Nomogram-Based Chronic Kidney Disease Prediction Model for Type 1 Diabetes Mellitus Patients Using Routine Pathological Data.

Type 1 diabetes mellitus (T1DM) patients are a significant threat to chronic kidney disease (CKD) development during their life. However, there is always a high chance of delay in CKD detection because CKD can be asymptomatic, and T1DM patients bypass traditional CKD tests during their routine checkups. This study aims to develop and validate a prediction model and nomogram of CKD in T1DM patients using readily available routine checkup data for early CKD detection. This research utilized 1375 T1DM patients' sixteen years of longitudinal data from multi-center Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials conducted at 28 sites in the USA and Canada and considered 17 routinely available features. Three feature ranking algorithms, extreme gradient boosting (XGB), random forest (RF), and extremely randomized trees classifier (ERT), were applied to create three feature ranking lists, and logistic regression analyses were performed to develop CKD prediction models using these ranked feature lists to identify the best performing top-ranked features combination. Finally, the most significant features were selected to develop a multivariate logistic regression-based CKD prediction model for T1DM patients. This model was evaluated using sensitivity, specificity, accuracy, precision, and F1 score on train and test data. A nomogram of the final model was further generated for easy application in clinical practices. Hypertension, duration of diabetes, drinking habit, triglycerides, ACE inhibitors, low-density lipoprotein (LDL) cholesterol, age, and smoking habit were the top-8 features ranked by the XGB model and identified as the most important features for predicting CKD in T1DM patients. These eight features were selected to develop the final prediction model using multivariate logistic regression, which showed 90.04% and 88.59% accuracy in internal and test data validation. The proposed model showed excellent performance and can be used for CKD identification in T1DM patients during routine checkups.

iACP-MultiCNN: Multi-channel CNN based anticancer peptides identification.

Cancer is one of the most dangerous diseases in the world that often leads to misery and death. Current treatments include different kinds of anticancer therapy which exhibit different types of side effects. Because of certain physicochemical properties, anticancer peptides (ACPs) have opened a new path of treatments for this deadly disease. That is why a well-performed methodology for identifying novel anticancer peptides has great importance in the fight against cancer. In addition to the laboratory techniques, various machine learning and deep learning methodologies have developed in recent years for this task. Although these models have shown reasonable predictive ability, there's still room for improvement in terms of performance and exploring new types of algorithms. In this work, we have proposed a novel multi-channel convolutional neural network (CNN) for identifying anticancer peptides from protein sequences. We have collected data from the existing state-of-the-art methodologies and applied binary encoding for data preprocessing. We have also employed k-fold cross-validation to train our models on benchmark datasets and compared our models' performance on the independent datasets. The comparison has indicated our models' superiority on various evaluation metrics. We think our work can be a valuable asset in finding novel anticancer peptides. We have provided a user-friendly web server for academic purposes and it is publicly available at: http://103.99.176.239/iacp-cnn/.

Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features.

The multichannel electrode array used for electromyogram (EMG) pattern recognition provides good performance, but it has a high cost, is computationally expensive, and is inconvenient to wear. Therefore, researchers try to use as few channels as possible while maintaining improved pattern recognition performance. However, minimizing the number of channels affects the performance due to the least separable margin among the movements possessing weak signal strengths. To meet these challenges, two time-domain features based on nonlinear scaling, the log of the mean absolute value (LMAV) and the nonlinear scaled value (NSV), are proposed. In this study, we validate the proposed features on two datasets, the existing four feature extraction methods, variable window size, and various signal-to-noise ratios (SNR). In addition, we also propose a feature extraction method where the LMAV and NSV are grouped with the existing 11 time-domain features. The proposed feature extraction method enhances accuracy, sensitivity, specificity, precision, and F1 score by 1.00%, 5.01%, 0.55%, 4.71%, and 5.06% for dataset 1, and 1.18%, 5.90%, 0.66%, 5.63%, and 6.04% for dataset 2, respectively. Therefore, the experimental results strongly suggest the proposed feature extraction method, for taking a step forward with regard to improved myoelectric pattern recognition performance.

Accurately predicting nitrosylated tyrosine sites using probabilistic sequence information.

Post-translational modification (PTM) is defined as the enzymatic changes of proteins after the translation process in protein biosynthesis. Nitrotyrosine, which is one of the most important modifications of proteins, is interceded by the active nitrogen molecule. It is known to be associated with different diseases including autoimmune diseases characterized by chronic inflammation and cell damage. Currently, nitrotyrosine sites are identified using experimental approaches which are laborious and costly. In this study, we propose a new machine learning method called PredNitro to accurately predict nitrotyrosine sites. To build PredNitro, we use sequence coupling information from the neighboring amino acids of tyrosine residues along with a support vector machine as our classification technique.Our results demonstrates that PredNitro achieves 98.0% accuracy with more than 0.96 MCC and 0.99 AUC in both 5-fold cross-validation and jackknife cross-validation tests which are significantly better than those reported in previous studies. PredNitro is publicly available as an online predictor at: http://103.99.176.239/PredNitro.

predML-Site: Predicting Multiple Lysine PTM Sites With Optimal Feature Representation and Data Imbalance Minimization.

Identifying of post-translational modifications (PTM) is crucial in the study of computational proteomics, cell biology, pathogenesis, and drug development due to its role in many bio-molecular mechanisms. Computational methods for predicting multiple PTM at the same lysine residues, often referred to as K-PTM, is still evolving. This paper presents a novel computational tool, abbreviated as predML-Site, for predicting KPTM, such as acetylation, crotonylation, methylation, succinylation from an uncategorized peptide sample involving single, multiple, or no modification. For informative feature representation, multiple sequence encoding schemes, such as the sequence-coupling, binary encoding, k-spaced amino acid pairs, amino acid factor have been used with ANOVA and incremental feature selection. As a core predictor, a cost-sensitive SVM classifier has been adopted which effectively mitigates the effect of class-label imbalance in the dataset. predML-Site predicts multi-label PTM sites with 84.18% accuracy using the top 91 features. It has also achieved 85.34% aiming and 86.58% coverage rate which are much better than the existing state-of-the-art predictors on the same rigorous validation test. This performance indicates that predML-Site can be used as a supportive tool for further K-PTM study. For the convenience of the experimental scientists, predML-Site has been deployed as a user-friendly web-server at http://103.99.176.239/predML-Site.

Performance Analysis of Conventional Machine Learning Algorithms for Identification of Chronic Kidney Disease in Type 1 Diabetes Mellitus Patients.

Chronic kidney disease (CKD) is one of the severe side effects of type 1 diabetes mellitus (T1DM). However, the detection and diagnosis of CKD are often delayed because of its asymptomatic nature. In addition, patients often tend to bypass the traditional urine protein (urinary albumin)-based CKD detection test. Even though disease detection using machine learning (ML) is a well-established field of study, it is rarely used to diagnose CKD in T1DM patients. This research aimed to employ and evaluate several ML algorithms to develop models to quickly predict CKD in patients with T1DM using easily available routine checkup data. This study analyzed 16 years of data of 1375 T1DM patients, obtained from the Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials directed by the National Institute of Diabetes, Digestive, and Kidney Diseases, USA. Three data imputation techniques (RF, KNN, and MICE) and the SMOTETomek resampling technique were used to preprocess the primary dataset. Ten ML algorithms including logistic regression (LR), k-nearest neighbor (KNN), Gaussian naïve Bayes (GNB), support vector machine (SVM), stochastic gradient descent (SGD), decision tree (DT), gradient boosting (GB), random forest (RF), extreme gradient boosting (XGB), and light gradient-boosted machine (LightGBM) were applied to developed prediction models. Each model included 19 demographic, medical history, behavioral, and biochemical features, and every feature's effect was ranked using three feature ranking techniques (XGB, RF, and Extra Tree). Lastly, each model's ROC, sensitivity (recall), specificity, accuracy, precision, and F-1 score were estimated to find the best-performing model. The RF classifier model exhibited the best performance with 0.96 (±0.01) accuracy, 0.98 (±0.01) sensitivity, and 0.93 (±0.02) specificity. LightGBM performed second best and was quite close to RF with 0.95 (±0.06) accuracy. In addition to these two models, KNN, SVM, DT, GB, and XGB models also achieved more than 90% accuracy.

Effects of Bacille Calmette Guerin (BCG) vaccination during COVID-19 infection.

The coronavirus disease 2019 (COVID-19) is caused by the infection of highly contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as the novel coronavirus. In most countries, the containment of this virus spread is not controlled, which is driving the pandemic towards a more difficult phase. In this study, we investigated the impact of the Bacille Calmette Guerin (BCG) vaccination on the severity and mortality of COVID-19 by performing transcriptomic analyses of SARS-CoV-2 infected and BCG vaccinated samples in peripheral blood mononuclear cells (PBMC). A set of common differentially expressed genes (DEGs) were identified and seeded into their functional enrichment analyses via Gene Ontology (GO)-based functional terms and pre-annotated molecular pathways databases, and their Protein-Protein Interaction (PPI) network analysis. We further analysed the regulatory elements, possible comorbidities and putative drug candidates for COVID-19 patients who have not been BCG-vaccinated. Differential expression analyses of both BCG-vaccinated and COVID-19 infected samples identified 62 shared DEGs indicating their discordant expression pattern in their respected conditions compared to control. Next, PPI analysis of those DEGs revealed 10 hub genes, namely ITGB2, CXCL8, CXCL1, CCR2, IFNG, CCL4, PTGS2, ADORA3, TLR5 and CD33. Functional enrichment analyses found significantly enriched pathways/GO terms including cytokine activities, lysosome, IL-17 signalling pathway, TNF-signalling pathways. Moreover, a set of identified TFs, miRNAs and potential drug molecules were further investigated to assess their biological involvements in COVID-19 and their therapeutic possibilities. Findings showed significant genetic interactions between BCG vaccination and SARS-CoV-2 infection, suggesting an interesting prospect of the BCG vaccine in relation to the COVID-19 pandemic. We hope it may potentially trigger further research on this critical phenomenon to combat COVID-19 spread.

Computational identification of multiple lysine PTM sites by analyzing the instance hardness and feature importance.

Identification of post-translational modifications (PTM) is significant in the study of computational proteomics, cell biology, pathogenesis, and drug development due to its role in many bio-molecular mechanisms. Though there are several computational tools to identify individual PTMs, only three predictors have been established to predict multiple PTMs at the same lysine residue. Furthermore, detailed analysis and assessment on dataset balancing and the significance of different feature encoding techniques for a suitable multi-PTM prediction model are still lacking. This study introduces a computational method named 'iMul-kSite' for predicting acetylation, crotonylation, methylation, succinylation, and glutarylation, from an unrecognized peptide sample with one, multiple, or no modifications. After successfully eliminating the redundant data samples from the majority class by analyzing the hardness of the sequence-coupling information, feature representation has been optimized by adopting the combination of ANOVA F-Test and incremental feature selection approach. The proposed predictor predicts multi-label PTM sites with 92.83% accuracy using the top 100 features. It has also achieved a 93.36% aiming rate and 96.23% coverage rate, which are much better than the existing state-of-the-art predictors on the validation test. This performance indicates that 'iMul-kSite' can be used as a supportive tool for further K-PTM study. For the convenience of the experimental scientists, 'iMul-kSite' has been deployed as a user-friendly web-server at http://103.99.176.239/iMul-kSite .

predForm-Site: Formylation site prediction by incorporating multiple features and resolving data imbalance.

Formylation is one of the newly discovered post-translational modifications in lysine residue which is responsible for different kinds of diseases. In this work, a novel predictor, named predForm-Site, has been developed to predict formylation sites with higher accuracy. We have integrated multiple sequence features for developing a more informative representation of formylation sites. Moreover, decision function of the underlying classifier have been optimized on skewed formylation dataset during prediction model training for prediction quality improvement. On the dataset used by LFPred and Formator predictor, predForm-Site achieved 99.5% sensitivity, 99.8% specificity and 99.8% overall accuracy with AUC of 0.999 in the jackknife test. In the independent test, it has also achieved more than 97% sensitivity and 99% specificity. Similarly, in benchmarking with recent method CKSAAP_FormSite, the proposed predictor significantly outperformed in all the measures, particularly sensitivity by around 20%, specificity by nearly 30% and overall accuracy by more than 22%. These experimental results show that the proposed predForm-Site can be used as a complementary tool for the fast exploration of formylation sites. For convenience of the scientific community, predForm-Site has been deployed as an online tool, accessible at http://103.99.176.239:8080/predForm-Site.

A new approach to expert reviewer detection and product rating derivation from online experiential product reviews.

Consumer reviews have emerged as one of the most influential factors in a person's purchase behavior. The existing open-source approaches for detecting expert reviewers and determining product ratings suffer from limitations and are susceptible to manipulation. In this work, we addressed these limitations by developing two algorithms and evaluated them on three datasets from amazon.com (the largest dataset contains nearly eight million reviews). In the first algorithm, we used a combination of the existing open-source approaches such as filtering by volume of contribution, helpfulness ratio, volume of helpfulness, and deviation from the estimated actual rating to detect the experts. The second algorithm is based on link analytic mutual iterative reinforcement of product ratings and reviewers' weights. In the second algorithm, both reviewers and products carry weights reflecting their relative importance. The reviewers influence the product rating according to their weight. Similarly, the reviewers' weights are impacted by their amount of deviation from the estimated actual product rating and the product's weight. Our evaluation using three datasets from amazon.com found the second algorithm superior to the other algorithms in detecting experts and deriving product ratings, significantly reducing the avg. error and avg. Mean Squared Error of the experts over the best of the other algorithms even after maintaining similar product coverage and quantity of reviews.

Force-Invariant Improved Feature Extraction Method for Upper-Limb Prostheses of Transradial Amputees.

A force-invariant feature extraction method derives identical information for all force levels. However, the physiology of muscles makes it hard to extract this unique information. In this context, we propose an improved force-invariant feature extraction method based on nonlinear transformation of the power spectral moments, changes in amplitude, and the signal amplitude along with spatial correlation coefficients between channels. Nonlinear transformation balances the forces and increases the margin among the gestures. Additionally, the correlation coefficient between channels evaluates the amount of spatial correlation; however, it does not evaluate the strength of the electromyogram signal. To evaluate the robustness of the proposed method, we use the electromyogram dataset containing nine transradial amputees. In this study, the performance is evaluated using three classifiers with six existing feature extraction methods. The proposed feature extraction method yields a higher pattern recognition performance, and significant improvements in accuracy, sensitivity, specificity, precision, and F1 score are found. In addition, the proposed method requires comparatively less computational time and memory, which makes it more robust than other well-known feature extraction methods.

System biology and bioinformatics pipeline to identify comorbidities risk association: Neurodegenerative disorder case study.

Alzheimer's disease (AD) is the commonest progressive neurodegenerative condition in humans, and is currently incurable. A wide spectrum of comorbidities, including other neurodegenerative diseases, are frequently associated with AD. How AD interacts with those comorbidities can be examined by analysing gene expression patterns in affected tissues using bioinformatics tools. We surveyed public data repositories for available gene expression data on tissue from AD subjects and from people affected by neurodegenerative diseases that are often found as comorbidities with AD. We then utilized large set of gene expression data, cell-related data and other public resources through an analytical process to identify functional disease links. This process incorporated gene set enrichment analysis and utilized semantic similarity to give proximity measures. We identified genes with abnormal expressions that were common to AD and its comorbidities, as well as shared gene ontology terms and molecular pathways. Our methodological pipeline was implemented in the R platform as an open-source package and available at the following link: https://github.com/unchowdhury/AD_comorbidity. The pipeline was thus able to identify factors and pathways that may constitute functional links between AD and these common comorbidities by which they affect each others development and progression. This pipeline can also be useful to identify key pathological factors and therapeutic targets for other diseases and disease interactions.

predPhogly-Site: Predicting phosphoglycerylation sites by incorporating probabilistic sequence-coupling information into PseAAC and addressing data imbalance.

Post-translational modification (PTM) involves covalent modification after the biosynthesis process and plays an essential role in the study of cell biology. Lysine phosphoglycerylation, a newly discovered reversible type of PTM that affects glycolytic enzyme activities, and is responsible for a wide variety of diseases, such as heart failure, arthritis, and degeneration of the nervous system. Our goal is to computationally characterize potential phosphoglycerylation sites to understand the functionality and causality more accurately. In this study, a novel computational tool, referred to as predPhogly-Site, has been developed to predict phosphoglycerylation sites in the protein. It has effectively utilized the probabilistic sequence-coupling information among the nearby amino acid residues of phosphoglycerylation sites along with a variable cost adjustment for the skewed training dataset to enhance the prediction characteristics. It has achieved around 99% accuracy with more than 0.96 MCC and 0.97 AUC in both 10-fold cross-validation and independent test. Even, the standard deviation in 10-fold cross-validation is almost negligible. This performance indicates that predPhogly-Site remarkably outperformed the existing prediction tools and can be used as a promising predictor, preferably with its web interface at http://103.99.176.239/predPhogly-Site.

COVID-19 health safety practices: Influence on grocery shopping behavior.

During COVID-19 lockdown, individuals were asked to leave their home only to meet the most urgent needs, such as grocery purchases and medical emergencies. This study aimed to know the consumers' health safety practices and their concerns toward grocery shopping and to know their adoption of healthier food as a result of the outbreak. An online survey was conducted during the second month of the COVID-19 lockdown. This study includes 212 respondents. Appropriate statistical tools were used to analyze the data. The findings of the study revealed that females were ahead compared to males in pursuing health safety practices during grocery shopping, but the frequency of following physical distancing for both males and females was not up to the mark. The most important concern about grocery shopping was fear of unavailability of stocks and fear of getting infected from grocery storekeepers. It was also found that, compared to earlier, people had reduced their frequency of grocery shopping and tried to shop quickly and efficiently. People bought more packaged foods and also made purchases from brands that were new to them. As a result of the COVID-19 pandemic, the adoption of healthier food habits varied significantly with gender, age, and household income of the respondents. This study indicates that there is a need to raise awareness among people on how to shop safely in grocery stores and that good hygiene practice should be followed in grocery stores to mitigate the risk of infection to consumers.

MEK inhibition reprograms CD8+ T lymphocytes into memory stem cells with potent antitumor effects.

Regenerative stem cell-like memory (TSCM) CD8+ T cells persist longer and produce stronger effector functions. We found that MEK1/2 inhibition (MEKi) induces TSCM that have naive phenotype with self-renewability, enhanced multipotency and proliferative capacity. This is achieved by delaying cell division and enhancing mitochondrial biogenesis and fatty acid oxidation, without affecting T cell receptor-mediated activation. DNA methylation profiling revealed that MEKi-induced TSCM cells exhibited plasticity and loci-specific profiles similar to bona fide TSCM isolated from healthy donors, with intermediate characteristics compared to naive and central memory T cells. Ex vivo, antigenic rechallenge of MEKi-treated CD8+ T cells showed stronger recall responses. This strategy generated T cells with higher efficacy for adoptive cell therapy. Moreover, MEKi treatment of tumor-bearing mice also showed strong immune-mediated antitumor effects. In conclusion, we show that MEKi leads to CD8+ T cell reprogramming into TSCM that acts as a reservoir for effector T cells with potent therapeutic characteristics.

Topical vitamin C, vitamin E, and acetylcysteine as corneal wound healing agents: A comparative study.

PURPOSE: The purpose of this study was to evaluate wound healing abilities and efficacy of topical Vitamin C, Vitamin E, and acetylcysteine for their possible clinical use. METHODS: The study was conducted on 36 eyes of 18 single-breed rabbits, Oryctolagus cuniculus, of both sexes. A 7.5 mm calibrated vacuum corneal trephine was used to induce a defect of 100 micron depth in center of both the corneas. The right eye of rabbits was selected as the control eye and the left eye as the trial eye, which received eyedrops as Group 1-10% Vitamin C eyedrops, Group 2-3% acetylcysteine eye drops, and Group 3-1% Vitamin E eye drops. Control eyes received ringer lactate. Evaluation was done for fluorescein stain positivity, epithelial defect size, and corneal haze on Day 2, Day 7, and Day 14 post induction of the epithelial defect. RESULTS: On day 14, three eyes of control group, all Vitamin C and acetylcysteine treated eyes, and four Vitamin E treated eyes were fluorescein stain negative. The mean defect area on day 14 was 0, 0, 0.13, and 1.86 mm2 in Vitamin C, Vitamin E, acetylcysteine, and control eyes, respectively. Vitamin C and Vitamin E control corneal haze better than acetylcysteine in experimentally induced corneal wounds in rabbits. CONCLUSION: The three trial drugs with different mechanism of action showed similar effect on healing of the experimentally created corneal wounds in rabbits, with comparison showing statistical insignificance.

5-Fluorouracil in combination with deoxyribonucleosides and deoxyribose as possible therapeutic options for the Coronavirus, COVID-19 infection.

The recent global pandemic created by the Coronavirus SARS-CoV-2, started in Wuhan, China in December 2019, has generated panic, both in term of human death (4-5% of infected patients identified through testing) and the global economy. Human sufferings seem to be continuing, and it is not clear how long this will continue and how much more destruction it is going to cause until complete control is achieved. One of the most disturbing issues is Covid-19 treatment; although a large number of medications, previously used successfully with other viruses (including Chinese herbal medicines and anti-malaria drugs), are under consideration, there remain questions as to whether they can play a satisfactory role for this disease. Global attempts are ongoing to find the drugs for the treatment of this virus but none of the antiviral drugs used for treatment of other human viral infection is working and hence attempts to find new drugs are continuing. Here the author is proposing that 5-Fluorouracil (5-FU) which when used on its own is failing as an antiviral agent due to the removal of this compound by proof reading ability exceptionally found in Coronaviruses. The author here is proposing to test 5-FU in combination with a number of deoxynucleosides on animal models infected with this Covid-19. Should encouraging results ensue, therapies could then be tried on patients.

Author Correction: PD-1 blockade in subprimed CD8 cells induces dysfunctional PD-1+CD38hi cells and anti-PD-1 resistance.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

PD-1 blockade in subprimed CD8 cells induces dysfunctional PD-1+CD38hi cells and anti-PD-1 resistance.

Understanding resistance to antibody to programmed cell death protein 1 (PD-1; anti-PD-1) is crucial for the development of reversal strategies. In anti-PD-1-resistant models, simultaneous anti-PD-1 and vaccine therapy reversed resistance, while PD-1 blockade before antigen priming abolished therapeutic outcomes. This was due to induction of dysfunctional PD-1+CD38hi CD8+ cells by PD-1 blockade in suboptimally primed CD8 cell conditions induced by tumors. This results in erroneous T cell receptor signaling and unresponsiveness to antigenic restimulation. On the other hand, PD-1 blockade of optimally primed CD8 cells prevented the induction of dysfunctional CD8 cells, reversing resistance. Depleting PD-1+CD38hi CD8+ cells enhanced therapeutic outcomes. Furthermore, non-responding patients showed more PD-1+CD38+CD8+ cells in tumor and blood than responders. In conclusion, the status of CD8+ T cell priming is a major contributor to anti-PD-1 therapeutic resistance. PD-1 blockade in unprimed or suboptimally primed CD8 cells induces resistance through the induction of PD-1+CD38hi CD8+ cells that is reversed by optimal priming. PD-1+CD38hi CD8+ cells serve as a predictive and therapeutic biomarker for anti-PD-1 treatment. Sequencing of anti-PD-1 and vaccine is crucial for successful therapy.

Antigen-Specific Antitumor Responses Induced by OX40 Agonist Are Enhanced by the IDO Inhibitor Indoximod.

Although an immune response to tumors may be generated using vaccines, so far, this approach has only shown minimal clinical success. This is attributed to the tendency of cancer to escape immune surveillance via multiple immune suppressive mechanisms. Successful cancer immunotherapy requires targeting these inhibitory mechanisms along with enhancement of antigen-specific immune responses to promote sustained tumor-specific immunity. Here, we evaluated the effect of indoximod, an inhibitor of the immunosuppressive indoleamine-(2,3)-dioxygenase (IDO) pathway, on antitumor efficacy of anti-OX40 agonist in the context of vaccine in the IDO- TC-1 tumor model. We demonstrate that although the addition of anti-OX40 to the vaccine moderately enhances therapeutic efficacy, incorporation of indoximod into this treatment leads to enhanced tumor regression and cure of established tumors in 60% of treated mice. We show that the mechanisms by which the IDO inhibitor leads to this therapeutic potency include (i) an increment of vaccine-induced tumor-infiltrating effector T cells that is facilitated by anti-OX40 and (ii) a decrease of IDO enzyme activity produced by nontumor cells within the tumor microenvironment that results in enhancement of the specificity and the functionality of vaccine-induced effector T cells. Our findings suggest a translatable strategy to enhance the overall efficacy of cancer immunotherapy. Cancer Immunol Res; 6(2); 201-8. ©2018 AACR.