Cancer vaccines exhibit specificity, effectiveness, and safety as an alternative immunotherapeutic strategy to struggle against malignant diseases, especially with the rapid development of mRNA cancer vaccines in recent years. However, how to maintain long-term immune memory after vaccination, especially T cells memory, to fulfill lasting surveillance against cancers, is still a challenging issue for researchers all over the world. IL-7 is critical for the development, maintenance, and proliferation of T lymphocytes, highlighting its potential role as an adjuvant in the development of cancer vaccines. Here, we summarized the IL-7/IL-7 receptor signaling in the development of T lymphocytes, the biological function of IL-7 in the maintenance and survival of T lymphocytes, the performance of IL-7 in pre-clinical and clinical trials of cancer vaccines, and the rationale to apply IL-7 as an adjuvant in cancer vaccine-based therapeutic strategy.

Pancreatic ductal adenocarcinoma is a highly lethal malignancy, which has now become the seventh most common cause of cancer death in the world, with the highest mortality rates in Europe and North America. In the past 30 years, there has been some progress in 5-year survival (rates increasing from 2.5 to 10%), but this is still extremely poor compared to all other common cancer types. Targeted therapies for advanced pancreatic cancer based on actionable mutations have been disappointing, with only 3–5% showing even a short clinical benefit. There is, however, a molecular diversity beyond mutations in genes responsible for producing classical canonical signaling pathways. Pancreatic cancer is almost unique in promoting an excess production of other components of the stroma, resulting in a complex tumor microenvironment that contributes to tumor development, progression, and response to treatment. Various transcriptional subtypes have also been described. Most notably, there is a strong alignment between the Classical/Pancreatic progenitor and Quasi-mesenchymal/Basal-like/Squamous subtype signatures of Moffit, Collinson, Bailey, Puleo, and Chan-Seng-Yue, which have potential clinical impact. Sequencing of epithelial cell populations enriched by laser capture microscopy combined with single-cell RNA sequencing has revealed the potential genomic evolution of pancreatic cancer as being a consequence of a gene expression continuum from mixed Basal-like and Classical cell populations within the same tumor, linked to allelic imbalances in mutant KRAS, with metastatic tumors being more copy number-unstable compared to primary tumors. The Basal-like subtype appears more chemoresistant with reduced survival compared to the Classical subtype. Chemotherapy and/or chemoradiation will also enrich the Basal-like subtype. Squamous/Basal-like programs facilitate immune infiltration compared with the Classical-like programs. The immune infiltrates associated with Basal and Classical type cells are distinct, potentially opening the door to differential strategies. Single-cell and spatial transcriptomics will now allow single cell profiling of tumor and resident immune cell populations that may further advance subtyping. Multiple clinical trials have been launched based on transcriptomic response signatures and molecular subtyping including COMPASS, Precision Promise, ESPAC6/7, PREDICT-PACA, and PASS1. We review several approaches to explore the clinical relevance of molecular profiling to provide optimal bench-to-beside translation with clinical impact.

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IntroductionDespite the many benefits immunotherapy has brought to patients with different cancers, its clinical applications and improvements are still hindered by drug resistance. Fostering a reliable approach to identifying sufferers who are sensitive to certain immunotherapeutic agents is of great clinical relevance.MethodsWe propose an ELISE (Ensemble Learning for Immunotherapeutic Response Evaluation) pipeline to generate a robust and highly accurate approach to predicting individual responses to immunotherapies. ELISE employed iterative univariable logistic regression to select genetic features of patients, using Monte Carlo Tree Search (MCTS) to tune hyperparameters. In each trial, ELISE selected multiple models for integration based on add or concatenate stacking strategies, including deep neural network, automatic feature interaction learning via self-attentive neural networks, deep factorization machine, compressed interaction network, and linear neural network, then adopted the best trial to generate a final approach. SHapley Additive exPlanations (SHAP) algorithm was applied to interpret ELISE, which was then validated in an independent test set.ResultRegarding prediction of responses to atezolizumab within esophageal adenocarcinoma (EAC) patients, ELISE demonstrated a superior accuracy (Area Under Curve [AUC] = 100.00%). AC005786.3 (Mean [|SHAP value|] = 0.0097) was distinguished as the most valuable contributor to ELISE output, followed by SNORD3D (0.0092), RN7SKP72 (0.0081), EREG (0.0069), IGHV4-80 (0.0063), and MIR4526 (0.0063). Mechanistically, immunoglobulin complex, immunoglobulin production, adaptive immune response, antigen binding and others, were downregulated in ELISE-neg EAC subtypes and resulted in unfavorable responses. More encouragingly, ELISE could be extended to accurately estimate the responsiveness of various immunotherapeutic agents against other cancers, including PD1/PD-L1 suppressor against metastatic urothelial cancer (AUC = 88.86%), and MAGE−A3 immunotherapy against metastatic melanoma (AUC = 100.00%).DiscussionThis study presented deep insights into integrating ensemble deep learning with self-attention as a mechanism for predicting immunotherapy responses to human cancers, highlighting ELISE as a potential tool to generate reliable approaches to individualized treatment.

Pancreatic ductal adenocarcinoma (PDAC) represents an aggressive tumor of the digestive system with still low five-year survival of less than 10%. Although there are improvements for multimodal therapy of PDAC, surgery still remains the effective way to treat the disease. Combined with adjuvant and/or neoadjuvant treatment, pancreatic surgery is able to enhance the five-year survival up to around 20%. However, pancreatic resection is always associated with a high risk of complications and regarded as one of the most complex fields in abdominal surgery. This review gives a summary on the surgical treatment for PDAC based on the current literature with a special focus on resection techniques.

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BackgroundDelayed gastric emptying (DGE) is one of the most frequent complications following pancreaticoduodenectomy. This meta-analysis aimed to evaluate the impact of Braun enteroenterostomy on DGE following pancreaticoduodenectomy.MethodsA systematic review of the literature was performed to identify relevant studies. Statistical analysis was carried out using Review Manager software 5.3.ResultsEleven studies involving 1672 patients (1005 in Braun group and 667 in non-Braun group) were included in the meta-analysis. Braun enteroenterostomy was associated with a statistically significant reduction in overall DGE (odds ratios [OR] 0.32, 95% confidence intervals [CI] 0.24 to 0.43; P <0.001), clinically significant DGE (OR 0.27, 95% CI 0.15 to 0.51; P <0.001), bile leak (OR 0.50, 95% CI 0.29 to 0.86; P = 0.01), and length of hospital stay (weighted mean difference -1.66, 95% CI -2.95 to 00.37; P = 0.01).ConclusionsBraun enteroenterostomy minimizes the rate and severity of DGE following pancreaticoduodenectomy.

Background:Although surgical resection is the recommended treatment for the patients with gastric cancer, lots of patients show advanced or metastatic gastric cancer at the time of diagnosis. Detection of gastric cancer at early stages is a huge challenge because of lack of appropriate detection tests. Unfortunately, existing clinical guidelines focusing on early diagnosis of gastric cancer do not provide consistent and prudent evidence. Serum carcinoembryonic antigen was considered as a complementary test, although it is not good enough to diagnose early gastric cancer. There are no other tumor markers recommended for diagnosing early gastric cancer. This study aims to evaluate and compare the diagnostic accuracy of 5 common tumor biomarkers (CA19–9, CA125, PG, IncRNA, and DNA methylation) and CEA and their combinations for diagnosing gastric cancer through network meta-analysis method, and to rank these tests using a superiority index.Methods:PubMed, EMBASE.com, and the Cochrane Central Register of Controlled Trials (CENTRAL) will be searched from their inception to March 2018. We will include diagnostic tests which assessed the accuracy of the above-mentioned tumor biomarkers and CEA for diagnosing gastric cancer. The risk of bias for each study will be independently assessed as low, moderate, or high using criteria adapted from Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Network meta-analysis will be performed using STATA 12.0 and R 3.4.1 software. The competing diagnostic tests will be ranked by a superiority index.Results:This study is ongoing and will be submitted to a peer-reviewed journal for publication.Conclusion:This study will provide systematically suggestions to select different tumor biomarkers for detecting the early gastric cancer.