chemotherapy, remains the only potentially curative treatment but only a minority of patients is diagnosed with locally resectable, non-metastatic disease. Patients with advanced disease are treated with chemotherapy but high rates of treatment resistance and unfavorable side-effect profiles of some of the used regimens remain major challenges. Biomarkers reflect pathophysiological or physiological processes linked to a disease and can be used as diagnostic, prognostic and predictive tools. Thus, accurate biomarkers can allow for better patient stratification and guide therapy choices. Currently, the only broadly used biomarker for PDAC, CA 19-9, has multiple limitations and the need for novel biomarkers is urgent. In this review, we highlight the current situation, recent discoveries and developments in the field of biomarkers of PDAC and their potential clinical applications.

Development and validation of a novel nomogram for predicting the prognosis of patients with resected pancreatic adenocarcinoma

The survival prediction for patients with resected pancreatic adenocarcinoma by using the Tumor-Node-Metastasis (TNM) staging system remains limited. A nomogram is a efficient tool that can be used to predict the outcome of patients with various types of malignancy. The present study aimed to develop and validate a nomogram for patients with resected pancreatic adenocarcinoma. A total of 368 patients (258 in the training set and 110 in the validation set) who underwent pancreatic adenocarcinoma resection at the China National Cancer Center between January 2008 and October 2018 were included in the present study. The nomogram was established according to the results from Cox multivariate analysis, which was validated by discrimination and calibration. The area under the receiver operating characteristic curve (AUC) was determined to assess the accuracy of survival predictions. The results from multivariate analysis in the training set demonstrated that blood transfusion, T-stage, N-stage, tumor grade, capsule invasion, carbohydrate antigen 199, neutrophil percentage and adjuvant therapy were independent prognostic factors for overall survival (OS; all P<0.05). Subsequently, a nomogram predicting the 1-year, 3-year and 5-year OS rates, with favorable calibration, was established based on the independent prognostic factors. The concordance indices of the nomogram were higher compared with the TNM staging system in both training and validation sets. Furthermore, a clear risk stratification system based on the nomogram was used to classify patients into the three following groups: Low-risk group (≤168), moderate-risk group (168–255) and high-risk group (>255). The risk stratification system demonstrated an improved ability in predicting the 1-year, 3-year and 5-year OS rates compared with the TNM system (AUC, 0.758, 0.709 and 0.672 vs. AUC, 0.614, 0.604 and 0.568; all P<0.05). The present study developed and validated a nomogram for patients with resected pancreatic adenocarcinoma by including additional independent prognostic factors, including tumor marker, immune index, surgical information, pathological data and adjuvant therapy. Taken together, the results from the present study indicated an improved performance of the nomogram in predicting the prognosis of patients with resected pancreatic adenocarcinoma compared with the TNM staging system.

Score for the Overall Survival Probability of Patients With Pancreatic Adenocarcinoma of the Body and Tail After Surgery: A Novel Nomogram-Based Risk Assessment

Pancreatic adenocarcinoma of the body and tail often has a dismal prognosis and lacks a specific prognostic stage. The aim of this study was to construct a nomogram for predicting survival of patients with pancreatic adenocarcinoma of the body and tail after surgery. Data of patients were selected from the Surveillance, Epidemiology, and End Results (SEER) database and from medical records of Sun Yat-sen University Cancer Center (SYSUCC). In a multivariate analysis for overall survival (OS), the following six variables were identified as independent predictors and incorporated into the nomogram: age, tumor differentiation, tumor size, lymph node ratio (LNR), and chemotherapy. A nomogram was built based on independent risk predictors. The concordance index (C-index) for nomogram, Tumor-Node-Metastasis (TNM) 7th and 8th stage system were 0.775 [95% confidence interval (CI), 0.731–0.819], 0.617 (95%CI, 0.575–0.659), and 0.632 (95%CI, 0.588–0.676), respectively. The calibrated nomogram predicted survival rates which closely corresponded to the actual survival rates. Furthermore, the values of the area under receiver operating characteristic (ROC) curves (AUC) of the nomograms were higher than those of the TNM 7th or 8th stage system in predicting 1-, 2-, and 3-year survival of patients in training and external validation cohorts. The well-calibrated nomogram could be used to predict prognosis for patients with pancreatic adenocarcinoma of the body and tail after surgery.

An Integrated Meta-Analysis of Secretome and Proteome Identify Potential Biomarkers of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is extremely aggressive, has an unfavorable prognosis, and there are no biomarkers for early detection of the disease or identification of individuals at high risk for morbidity or mortality. The cellular and molecular complexity of PDAC leads to inconsistences in clinical validations of many proteins that have been evaluated as prognostic biomarkers of the disease. The tumor secretome, a potential source of biomarkers in PDAC, plays a crucial role in cell proliferation and metastasis, as well as in resistance to treatments, which together contribute to a worse clinical outcome. The massive amount of proteomic data from pancreatic cancer that has been generated from previous studies can be integrated and explored to uncover secreted proteins relevant to the diagnosis and prognosis of the disease. The present study aimed to perform an integrated meta-analysis of PDAC proteome and secretome public data to identify potential biomarkers of the disease. Our meta-analysis combined mass spectrometry data obtained from two systematic reviews of the pancreatic cancer literature, which independently selected 20 studies of the secretome and 35 of the proteome. Next, we predicted the secreted proteins using seven in silico tools or databases, which identified 39 secreted proteins shared between the secretome and proteome data. Notably, the expression of 31 genes of these secretome-related proteins was upregulated in PDAC samples from The Cancer Genome Atlas (TCGA) when compared to control samples from TCGA and The Genotype-Tissue Expression (GTEx). The prognostic value of these 39 secreted proteins in predicting survival outcome was confirmed using gene expression data from four PDAC datasets (validation set). The gene expression of these secreted proteins was able to distinguish high- and low-survival patients in nine additional tumor types from TCGA, demonstrating that deregulation of these secreted proteins may also contribute to the prognosis in multiple cancers types. Finally, we compared the prognostic value of the identified secreted proteins in PDAC biomarkers studies from the literature. This analysis revealed that our gene signature performed equally well or better than the signatures from these previous studies. In conclusion, our integrated meta-analysis of PDAC proteome and secretome identified 39 secreted proteins as potential biomarkers, and the tumor gene expression profile of these proteins in patients with PDAC is associated with worse overall survival.

Development and validation of novel nomograms for predicting the survival of patients after surgical resection of pancreatic ductal adenocarcinoma

Background/Aims

Pancreatic ductal adenocarcinoma (PDAC) is associated with high mortality, even after surgical resection. The existing predictive models for survival have limitations. This study aimed to develop better nomograms for predicting overall survival (OS) and cancer‐specific survival (CSS) in PDAC patients after surgery.

Methods

A total of 6323 PDAC patients were retrospectively recruited from the Surveillance, Epidemiology, and End Results (SEER) database and randomly allocated into training, validation, and test cohorts. Multivariate Cox regression analysis was conducted to identify significant independent factors for OS and CSS, which were used for construction of nomograms. The performance was evaluated, validated, and compared with that of the 8th edition AJCC staging system.

Results

Ten independent factors were significantly correlated with OS and CSS. The 1‐, 3‐, and 5‐year OS rates were 40%, 20%, and 15%, and 1‐, 3‐, and 5‐year CSS rates were 45%, 24%, and 19%, respectively. The nomograms were calibrated well, with c‐indexes of 0.640 for OS and 0.643 for CSS, respectively. Notably, relative to the 8th edition AJCC staging system, the nomograms were able to stratify each AJCC stage into three prognostic subgroups for more robust risk stratification. Furthermore, the nomograms achieved significant clinical validity, exhibiting wide threshold probabilities and high net benefit. Performance assessment also showed high predictive accuracy and reliability.

Conclusions

The predictive ability and reliability of the established nomograms have been validated, and therefore, these nomograms hold potential as novel approaches to predicting survival and assessing survival risks for PDAC patients after surgery.

External validation of the Besançon nomogram in Asian patients with advanced pancreatic cancer receiving second-line chemotherapy: A multi-institute experience in Taiwan

BACKGROUND

Determining survival outcome in advanced pancreatic ductal adenocarcinoma (aPDAC) patients receiving second-line (L2) chemotherapy is important for clinical decision-making. The Besançon group from France recently proposed a prognostic nomogram to predict overall survival (OS) for aPDAC patients receiving L2 chemotherapy. The present study aimed to externally validate the performance of the Besançon nomogram in predicting OS in an Asian cohort.

METHODS

We retrospectively enrolled 349 patients who received L2 chemotherapy for aPDAC between 2010 and 2016 at four institutes in Taiwan. The performance of the Besançon model in this cohort was evaluated with C-index and calibration plots.

RESULTS

The median OS time in our patient cohort was 4.5 months (95% confidence interval [CI], 3.0-5.0). Using the Besançon nomogram-predicted risk groups, the median OS times in the low, intermediate, and high-risk groups were 6.7 (95% CI, 5.3-8.2), 3.2 (95% CI, 2.4-3.9), and 1.7 months (95% CI, 0.6-2.7), respectively. The C-index of the predicted six- and 12-month survival probabilities for the Besançon nomogram were 0.766 (95% CI, 0.715-0.816) and 0.698 (95% CI, 0.641-0.754), respectively. The calibration plot showed that the observed six-month survival probability was close to the diagonal line, while that for 12-month survival deviated below the diagonal line compared to the survival probability predicted by the Besançon nomogram.

CONCLUSIONS

Although the Besançon nomogram tended to over-estimate the 12-month survival probability, our study demonstrated that the nomogram is a reliable and readily applicable model to estimate survival outcomes of aPDAC patients receiving L2 chemotherapy.

Validation and application of a prognostic model for patients with advanced pancreatic cancer receiving palliative chemotherapy

BACKGROUND

We previously developed a robust prognostic model (GS model) to predict the survival outcome of patients with advanced pancreatic cancer (APC) receiving palliative chemotherapy with gemcitabine plus S-1 (GS). This study aimed to validate the application of the GS model in APC patients receiving chemotherapy other than the GS regimen.

PATIENTS AND METHODS

We retrospectively analyzed 727 APC patients who received first-line palliative chemotherapy other than the GS regimen between 2010 and 2016 at four institutions in Taiwan. The patients were categorized into three prognostic groups based on the GS model for comparisons of survival outcome, best tumor response, and in-group survival differences with monotherapy or combination therapy.

RESULTS

The median survival times for the good, intermediate, and poor prognostic groups were 13.4, 8.4, and 4.6 months, respectively. The hazard ratios for the comparisons of intermediate and poor to good prognostic groups were 1.51 (95% confidence interval [CI]), 1.22-1.88, P < .001) and 2.84 (95% CI, 2.34-3.45, P < .001). The best tumor responses with either partial response or stable disease were 57.5%, 40.4%, and 17.2% of patients in the good, intermediate, and poor prognostic groups (P < .001), respectively. For patients in the good prognostic group, first-line chemotherapy with monotherapy and combination therapy had similar median survival times (13.8 vs 12.9 months, P = .26), while combination therapy showed a better median survival time than monotherapy in patients in the intermediate and poor prognostic groups (8.5 vs 8.0 months, P = .038 and 5.7 vs 3.7 months, P = .001, respectively).

CONCLUSION

The results of our study supported the application of the GS model as a general prognostic tool for patients with pancreatic cancer receiving first-line palliative chemotherapy with gemcitabine-based regimens.

Age-adjusted Charlson Comorbidity Index (ACCI) is a significant factor for predicting survival after radical gastrectomy in patients with gastric cancer

INTRODUCTION

To assess the ability of the Age-Adjusted Charlson Comorbidity Index (ACCI) to predict survival after radical gastrectomy in patients with gastric cancer (GC).

METHOD

Data from patients with GC who underwent radical gastrectomy from January 2008 to December 2012 in Fujian Medical University Union Hospital were retrospectively analyzed. Patients were categorized into either high ACCI group or low ACCI group based on the effect of ACCI on long-term GC prognosis. 1:1 propensity score matching (PSM) was used to reduce confounding bias. To further analyze the impact of ACCI on the long-term prognosis of patients after radical gastrectomy, a nomogram was built based on the Cox proportional hazards regression model.

RESULTS

A total of 1476 patients were included in the analysis. After PSM, there was no statistically significant differences in tumor location, tumor size and tumor stage between low ACCI group (429 cases) and high ACCI group (429 cases) (all P > 0.05). Before and after PSM, the incidence of postoperative complications in high ACCI group was significantly higher than that in low ACCI group (P < 0.05). The 5-year overall survival rate (OS) in low ACCI group was significantly higher than that in high ACCI group. Multivariate analysis showed that ACCI was an independent risk factor for OS (P < 0.05). The Harrell's C-statistics (C-index) of TNMA, a prognostic evaluation system combining ACCI and TNM staging system, was significantly higher than that of TNM staging system in both the modeling and validation groups (all P < 0.05).

CONCLUSIONS

ACCI was an independent risk factor for the long-term prognosis of GC patients after radical gastrectomy that could effectively improve the predictive efficacy of the TNM staging system for GC.

Developing a Prognostic Information System for Personalized Care in Real Time

Context

Electronic medical records hold promise to transform clinical practice. However, technological and other barriers may preclude using them to guide care in real time. We used the Virtual Data Warehouse (VDW) to develop a tool that enables physicians to generate real-time, personalized prognostic information about survival after cancer.

Case description

Patients with cancer often ask their oncologists, "Have you ever seen a patient like me?" To help oncologists answer this question, we developed a prototype Prognostic Information System (PRISM), a web-based tool that gathers data about the index patient from Kaiser Permanente's clinical information systems, selects a historical cohort of similar patients, and displays the survival curve of the similar patients relative to key points in their treatment course.

Findings and major themes

The prototype was developed by a multidisciplinary team with expertise in oncology, research, and technology. We have completed two rounds of user testing and refinement. Successful development rested on: (1) executive support and a clinical champion; (2) collaboration among experts from multiple disciplines; (3) starting with simple cases rather than ambitious ones; (4) extensive research experience with the Virtual Data Warehouse, related databases, and an existing query tool; and (5) following agile software development principles, especially iterative user testing.

Conclusion

Clinical data stored in health care systems' electronic medical records can be used to personalize clinical care in real time. Development of prognostic information systems can be accelerated by collaborations among researchers, technology specialists, and clinicians and by use of existing technology like the Virtual Data Warehouse.

Prognostic value of γ-glutamyltransferase-to-albumin ratio in patients with pancreatic ductal adenocarcinoma following radical surgery

Pancreatic ductal adenocarcinoma (PDAC) is a devastating malignancy with poor prognosis. Many preoperative biomarkers can predict postoperative survival of PDAC patients. In this study, we created a novel ratio index based on preoperative liver function test, γ-glutamyltransferase-to-albumin ratio (GAR), and evaluated its prognostic value in predicting clinical outcomes of PDAC patients following radical surgery. We retrospectively enrolled 833 PDAC patients who had underwent radical surgery at our institution between January 2010 and January 2017. Patients were divided into two groups according to the cut-off value of GAR. Univariate and multivariate survival analysis between the groups were evaluated. TNM stage, GAR, preoperative serum carbohydrate antigen 19-9 (CA19-9) and tumor differentiation were combined to generate a more accurate prognostic model. The optimal cut-off value of GAR was 0.65. Significant correlations were found between GAR and tumor location, tumor size, vascular invasion, obstructive jaundice, biliary drainage and parameters of liver function test. Univariate and multivariate analysis showed that high level of GAR independently predicted poorer postoperative overall survival (OS, P < 0.001) and recurrence-free survival (RFS, P < 0.001). Subgroup analysis demonstrated that GAR was predictive of survival in patients without biliary obstruction or severely impaired liver function. In addition, integration of GAR, preoperative serum CA19-9, and tumor differentiation into TNM staging system could better stratify the prognosis for PDAC patients compared with TNM stage alone. Our study demonstrates that preoperative GAR is an independent prognostic factor for prediction of surgical outcomes in PDAC patients. Combination of TNM stage, GAR, preoperative serum CA19-9, and tumor differentiation can enhance the prognostic accuracy.

Nomogram to Predict Cancer-Specific Survival in Patients with Pancreatic Acinar Cell Carcinoma: A Competing Risk Analysis

Background: The objective of this study was to evaluate the probability of cancer-specific death of patients with acinar cell carcinoma (ACC) and build nomograms to predict overall survival (OS) and cancer-specific survival (CSS) of these patients. Methods: Data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Patients diagnosed with ACC between 2004 and 2014 were retrospectively collected. Cancer-specific mortality and competing risk mortality were evaluated. Nomograms for estimating 1-, 2- and 3-year OS and CSS were established based on Cox regression model and Fine and Grey's model. The precision of the 1-, 2- and 3-year survival of the nomograms was evaluated and compared using the area under receiver operating characteristic (ROC) curve (AUC). Results: The study cohort included 227 patients with ACC. The established nomograms were well calibrated, and had good discriminative ability, with a concordance index (C-index) of 0.742 for OS prediction and 0.766 for CSS prediction. The nomograms displayed better discrimination power than 7th or 8th edition Tumor-Node-Metastasis (TNM) stage systems in training set and validation set for predicting both OS and CSS. The AUC values of the nomogram predicting 1-, 2-, and 3-year OS rates were 0.784, 0.797 and 0.805, respectively, which were higher than those of 7th or 8th edition TNM stage systems. Regard to the prediction of CSS rates, the AUC values of the nomogram were also higher than those of 7th or 8th edition TNM stage systems. Conclusion: We evaluated the 1-, 2- and 3-year OS and CSS in patients with ACC for the first time. Our nomograms showed relatively good performance and could be considered as convenient individualized predictive tools for prognosis.

A Prognostic Nomogram for Disease-Specific Survival in Patients with Pancreatic Ductal Adenocarcinoma of the Head of the Pancreas Following Pancreaticoduodenectomy

BACKGROUND This study developed and validated a nomogram to predict patient prognosis for pancreatic ductal adenocarcinoma (PDAC) of the head of the pancreas following pancreaticoduodenectomy. MATERIAL AND METHODS Retrospective data were obtained from 4,383 patients with PDAC of the head of the pancreas who underwent pancreaticoduodenectomy between 2004-2013 from 11 Registries Research Data of the Surveillance, Epidemiology,and End Results (SEER) database. Cox proportional hazards model was used to identify independent risk factors. The predictive accuracy of the nomogram was determined by the concordance index (C-index) and calibration curve. The results were externally validated by comparison with data from 1,743 patients from 7 other Registries Research Data. RESULTS Of the 4,383 patients in the training dataset, median disease-specific survival (DSS) was 17.0 months (range, 1.0-131 months), and postoperative 1-year, 3-year, and 5-year DSS rates were 70.3%, 26.1%, and 16.8%, respectively. Multivariate analysis showed that patient sex, age, tumor grade, regional lymph nodes examined, positive regional lymph nodes, tumor size, extent of local invasion, and tumor metastases were independent risk factors for DSS. The C-index of the internal validation dataset for prediction of DSS was 0.64 (95% CI, 0.63-0.65), which was superior to the American Joint Committee on Cancer (AJCC) staging, 0.57 (95% CI, 0.56-0.58) (P<0.001). The 5-year DSS rates and median DSS time for patients in the low-risk group were significantly greater compared with high-risk group (P<0.001). CONCLUSIONS A validated prognostic disease-specific nomogram for patient survival in PDAC of the head of the pancreas following pancreaticoduodenectomy was developed.

Identification of hub genes and analysis of prognostic values in pancreatic ductal adenocarcinoma by integrated bioinformatics methods

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers in the world, and more molecular mechanisms should be illuminated to meet the urgent need of developing novel detection and therapeutic strategies. We analyzed the related microarray data to find the possible hub genes and analyzed their prognostic values using bioinformatics methods. The mRNA microarray datasets GSE62452, GSE15471, GSE102238, GSE16515, and GSE62165 were finally chosen and analyzed using GEO2R. The overlapping genes were found by Venn Diagrams, functional and pathway enrichment analyses were performed using the DAVID database, and the protein-protein interaction (PPI) network was constructed by STRING and Cytoscape. OncoLnc, which was linked to TCGA survival data, was used to investigate the prognostic values. In total, 179 differentially expressed genes (DEGs) were found in PDAC, among which, 130 were up-regulated genes and 49 were down-regulated. DAVID showed that the up-regulated genes were significantly enriched in extracellular matrix and structure organization, collagen catabolic and metabolic process, while the down-regulated genes were mainly involved in proteolysis, reactive oxygen species metabolic process, homeostatic process and cellular response to starvation. From the PPI network, the 21 nodes with the highest degree were screened as hub genes. Based on Molecular Complex Detection (MCODE) plug-in, the top module was formed by ALB, TGM, PLAT, PLAU, EGF, MMP7, MMP1, LAMC2, LAMA3, LAMB3, COLA1, FAP, CDH11, COL3A1, ITGA2, and VCAN. OncoLnc survival analysis showed that, high expression of ITGA2, MMP7, ITGB4, ITGA3, VCAN and PLAU may predict poor survival results in PDAC. The present study identified hub genes and pathways in PDAC, which may be potential targets for its diagnosis, treatment, and prognostic prediction.

A nomogram for predicting survival of patients with locally advanced pancreatic cancer treated with chemoradiotherapy

BACKGROUND

We developed a nomogram for predicting survival of patients with locally advanced pancreatic cancer (LAPC) after concurrent chemoradiotherapy (CRT) using ¹⁸F-flurodeoxyglucose-positron emission tomography (FDG-PET) parameters and CA 19-9 levels.

METHODS

Based on 426 patients with LAPC who received concurrent CRT between 2004 and 2015, we investigated significant prognostic factors for survival to build a nomogram, including the maximum standardized uptake value (SUV_max) and CA 19-9 levels. Predictive accuracy and discriminative ability were then measured.

RESULTS

Median progression-free survival and overall survival (OS) were 9.4 and 15.4 months, respectively, at a median 15-month follow-up. High-dose radiation (EQD2, ≥61 Gy), initial SUV_max <3.5 and CA 19-9 ≤400 U/mL, and surgical resection after CRT were significantly related to prolonged OS by multivariate analysis (p < 0.05). A nomogram model for OS was established and showed good calibration and acceptable discrimination (c-index 0.656). Using the nomogram, 3 different prognosis groups could be identified with a median OS of 25, 15, and 11 months (p < 0.001).

CONCLUSION

A nomogram was developed with high-dose radiation (EQD2, ≥61 Gy), initial SUV_max <3.5, CA 19-9 ≤400 U/mL, and surgical resection after CRT for patients with LAPC. This will help in clinical decision-making and in selecting patients for CRT.

Patient-derived induced pluripotent stem cells for models of cancer and cancer stem cell research

Induced pluripotent stem cells (iPSCs) are embryonic stem cell-like cells reprogrammed from somatic cells by four transcription factors, OCT4, SOX2, KLF4 and c-MYC. iPSCs derived from cancer cells (cancer-iPSCs) could be a novel strategy for studying cancer. During cancer cell reprogramming, the epigenetic status of the cancer cell may be altered, such that it acquires stemness and pluripotency. The cellular behavior of the reprogrammed cells exhibits dynamic changes during the different stages of reprogramming. The cells may acquire the properties of cancer stem cells (CSCs) during the process of reprogramming, and lose their carcinogenic properties during reprogramming into a cancer-iPSCs. Differentiation of cancer-iPSCs by teratoma formation or organoid culturing could mimic the process of tumorigenesis. Some of the molecular mechanisms associated with cancer progression could be elucidated using the cancer-iPSC model. Furthermore, cancer-iPSCs could be expanded in culture system or bioreactors, and serve as cell sources for research, and as personal disease models for therapy and drug screening. This article introduces cancer studies that used the cell reprogramming strategy.

Overall survival and cancer-specific survival in patients with surgically resected pancreatic head adenocarcinoma: A competing risk nomogram analysis

Background: The objective of this study was to estimate probabilities of overall survival (OS) and cancer-specific survival (CSS) in patients with pancreatic head adenocarcinoma after surgery. In addition, we attempted to build nomograms to predict prognosis of these patients.

Methods: Patients diagnosed with surgically resected pancreatic head adenocarcinoma between 2004 and 2014 were selected for the study from the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms were established for estimating 1-, 2- and 3-year OS and CSS based on Cox regression model and Fine and Grey's model. The performance of the nomogram was measured by concordance index (C-index) and the area under receiver operating characteristic (ROC) curve (AUC).

Results: A total of 2374 patients were retrospectively collected from the SEER database. The discrimination of nomogram for OS prediction was superior to that of the Tumor-Node-Metastasis (TNM) 7th or 8th edition stage systems (C-index = 0.640, 95% CI, 0.618 - 0.662 vs 0.573, 95% CI, 0.554 - 0.593, P < 0.001; 0.640, 95% CI, 0.618 - 0.662 vs 0.596, 95% CI, 0.586 - 0.607, P < 0.001, respectively). The comparisons of values of AUC showed that the established nomograms displayed better discrimination power than TNM 7th or 8th stage systems for predicting both OS and CSS.

Conclusions: The nomograms which could predict 1-, 2- and 3-year OS and CSS were established in this study. Our nomograms showed a relatively good performance and could be served as an effective tool for prognostic evaluation of patients with pancreatic head adenocarcinoma after surgery.

Nomograms predict long-term survival for patients with periampullary adenocarcinoma after pancreatoduodenectomy

BACKGROUND

The prognosis of patients with periampullary adenocarcinoma after pancreatoduodenectomy is diverse and not yet clearly illustrated. The aim of this study was to develop a nomogram to predict individual risk of overall survival (OS) and progression-free survival (PFS) in patients with periampullary adenocarcinoma after pancreatoduodenectomy.

METHODS

A total of 205 patients with periampullary adenocarcinoma after pancreatoduodenectomy were retrospectively included. OS and PFS were evaluated by the Kaplan-Meier method. Two nomograms for predicting OS and PFS were established, and the predictive accuracy was measured by the concordance index (Cindex) and calibration plots.

RESULTS

Lymph node ratio (LNR), carbohydrate antigen 19-9 (CA19-9) and anatomical location were incorporated into the nomogram for OS prediction and LNR, CA19-9; anatomical location and tumor differentiation were incorporated into the nomogram for PFS prediction. All calibration plots for the probability of OS and PFS fit well. The Cindexes of the nomograms for OS and PFS prediction were 0.678 and 0.68, respectively. The OS and PFS survival times were stratified significantly using the nomogram-predicted survival probabilities.

CONCLUSIONS

The present nomograms for OS and PFS prediction can provide valuable information for tailored decision-making for patients with periampullary adenocarcinoma after pancreatoduodenectomy.