Optimal population-specific HLA imputation with dimension reduction

Human genomics has quickly evolved, powering genome-wide association studies (GWASs). SNP-based GWASs cannot capture the intense polymorphism of HLA genes, highly associated with disease susceptibility. There are methods to statistically impute HLA genotypes from SNP-genotypes data, but lack of diversity in reference panels hinders their performance. We evaluated the accuracy of the 1000 Genomes data as a reference panel for imputing HLA from admixed individuals of African and European ancestries, focusing on (a) the full dataset, (b) 10 replications from 6 populations, and (c) 19 conditions for the custom reference panels. The full dataset outperformed smaller models, with a good F1-score of 0.66 for HLA-B. However, custom models outperformed the multiethnic or population models of similar size (F1-scores up to 0.53, against up to 0.42). We demonstrated the importance of using genetically specific models for imputing populations, which are currently underrepresented in public datasets, opening the door to HLA imputation for every genetic population.

Harnessing tumor immunogenomics: Tumor neoantigens in ovarian cancer and beyond

Ovarian cancer is a major cause of death among gynecological cancers due to its highly aggressive nature. Immunotherapy has emerged as a promising avenue for ovarian cancer treatment, offering targeted approaches with reduced off-target effects. With the advent of next-generation sequencing, it has become possible to identify genomic alterations that can serve as potential targets for immunotherapy. Furthermore, immunogenomics research has revealed the importance of genetic alterations in shaping the cancer immune responses. However, the heterogeneity of immunogenicity and the low tumor mutation burden pose challenges for neoantigen-based immunotherapies. Further research is needed to identify neoantigen-specific tumor-infiltrating lymphocytes (TIL) and establish guidelines for patient inclusion criteria in TIL-based therapy. The study of neoantigens and their implications in ovarian cancer immunotherapy holds great promise, and efforts focused on personalized treatment strategies, refined neoantigen selection, and optimized therapeutic combinations will contribute to improving patient outcomes in the future.

Identification of cytokine-predominant immunosuppressive class and prognostic risk signatures in glioma

PURPOSE

The advent of immune checkpoint blockade (ICB) therapies this year has changed the way glioblastoma (GBM) is treated. Meanwhile, some patients with strong PD-L1 expression remain immune checkpoint resistant. To better understand the molecular processes that influence the immune environment, there is an urgent need to characterize the immunosuppressive tumor microenvironment and identify biomarkers to predict patient survival outcomes.

PATIENTS AND METHODS

Our study analyzed RNA-sequencing data from 178 GBM samples. Their unique gene expression patterns in the tumor microenvironment were analyzed by an unsupervised clustering algorithm. Through these expression patterns, a panel of T-cell exhaustion signatures, immunosuppressive cells, and clinical features correlates with immunotherapy response. The presence or absence of immune status and prognostic signatures was then validated with the test dataset.

RESULTS

38.2% of GBM patients showed increased expression of anti-inflammatory cytokines, significant enrichment of T cell exhaustion signals, higher proportion of immunosuppressive cells (macrophages and CD4 regulatory T cells) and nine inhibitory checkpoints (CTLA4, PDCD1, LAG3, BTLA, TIGIT, HAVCR2, IDO1, SIGLEC7, and VISTA). The immunodepleted class (IDC) was used to classify these immunocompromised individuals. Despite the high density of tumor-infiltrating lymphocytes shown by IDC, such patients have a poor prognosis. Although PD-L1 was highly expressed in IDC, it suggested that there might be ICB resistance. There are many IDC predictive signatures to discover.

CONCLUSION

PD-1 is strongly expressed in a novel immunosuppressive class of GBM, but this cluster may be resistant to ICB therapy. A comprehensive description of this drug-resistant tumor microenvironment could provide new insights into drug resistance mechanisms and improved immunotherapy techniques.

Transcriptional immunogenomic analysis reveals distinct immunological clusters in paediatric nervous system tumours

BACKGROUND

Cancer immunotherapies including immune checkpoint inhibitors and Chimeric Antigen Receptor (CAR) T-cell therapy have shown variable response rates in paediatric patients highlighting the need to establish robust biomarkers for patient selection. While the tumour microenvironment in adults has been widely studied to delineate determinants of immune response, the immune composition of paediatric solid tumours remains relatively uncharacterized calling for investigations to identify potential immune biomarkers.

METHODS

To inform immunotherapy approaches in paediatric cancers with embryonal origin, we performed an immunogenomic analysis of RNA-seq data from 925 treatment-naïve paediatric nervous system tumours (pedNST) spanning 12 cancer types from three publicly available data sets.

RESULTS

Within pedNST, we uncovered four broad immune clusters: Paediatric Inflamed (10%), Myeloid Predominant (30%), Immune Neutral (43%) and Immune Desert (17%). We validated these clusters using immunohistochemistry, methylation immune inference and segmentation analysis of tissue images. We report shared biology of these immune clusters within and across cancer types, and characterization of specific immune cell frequencies as well as T- and B-cell repertoires. We found no associations between immune infiltration levels and tumour mutational burden, although molecular cancer entities were enriched within specific immune clusters.

CONCLUSIONS

Given the heterogeneity of immune infiltration within pedNST, our findings suggest personalized immunogenomic profiling is needed to guide selection of immunotherapeutic strategies.

Multi-omics analysis uncovers clinical, immunological, and pharmacogenomic implications of cuproptosis in clear cell renal cell carcinoma

OBJECTIVE

The latest research proposed a novel copper-dependent programmed cell death named cuproptosis. We aimed to elucidate the influence of cuproptosis in clear cell renal cell carcinoma (ccRCC) from a multi-omic perspective.

METHODS

This study systematically assessed mRNA expression, methylation, and genetic alterations of cuproptosis genes in TCGA ccRCC samples. Through unsupervised clustering analysis, the samples were classified as different cuproptosis subtypes, which were verified through NTP method in the E-MTAB-1980 dataset. Next, the cuproptosis score (Cuscore) was computed based on cuproptosis-related genes via PCA. We also evaluated clinical and immunogenomic features, drug sensitivity, immunotherapeutic response, and post-transcriptional regulation.

RESULTS

Cuproptosis genes presented multi-layer alterations in ccRCC, and were linked with patients' survival and immune microenvironment. We defined three cuproptosis subtypes [C1 (moderate cuproptosis), C2 (low cuproptosis), and C3 (high cuproptosis)], and the robustness and reproducibility of this classification was further proven. Overall survival was best in C3, moderate in C1, and worst in C2. C1 had the highest sensitivity to pazopanib, and sorafenib, while C2 was most sensitive to sunitinib. Furthermore, C1 patients benefited more from anti-PD-1 immunotherapy. Patients with high Cuscore presented the notable survival advantage. Cuscore was highly linked with immunogenomic features, and post-transcriptional events that contributed to ccRCC development. Finally, several potential compounds and druggable targets (NMU, RARRES1) were selected for low Cuscore group.

CONCLUSION

Overall, our study revealed the non-negligible role of cuproptosis in ccRCC development. Evaluation of the cuproptosis subtypes improves our cognition of immunogenomic features and better guides personalized prognostication and precision therapy.

Genetic regulators of cytokine responses upon BCG vaccination in children from West Africa

Genetic variation is a key factor influencing cytokine production capacity, but which genetic loci regulate cytokine production before and after vaccination, particularly in African population is unknown. Here, we aimed to identify single-nucleotide polymorphisms (SNPs) controlling cytokine responses (cQTLs) after microbial stimulation in infants of West-African ancestry, comprising of low-birth-weight neonates randomized to bacillus Calmette-Guérin (BCG) vaccine-at-birth (intervention) or to the usual delayed BCG (control). Genome-wide cytokine QTL mapping revealed 12 independent cQTLs loci, of which the LINC01082-LINC00917 locus influenced more than half of the cytokine-stimulation pairs assessed. Furthermore, nine distinct cQTLs were found among infants randomized to BCG. Functional validation confirmed that several complement genes affect cytokine response after BCG vaccination. We observed a limited overlap of common cQTLs between the West-African infants and cohorts of Western European individuals. These data reveal strong population-specific genetic effects on cytokine production and may indicate new opportunities for therapeutic intervention and vaccine development in African populations.

Analysis of KIR gene variants in The Cancer Genome Atlas and UK Biobank using KIRCLE

BACKGROUND

Natural killer (NK) cells represent a critical component of the innate immune system's response against cancer and viral infections, among other diseases. To distinguish healthy host cells from infected or tumor cells, killer immunoglobulin receptors (KIR) on NK cells bind and recognize Human Leukocyte Antigen (HLA) complexes on their target cells. However, NK cells exhibit great diversity in their mechanism of activation, and the outcomes of their activation are not yet understood fully. Just like the HLAs they bind, KIR receptors exhibit high allelic diversity in the human population. Here we provide a method to identify KIR allele variants from whole exome sequencing data and uncover novel associations between these variants and various molecular and clinical correlates.

RESULTS

In order to better understand KIRs, we have developed KIRCLE, a novel method for genotyping individual KIR genes from whole exome sequencing data, and used it to analyze approximately sixty-thousand patient samples in The Cancer Genome Atlas (TCGA) and UK Biobank. We were able to assess population frequencies for different KIR alleles and demonstrate that, similar to HLA alleles, individuals' KIR alleles correlate strongly with their ethnicities. In addition, we observed associations between different KIR alleles and HLA alleles, including HLA-B*53 with KIR3DL2*013 (Fisher's exact FDR = 7.64e-51). Finally, we showcased statistically significant associations between KIR alleles and various clinical correlates, including peptic ulcer disease (Fisher's exact FDR = 0.0429) and age of onset of atopy (Mann-Whitney U FDR = 0.0751).

CONCLUSIONS

We show that KIRCLE is able to infer KIR variants accurately and consistently, and we demonstrate its utility using data from approximately sixty-thousand individuals from TCGA and UK Biobank to discover novel molecular and clinical correlations with KIR germline variants. Peptic ulcer disease and atopy are just two diseases in which NK cells may play a role beyond their "classical" realm of anti-tumor and anti-viral responses. This tool may be used both as a benchmark for future KIR-variant-inference algorithms, and to better understand the immunogenomics of and disease processes involving KIRs.

Identification of a cytokine-dominated immunosuppressive class in squamous cell lung carcinoma with implications for immunotherapy resistance

BACKGROUND

Immune checkpoint blockade (ICB) therapy has revolutionized the treatment of lung squamous cell carcinoma (LUSC). However, a significant proportion of patients with high tumour PD-L1 expression remain resistant to immune checkpoint inhibitors. To understand the underlying resistance mechanisms, characterization of the immunosuppressive tumour microenvironment and identification of biomarkers to predict resistance in patients are urgently needed.

METHODS

Our study retrospectively analysed RNA sequencing data of 624 LUSC samples. We analysed gene expression patterns from tumour microenvironment by unsupervised clustering. We correlated the expression patterns with a set of T cell exhaustion signatures, immunosuppressive cells, clinical characteristics, and immunotherapeutic responses. Internal and external testing datasets were used to validate the presence of exhausted immune status.

RESULTS

Approximately 28 to 36% of LUSC patients were found to exhibit significant enrichments of T cell exhaustion signatures, high fraction of immunosuppressive cells (M2 macrophage and CD4 Treg), co-upregulation of 9 inhibitory checkpoints (CTLA4, PDCD1, LAG3, BTLA, TIGIT, HAVCR2, IDO1, SIGLEC7, and VISTA), and enhanced expression of anti-inflammatory cytokines (e.g. TGFβ and CCL18). We defined this immunosuppressive group of patients as exhausted immune class (EIC). Although EIC showed a high density of tumour-infiltrating lymphocytes, these were associated with poor prognosis. EIC had relatively elevated PD-L1 expression, but showed potential resistance to ICB therapy. The signature of 167 genes for EIC prediction was significantly enriched in melanoma patients with ICB therapy resistance. EIC was characterized by a lower chromosomal alteration burden and a unique methylation pattern. We developed a web application ( http://lilab2.sysu.edu.cn/tex & http://liwzlab.cn/tex ) for researchers to further investigate potential association of ICB resistance based on our multi-omics analysis data.

CONCLUSIONS

We introduced a novel LUSC immunosuppressive class which expressed high PD-L1 but showed potential resistance to ICB therapy. This comprehensive characterization of immunosuppressive tumour microenvironment in LUSC provided new insights for further exploration of resistance mechanisms and optimization of immunotherapy strategies.

Practical Implementation of Genetics: New Concepts in Immunogenomics to Predict, Prevent, and Diagnose Drug Hypersensitivity

Delayed drug hypersensitivities are CD8⁺ T cell-mediated reactions associated with up to 50% mortality. Human leukocyte antigen (HLA) alleles are known to predispose disease and are specific to drug, reaction, and patient ethnicity. Pretreatment screening is recommended for a handful of the strongest associations to identify and prevent drug use in high-risk patients. However, an incomplete predictive value implicates other HLA-imposed risk factors, and low carriage of many identified HLA-risk alleles combined with the high cost of sequence-based typing has limited economic viability for similar recommendation of screening across drugs and health care systems. For mitigation, an expanding armory of low-cost polymerase chain reaction-based screens is being developed, and HLA-imposed risk factors are being discovered. These include (1) polymorphic variants of metabolic and endoplasmic reticulum aminopeptidase enzymes toward multiallelic screening with increased predictivity; (2) regulation by immune checkpoint inhibitors, enabling detolerized animal models of human disease; and (3) immunodominant T cell receptors (TCR) on clonally expanded CD8⁺ T cells. For the latter, HLA risk-restricted TCR provides immunogenomic strategies and samples from a single patient to identify novel HLA-risk associations in underserved minority populations, tissue-relevant effector biomarkers toward earlier diagnosis and treatment, and HLA-TCR-presented immunogenic structures to aid future drug development.

Novel Analysis Methods to Mine Immune-Mediated Phenotypes and Find Genetic Variation Within the Electronic Health Record (Roadmap for Phenotype to Genotype: Immunogenomics)

The field of immunogenomics has the opportunity for accelerated genetic discovery aided by the maturation of electronic health records (EHRs) linked to DNA biobanks. Novel analysis methods in deep phenotyping of EHR data allow the full realization of the paired and increasingly dense genetic/phenotypic information available. This enables researchers to uncover genetic risk factors for the prevention and optimal treatment of immune-mediated diseases and immune-mediated adverse drug reactions. This article reviews the background of EHRs linked to DNA biobanks, potential applications to immunogenomic discovery, and current and emerging techniques in EHR-based deep phenotyping.

Novel Transcriptomic Interactions Between Immune Content and Genomic Classifier Predict Lethal Outcomes in High-grade Prostate Cancer

Grade group 4 and 5 (GG-45) prostate cancer (PCa) patients are at the highest risk of lethal outcomes, yet lack genomic risk stratification for prognosis and treatment selection. Here, we assess whether transcriptomic interactions between tumor immune content score (ICS) and the Decipher genomic classifier can identify most lethal subsets of GG-45 PCa. We utilized whole transcriptome data from 8071 tumor tissue (6071 prostatectomy and 2000 treatment-naïve biopsy samples) to derive four immunogenomic subtypes using ICS and Decipher. When compared across all grade groups, GG-45 samples had the highest proportion of most aggressive subtype-ICS^High/Decipher^High. Subsequent analyses within the GG-45 patient samples (n = 1420) revealed that the ICS^High/Decipher^High subtype was associated with increased genomic radiosensitivity. Additionally, in a multivariable model (n = 335), ICS^High/Decipher^High subtype had a significantly higher risk of distant metastasis (hazard ratio [HR] = 5.41; 95% confidence interval [CI], 2.76-10.6; p ≤  0.0001) and PCa-specific mortality (HR = 10.6; 95% CI, 4.18-26.94; p ≤  0.0001) as compared with ICS^Low/Decipher^Low. The novel immunogenomic subtypes establish a very strong synergistic interaction between ICS and Decipher in identifying GG-45 patients who experience the most lethal outcomes. PATIENT SUMMARY: In this analysis, we identified a novel interaction between the total immune content of prostate tumors and genomic classifier to identify the most lethal subset of patients with grade groups 4 and 5. Our results will aid in the subtyping of aggressive prostate cancer patients who may benefit from combined immune-radiotherapy modalities.

Maintenance of the human memory T cell repertoire by subset and tissue site

BACKGROUND

Immune-mediated protection is mediated by T cells expressing pathogen-specific T cell antigen receptors (TCR) that are maintained at diverse sites of infection as tissue-resident memory T cells (TRM) or that disseminate as circulating effector-memory (TEM), central memory (TCM), or terminal effector (TEMRA) subsets in blood and tissues. The relationship between circulating and tissue resident T cell subsets in humans remains elusive, and is important for promoting site-specific protective immunity.

METHODS

We analyzed the TCR repertoire of the major memory CD4+ and CD8+T cell subsets (TEM, TCM, TEMRA, and TRM) isolated from blood and/or lymphoid organs (spleen, lymph nodes, bone marrow) and lungs of nine organ donors, and blood of three living individuals spanning five decades of life. High-throughput sequencing of the variable (V) portion of individual TCR genes for each subset, tissue, and individual were analyzed for clonal diversity, expansion and overlap between lineage, T cell subsets, and anatomic sites. TCR repertoires were further analyzed for TRBV gene usage and CDR3 edit distance.

RESULTS

Across blood, lymphoid organs, and lungs, human memory, and effector CD8+T cells exhibit greater clonal expansion and distinct TRBV usage compared to CD4+T cell subsets. Extensive sharing of clones between tissues was observed for CD8+T cells; large clones specific to TEMRA cells were present in all sites, while TEM cells contained clones shared between sites and with TRM. For CD4+T cells, TEM clones exhibited the most sharing between sites, followed by TRM, while TCM clones were diverse with minimal sharing between sites and subsets. Within sites, TRM clones exhibited tissue-specific expansions, and maintained clonal diversity with age, compared to age-associated clonal expansions in circulating memory subsets. Edit distance analysis revealed tissue-specific biases in clonal similarity.

CONCLUSIONS

Our results show that the human memory T cell repertoire comprises clones which persist across sites and subsets, along with clones that are more restricted to certain subsets and/or tissue sites. We also provide evidence that the tissue plays a key role in maintaining memory T cells over age, bolstering the rationale for site-specific targeting of memory reservoirs in vaccines and immunotherapies.

Integrative molecular characterisation of gallbladder cancer reveals micro-environment-associated subtypes

BACKGROUND & AIMS

Gallbladder cancer (GBC) is the most common type of biliary tract cancer, but the molecular mechanisms involved in gallbladder carcinogenesis remain poorly understood. In this study, we applied integrative genomics approaches to characterise GBC and explore molecular subtypes associated with patient survival.

METHODS

We profiled the mutational landscape of GBC tumours (whole-exome sequencing on 92, targeted sequencing on 98, in total 190 patients). In a subset (n = 45), we interrogated the matched transcriptomes, DNA methylomes, and somatic copy number alterations. We explored molecular subtypes identified through clustering tumours by genes whose expression was associated with survival in 47 tumours and validated subtypes on 34 publicly available GBC cases.

RESULTS

Exome analysis revealed TP53 was the most mutated gene. The overall mutation rate was low (median 0.82 Mut/Mb). APOBEC-mediated mutational signatures were more common in tumours with higher mutational burden. Aflatoxin-related signatures tended to be highly clonal (present in ≥50% of cancer cells). Transcriptome-wide survival association analysis revealed a 95-gene signature that stratified all GBC patients into 3 subtypes that suggested an association with overall survival post-resection. The 2 poor-survival subtypes were associated with adverse clinicopathologic features (advanced stage, pN1, pM1), immunosuppressive micro-environments (myeloid-derived suppressor cell accumulation, extensive desmoplasia, hypoxia) and T cell dysfunction, whereas the good-survival subtype showed the opposite features.

CONCLUSION

These data suggest that the tumour micro-environment and immune profiles could play an important role in gallbladder carcinogenesis and should be evaluated in future clinical studies, along with mutational profiles.

LAY SUMMARY

Gallbladder cancer is highly fatal, and its causes are poorly understood. We evaluated gallbladder tumours to see if there were differences between tumours in genetic information such as DNA and RNA. We found evidence of aflatoxin exposure in these tumours, and immune cells surrounding the tumours were associated with survival.

Therapeutic applications of the cancer immunoediting hypothesis

Since the late 19th century, the immune system has increasingly garnered interest as a novel avenue for cancer therapy, particularly given scientific breakthroughs in recent decades delineating the fundamental role of the immune system in tumorigenesis. The immunoediting hypothesis has articulated this role, describing three phases of the tumor-immune system interaction: Elimination, Equilibrium, and Escape wherein tumors progress from active immunologic surveillance and destruction through dynamic immunologic stasis to unfettered growth. The primary goals of immunotherapy are to restrict and revert progression through these phases, thereby improving the immune system's ability to control tumor growth. In this review, we detail the development and foundation of the cancer immunoediting hypothesis and apply this hypothesis to the dynamic immunotherapy field that includes checkpoint blockade, vaccine therapy, and adoptive cell transfer.

Construction of the optimization prognostic model based on differentially expressed immune genes of lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most common pathology subtype of lung cancer. In recent years, immunotherapy, targeted therapy and chemotherapeutics conferred a certain curative effects. However, the effect and prognosis of LUAD patients are different, and the efficacy of existing LUAD risk prediction models is unsatisfactory.

Methods

The Cancer Genome Atlas (TCGA) LUAD dataset was downloaded. The differentially expressed immune genes (DEIGs) were analyzed with edgeR and DESeq2. The prognostic DEIGs were identified by COX regression. Protein-protein interaction (PPI) network was inferred by STRING using prognostic DEIGs with p value< 0.05. The prognostic model based on DEIGs was established using Lasso regression. Immunohistochemistry was used to assess the expression of FERMT2, FKBP3, SMAD9, GATA2, and ITIH4 in 30 cases of LUAD tissues.

Results

In total,1654 DEIGs were identified, of which 436 genes were prognostic. Gene functional enrichment analysis indicated that the DEIGs were involved in inflammatory pathways. We constructed 4 models using DEIGs. Finally, model 4, which was constructed using the 436 DEIGs performed the best in prognostic predictions, the receiver operating characteristic curve (ROC) was 0.824 for 3 years, 0.838 for 5 years, 0.834 for 10 years. High levels of FERMT2, FKBP3 and low levels of SMAD9, GATA2, ITIH4 expression are related to the poor overall survival in LUAD (p < 0.05). The prognostic model based on DEIGs reflected infiltration by immune cells.

Conclusions

In our study, we built an optimal prognostic signature for LUAD using DEIGs and verified the expression of selected genes in LUAD. Our result suggests immune signature can be harnessed to obtain prognostic insights.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-07911-8.

The head and neck cancer genome in the era of immunotherapy

The recent success of immunotherapy in head and neck squamous cell carcinoma (HNSCC) has necessitated a new perspective on the cancer genome. Here we review recent advances in the carcinogenesis and molecular genetics of HNSCC with an eye on their implications for cancer immunity. Newer sequencing technologies have recently facilitated dissection of the complex interaction between the HPV virus, tumor, host factors, and the tumor microenvironment (TME) that help shed light on how the immune system interacts with head and neck malignancies.

Avian Immunome DB: an example of a user-friendly interface for extracting genetic information

BACKGROUND

Genomic and genetic studies often require a target list of genes before conducting any hypothesis testing or experimental verification. With the ever-growing number of sequenced genomes and a variety of different annotation strategies, comes the potential for ambiguous gene symbols, making it cumbersome to capture the "correct" set of genes. In this article, we present and describe the Avian Immunome DB (AVIMM) for easy gene property extraction as exemplified by avian immune genes. The avian immune system is characterised by a cascade of complex biological processes underlaid by more than 1000 different genes. It is a vital trait to study particularly in birds considering that they are a significant driver in spreading zoonotic diseases. With the completion of phase II of the B10K ("Bird 10,000 Genomes") consortium's whole-genome sequencing effort, we have included 363 annotated bird genomes in addition to other publicly available bird genome data which serve as a valuable foundation for AVIMM.

CONSTRUCTION AND CONTENT

A relational database with avian immune gene evidence from Gene Ontology, Ensembl, UniProt and the B10K consortium has been designed and set up. The foundation stone or the "seed" for the initial set of avian immune genes is based on the well-studied model organism chicken (Gallus gallus). Gene annotations, different transcript isoforms, nucleotide sequences and protein information, including amino acid sequences, are included. Ambiguous gene names (symbols) are resolved within the database and linked to their canonical gene symbol. AVIMM is supplemented by a command-line interface and a web front-end to query the database.

UTILITY AND DISCUSSION

The internal mapping of unique gene symbol identifiers to canonical gene symbols allows for an ambiguous gene property search. The database is organised within core and feature tables, which makes it straightforward to extend for future purposes. The database design is ready to be applied to other taxa or biological processes. Currently, the database contains 1170 distinct avian immune genes with canonical gene symbols and 612 synonyms across 363 bird species. While the command-line interface readily integrates into bioinformatics pipelines, the intuitive web front-end with download functionality offers sophisticated search functionalities and tracks the origin for each record. AVIMM is publicly accessible at https://avimm.ab.mpg.de .

Characterization of bovine MHC DRB3 diversity in global cattle breeds, with a focus on cattle in Myanmar

Background

Myanmar cattle populations predominantly consist of native cattle breeds (Pyer Sein and Shwe), characterized by their geographical location and coat color, and the Holstein-Friesian crossbreed, which is highly adapted to the harsh tropical climates of this region. Here, we analyzed the diversity and genetic structure of the BoLA-DRB3 gene, a genetic locus that has been linked to the immune response, in Myanmar cattle populations.

Methods

Blood samples (n = 294) were taken from two native breeds (Pyer Sein, n = 163 and Shwe Ni, n = 69) and a cattle crossbreed (Holstein-Friesian, n = 62) distributed across six regions of Myanmar (Bago, n = 38; Sagaing, n = 77; Mandalay, n = 46; Magway, n = 46; Kayin, n = 43; Yangon, n = 44). In addition, a database that included 2428 BoLA-DRB3 genotypes from European (Angus, Hereford, Holstein, Shorthorn, Overo Negro, Overo Colorado, and Jersey), Zebuine (Nellore, Brahman and Gir), Asian Native from Japan and Philippine and Latin-American Creole breeds was also included. Furthermore, the information from the IPD–MHC database was also used in the present analysis. DNA was genotyped using the sequence-based typing method. DNA electropherograms were analyzed using the Assign 400ATF software.

Results

We detected 71 distinct alleles, including three new variants for the BoLA-DRB3 gene. Venn analysis showed that 11 of these alleles were only detected in Myanmar native breeds and 26 were only shared with Asian native and/or Zebu groups. The number of alleles ranged from 33 in Holstein-Friesians to 58 in Pyer Seins, and the observed versus unbiased expected heterozygosity were higher than 0.84 in all the three the populations analyzed. The F_ST analysis showed a low level of genetic differentiation between the two Myanmar native breeds (F_ST = 0.003), and between these native breeds and the Holstein-Friesians (F_ST <  0.021). The average F_ST value for all the Myanmar Holstein-Friesian crossbred and Myanmar native populations was 0.0136 and 0.0121, respectively. Principal component analysis (PCA) and tree analysis showed that Myanmar native populations grouped in a narrow cluster that diverged clearly from the Holstein-Friesian populations. Furthermore, the BoLA-DRB3 allele frequencies suggested that while some Myanmar native populations from Bago, Mandalay and Yangon regions were more closely related to Zebu breeds (Gir and Brahman), populations from Kayin, Magway and Sagaing regions were more related to the Philippines native breeds. On the contrary, PCA showed that the Holstein-Friesian populations demonstrated a high degree of dispersion, which is likely the result of the different degrees of native admixture in these populations.

Conclusion

This study is the first to report the genetic diversity of the BoLA-DRB3 gene in two native breeds and one exotic cattle crossbreed from Myanmar. The results obtained contribute to our understanding of the genetic diversity and distribution of BoLA-DRB3 gene alleles in Myanmar, and increases our knowledge of the worldwide variability of cattle BoLA-DRB3 genes, an important locus for immune response and protection against pathogens.

Immunogenomic pathways associated with cytotoxic lymphocyte infiltration and survival in colorectal cancer

Background

Colorectal cancer (CRC) is the second leading cancer killer in the US today and patients with metastatic disease have only a 14% 5-year survival. One of the most impactful recent advances in cancer therapy, immune checkpoint inhibition, has not been shown to be effective for the majority of these patients. In this study, we use The Cancer Genome Atlas (TCGA) and recently developed informatic-based tools to identify targets for immune based therapy in colorectal cancer patients.

Methods

Open access, pre-processed (level 3) mRNA data and clinical data from colorectal patients from the TCGA was downloaded from FireCloud. Using the Microenvironment Cell Populations-Counter method (MCP-Counter), cytotoxic lymphocyte scores were calculated for all patients. Patients were then grouped by cytotoxic lymphocyte score (High vs Low), pathologic stage, and location to identify differentially expressed genes. Pathway enrichment analysis was performed using Reactome to determine differentially expressed genes associated with immune pathways. Survival analysis was performed with identified differentially expressed genes.

Results

In the TCGA dataset, there are 461 colon and 172 rectal cancer patients. After stratifying patients by cytotoxic lymphocyte score, anatomical location, and stage, we found a significant number of differentially expressed genes. We identified one pathway, “immunoregulatory interactions between a lymphoid and non-lymphoid cell”, that was highly enriched and included in all tumor locations and stages. Survival analysis performed with differentially expressed genes in this pathway identified 21 different genes associated with survival and cytotoxic lymphocyte infiltration, with ~ 70% of these genes occurring in the metastatic right-sided CRC group. Specifically, all genes associated with survival in the metastatic right-sided colorectal cancer group with low cytotoxic lymphocyte scores positively impacted survival.

Conclusions

Utilizing the TCGA, a publicly available dataset, and informatics-based analyses, we identified potential targets to improve immune based therapy in colorectal cancer. Additionally, we note the most targets in metastatic right-sided CRC patients, the patient group with the worst predicted survival. The results from this study demonstrate the ability of informatics-based analytic techniques to identify new therapeutic targets as well as improve patient selection for intervention, helping us to achieve the goals of precision-based oncology.

Structural and immunogenomic insights into B-cell receptor activation

B cells express B-cell receptors (BCRs) which recognize antigen to trigger signaling cascades for B-cell activation and subsequent antibody production. BCR activation has a crucial influence on B-cell fate. How BCR is activated upon encountering antigen remains to be solved, although tremendous progresses have been achieved in the past few years. Here, we summarize the models that have been proposed to explain BCR activation, including the cross-linking model, the conformation-induced oligomerization model, the dissociation activation model, and the conformational change model. Especially, we elucidate the partially resolved structures of antibodies and/or BCRs by far and discusse how these current structural and further immunogenomic messages and more importantly the future studies may shed light on the explanation of BCR activation and the relevant diseases in the case of dysregulation.

Using RNA Sequencing to Characterize the Tumor Microenvironment

RNA sequencing (RNA-seq) is an integral tool in immunogenomics, allowing for interrogation of the transcriptome of a tumor and its microenvironment. Analytical methods to deconstruct the genomics data can then be applied to infer gene expression patterns associated with the presence of various immunocyte populations. High quality RNA-seq is possible from formalin-fixed, paraffin-embedded (FFPE), fresh-frozen, and fresh tissue, with a wide variety of sequencing library preparation methods, sequencing platforms, and downstream bioinformatics analyses currently available. Selection of an appropriate library preparation method is largely determined by tissue type, quality of RNA, and quantity of RNA. Downstream of sequencing, many analyses can be applied to the data, including differential gene expression analysis, immune gene signature analysis, gene pathway analysis, T/B-cell receptor inference, HLA inference, and viral transcript quantification. In this chapter, we will describe our workflow for RNA-seq from bulk tissue to evaluable data, including extraction of RNA, library preparation methods, sequencing of libraries, alignment and quality assurance of data, and initial downstream analyses of RNA-seq data to extract relevant immunogenomics features. Systems biology methods that draw additional insights by integrating these features are covered further in Chapters 28 - 30 .

Analysis of TCR β CDR3 sequencing data for tracking anti-tumor immunity

Anti-tumor T cells are the soldiers in the body's war against cancer. Effector T cells can detect and eliminate cells expressing their cognate antigen via activation through engagement of the T cell receptor (TCR) with its cognate peptide:MHC complex. Owing to the recent success of immunotherapy in the treatment of many different cancer types, research efforts have shifted toward identifying and tracking anti-tumor T cell responses upon treatment in cancer patients. While traditional methods, such as ELISpot and flow cytometric intracellular staining have had limited success, likely owing to the inability to get viable biospecimens or the lower magnitude of tumor-specific T cell responses relative to virus-specific responses, new techniques that utilize next generation sequencing enable T cell response tracking independent of cytokine production or cell viability. The TCR, which confers T cell antigen-specificity, can be used as a molecular barcode to track T cell clonotypic dynamics across biological compartments and over time in cancer patients undergoing treatment. Because this method does not require viable cells, these T cell clonotypes can also be tracked in archival tumor tissue and flash frozen cell pellets. While exciting, quantitative TCR sequencing (TCRseq) technologies have been met with the conundrum of how to properly analyze and interpret the data. Here we provide a comprehensive guide on how to acquire, analyze, and interpret TCRseq data, as well as special considerations that should be taken prior to experimental setup.

Immunologic and immunogenomic aspects of tumor progression

Tumor progression to metastatic disease is characterized by continuous genetic alterations due to instability of the genome. Immune sensitivity was found to be linked to tumor mutational burden (TMB) and the resulting amount of neoantigens. However, APOBEC activity resulting in increase in TMB causes immune evasion. On the other hand, clonal or acquired genetic loss of HLA class I also hampers immune sensitivity of tumors. Rare amplification of the PD-L1 gene in cancers may render them sensitive to immune checkpoint inhibitors but involvement of broader regions of chromosome 9p may ultimately lead again to immune evasion due to inactivation of the IFN-γ signaling pathway. Such genetic changes may occur not only in the primary tumor but at any phase of progression: in lymphatic as well as in visceral metastases. Accordingly, it is rational to monitor these changes continuously during disease progression similar to target therapies. Moreover, beside temporal variability, genomic features of tumors such as mutation profiles, as well as the tumor immune microenvironment also show considerable inter- and intratumoral spatial heterogeneity, suggesting the necessity of multiple sampling in biomarker studies.

Neoantigens retention in patient derived xenograft models mediates autologous T cells activation in ovarian cancer

Ovarian cancer (OC) has an overall modest number of mutations that facilitate a functional immune infiltrate able to recognize tumor mutated antigens, or neoantigens. Although patient-derived xenografts (PDXs) can partially model the tumor mutational load and mimic response to chemotherapy, no study profiled a neoantigen-driven response in OC PDXs. Here we demonstrate that the genomic status of the primary tumor from an OC patient can be recapitulated in vivo in a PDX model, with the goal of defining autologous T cells activation by neoantigens using in silico, in vitro and in vivo approaches. By profiling the PDX mutanome we discovered three main clusters of mutations defining the expansion, retraction or conservation of tumor clones based on their variant allele frequencies (VAF). RNASeq analyses revealed a strong functional conservation between the primary tumor and PDXs, highlighted by the upregulation of antigen presenting pathways. We tested in vitro a set of 30 neoantigens for recognition by autologous T cells and identified a core of six neoantigens that define a potent T cell activation able to slow tumor growth in vivo. The pattern of recognition of these six neoantigens indicates the pre-existence of anti-tumor immunity in the patient. To evaluate the breadth of T cell activation, we performed single cell sequencing profiling the TCR repertoire upon stimulation with neoantigenic moieties and identified sequence motifs that define an oligoclonal and autologous T cell response. Overall, these results indicate that OC PDXs can be a valid tool to model OC response to immunotherapy.

Overview of methodologies for T-cell receptor repertoire analysis

Background

The T-cell receptor (TCR), located on the surface of T cells, is responsible for the recognition of the antigen-major histocompatibility complex, leading to the initiation of an inflammatory response. Analysing the TCR repertoire may help to gain a better understanding of the immune system features and of the aetiology and progression of diseases, in particular those with unknown antigenic triggers. The extreme diversity of the TCR repertoire represents a major analytical challenge; this has led to the development of specialized methods which aim to characterize the TCR repertoire in-depth. Currently, next generation sequencing based technologies are most widely employed for the high-throughput analysis of the immune cell repertoire.

Results

Here, we report on the latest methodological advancements in the field by describing and comparing the available tools; from the choice of the starting material and library preparation method, to the sequencing technologies and data analysis.

Finally, we provide a practical example and our own experience by reporting some exemplary results from a small internal benchmark study, where current approaches from the literature and the market are employed and compared.

Conclusions

Several valid methods for clonotype identification and TCR repertoire analysis exist, however, a gold standard method for the field has not yet been identified. Depending on the purpose of the scientific study, some approaches may be more suitable than others. Finally, due to possible method specific biases, scientists must be careful when comparing results obtained using different methods.

Electronic supplementary material

The online version of this article (doi:10.1186/s12896-017-0379-9) contains supplementary material, which is available to authorized users.

Identification of innate lymphoid cells in single-cell RNA-Seq data

Innate lymphoid cells (ILCs) consist of natural killer (NK) cells and non-cytotoxic ILCs that are broadly classified into ILC1, ILC2, and ILC3 subtypes. These cells recently emerged as important early effectors of innate immunity for their roles in tissue homeostasis and inflammation. Over the last few years, ILCs have been extensively studied in mouse and human at the functional and molecular level, including gene expression profiling. However, sorting ILCs with flow cytometry for gene expression analysis is a delicate and time-consuming process. Here we propose and validate a novel framework for studying ILCs at the transcriptomic level using single-cell RNA-Seq data. Our approach combines unsupervised clustering and a new cell type classifier trained on mouse ILC gene expression data. We show that this approach can accurately identify different ILCs, especially ILC2 cells, in human lymphocyte single-cell RNA-Seq data. Our new model relies only on genes conserved across vertebrates, thereby making it in principle applicable in any vertebrate species. Considering the rapid increase in throughput of single-cell RNA-Seq technology, our work provides a computational framework for studying ILC2 cells in single-cell transcriptomic data and may help exploring their conservation in distant vertebrate species.

Neoantigens in immunotherapy and personalized vaccines: Implications for head and neck squamous cell carcinoma

The recent success of immunotherapies has demonstrated the potency of tumor-specific immune cells in mediating tumor rejection and generating durable tumor immunity. Our understanding of the scientific basis of these responses results from the confluence of a better comprehension of the cancer immunoediting process and the revolution in next generation sequencing of cancer genomes. Recent evidence suggests that T cell specificity for cancer cell expressed mutant proteins - termed neoantigens - is an important component of immune mediated tumor rejection. Improved neoantigen prediction algorithms have made it possible to predict and monitor immune responses to checkpoint inhibitors and adoptively transferred autologous lymphocytes and have enabled the development of tumor-specific therapeutic vaccines. Herein, we review the current research on cancer neoantigens in immunotherapies and its implications for the future of head and neck cancer management.

Identification of immune-related SNPs in the transcriptome of Mytilus chilensis through high-throughput sequencing

Single nucleotide polymorphisms (SNPs) identified in coding regions represent a useful tool for understanding the immune response against pathogens and stressful environmental conditions. In this study, a SNPs database was generated from transcripts involved in the innate immune response of the mussel Mytilus chilensis. The SNPs were identified through hemocytes transcriptome sequencing from 18 individuals, and SNPs mining was performed in 225,336 contigs, yielding 20,306 polymorphisms associated to immune-related genes. Classification of identified SNPs was based on different pathways of the immune response for Mytilus sp. A total of 28 SNPs were identified in the Toll-like receptor pathway and included 5 non-synonymous polymorphisms; 19 SNPs were identified in the apoptosis pathway and included 3 non-synonymous polymorphisms; 35 SNPs were identified in the Ubiquitin-mediated proteolysis pathway and included 4 non-synonymous variants; and 54 SNPs involved in other molecular functions related to the immune response, such as molecular chaperones, antimicrobial peptides, and genes that interacts with marine toxins were also identified. The molecular markers identified in this work could be useful for novel studies, such as those related to associations between high-resolution molecular markers and functional response to pathogen agents.