Neoantigen screening identifies EGFR L858R mutation immunogenicity in 1862 Chinese NSCLC patients.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e21512-e21512
Author(s):  
Jin Lin ◽  
Yu Chen ◽  
Gang Chen ◽  
Liu Jun ◽  
Shiguang Hao ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Binding Affinity ◽  
Immune Cell ◽  
Cell Types ◽  
Hla Typing ◽  
Chinese Patients ◽  
Sequencing Data ◽  
Immunological Mechanisms ◽  
L858r Mutation ◽  
Nsclc Patients

e21512 Background: Therapeutic vaccines targeting mutation-derived neoantigens prime T-cell responses and deliver long-term clinical benefit to patients. Last year we reported EGFR L858R mutation could be a possible target for individual-independent vaccine in 799 Chinese patients with non-small cell lung cancer (NSCLC). Here we verified the findings and explored possible immunological mechanisms in a larger sample size of 1862 Chinese NSCLC. Peripheral blood or normal tissue was used as control. Methods: DNA sequencing data was acquired using our targeted 1021-gene panel and HLA-I genotyping was determined based on DNA sequencing by OptiType v1.0. The peptide-HLA binding affinity was predicted with netMHCpan v4.0. Neoantigen was identified if the IC50 MT < 500 nM and IC50 WT > 500 nM, furthermore, IC50 MT < 50 nM was considered as strong-binders. Results: EGFR (50.05%) was the most prevalent mutated gene, with 22.61% and 13.16% harboring L858R mutations and E746_A750del respectively. HLA typing showed HLA-A*11:01(42.59%) was the top one allele and HLA-A*33:03(12.94%) ranked 12th. The combination of EGFR L858R and HLA-A*33:03 had both relatively strongest binding affinity (IC50 MT = 22.9 nM and IC50 WT = 12734.0 nM, 2.93%) and highest shared frequency (2.93%). In terms of mechanism, we examined 7 of 1862 (0.37%) B2M gene mutation and 1092 of 1731 (63.1%) HLA-I LOH (loss of heterozygosity) in our population, but didn’t see any statistical difference between L858R subtype and other position mutations in EGFR. We also calculated 26 immune cell types’ signature scores from Chinese CHOICE study and found L858R subtype was associated with elevated level of pDC (KW-test p = 0.02) and lower level of neutrophils signature(KW-test p = 0.07) compared with exon 19 deletion and other uncommon mutations of EGFR. Conclusions: (1) Targeting EGFR L858R mutation in patients with HLA-A*33:03 allele appeared to be valuable for the neoantigen-based vaccine designed for Chinese NSCLC patients. (2) The specific immunogenicity of L858R seemed to be little influenced by B2M mutation and HLA-I LOH which were important factors for neoantigen presentation. (3) High pDC and low neutrophils infiltration might contribute to the inhibitive microenvironment associated with L858R mutation.

484 Bioturing browser: interactively explore public single cell sequencing data

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A520-A520
Author(s):  
Son Pham ◽  
Tri Le ◽  
Tan Phan ◽  
Minh Pham ◽  
Huy Nguyen ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Cell Types ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Data Formats ◽  
Cancer Types ◽  
Cell Data

BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual datasets and meta-analysis.MethodsN/AResultsWe present BioTuring Browser, an interactive platform for accessing and reanalyzing published single-cell omics data. The platform is currently hosting a curated database of more than 10 million cells from 247 projects, covering more than 120 immune cell types and subtypes, and 15 different cancer types. All data are processed and annotated with standardized labels of cell types, diseases, therapeutic responses, etc. to be instantly accessed and explored in a uniform visualization and analytics interface. Based on this massive curated database, BioTuring Browser supports searching similar expression profiles, querying a target across datasets and automatic cell type annotation. The platform supports single-cell RNA-seq, CITE-seq and TCR-seq data. BioTuring Browser is now available for download at www.bioturing.com.ConclusionsN/A

scholarly journals GSDMA drives the most replicated association with asthma in naïve CD4+ T cells

2019 ◽  
Author(s):  
Anne-Marie Madore ◽  
Lucile Pain ◽  
Anne-Marie Boucher-Lafleur ◽  
Jolyane Meloche ◽  
Andréanne Morin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
T Cells ◽  
Genetic Variants ◽  
Immune Cell ◽  
Cell Types ◽  
Sequencing Data ◽  
Opposite Pattern ◽  
Genetic Mechanisms ◽  
New Gene

AbstractBackgroundThe 17q12-21 locus is the most replicated association with asthma. However, no study had described the genetic mechanisms underlying this association considering all genes of the locus in immune cell samples isolated from asthmatic and non-asthmatic individuals.ObjectiveThis study takes benefit of samples from naïve CD4+ T cells and eosinophils isolated from the same 200 individuals to describe specific interactions between genetic variants, gene expression and DNA methylation levels for the 17q12-21 asthma locus.Methods and ResultsAfter isolation of naïve CD4+ T cells and eosinophils from blood samples, next generation sequencing was used to measure DNA methylation levels and gene expression counts. Genetic interactions were then evaluated considering genetic variants from imputed genotype data. In naïve CD4+ T cells but not eosinophils, 20 SNPs in the fourth and fifth haplotype blocks modulated both GSDMA expression and methylation levels, showing an opposite pattern of allele frequencies and expression counts in asthmatics compared to controls. Moreover, negative correlations have been measured between methylation levels of CpG sites located within the 1.5 kb region from the transcription start site of GSDMA and its expression counts.ConclusionAvailability of sequencing data from two key cell types isolated from asthmatic and non-asthmatic individuals allowed identifying a new gene in naïve CD4+ T cells that drives the association with the 17q12-21 locus, leading to a better understanding of the genetic mechanisms taking place in it.

scholarly journals Genetic associations at regulatory phenotypes improve fine-mapping of causal variants for twelve immune-mediated diseases

2020 ◽  
Author(s):  
Kousik Kundu ◽  
Alice L. Mann ◽  
Manuel Tardaguila ◽  
Stephen Watt ◽  
Hannes Ponstingl ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Meta Analysis ◽  
Cell Types ◽  
Genetic Associations ◽  
Sequencing Data ◽  
Immune Mediated ◽  
Causal Genes ◽  
Causal Variants

AbstractThe identification of causal genetic variants for common diseases improves understanding of disease biology. Here we use data from the BLUEPRINT project to identify regulatory quantitative trait loci (QTL) for three primary human immune cell types and use these to fine-map putative causal variants for twelve immune-mediated diseases. We identify 340 unique, non major histocompatibility complex (MHC) disease loci that colocalise with high (>98%) posterior probability with regulatory QTLs, and apply Bayesian frameworks to fine-map associations at each locus. We show that fine-mapping applied to regulatory QTLs yields smaller credible set sizes and higher posterior probabilities for candidate causal variants compared to disease summary statistics. We also describe a systematic under-representation of insertion/deletion (INDEL) polymorphisms in credible sets derived from publicly available disease meta-analysis when compared to QTLs based on genome-sequencing data. Overall, our findings suggest that fine-mapping applied to disease-colocalising regulatory QTLs can enhance the discovery of putative causal disease variants and provide insights into the underlying causal genes and molecular mechanisms.

scholarly journals Higher cytolytic score correlates with an immunosuppressive tumor microenvironment and reduced survival in glioblastoma

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Alexander F. Haddad ◽  
Jia-Shu Chen ◽  
Taemin Oh ◽  
Matheus P. Pereira ◽  
Rushikesh S. Joshi ◽  
...  
Keyword(s):  
T Cell ◽  
Immune Cell ◽  
Cell Types ◽  
Cd8 T Cell ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Sequencing Data ◽  
Gene Score ◽  
T Cell Infiltration ◽  
The Relationship

Abstract Cytolytic score (CYT), calculated from mRNA expression levels of granzyme and perforin, positively correlates with CD8+ T cell infiltration/activity in a variety of cancers. Unlike other cancers, higher CYT has been associated with worse prognosis in glioblastoma (GBM). To address this discrepancy, we sought to investigate the relationship between CYT and immune checkpoint gene score (ICGscore), as well as their correlation with patient survival and tumor immune cell infiltration. Clinical and RNA-sequencing data for patients with newly diagnosed GBM were obtained from The Cancer Genome Atlas. Maximally-selected rank statistics was used to dichotomize subgroups. CIBERSORT was used to estimate abudence of immune cell-types. Spearman correlation was used to characterize the relationship between CYT and ICGscore. Kaplan–Meier curves were generated for survival analysis. Overall, 28/151 patients had high CYT. High CYT was associated with a mesenchymal subtype (p < 0.001) and worse survival (7.45 vs. 12.2 months, p < 0.001). There were no differences in patient demographics, IDH/MGMT mutation status, or treatment. On subgroup analysis, patients with high CYT/ICGscore had significantly increased CD8+ infiltration (p < 0.001), as expected, and worse survival (HR 0.445, p < 0.01). Furthermore, CYT strongly correlated with ICGscore (RS = 0.675, p < 0.001). The high CYT/ICGscore subgroup was associated with greater infiltration of M2 macrophages (p = 0.011) and neutrophils (p = 0.055). Our study highlights a multidimensional immunosuppressive GBM microenvironment in patients with higher CYT and potentially identifies patients with high CYT/ICGscore as a subgroup that may particularly benefit from multi-faceted immunotherapies, given their already elevated tumor CD8+ T cell levels.

EGFR L858R mutation as a possible target for individual-independent immunotherapy in Chinese population.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 8538-8538
Author(s):  
Yu Chen ◽  
Shiguang Hao ◽  
Zeng-qing Guo ◽  
Jing Lin ◽  
Li-zhu Chen ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Vaccine Development ◽  
Gene Panel ◽  
Hla Typing ◽  
Sequencing Data ◽  
Nsclc Tissue ◽  
Tissue Samples ◽  
Hla Alleles ◽  
L858r Mutation ◽  
Frequent Allele

8538 Background: Neoantigens arise from tumor-specific mutations and potentially provoke immune responses. General vaccines targeting these peptides could be beneficial for patients suffering from common cancers, like lung cancer. Therefore, a retrospective analysis was performed on 799 non-small cell lung cancer (NSCLC) tissue samples previously profiled using our 1021-gene panel. Each sample was collected from a unique patient, from whom peripheral blood or normal tissue was also obtained as control. Methods: Sequencing data were generated and pre-analyzed according to our in-house procedures. HLA typing was done using OptiType v1.0 (required sequences were captured by 1021-gene panel) and neoantigens were predicted by netMHCpan v4.0 based on typed HLA alleles and curated non-frameshift somatic mutations with frequency > 5%, which were called in pre-analysis. A neoantigen is considered mutant-specific if IC50 mut is < 500 nM and IC50 wild is > 500 nM, and especially, it is considered a strong-binder if IC50 mut is < 50 nM. Results: HLA typing returned 141 unique alleles, with the top 3 by carrier frequency being A*1101 (39%), C*0102 (33%) and A*2402 (28%). A further investigation into HLA alleles, mutations and neoantigens revealed two mutations on EGFR as candidates for off-the-shelf vaccine development: (1) L858R mutation (19%, 151 out of 799) and (2) E746_A750del mutation (13%, 106 out of 799). Among the four neoantigens derived from EGFR L858R mutation is HVKITDFGR, which can be recognized by A*3303 (IC50 mut = 22.93 nM and IC50 wild = 12,733.96 nM) and the combination is shared by 3% of the patients (23 out of 799), despite that A*3303 is not a very frequent allele in this population (16%, 127 out of 799). Two neoantigens were derived from EGFR E746_A750del mutation, including IPVAIKTSPK, which is mainly recognized by A*1101 (IC50 mut = 158.16 nM and IC50 wild = 31,132.66 nM). This combination is shared by 5% of the patients (41 out of 799). Conclusions: (1) EGFR L858R mutation and HLA-A*3303 could be a good target for individual-independent vaccine development. (2) HLA-A*1101 is the most frequent allele in this population. However, HLA-A*1101 and E746_A750del mutation is not so ideal for off-the-shelf vaccine development.

scholarly journals Immunological mechanisms and therapeutic targets of fatty liver diseases

2020 ◽  
Vol 18 (1) ◽  
pp. 73-91 ◽  
Author(s):  
Hua Wang ◽  
Wajahat Mehal ◽  
Laura E. Nagy ◽  
Yaron Rotman
Keyword(s):  
Liver Disease ◽  
Fatty Liver ◽  
Fatty Liver Disease ◽  
Liver Diseases ◽  
Bacterial Infections ◽  
Immune Cell ◽  
Therapeutic Targets ◽  
Cell Types ◽  
Nonalcoholic Fatty Liver ◽  
Immunological Mechanisms

AbstractAlcoholic liver disease (ALD) and nonalcoholic fatty liver disease (NAFLD) are the two major types of chronic liver disease worldwide. Inflammatory processes play key roles in the pathogeneses of fatty liver diseases, and continuous inflammation promotes the progression of alcoholic steatohepatitis (ASH) and nonalcoholic steatohepatitis (NASH). Although both ALD and NAFLD are closely related to inflammation, their respective developmental mechanisms differ to some extent. Here, we review the roles of multiple immunological mechanisms and therapeutic targets related to the inflammation associated with fatty liver diseases and the differences in the progression of ASH and NASH. Multiple cell types in the liver, including macrophages, neutrophils, other immune cell types and hepatocytes, are involved in fatty liver disease inflammation. In addition, microRNAs (miRNAs), extracellular vesicles (EVs), and complement also contribute to the inflammatory process, as does intertissue crosstalk between the liver and the intestine, adipose tissue, and the nervous system. We point out that inflammation also plays important roles in promoting liver repair and controlling bacterial infections. Understanding the complex regulatory process of disrupted homeostasis during the development of fatty liver diseases may lead to the development of improved targeted therapeutic intervention strategies.

scholarly journals Infino: a Bayesian hierarchical model improves estimates of immune infiltration into tumor microenvironment

2017 ◽  
Author(s):  
Maxim Zaslavsky ◽  
Jacqueline Buros Novik ◽  
Eliza Chang ◽  
Jeffrey Hammerbacher
Keyword(s):  
T Cell ◽  
Tumor Microenvironment ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Cell Types ◽  
Training Data ◽  
Cell Type ◽  
Sequencing Data ◽  
Effective Response ◽  
Immune Infiltration

AbstractRobust quantification of immune cell infiltration into the tumor microenvironment may shed light on why only a small proportion of patients benefit from checkpoint therapy. The immune cells surrounding a tumor have been suggested to mediate an effective response to immunotherapy. However, traditional measurement of immune cell content around a tumor by immunohistochemistry, flow cytometry, or mass cytometry allows measurement of only up to a few dozen markers at a time, limiting the number of immune cell types identified. Immune cell type abundances may instead be estimated in silico by deconvolving gene expression mixtures from bulk RNA sequencing of tumor tissue. By measuring tens of thousands of transcripts at once, bulk RNA-seq provides a rich input to algorithms that quantify cell type abundances in the tumor microenvironment, affording the potential to quantify the states of a greater number of immune cell types (given adequate training data). Here, we first review existing methods for deconvolution and evaluate their performance on synthetic mixtures. Then we develop a Bayesian inference approach, named infino, that learns to distinguish immune cell expression phenotypes and deconvolve mixtures. In contrast to earlier approaches, infino accepts RNA sequencing data, models transcript expression variability, and exploits the relationships between cell types to improve deconvolution accuracy and allow interrogation from the level of broad categories to the level of finest granularity. The resulting probability distributions of immune infiltration could be applied to numerous questions concerning the diverse ecology of immune cell types, including assessment of the association of immune infiltration with response to immunotherapy, and study of the expression profile and presence of elusive T cell subcompartments, such as T cell exhaustion.

scholarly journals ImmuCellAI: a unique method for comprehensive T-cell subsets abundance prediction and its application in cancer immunotherapy

2019 ◽  
Author(s):  
Ya-Ru Miao ◽  
Qiong Zhang ◽  
Qian Lei ◽  
Mei Luo ◽  
Gui-Yan Xie ◽  
...  
Keyword(s):  
T Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
Response Prediction ◽  
Cell Types ◽  
Cancer Immunology ◽  
T Cell Subsets ◽  
Expression Data ◽  
Sequencing Data ◽  
Pre Treatment

AbstractThe distribution and abundance of immune cells, particularly T-cell subsets, play pivotal roles in cancer immunology and therapy. There are many T-cell subsets with specific function, however current methods are limited in estimating them, thus, a method for predicting comprehensive T-cell subsets is urgently needed in cancer immunology research. Here we introduce Immune Cell Abundance Identifier (ImmuCellAI), a novel gene set signature-based method, for precisely estimating the abundance of 24 immune cell types including 18 T-cell subsets, from gene expression data. Performance evaluation on both our sequencing data with flow cytometry results and public expression data indicated that ImmuCellAI can estimate immune cells with superior accuracy than other methods especially on many T-cell subsets. Application of ImmuCellAI to immunotherapy datasets revealed that the abundance of dendritic cells (DC), cytotoxic T, and gamma delta T cells was significantly higher both in comparisons of on-treatment vs. pre-treatment and responders vs. non-responders. Meanwhile, we built an ImmuCellAI result-based model for predicting the immunotherapy response with high accuracy (AUC 0.80~0.91). These results demonstrated the powerful and unique function of ImmuCellAI in tumor immune infiltration estimation and immunotherapy response prediction. The ImmuCellAI online server is freely available at http://bioinfo.life.hust.edu.cn/web/ImmuCellAI/.

scholarly journals Distinguishing linear and branched evolution given single-cell DNA sequencing data of tumors

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Leah L. Weber ◽  
Mohammed El-Kebir
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Evolutionary Process ◽  
Treatment Decision ◽  
Real Data ◽  
Current Data ◽  
Fast Method ◽  
Sequencing Data ◽  
Evolutionary Trajectory ◽  
Cancer Types

Abstract Background Cancer arises from an evolutionary process where somatic mutations give rise to clonal expansions. Reconstructing this evolutionary process is useful for treatment decision-making as well as understanding evolutionary patterns across patients and cancer types. In particular, classifying a tumor’s evolutionary process as either linear or branched and understanding what cancer types and which patients have each of these trajectories could provide useful insights for both clinicians and researchers. While comprehensive cancer phylogeny inference from single-cell DNA sequencing data is challenging due to limitations with current sequencing technology and the complexity of the resulting problem, current data might provide sufficient signal to accurately classify a tumor’s evolutionary history as either linear or branched. Results We introduce the Linear Perfect Phylogeny Flipping (LPPF) problem as a means of testing two alternative hypotheses for the pattern of evolution, which we prove to be NP-hard. We develop Phyolin, which uses constraint programming to solve the LPPF problem. Through both in silico experiments and real data application, we demonstrate the performance of our method, outperforming a competing machine learning approach. Conclusion Phyolin is an accurate, easy to use and fast method for classifying an evolutionary trajectory as linear or branched given a tumor’s single-cell DNA sequencing data.

scholarly journals Biomarkers and Gene Signatures to Predict Durable Response to Pembrolizumab in Non-Small Cell Lung Cancer

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3828
Author(s):  
Anello Marcello Poma ◽  
Rossella Bruno ◽  
Iacopo Pietrini ◽  
Greta Alì ◽  
Giulia Pasquini ◽  
...  
Keyword(s):  
Lung Cancer ◽  
T Cells ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Immune Cell ◽  
Small Cell ◽  
Small Cell Lung ◽  
First Line ◽  
Durable Response ◽  
Nsclc Patients

Pembrolizumab has been approved as first-line treatment for advanced Non-small cell lung cancer (NSCLC) patients with tumors expressing PD-L1 and in the absence of other targetable alterations. However, not all patients that meet these criteria have a durable benefit. In this monocentric study, we aimed at refining the selection of patients based on the expression of immune genes. Forty-six consecutive advanced NSCLC patients treated with pembrolizumab in first-line setting were enrolled. The expression levels of 770 genes involved in the regulation of the immune system was analysed by the nanoString system. PD-L1 expression was evaluated by immunohistochemistry. Patients with durable clinical benefit had a greater infiltration of cytotoxic cells, exhausted CD8, B-cells, CD45, T-cells, CD8 T-cells and NK cells. Immune cell scores such as CD8 T-cell and NK cell were good predictors of durable response with an AUC of 0.82. Among the immune cell markers, XCL1/2 showed the better performance in predicting durable benefit to pembrolizumab, with an AUC of 0.85. Additionally, CD8A, CD8B and EOMES showed a high specificity (>0.86) in identifying patients with a good response to treatment. In the same series, PD-L1 expression levels had an AUC of 0.61. The characterization of tumor microenvironment, even with the use of single markers, can improve patients’ selection for pembrolizumab treatment.

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