Immunoinformatics (Amsterdam, Netherlands)最新文献

{"title":"Immune checkpoint therapy modeling of PD-1/PD-L1 blockades reveals subtle difference in their response dynamics and potential synergy in combination","authors":"Kamran Kaveh ,&nbsp;Feng Fu","doi":"10.1016/j.immuno.2021.100004","DOIUrl":"https://doi.org/10.1016/j.immuno.2021.100004","url":null,"abstract":"<div><p>Immune checkpoint therapy is one of the most promising immunotherapeutic methods that are likely able to give rise to durable treatment response for various cancer types. Despite much progress in the past decade, there are still critical open questions with particular regards to quantifying and predicting the efficacy of treatment and potential optimal regimens for combining different immune checkpoint blockades. To shed light on this issue, here we develop clinically-relevant, dynamical systems models of cancer immunotherapy with a focus on the immune checkpoint PD-1/PD-L1 blockades. Our model allows the acquisition of adaptive immune resistance in the absence of treatment, whereas immune checkpoint blockades can reverse such resistance and boost anti-tumor activities of effector cells. Our numerical analysis predicts that anti-PD-1 agents are commonly less effective than anti-PD-L1 agents for a wide range of model parameters. We also observe that combination treatment of anti-PD-1 and anti-PD-L1 blockades leads to a desirable synergistic effect. Our modeling framework lays the ground for future data-driven analysis on combination therapeutics of immune checkpoint treatment regimes and thorough investigation of optimized treatment on a patient-by-patient basis.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"1 ","pages":"Article 100004"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.immuno.2021.100004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92022448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ImmunoInformatics: at the crossroads between immunology and informatics, and beyond","authors":"Niels Halama,&nbsp;Doron Levy","doi":"10.1016/j.immuno.2021.100001","DOIUrl":"https://doi.org/10.1016/j.immuno.2021.100001","url":null,"abstract":"","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"1 ","pages":"Article 100001"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.immuno.2021.100001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92022447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CelltrackR: An R package for fast and flexible analysis of immune cell migration data","authors":"Inge M.N. Wortel ,&nbsp;Annie Y. Liu ,&nbsp;Katharina Dannenberg ,&nbsp;Jeffrey C. Berry ,&nbsp;Mark J. Miller ,&nbsp;Johannes Textor","doi":"10.1016/j.immuno.2021.100003","DOIUrl":"10.1016/j.immuno.2021.100003","url":null,"abstract":"<div><p>Visualization of cell migration via time-lapse microscopy has greatly advanced our understanding of the immune system. However, subtle differences in migration dynamics are easily obscured by biases and imaging artifacts. While several analysis methods have been suggested to address these issues, an integrated tool implementing them is currently lacking. Here, we present celltrackR, an R package containing a diverse set of state-of-the-art analysis methods for (immune) cell tracks. CelltrackR supports the complete pipeline for track analysis by providing methods for data management, quality control, extracting and visualizing migration statistics, clustering tracks, and simulating cell migration. CelltrackR supports the analysis of both 2D and 3D cell tracks. CelltrackR is an open-source package released under the GPL-2 license, and is freely available on both GitHub and CRAN. Although the package was designed specifically for immune cell migration data, many of its methods will also be of use in other research areas dealing with moving objects.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"1 ","pages":"Article 100003"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.immuno.2021.100003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9272309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating single cell sequencing with a spatial quantitative systems pharmacology model spQSP for personalized prediction of triple-negative breast cancer immunotherapy response","authors":"Shuming Zhang ,&nbsp;Chang Gong ,&nbsp;Alvaro Ruiz-Martinez ,&nbsp;Hanwen Wang ,&nbsp;Emily Davis-Marcisak ,&nbsp;Atul Deshpande ,&nbsp;Aleksander S. Popel ,&nbsp;Elana J. Fertig","doi":"10.1016/j.immuno.2021.100002","DOIUrl":"10.1016/j.immuno.2021.100002","url":null,"abstract":"<div><p>Response to cancer immunotherapies depends on the complex and dynamic interactions between T cell recognition and killing of cancer cells that are counteracted through immunosuppressive pathways in the tumor microenvironment. Therefore, while measurements such as tumor mutational burden provide biomarkers to select patients for immunotherapy, they neither universally predict patient response nor implicate the mechanisms that underlie immunotherapy resistance. Recent advances in single-cell RNA sequencing technology measure cellular heterogeneity within cells of an individual tumor but have yet to realize the promise of predictive oncology. In addition to data, mechanistic multiscale computational models are developed to predict treatment response. Incorporating single-cell data from tumors to parameterize these computational models provides deeper insights into prediction of clinical outcome in individual patients. Here, we integrate whole-exome sequencing and scRNA-seq data from Triple-Negative Breast Cancer patients to model neoantigen burden in tumor cells as input to a spatial Quantitative System Pharmacology model. The model comprises a four-compartmental Quantitative System Pharmacology sub-model to represent a whole patient and a spatial agent-based sub-model to represent tumor volumes at the cellular scale. We use the high-throughput single-cell data to model the role of antigen burden and heterogeneity relative to the tumor microenvironment composition on predicted immunotherapy response. We demonstrate how this integrated modeling and single-cell analysis framework can be used to relate neoantigen heterogeneity to immunotherapy treatment outcomes. Our results demonstrate feasibility of merging single-cell data to initialize cell states in multiscale computational models such as the spQSP for personalized prediction of clinical outcomes to immunotherapy.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"1 ","pages":"Article 100002"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.immuno.2021.100002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39565053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NetMHCphosPan - Pan-specific prediction of MHC class I antigen presentation of phosphorylated ligands","authors":"Carina Thusgaard Refsgaard ,&nbsp;Carolina Barra ,&nbsp;Xu Peng ,&nbsp;Nicola Ternette ,&nbsp;Morten Nielsen","doi":"10.1016/j.immuno.2021.100005","DOIUrl":"10.1016/j.immuno.2021.100005","url":null,"abstract":"<div><p>Post-translational modifications of proteins play a crucial part in carcinogenesis. Phosphorylated peptides have shown to be presented by MHC class I molecules and recognised by cytotoxic T cells, making them a promising target for immunotherapy. Identification of phosphorylated MHC class I ligands has so far predominantly been done using bioinformatic tools trained on unmodified peptides. Only one tool, PhosMHCpred, has been developed specifically for the prediction of phosphorylated MHC class I ligands so far and this tool has been trained only on a limited number of alleles and provides a limited peptide length coverage (only including 9-mers).</p><p>Here we propose a method, termed NetMHCphosPan, for the prediction of MHC presented phosphopeptides. The method is trained using the NNAlign_MA framework, which allows incorporating mixed data types and information leverage between data sets resulting in a greatly improved MHC and peptide length coverage and an overall increased predictive power compared to PhosMHCpred. Motif deconvolution suggested a strong preference for phosphosites to be located in position 4 of the binding motif, and enrichment of proline at P5 and arginine at P1. The improved performance, driven by the extended length and allelic coverage, of NetMHCphosPan over current state-of-the-art methods, was further validated on a large benchmark data set independent from the model development.</p><p>In conclusion, we have confirmed the high power of NNAlign_MA for motif deconvolution of complex immuno-peptidomics data and have developed a novel method for prediction of MHC presented phosphopeptides with improved predictive power and a broader peptide length and MHC coverage compared to current state-of-the-art methods. The developed method is available at <span>http://www.cbs.dtu.dk/services/NetMHCphosPan-1.0</span><svg><path></path></svg>.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"1 ","pages":"Article 100005"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.immuno.2021.100005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49638856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Immunoinformatics","authors":"Namrata Tomar","doi":"10.1007/978-1-0716-0389-5","DOIUrl":"https://doi.org/10.1007/978-1-0716-0389-5","url":null,"abstract":"","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-1-0716-0389-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"51706106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}