Preliminary clinical study of personalized neoantigen vaccine therapy for microsatellite stability (MSS)-advanced colorectal cancer.

Immunotherapy based on immune checkpoint inhibitors (ICIs) has provided revolutionary results in treating various cancers. However, its efficacy in colorectal cancer (CRC), especially in microsatellite stability-CRC, is limited. This study aimed to observe the efficacy of personalized neoantigen vaccine in treating MSS-CRC patients with recurrence or metastasis after surgery and chemotherapy. Candidate neoantigens were analyzed from whole-exome and RNA sequencing of tumor tissues. The safety and immune response were assessed through adverse events and ELISpot. The clinical response was evaluated by progression-free survival (PFS), imaging examination, clinical tumor marker detection, circulating tumor DNA (ctDNA) sequencing. Changes in health-related quality of life were measured by the FACT-C scale. A total of six MSS-CRC patients with recurrence or metastasis after surgery and chemotherapy were administered with personalized neoantigen vaccines. Neoantigen-specific immune response was observed in 66.67% of the vaccinated patients. Four patients remained progression-free up to the completion of clinical trial. They also had a significantly longer progression-free survival time than the other two patients without neoantigen-specific immune response (19 vs. 11 months). Changes in health-related quality of life improved for almost all patients after the vaccine treatment. Our results shown that personalized neoantigen vaccine therapy is likely to be a safe, feasible and effective strategy for MSS-CRC patients with postoperative recurrence or metastasis.

Establishment and Validation of a Pathologic Upgrade Prediction Nomogram Model for Gastric Low-Grade Intraepithelial Neoplasia Patients After the Eradication of Helicobacter pylori.

BACKGROUND: As yet, there is no unified method of treatment for the evaluation and management of gastric low-grade intraepithelial neoplasia (LGIN) worldwide. METHODS: Patients with gastric LGIN who had been treated with Helicobacter pylori eradication were gathered retrospectively. Based on several relevant characteristics described and analyzed by LASSO regression analysis and multivariable logistic regression, a prediction nomogram model was established. C-index, the area under the receiver operating characteristic curve (AUC), calibration plot, and decision curve analysis (DCA) were adopted to evaluate the accuracy and reliability of the model. RESULTS: A total of 309 patients with LGIN were randomly divided into the training groups and the validation groups. LASSO regression analysis and multivariable logistic regression identified that 6 variables including gender, size, location, borderline, number, and erosion were independent risk factors. The nomogram model displayed good discrimination with a C-index of .765 (95% confidence interval: .702-.828). The accuracy and reliability of the model were also verified by an AUC of .764 in the training group and .757 in the validation group. Meanwhile, the calibration curve and the DCA suggested that the predictive nomogram had promising accuracy and clinical utility. CONCLUSIONS: A predictive nomogram model was constructed and proved to be clinically applicable to identify high-risk groups with possible pathologic upgrade in patients with gastric LGIN. Since it is regarded that strengthening follow-up or endoscopic treatment of high-risk patients may contribute to improving the detection rate or reducing the incidence of gastric cancer, the predictive nomogram model provides a reliable basis for the treatment of LGIN.

DEMA: a distance-bounded energy-field minimization algorithm to model and layout biomolecular networks with quantitative features.

SUMMARY: In biology, graph layout algorithms can reveal comprehensive biological contexts by visually positioning graph nodes in their relevant neighborhoods. A layout software algorithm/engine commonly takes a set of nodes and edges and produces layout coordinates of nodes according to edge constraints. However, current layout engines normally do not consider node, edge or node-set properties during layout and only curate these properties after the layout is created. Here, we propose a new layout algorithm, distance-bounded energy-field minimization algorithm (DEMA), to natively consider various biological factors, i.e., the strength of gene-to-gene association, the gene's relative contribution weight and the functional groups of genes, to enhance the interpretation of complex network graphs. In DEMA, we introduce a parameterized energy model where nodes are repelled by the network topology and attracted by a few biological factors, i.e., interaction coefficient, effect coefficient and fold change of gene expression. We generalize these factors as gene weights, protein-protein interaction weights, gene-to-gene correlations and the gene set annotations-four parameterized functional properties used in DEMA. Moreover, DEMA considers further attraction/repulsion/grouping coefficient to enable different preferences in generating network views. Applying DEMA, we performed two case studies using genetic data in autism spectrum disorder and Alzheimer's disease, respectively, for gene candidate discovery. Furthermore, we implement our algorithm as a plugin to Cytoscape, an open-source software platform for visualizing networks; hence, it is convenient. Our software and demo can be freely accessed at http://discovery.informatics.uab.edu/dema. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Girdin influences pancreatic cancer progression by regulating c-MYC expression.

Pancreatic ductal adenocarcinoma is a complex gastrointestinal tumor with high metastatic potential and poor prognosis. Actin-binding protein Girdin is highly expressed in a variety of tumors and promotes tumorigenesis and progression. However, the mechanisms underlying the involvement of Girdin in pancreatic cancer have not been clarified. In this study, we observed that the expression of Girdin was upregulated in pancreatic cancer cells. The siRNA-mediated gene knockdown experiments showed that reduced expression of Girdin in pancreatic cancer cells inhibited cell proliferation, migration, and invasion while promoting cell apoptosis. Functional assays revealed that c-MYC overexpression in pancreatic cancer cells could significantly increase the cell proliferation ability and rates of cell migration and invasion while decreasing the apoptosis rate. It has been shown that phosphorylation plays a role in the functional regulation of the c-MYC gene. Subsequently, we examined the expression level of c-MYC in cells with manipulated expression of Girdin and identified a positive correlation between Girdin expression and c-MYC expression. Moreover, we found that Girdin knockdown in c-MYC-overexpressing pancreatic cancer cells slowed cell growth, blocked the cell cycle progression, significantly promoted apoptosis, and markedly decreased the cell migration and invasion. This finding indicated that silencing Girdin could mitigate the effect of c-MYC on promoting proliferation and metastasis of pancreatic cancer. Overall, this study provided evidence that Girdin promoted pancreatic cancer development presumably by regulating the c-MYC overexpression.

Neoantigen-reactive T cells exhibit effective anti-tumor activity against colorectal cancer.

Neoantigens play a crucial role in cancer immunotherapy. However, the effectiveness and safety of neoantigen-based immunotherapies in patients with colorectal cancer (CRC), particularly in the Chinese population, have not been well studied. This study explored the feasibility and effectiveness of neoantigens in the treatment of CRC. Whole-exome sequencing (WES) and transcriptome sequencing were used to identify somatic mutations, RNA expression, and human leukocyte antigen (HLA) alleles. Neoantigen candidates were predicted, and immunogenicity was assessed. The neoantigens TSHZ3-L523P, RARA-R83H, TP53-R248W, EYA2-V333I, and NRAS-G12D from Patient 4 (PW4); TASP1-P161L, RAP1GAP-S215R, MOSPD1-V63I, and NAV2-D1973N from Patient 10 (PW10); and HAVCR2-F39V, SEC11A-R11L, SMPDL3B-T452M, LRFN3-R118Q, and ULK1-S248L from Patient 11 (HLA-A0201+PW11) induced a heightened neoantigen-reactive T cell (NRT) response as compared with the controls in peripheral blood lymphocytes (PBLs) isolated from patients with CRC. In addition, we identified neoantigen-containing peptides SEC11A-R11L and ULK1-S248L from HLA-A0201+PW11, which more effectively elicited specific CTL responses than the corresponding native peptides in PBLs isolated from HLA-A0201+PW11 as well as in HLA-A2.1/Kb transgenic mice. Importantly, adoptive transfer of NRTs induced by vaccination with two mutant peptides could effectively inhibit tumor growth in tumor-bearing mouse models. These data indicate that neoantigen-containing peptides with high immunogenicity represent promising candidates for peptide-mediated personalized therapy.Abbreviations: CRC: colorectal cancer; DCs: dendritic cells; ELISPOT: enzyme-linked immunosorbent spot; E:T: effector:target; HLA: human leukocyte antigen; MHC: major histocompatibility complex; Mut: mutant type; NGS: next-generation sequencing; NRTs: neoantigen-reactive T cells; PBMCs: peripheral blood mononuclear cells; STR: short tandem repeat; PBLs: peripheral blood lymphocytes; PBS: phosphate-buffered saline; PD-1: programmed cell death protein 1; TILs: tumor-infiltrating lymphocytes; RNA-seq: RNA sequencing; Tg: transgenic; TMGs: tandem minigenes; WES: whole-exome sequencing; WT: wild-type.

IncRNA LUCAT1 acts as a Potential Biomarker and Demonstrates Malignant Biological Behaviors in Gastric Cancer.

BACKGROUND: Gastric cancer (GC) remains the fourth-leading malignancy worldwide and has a high mortality rate. Accumulating evidence reveals that long noncoding RNAs (lncRNAs) play essential roles in tumorigenesis and metastasis and can be used as potential biomarkers for diagnosis and prognosis. METHODS: We downloaded gene expression profiles from the National Center of Biotechnology Information Gene Expression Omnibus (GEO), screened lncRNAs differentially expressed in gastric cancer tissues and adjacent tissues, and then constructed a lncRNA-miRNA-mRNA network. Seventy patients with gastric cancer were divided into two groups according to different clinical characteristics. The expression of lncRNA LUCAT1 in gastric cancer was detected by reverse transcription polymerase chain reaction (RT-PCR). The AGS and SGC-7901 cell lines were used in CCK8 assay, apoptosis, cell cycle test, transwell assay, and wound healing assay. RESULTS: The expression level of LUCAT1 was associated with tumor diameter (p < 0.001), tissue differentiation grade (p = 0.026), and LNM status (p = 0.020) in GC. The results showed that the lncRNA LUCAT1 could promote the proliferation, invasion, and migration of GC cells, inhibit the apoptosis of GC cells, and affect the process of cell cycles. CONCLUSIONS: The lncRNA LUCAT1 may be used as a potential biomarker for early signs of LNM in GC and may play a crucial role in the development of GC.

Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review.

Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic oscillations of neural activity, which is one of the core technologies of brain-computer interface systems (BCIs). However, extraction of the features from non-linear and non-stationary EEG signals is still a challenging task in current algorithms. With the development of artificial intelligence, various advanced algorithms have been proposed for signal classification in recent years. Among them, deep neural networks (DNNs) have become the most attractive type of method due to their end-to-end structure and powerful ability of automatic feature extraction. However, it is difficult to collect large-scale datasets in practical applications of BCIs, which may lead to overfitting or weak generalizability of the classifier. To address these issues, a promising technique has been proposed to improve the performance of the decoding model based on data augmentation (DA). In this article, we investigate recent studies and development of various DA strategies for EEG classification based on DNNs. The review consists of three parts: what kind of paradigms of EEG-based on BCIs are used, what types of DA methods are adopted to improve the DNN models, and what kind of accuracy can be obtained. Our survey summarizes the current practices and performance outcomes that aim to promote or guide the deployment of DA to EEG classification in future research and development.

A Disocclusion Inpainting Framework for Depth-Based View Synthesis.

This paper proposes a disocclusion inpainting framework for depth-based view synthesis. It consists of four modules: foreground extraction, motion compensation, improved background reconstruction, and inpainting. The foreground extraction module detects the foreground objects and removes them from both depth map and rendered video; the motion compensation module guarantees the background reconstruction model to suit for moving camera scenarios; the improved background reconstruction module constructs a stable background video by exploiting the temporal correlation information in both 2D video and its corresponding depth map; and the constructed background video and inpainting module are used to eliminate the holes in the synthesized view. The analysis and experiment indicate that the proposed framework has good generality, scalability and effectiveness, which means most of the existing background reconstruction methods and image inpainting methods can be employed or extended as the modules in our framework. Our comparison results have demonstrated that the proposed framework achieves better synthesized quality, temporal consistency, and has lower running time compared to the other methods.

Overexpression of calpain­1 predicts poor outcome in patients with colorectal cancer and promotes tumor cell progression associated with downregulation of FLNA.

Several studies have demonstrated that calpain­1 is involved in a variety of pathophysiological processes, including tumorigenesis. However, the clinical relevance and role of calpain­1 in colorectal cancer (CRC) are unclear. Filamin A (FLNA) is an actin­binding protein that participates in cancer progression and can be cleaved by calpain­1. In the present study, the protein expression levels of calpain­1 and FLNA were detected by immunohistochemistry in 467 matched cancerous and paracancerous tissues from patients with CRC. The staining results and the clinicopathological characteristics of the patients were comprehensively analyzed. A high expression level of calpain­1 was strongly associated with age, metastasis, Dukes stage and survival time but not with sex, histologic grade, tumour location or tumor size. By contrast, a low expression level of FLNA was significantly associated with tumor size, histological grade, metastasis, Dukes stage and survival time, but not with age, sex, or tumor location. Kaplan­Meier survival analysis demonstrated that patients with calpain­1 overexpression had a shorter mean overall survival (OS) than patients with lower levels of calpain­1 expression. Unlike high levels of calpain­1, high levels of FLNA were associated with longer OS than lower levels of FLNA expression. Furthermore, calpain­1 expression was inversely correlated with FLNA expression. The relationship between calpain­1 and FLNA was further confirmed using CRC cell lines in vitro. When calpain­1 expression decreased in CRC cells, FLNA expression increased. Furthermore, calpain­1 knockdown in CRC cells resulted in decreased proliferation, colony formation, migration and invasion. The present findings suggest that calpain­1 overexpression predicted a poor outcome in patients with CRC and promoted tumor progression, possibly via FLNA downregulation.

The silencing of replication protein A1 induced cell apoptosis via regulating Caspase 3.

AIMS: Gastrointestinal cancers are a kind of deadly malignancy afflicting close to a million peoples worldwide. 5-Fluorouracil (5-Fu) is a main chemotherapeutic agent for cancer treatment. However, prolonged exposure of 5-Fu to cancer cells may cause chemoresistance and decrease the therapeutic potential of 5-Fu. MAIN METHODS: Replication protein A (RPA) is a component of the origin recognition complex. In our study, we explored the role of RPA1 in hepatocellular carcinoma cell SMMC-7721, gastric cancer cell SGC-7901 and colorectal cancer HT-29 via lentiviral particles infection. Flow cytometry assay was used to examine the effect of RPA1 on cell proliferation, cell cycle and apoptosis. Western blot was employed to determine the role of RPA1 on Caspase 3 expression. KEY FINDINGS: Immunohistochemstry results showed that RPA1 was highly expressed in colorectal cancer tissues. Only 5-Fu or the knockdown of RPA1 suppressed cell clone formation, induced cell cycle arrest at the G1 phase and promoted cell apoptosis by regulating the protein level of Caspase 3. And the combination of the application of 5-Fu and RPA1 silencing significantly enhanced the above effects. SIGNIFICANCE: RPA1 serves as an oncogene during gastrointestinal cancers progression. These studies reveal a new target for gastrointestinal cancers therapy, and the combination of 5-Fu and silencing of RPA1 provides a new attractive therapeutic measure for gastrointestinal cancers.

A Dynamic Survival Detection and Analysis System for Mosquito Larvae Viability in Drug Assays.

Bioinformatics studies have emerged in the domain of larval behavior analysis in recent years. A dynamic survival detection and analysis system for automatically monitoring a large amount of mosquito larvae in bioassays with multiwell plates by acquiring and processing videos is proposed in this article. In our system, equipment is designed for acquiring the video of the mosquito larvae in several multiwell plates simultaneously by a camera, and a video analysis module is developed for detecting the survival states of larvae in each well in real time. Also, a novel model and a new image registration algorithm are proposed to accurately obtain the survival state by analyzing the larval motion activities and the weights of larvae in each well. In our experiments, several spinosad bioassays against 2-instar Aedes aegypti with 96-well plates are used to evaluate the proposed system, and the accuracy of the larval survival state in our system is more than 85%. Moreover, this investigation has indicated that the developed system not only can be used in the mosquito larval bioassays but also can be suitable to detect and analyze the behaviors of large amount of other larvae.