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Article ; Online: Integrative HLA typing of tumor and adjacent normal tissue can reveal insights into the tumor immune response.

Sverchkova, Angelina / Burkholz, Scott / Rubsamen, Reid / Stratford, Richard / Clancy, Trevor

2024 Volume 17, Issue 1, Page(s) 37

Abstract: Background: The HLA complex is the most polymorphic region of the human genome, and its improved characterization can help us understand the genetics of human disease as well as the interplay between cancer and the immune system. The main function of ... ...

Abstract	Background: The HLA complex is the most polymorphic region of the human genome, and its improved characterization can help us understand the genetics of human disease as well as the interplay between cancer and the immune system. The main function of HLA genes is to recognize "non-self" antigens and to present them on the cell surface to T cells, which instigate an immune response toward infected or transformed cells. While sequence variation in the antigen-binding groove of HLA may modulate the repertoire of immunogenic antigens presented to T cells, alterations in HLA expression can significantly influence the immune response to pathogens and cancer. Methods: RNA sequencing was used here to accurately genotype the HLA region and quantify and compare the level of allele-specific HLA expression in tumors and patient-matched adjacent normal tissue. The computational approach utilized in the study types classical and non-classical Class I and Class II HLA alleles from RNA-seq while simultaneously quantifying allele-specific or personalized HLA expression. The strategy also uses RNA-seq data to infer immune cell infiltration into tumors and the corresponding immune cell composition of matched normal tissue, to reveal potential insights related to T cell and NK cell interactions with tumor HLA alleles. Results: The genotyping method outperforms existing RNA-seq-based HLA typing tools for Class II HLA genotyping. Further, we demonstrate its potential for studying tumor-immune interactions by applying the method to tumor samples from two different subtypes of breast cancer and their matched normal breast tissue controls. Conclusions: The integrative RNA-seq-based HLA typing approach described in the study, coupled with HLA expression analysis, neoantigen prediction and immune cell infiltration, may help increase our understanding of the interplay between a patient's tumor and immune system; and provide further insights into the immune mechanisms that determine a positive or negative outcome following treatment with immunotherapy such as checkpoint blockade.
MeSH term(s)	Humans ; Female ; Histocompatibility Antigens Class I ; Genotype ; Breast Neoplasms/genetics ; Immunity ; Histocompatibility Testing/methods ; HLA Antigens/genetics
Chemical Substances	Histocompatibility Antigens Class I ; HLA Antigens
Language	English
Publishing date	2024-01-27
Publishing country	England
Document type	Journal Article
ZDB-ID	2411865-5
ISSN	1755-8794 ; 1755-8794
ISSN (online)	1755-8794
ISSN	1755-8794
DOI	10.1186/s12920-024-01808-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Deep learning of antibody epitopes using molecular permutation vectors

Vardaxis, Ioannis / Simovski, Boris / Anzar, Irantzu / Stratford, Richard / Clancy, Trevor

bioRxiv

Abstract: The accurate computational prediction of B cell epitopes can vastly reduce the cost and time required for identifying potential epitope candidates for the design of vaccines and immunodiagnostics. However, current computational tools for B cell epitope ... ...

Abstract	The accurate computational prediction of B cell epitopes can vastly reduce the cost and time required for identifying potential epitope candidates for the design of vaccines and immunodiagnostics. However, current computational tools for B cell epitope prediction perform poorly and are not fit-for-purpose, and there remains enormous room for improvement and the need for superior prediction strategies. Here we propose a novel approach that improves B cell epitope prediction by encoding epitopes as binary molecular permutation vectors that represent the position and structural properties of the amino acids within a protein antigen sequence that interact with an antibody, rather than the traditional approach of defining epitopes as scores per amino acid on a protein sequence that pertain to their probability of partaking in a B cell epitope antibody interaction. In addition to defining epitopes as a binary molecular permutation vectors, the approach also uses the 3D macrostructure features of the unbound 3D protein structures, and in turn uses these features to train another deep learning model on the corresponding antibody-bound protein 3D structures. We demonstrate that the strategy predicts B cell epitopes with improved accuracy compared to the existing tools, and reliably identifies the majority of experimentally verified epitopes on the spike protein of SARS-CoV-2 not seen by the model in training. With the approach described herein, a primary protein sequence with the query molecular permutation vector alone is required to predict B cell epitopes in a reliable manner, potentially advancing the use of computational prediction of B cell epitopes in biomedical research applications.
Keywords	covid19
Language	English
Publishing date	2024-03-21
Publisher	Cold Spring Harbor Laboratory
Document type	Article ; Online
DOI	10.1101/2024.03.20.585661
Database	COVID19

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Article: The T Cell Epitope Landscape of SARS-CoV-2 Variants of Concern.

Tennøe, Simen / Gheorghe, Marius / Stratford, Richard / Clancy, Trevor

Vaccines

2022 Volume 10, Issue 7

Abstract: During the COVID-19 pandemic, several SARS-CoV-2 variants of concern (VOC) emerged, bringing with them varying degrees of health and socioeconomic burdens. In particular, the Omicron VOC displayed distinct features of increased transmissibility ... ...

Abstract	During the COVID-19 pandemic, several SARS-CoV-2 variants of concern (VOC) emerged, bringing with them varying degrees of health and socioeconomic burdens. In particular, the Omicron VOC displayed distinct features of increased transmissibility accompanied by antigenic drift in the spike protein that partially circumvented the ability of pre-existing antibody responses in the global population to neutralize the virus. However, T cell immunity has remained robust throughout all the different VOC transmission waves and has emerged as a critically important correlate of protection against SARS-CoV-2 and its VOCs, in both vaccinated and infected individuals. Therefore, as SARS-CoV-2 VOCs continue to evolve, it is crucial that we characterize the correlates of protection and the potential for immune escape for both B cell and T cell human immunity in the population. Generating the insights necessary to understand T cell immunity, experimentally, for the global human population is at present a critical but a time consuming, expensive, and laborious process. Further, it is not feasible to generate global or universal insights into T cell immunity in an actionable time frame for potential future emerging VOCs. However, using computational means we can expedite and provide early insights into the correlates of T cell protection. In this study, we generated and revealed insights on the T cell epitope landscape for the five main SARS-CoV-2 VOCs observed to date. We demonstrated using a unique AI prediction platform, a significant conservation of presentable T cell epitopes across all mutated peptides for each VOC. This was modeled using the most frequent HLA alleles in the human population and covers the most common HLA haplotypes in the human population. The AI resource generated through this computational study and associated insights may guide the development of T cell vaccines and diagnostics that are even more robust against current and future VOCs, and their emerging subvariants.
Language	English
Publishing date	2022-07-14
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2703319-3
ISSN	2076-393X
ISSN	2076-393X
DOI	10.3390/vaccines10071123
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Article ; Online: The T cell epitope landscape of SARS-CoV-2 variants of concern

Tennoe, Simen / Gheorghe, Marius / Stratford, Richard / Clancy, Trevor

bioRxiv

Abstract	During the COVID-19 pandemic, several SARS-CoV-2 variants of concern (VOC) emerged, bringing with them varying degrees of health and socioeconomic burdens. In particular, the Omicron VOC displayed distinct features of increased transmissibility accompanied by antigenic drift in the spike protein that partially circumvented the ability of pre-existing antibody responses in the global population to neutralize the virus. However, T cell immunity has remained robust throughout all the different VOC transmission waves and has emerged as a critically important correlate of protection against SARS-CoV-2 VOCs, in both vaccinated and infected individuals. Therefore, as SARS-CoV-2 VOCs continue to evolve, it is crucial that we characterize the correlates of protection and the potential for immune es-cape for both B cell and T cell human immunity in the population. Generating the insights necessary to understand T cell immunity, experimentally, for the global human population is at present critical but a time consuming, expensive, and laborious process. Further, it is not feasible to generate global or universal insights into T cell immunity in an actionable time frame for potential future emerging VOCs. However, using computational means we can expedite and provide early insights into the correlates of T cell protection. In this study, we generated and reveal insights on the T cell epitope landscape for the five main SARS-CoV-2 VOCs observed to date. We demonstrated here using a unique AI prediction platform, a strong concordance in global T cell protection across all mutated peptides for each VOC. This was modeled using the most frequent HLA alleles in the human population and covers the most common HLA haplotypes in the human population. The AI resource generated through this computational study and associated insights may guide the development of T cell vaccines and diagnostics that are even more robust against current and future VOCs, and their emerging subvariants.
Keywords	covid19
Language	English
Publishing date	2022-06-06
Publisher	Cold Spring Harbor Laboratory
Document type	Article ; Online
DOI	10.1101/2022.06.06.491344
Database	COVID19

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Article ; Online: Clinical Activity of Combined Telomerase Vaccination and Pembrolizumab in Advanced Melanoma: Results from a Phase I Trial.

Ellingsen, Espen B / O'Day, Steven / Mezheyeuski, Artur / Gromadka, Agnieszka / Clancy, Trevor / Kristedja, Timothy S / Milhem, Mohammed / Zakharia, Yousef

Clinical cancer research : an official journal of the American Association for Cancer Research

2023 Volume 29, Issue 16, Page(s) 3026–3036

Abstract: Purpose: Cancer vaccines represent a novel treatment modality with a complementary mode of action addressing a crucial bottleneck for checkpoint inhibitor (CPI) efficacy. CPIs are expected to release brakes in T-cell responses elicited by vaccination, ... ...

Abstract	Purpose: Cancer vaccines represent a novel treatment modality with a complementary mode of action addressing a crucial bottleneck for checkpoint inhibitor (CPI) efficacy. CPIs are expected to release brakes in T-cell responses elicited by vaccination, leading to more robust immune responses. Increased antitumor T-cell responses may confer increased antitumor activity in patients with less immunogenic tumors, a subgroup expected to achieve reduced benefit from CPIs alone. In this trial, a telomerase-based vaccine was combined with pembrolizumab to assess the safety and clinical activity in patients with melanoma. Patients and methods: Thirty treatment-naïve patients with advanced melanoma were enrolled. Patients received intradermal injections of UV1 with adjuvant GM-CSF at two dose levels, and pembrolizumab according to the label. Blood samples were assessed for vaccine-induced T-cell responses, and tumor tissues were collected for translational analyses. The primary endpoint was safety, with secondary objectives including progression-free survival (PFS), overall survival (OS), and objective response rate (ORR). Results: The combination was considered safe and well-tolerated. Grade 3 adverse events were observed in 20% of patients, with no grade 4 or 5 adverse events reported. Vaccination-related adverse events were mostly mild injection site reactions. The median PFS was 18.9 months, and the 1- and 2-year OS rates were 86.7% and 73.3%, respectively. The ORR was 56.7%, with 33.3% achieving complete responses. Vaccine-induced immune responses were observed in evaluable patients, and inflammatory changes were detected in posttreatment biopsies. Conclusions: Encouraging safety and preliminary efficacy were observed. Randomized phase II trials are currently ongoing.
MeSH term(s)	Humans ; Antibodies, Monoclonal, Humanized ; Melanoma/pathology ; Telomerase ; Vaccination
Chemical Substances	Antibodies, Monoclonal, Humanized ; pembrolizumab (DPT0O3T46P) ; Telomerase (EC 2.7.7.49)
Language	English
Publishing date	2023-06-12
Publishing country	United States
Document type	Clinical Trial, Phase I ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1225457-5
ISSN	1557-3265 ; 1078-0432
ISSN (online)	1557-3265
ISSN	1078-0432
DOI	10.1158/1078-0432.CCR-23-0416
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Book ; Online: T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precesion Immunotherapy

Chen, Ziqi / Min, Martin Renqiang / Guo, Hongyu / Cheng, Chao / Clancy, Trevor / Ning, Xia

2023

Abstract: T cells monitor the health status of cells by identifying foreign peptides displayed on their surface. T-cell receptors (TCRs), which are protein complexes found on the surface of T cells, are able to bind to these peptides. This process is known as TCR ... ...

Abstract	T cells monitor the health status of cells by identifying foreign peptides displayed on their surface. T-cell receptors (TCRs), which are protein complexes found on the surface of T cells, are able to bind to these peptides. This process is known as TCR recognition and constitutes a key step for immune response. Optimizing TCR sequences for TCR recognition represents a fundamental step towards the development of personalized treatments to trigger immune responses killing cancerous or virus-infected cells. In this paper, we formulated the search for these optimized TCRs as a reinforcement learning (RL) problem, and presented a framework TCRPPO with a mutation policy using proximal policy optimization. TCRPPO mutates TCRs into effective ones that can recognize given peptides. TCRPPO leverages a reward function that combines the likelihoods of mutated sequences being valid TCRs measured by a new scoring function based on deep autoencoders, with the probabilities of mutated sequences recognizing peptides from a peptide-TCR interaction predictor. We compared TCRPPO with multiple baseline methods and demonstrated that TCRPPO significantly outperforms all the baseline methods to generate positive binding and valid TCRs. These results demonstrate the potential of TCRPPO for both precision immunotherapy and peptide-recognizing TCR motif discovery.
Keywords	Quantitative Biology - Quantitative Methods ; Computer Science - Machine Learning
Subject code	006
Publishing date	2023-03-02
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: NeoMutate: an ensemble machine learning framework for the prediction of somatic mutations in cancer.

Anzar, Irantzu / Sverchkova, Angelina / Stratford, Richard / Clancy, Trevor

BMC medical genomics

2019 Volume 12, Issue 1, Page(s) 63

Abstract: Background: The accurate screening of tumor genomic landscapes for somatic mutations using high-throughput sequencing involves a crucial step in precise clinical diagnosis and targeted therapy. However, the complex inherent features of cancer tissue, ... ...

Abstract	Background: The accurate screening of tumor genomic landscapes for somatic mutations using high-throughput sequencing involves a crucial step in precise clinical diagnosis and targeted therapy. However, the complex inherent features of cancer tissue, especially, tumor genetic intra-heterogeneity coupled with the problem of sequencing and alignment artifacts, makes somatic variant calling a challenging task. Current variant filtering strategies, such as rule-based filtering and consensus voting of different algorithms, have previously helped to increase specificity, although comes at the cost of sensitivity. Methods: In light of this, we have developed the NeoMutate framework which incorporates 7 supervised machine learning (ML) algorithms to exploit the strengths of multiple variant callers, using a non-redundant set of biological and sequence features. We benchmarked NeoMutate by simulating more than 10,000 bona fide cancer-related mutations into three well-characterized Genome in a Bottle (GIAB) reference samples. Results: A robust and exhaustive evaluation of NeoMutate's performance based on 5-fold cross validation experiments, in addition to 3 independent tests, demonstrated a substantially improved variant detection accuracy compared to any of its individual composite variant callers and consensus calling of multiple tools. Conclusions: We show here that integrating multiple tools in an ensemble ML layer optimizes somatic variant detection rates, leading to a potentially improved variant selection framework for the diagnosis and treatment of cancer.
MeSH term(s)	Genomics/methods ; High-Throughput Nucleotide Sequencing ; Mutation ; Neoplasms/genetics ; Supervised Machine Learning ; Workflow
Language	English
Publishing date	2019-05-16
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1755-8794
ISSN (online)	1755-8794
DOI	10.1186/s12920-019-0508-5
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Article ; Online: Corrigendum: Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence.

Federico, Lorenzo / Malone, Brandon / Tennøe, Simen / Chaban, Viktoriia / Osen, Julie Røkke / Gainullin, Murat / Smorodina, Eva / Kared, Hassen / Akbar, Rahmad / Greiff, Victor / Stratford, Richard / Clancy, Trevor / Munthe, Ludvig Andre

Frontiers in immunology

2024 Volume 15, Page(s) 1377041

Abstract: This corrects the article DOI: 10.3389/fimmu.2023.1265044.]. ...

Abstract	[This corrects the article DOI: 10.3389/fimmu.2023.1265044.].
Language	English
Publishing date	2024-02-21
Publishing country	Switzerland
Document type	Published Erratum
ZDB-ID	2606827-8
ISSN	1664-3224 ; 1664-3224
ISSN (online)	1664-3224
ISSN	1664-3224
DOI	10.3389/fimmu.2024.1377041
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Article ; Online: Improved HLA typing of Class I and Class II alleles from next-generation sequencing data.

Sverchkova, Angelina / Anzar, Irantzu / Stratford, Richard / Clancy, Trevor

HLA

2019 Volume 94, Issue 6, Page(s) 504–513

Abstract: Precise HLA genotyping is of great clinical importance, albeit a challenging bioinformatics endeavor because of the hyper polymorphism of the HLA region. The ever-increasing availability of next-generation sequencing (NGS) solutions has spurred the ... ...

Abstract	Precise HLA genotyping is of great clinical importance, albeit a challenging bioinformatics endeavor because of the hyper polymorphism of the HLA region. The ever-increasing availability of next-generation sequencing (NGS) solutions has spurred the development of several computational methods for predicting HLA genotypes from NGS data. Although some of these tools genotype HLA Class I alleles reasonably well, there is a need to incorporate integrative parameters related to ethnicity frequency information, in order to improve performance for both Class I and Class II alleles. Here, we present a bioinformatics method that addresses some of the current shortfalls in HLA genotyping from NGS. First, reads that map to the HLA region is aligned against a comprehensive library of reference HLA alleles. The allele type was then subsequently determined on the basis of the distribution of aligned reads, and the prior probabilities of the ethnic frequencies of alleles. Three public NGS datasets were used to benchmark the approach against six similar tools. The method outlined in this manuscript displayed an overall accuracy of 98.73% for Class I and 96.37% for Class II alleles. We illustrate an improved integrative approach that outperforms existing tools and is able to predict HLA alleles with improved fidelity for both Class I and Class II alleles.
MeSH term(s)	Alleles ; Computational Biology/methods ; Databases, Genetic ; Datasets as Topic ; Ethnic Groups/genetics ; Gene Frequency ; Genotype ; Genotyping Techniques/methods ; High-Throughput Nucleotide Sequencing/methods ; Histocompatibility Antigens Class I/genetics ; Histocompatibility Antigens Class II/genetics ; Histocompatibility Testing/methods ; Humans ; Sequence Analysis, DNA/methods
Chemical Substances	Histocompatibility Antigens Class I ; Histocompatibility Antigens Class II
Language	English
Publishing date	2019-10-08
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Validation Study
ZDB-ID	2845111-9
ISSN	2059-2310 ; 2059-2302
ISSN (online)	2059-2310
ISSN	2059-2302
DOI	10.1111/tan.13685
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Zs.A 661: Show issues

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Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Profiling networks of distinct immune-cells in tumors.

Clancy, Trevor / Hovig, Eivind

BMC bioinformatics

2016 Volume 17, Issue 1, Page(s) 263

Abstract: Background: It is now clearly evident that cancer outcome and response to therapy is guided by diverse immune-cell activity in tumors. Presently, a key challenge is to comprehensively identify networks of distinct immune-cell signatures present in ... ...

Abstract	Background: It is now clearly evident that cancer outcome and response to therapy is guided by diverse immune-cell activity in tumors. Presently, a key challenge is to comprehensively identify networks of distinct immune-cell signatures present in complex tissue, at higher-resolution and at various stages of differentiation, activation or function. This is particularly so for closely related immune-cells with diminutive, yet critical, differences. Results: To predict networks of infiltrated distinct immune-cell phenotypes at higher resolution, we explored an integrated knowledge-based approach to select immune-cell signature genes integrating not only expression enrichment across immune-cells, but also an automatic capture of relevant immune-cell signature genes from the literature. This knowledge-based approach was integrated with resources of immune-cell specific protein networks, to define signature genes of distinct immune-cell phenotypes. We demonstrate the utility of this approach by profiling signatures of distinct immune-cells, and networks of immune-cells, from metastatic melanoma patients who had undergone chemotherapy. The resultant bioinformatics strategy complements immunohistochemistry from these tumors, and predicts both tumor-killing and immunosuppressive networks of distinct immune-cells in responders and non-responders, respectively. The approach is also shown to capture differences in the immune-cell networks of BRAF versus NRAS mutated metastatic melanomas, and the dynamic changes in resistance to targeted kinase inhibitors in MAPK signalling. Conclusions: This integrative bioinformatics approach demonstrates that capturing the protein network signatures and ratios of distinct immune-cell in the tumor microenvironment maybe an important factor in predicting response to therapy. This may serve as a computational strategy to define network signatures of distinct immune-cells to guide immuno-pathological discovery.
MeSH term(s)	Computational Biology/methods ; GTP Phosphohydrolases/genetics ; Gene Expression Profiling/methods ; Gene Regulatory Networks ; Genes, Neoplasm/genetics ; Humans ; Immune System/immunology ; Immune System/metabolism ; MAP Kinase Signaling System ; Melanoma/genetics ; Melanoma/immunology ; Melanoma/secondary ; Membrane Proteins/genetics ; Mutation/genetics ; Proto-Oncogene Proteins B-raf/genetics
Chemical Substances	Membrane Proteins ; BRAF protein, human (EC 2.7.11.1) ; Proto-Oncogene Proteins B-raf (EC 2.7.11.1) ; GTP Phosphohydrolases (EC 3.6.1.-) ; NRAS protein, human (EC 3.6.1.-)
Language	English
Publishing date	2016-07-04
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-016-1141-3
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