Human equilibrative nucleoside transporter-1 expression is a predictor in patients with resected pancreatic cancer treated with adjuvant S-1 chemotherapy

Machine Learning of Histopathological Images Predicts Recurrences of Resected Pancreatic Ductal Adenocarcinoma With Adjuvant Treatment

OBJECTIVES

Pancreatic ductal adenocarcinoma is an intractable disease with frequent recurrence after resection and adjuvant therapy. The present study aimed to clarify whether artificial intelligence-assisted analysis of histopathological images can predict recurrence in patients with pancreatic ductal adenocarcinoma who underwent resection and adjuvant chemotherapy with tegafur/5-chloro-2,4-dihydroxypyridine/potassium oxonate.

MATERIALS AND METHODS

Eighty-nine patients were enrolled in the study. Machine-learning algorithms were applied to 10-billion-scale pixel data of whole-slide histopathological images to generate key features using multiple deep autoencoders. Areas under the curve were calculated from receiver operating characteristic curves using a support vector machine with key features alone and by combining with clinical data (age and carbohydrate antigen 19-9 and carcinoembryonic antigen levels) for predicting recurrence. Supervised learning with pathological annotations was conducted to determine the significant features for predicting recurrence.

RESULTS

Areas under the curves obtained were 0.73 (95% confidence interval, 0.59-0.87) by the histopathological data analysis and 0.84 (95% confidence interval, 0.73-0.94) by the combinatorial analysis of histopathological data and clinical data. Supervised learning model demonstrated that poor tumor differentiation was significantly associated with recurrence.

CONCLUSIONS

Results indicate that machine learning with the integration of artificial intelligence-driven evaluation of histopathological images and conventional clinical data provides relevant prognostic information for patients with pancreatic ductal adenocarcinoma.

The prognostic impact of ABO blood type in pancreatic cancer: Relevance to adjuvant chemotherapy

BACKGROUND/PURPOSE

The aim of this study was to investigate the prognostic impact of ABO blood type in resected pancreatic cancer (PC), with a focus on adjuvant chemotherapy.

METHODS

We retrospectively analyzed 510 patients who underwent pancreatectomy for PC between 2006 and 2017. Survival outcomes were investigated according to blood type and adjuvant chemotherapy regimen (S-1, gemcitabine, or no adjuvant chemotherapy).

RESULTS

Among the 510 patients, the overall survival (OS) of patients with blood type O was significantly better compared to those with blood type non-O (5-year OS rate, 46.6% vs 30.5%, P = .025). In 241 patients treated with adjuvant S-1, the 5-year OS of patients with blood type O was significantly better than those with blood type non-O (70.7% vs 44.2%, P = .001). Multivariate analysis showed that blood type non-O was an independent prognostic factor for OS in the overall cohort (hazard ratio [HR]: 1.58, P = .002) and in patients treated with adjuvant S-1 (HR: 2.99, P < .001). In patients treated with adjuvant gemcitabine or no adjuvant chemotherapy, there was no significant difference in OS between patients with blood type O and those with blood type non-O.

CONCLUSIONS

Blood type O predicted favorable survival in patients with resected PC, which was associated with adjuvant S-1.

Concordance of human equilibrative nucleoside transporter-1 expressions between murine (10D7G2) and rabbit (SP120) antibodies and association with clinical outcomes of adjuvant chemotherapy for pancreatic cancer: A collaborative study from the JASPAC 01 trial

BACKGROUND

Expression of human equilibrative nucleoside transporter-1 (hENT1) is reported to predict survival of gemcitabine (GEM)-treated patients. However, predictive values of immunohistochemical hENT1 expression may differ according to the antibodies, 10D7G2 and SP120.

AIM

We aimed to investigate the concordance of immunohistochemical hENT1 expression between the two antibodies and prognosis.

METHODS

The subjects of this study were totally 332 whose formalin-fixed paraffin-embedded specimens and/or unstained sections were obtained. The individual H-scores and four classifications according to the staining intensity were applied for the evaluation of hENT1 expression by 10D7G2 and SP120, respectively.

RESULTS

The highest concordance rate (79.8%) was obtained when the cut-off between high and low hENT1 expression using SP120 was set between moderate and strong. There were no correlations of hENT1 mRNA level with H-score (p = .258). Although the hENT1 mRNA level was significantly different among four classifications using SP120 (p = .011), there was no linear relationship among them. Multivariate analyses showed that adjuvant GEM was a significant predictor of the patients with low hENT1 expression using either 10D7G2 (Hazard ratio [HR] 2.39, p = .001) or SP120 (HR 1.84, p < .001). In contrast, agent for adjuvant chemotherapy was not significant predictor for the patients with high hENT1 expression regardless of the kind of antibody.

CONCLUSION

The present study suggests that the two antibodies for evaluating hENT1 expression are equivalent depending on the cut-off point and suggests that S-1 is the first choice of adjuvant chemotherapy for pancreatic cancer with low hENT1 expression, whereas either S-1 or GEM can be introduced for the pancreatic cancer with high hENT1 expression, no matter which antibody is used.

Nucleoside transporter-guided cytarabine-conjugated liposomes for intracellular methotrexate delivery and cooperative choriocarcinoma therapy

Gestational trophoblastic tumors seriously endanger child productive needs and the health of women in childbearing age. Nanodrug-based therapy mediated by transporters provides a novel strategy for the treatment of trophoblastic tumors. Focusing on the overexpression of human equilibrative nucleoside transporter 1 (ENT1) on the membrane of choriocarcinoma cells (JEG-3), cytarabine (Cy, a substrate of ENT1)-grafted liposomes (Cy-Lipo) were introduced for the targeted delivery of methotrexate (Cy-Lipo@MTX) for choriocarcinoma therapy in this study. ENT1 has a high affinity for Cy-Lipo and can mediate the endocytosis of the designed nanovehicles into JEG-3 cells. The ENT1 protein maintains its transportation function through circulation and regeneration during endocytosis. Therefore, Cy-Lipo-based formulations showed high tumor accumulation and retention in biodistribution studies. More importantly, the designed DSPE-PEG2k-Cy conjugation exhibited a synergistic therapeutic effect on choriocarcinoma. Finally, Cy-Lipo@MTX exerted an extremely powerful anti-choriocarcinoma effect with fewer side effects. This study suggests that the overexpressed ENT1 on choriocarcinoma cells holds great potential as a high-efficiency target for the rational design of active targeting nanotherapeutics.

Targeting Pyrimidine Metabolism in the Era of Precision Cancer Medicine

Metabolic rewiring is considered as a primary feature of cancer. Malignant cells reprogram metabolism pathway in response to various intrinsic and extrinsic drawback to fuel cell survival and growth. Among the complex metabolic pathways, pyrimidine biosynthesis is conserved in all living organism and is necessary to maintain cellular fundamental function (i.e. DNA and RNA biosynthesis). A wealth of evidence has demonstrated that dysfunction of pyrimidine metabolism is closely related to cancer progression and numerous drugs targeting pyrimidine metabolism have been approved for multiple types of cancer. However, the non-negligible side effects and limited efficacy warrants a better strategy for negating pyrimidine metabolism in cancer. In recent years, increased studies have evidenced the interplay of oncogenic signaling and pyrimidine synthesis in tumorigenesis. Here, we review the recent conceptual advances on pyrimidine metabolism, especially dihydroorotate dehydrogenase (DHODH), in the framework of precision oncology medicine and prospect how this would guide the development of new drug precisely targeting the pyrimidine metabolism in cancer.

An EMT-Related Gene Signature for Predicting Response to Adjuvant Chemotherapy in Pancreatic Ductal Adenocarcinoma

Background

For pancreatic ductal adenocarcinoma (PDAC) patients, chemotherapy failure is the major reason for postoperative recurrence and poor outcomes. Establishment of novel biomarkers and models for predicting chemotherapeutic efficacy may provide survival benefits by tailoring treatments.

Methods

Univariate cox regression analysis was employed to identify EMT-related genes with prognostic potential for DFS. These genes were subsequently submitted to LASSO regression analysis and multivariate cox regression analysis to identify an optimal gene signature in TCGA training cohort. The predictive accuracy was assessed by Kaplan–Meier (K-M), receiver operating characteristic (ROC) and calibration curves and was validated in PACA-CA cohort and our local cohort. Pathway enrichment and function annotation analyses were conducted to illuminate the biological implication of this risk signature.

Results

LASSO and multivariate Cox regression analyses selected an 8-gene signature comprised DLX2, FGF9, IL6R, ITGB6, MYC, LGR5, S100A2, and TNFSF12. The signature had the capability to classify PDAC patients with different DFS, both in the training and validation cohorts. It provided improved DFS prediction compared with clinical indicators. This signature was associated with several cancer-related pathways. In addition, the signature could also predict the response to immune-checkpoint inhibitors (ICIs)-based immunotherapy.

Conclusion

We established a novel EMT-related gene signature that was capable of predicting therapeutic response to adjuvant chemotherapy and immunotherapy. This signature might facilitate individualized treatment and appropriate management of PDAC patients.

Diagnostic, Predictive and Prognostic Molecular Biomarkers in Pancreatic Cancer: An Overview for Clinicians

Simple Summary

Pancreatic cancer is the fourth most common cancer-related cause of death in the United States and is usually asymptomatic in early stages. There is a scarcity of tests that facilitate early diagnosis or accurately predict the disease progression. To this end, biomarkers have been identified as important tools in the diagnosis and management of pancreatic cancer. Despite the increasing number of biomarkers described in the literature, most of them have demonstrated moderate sensitivity and/or specificity and are far from being considered as screening tests. More efficient non-invasive biomarkers are needed to facilitate early-stage diagnosis and interventions. Multi-disciplinary collaboration might be required to facilitate the identification of such markers.

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is the most common pancreatic malignancy and is associated with aggressive tumor behavior and poor prognosis. Most patients with PDAC present with an advanced disease stage and treatment-resistant tumors. The lack of noninvasive tests for PDAC diagnosis and survival prediction mandates the identification of novel biomarkers. The early identification of high-risk patients and patients with PDAC is of utmost importance. In addition, the identification of molecules that are associated with tumor biology, aggressiveness, and metastatic potential is crucial to predict survival and to provide patients with personalized treatment regimens. In this review, we summarize the current literature and focus on newer biomarkers, which are continuously added to the armamentarium of PDAC screening, predictive tools, and prognostic tools.

Score for predicting overall survival in pancreatic adenocarcinoma patients with positive lymph nodes after surgery: a novel nomogram-based risk assessment

Background

Pancreatic adenocarcinoma (PaC) patients with positive lymph nodes (PLNs) have a dismal prognosis and lack a specific prognostic stage. This study aimed to construct a nomogram for the prediction of overall survival (OS) in these patients.

Methods

A total of 1,340 patients screened from the Surveillance, Epidemiology, and End Results database were included and randomly divided at a ratio of 7:3 into a training set (n=940) and an internal validation set (n=400). Cox regression analyses were conducted to select independent predictors in the training set, and a nomogram was constructed. The model was verified in the internal validation set and in an external validation set, which comprised 64 patients from a Chinese institute.

Results

Six independent prognostic factors (age at diagnosis, tumor grade, lymph node ratio, T stage, radiotherapy, and chemotherapy) were identified in PaC patients with PLNs and were entered into the nomogram. The final model had a higher C-index for predicting OS than the American Joint Committee on Cancer-8th edition staging system (training set: 0.658 vs. 0.546; internal validation set: 0.661 vs. 0.546; external validation set: 0.691 vs. 0.581). The 1-, 2-, and 3-year area under the receiver operating characteristic curve values indicated better discrimination power for the established nomogram with respect to the prediction of OS in the training, internal validation, and external validation sets than for the American Joint Committee on Cancer-8th edition staging system. Furthermore, the nomogram performed well in both calibration and decision curve analyses (DCA) of clinical applicability. OS in PaC patients with PLNs was significantly distinguished among the three risk groups stratified according to the nomogram score (P<0.001).

Conclusions

The well-calibrated nomogram was determined to be extremely efficient in predicting survival, and defining a high-risk population based on the nomogram score among PaC patients with PLNs after surgery.

Research advances of intracellular mechanisms underlying gemcitabine resistance in pancreatic cancer

Pancreatic cancer is one of the most deadly malignant tumors that endanger human health, and pancreatic ductal adenocarcinoma (PDAC) is the most common histological type. Due to the lack of specific clinical symptoms, physical signs, and effective screening biomarkers for early stage PDAC, only 15%-20% of patients are qualified for surgical resection. Consequently, gemcitabine (GEM)-based monotherapy or combination therapy is still the most important or even the only treatment option. However, the overall response rate of PDAC to GEM is less than 20%, and GEM resistance is one of the most important factors affecting the efficacy of chemotherapy. At present, the mechanism of GEM resistance has not been clarified, which may involve congenital and acquired regulation. The heterogeneity of PDAC further increases its complexity. However, regulation of intracellular signaling pathways is the ultimate event to induce GEM resistance. This article will review the recent advances in research of GEM metabolism and regulation of signaling pathways in PDAC cells, and discuss potential GEM chemosensitization strategies, in order to improve the effective rate of chemotherapy and the outcome.

Human equilibrative nucleoside transporter-1 expression is a predictor in patients with resected pancreatic cancer treated with adjuvant S-1 chemotherapy

The high expression of human equilibrative nucleoside transporter-1 (hENT1) and the low expression of dihydropyrimidine dehydrogenase (DPD) are reported to predict a favorable prognosis in patients treated with gemcitabine (GEM) and 5-fluorouracil (5FU) as the adjuvant setting, respectively. The expression of hENT1 and DPD were analyzed in patients registered in the JASPAC 01 trial, which showed a better survival of S-1 over GEM as adjuvant chemotherapy after resection for pancreatic cancer, and their possible roles for predicting treatment outcomes and selecting a chemotherapeutic agent were investigated. Intensity of hENT1 and DPD expression was categorized into no, weak, moderate or strong by immunohistochemistry staining, and the patients were classified into high (strong/moderate) and low (no/weak) groups. Specimens were available for 326 of 377 (86.5%) patients. High expression of hENT1 and DPD was detected in 100 (30.7%) and 63 (19.3%) of 326 patients, respectively. In the S-1 arm, the median overall survival (OS) with low hENT1, 58.0 months, was significantly better than that with high hENT1, 30.9 months (hazard ratio 1.75, P = 0.007). In contrast, there were no significant differences in OS between DPD low and high groups in the S-1 arm and neither the expression levels of hENT1 nor DPD revealed a relationship with treatment outcomes in the GEM arm. The present study did not show that the DPD and hENT1 are useful biomarkers for choosing S-1 or GEM as adjuvant chemotherapy. However, hENT1 expression is a significant prognostic factor for survival in the S-1 arm.