Neoantigen Landscape Supports Feasibility of Personalized Cancer Vaccine for Follicular Lymphoma.

Personalized cancer vaccines designed to target neoantigens represent a promising new treatment paradigm in oncology. In contrast to classical idiotype vaccines, we hypothesized that 'polyvalent' vaccines could be engineered for the personalized treatment of follicular lymphoma (FL) using neoantigen discovery by combined whole exome sequencing (WES) and RNA sequencing (RNA-Seq). Fifty-eight tumor samples from 57 patients with FL underwent WES and RNA-Seq. Somatic and B-cell clonotype neoantigens were predicted and filtered to identify high-quality neoantigens. B-cell clonality was determined by alignment of B-cell receptor (BCR) CDR3 regions from RNA-Seq data, grouping at the protein level, and comparison to the BCR repertoire from healthy individuals using RNA-Seq data. An average of 52 somatic mutations per patient (range: 2-172) were identified, and two or more (median: 15) high-quality neoantigens were predicted for 56 of 58 FL samples. The predicted neoantigen peptides were composed of missense mutations (77%), indels (9%), gene fusions (3%), and BCR sequences (11%). Building off of these preclinical analyses, we initiated a pilot clinical trial using personalized neoantigen vaccination combined with PD-1 blockade in patients with relapsed or refractory FL (#NCT03121677). Synthetic long peptide (SLP) vaccines targeting predicted high-quality neoantigens were successfully synthesized for and administered to all four patients enrolled. Initial results demonstrate feasibility, safety, and potential immunologic and clinical responses. Our study suggests that a genomics-driven personalized cancer vaccine strategy is feasible for patients with FL, and this may overcome prior challenges in the field.

Procrustes is a machine-learning approach that removes cross-platform batch effects from clinical RNA sequencing data.

With the increased use of gene expression profiling for personalized oncology, optimized RNA sequencing (RNA-seq) protocols and algorithms are necessary to provide comparable expression measurements between exome capture (EC)-based and poly-A RNA-seq. Here, we developed and optimized an EC-based protocol for processing formalin-fixed, paraffin-embedded samples and a machine-learning algorithm, Procrustes, to overcome batch effects across RNA-seq data obtained using different sample preparation protocols like EC-based or poly-A RNA-seq protocols. Applying Procrustes to samples processed using EC and poly-A RNA-seq protocols showed the expression of 61% of genes (N = 20,062) to correlate across both protocols (concordance correlation coefficient > 0.8, versus 26% before transformation by Procrustes), including 84% of cancer-specific and cancer microenvironment-related genes (versus 36% before applying Procrustes; N = 1,438). Benchmarking analyses also showed Procrustes to outperform other batch correction methods. Finally, we showed that Procrustes can project RNA-seq data for a single sample to a larger cohort of RNA-seq data. Future application of Procrustes will enable direct gene expression analysis for single tumor samples to support gene expression-based treatment decisions.

Multi-omic profiling of follicular lymphoma reveals changes in tissue architecture and enhanced stromal remodeling in high-risk patients.

Follicular lymphoma (FL) is a generally incurable malignancy that evolves from developmentally blocked germinal center (GC) B cells. To promote survival and immune escape, tumor B cells undergo significant genetic changes and extensively remodel the lymphoid microenvironment. Dynamic interactions between tumor B cells and the tumor microenvironment (TME) are hypothesized to contribute to the broad spectrum of clinical behaviors observed among FL patients. Despite the urgent need, existing clinical tools do not reliably predict disease behavior. Using a multi-modal strategy, we examined cell-intrinsic and -extrinsic factors governing progression and therapeutic outcomes in FL patients enrolled onto a prospective clinical trial. By leveraging the strengths of each platform, we identify several tumor-specific features and microenvironmental patterns enriched in individuals who experience early relapse, the most high-risk FL patients. These features include stromal desmoplasia and changes to the follicular growth pattern present 20 months before first progression and first relapse.

Spatial mapping of human hematopoiesis at single-cell resolution reveals aging-associated topographic remodeling.

ABSTRACT: The spatial anatomy of hematopoiesis in the bone marrow (BM) has been extensively studied in mice and other preclinical models, but technical challenges have precluded a commensurate exploration in humans. Institutional pathology archives contain thousands of paraffinized BM core biopsy tissue specimens, providing a rich resource for studying the intact human BM topography in a variety of physiologic states. Thus, we developed an end-to-end pipeline involving multiparameter whole tissue staining, in situ imaging at single-cell resolution, and artificial intelligence-based digital whole slide image analysis and then applied it to a cohort of disease-free samples to survey alterations in the hematopoietic topography associated with aging. Our data indicate heterogeneity in marrow adipose tissue (MAT) content within each age group and an inverse correlation between MAT content and proportions of early myeloid and erythroid precursors, irrespective of age. We identify consistent endosteal and perivascular positioning of hematopoietic stem and progenitor cells (HSPCs) with medullary localization of more differentiated elements and, importantly, uncover new evidence of aging-associated changes in cellular and vascular morphologies, microarchitectural alterations suggestive of foci with increased lymphocytes, and diminution of a potentially active megakaryocytic niche. Overall, our findings suggest that there is topographic remodeling of human hematopoiesis associated with aging. More generally, we demonstrate the potential to deeply unravel the spatial biology of normal and pathologic human BM states using intact archival tissue specimens.

B-Cell Infiltrate in the Tumor Microenvironment Is Associated With Improved Survival in Resected Lung Adenocarcinoma.

Introduction: Relapse is common after resection of lung adenocarcinoma (LUAD). Features of the tumor microenvironment (TME) which influence postsurgical survival outcomes are poorly characterized. Here, we analyzed the TME of more than 1500 LUAD specimens to identify the relationship between B-cell infiltration and prognosis. Methods: Whole exome sequencing and bulk RNA sequencing were performed on LUADs and adjacent normal lung tissue. Relapse-free survival and overall survival (OS) were retrospectively correlated with characteristics of the tumor and TME in three data sets. Results: High B-cell content (defined as >10% B cells) was associated with improved OS in both a The Cancer Genome Atlas-resected LUAD data set (p = 0.01) and a separate institutional stage II LUAD data set (p = 0.04, median not reached versus 89.5 mo). A validation cohort consisting of pooled microarray data representing more than 1400 resected stage I to III LUADs confirmed the association between greater B-cell abundance, specifically higher B-cell expression, and longer postsurgical survival (median OS 90 versus 71 mo, p < 0.01). Relapse-free survival was longer for patients with adenocarcinomas with high B-cell content across data sets, but it did not reach statistical significance. Subcategorization of B-cell subsets indicated that high naive B-cell content was most predictive of survival. There was no correlation between programmed death-ligand 1 expression, lymphoid aggregates, or overall immune infiltrate density and survival outcomes across the cohorts. Conclusions: The growing adjuvant immunotherapy repertoire has increased the urgency for identifying prognostic and predictive biomarkers. Comprehensive profiling of more than 1500 LUADs suggests that high tumor-infiltrating B-cell content is a favorable prognostic marker.

B cell-dependent subtypes and treatment-based immune correlates to survival in stage 3 and 4 lung adenocarcinomas.

Lung cancer is the leading cause of cancer-related deaths worldwide. Surgery and chemoradiation are the standard of care in early stages of non-small cell lung cancer (NSCLC), while immunotherapy is the standard of care in late-stage NSCLC. The immune composition of the tumor microenvironment (TME) is recognized as an indicator for responsiveness to immunotherapy, although much remains unknown about its role in responsiveness to surgery or chemoradiation. In this pilot study, we characterized the NSCLC TME using mass cytometry (CyTOF) and bulk RNA sequencing (RNA-Seq) with deconvolution of RNA-Seq being performed by Kassandra, a recently published deconvolution tool. Stratification of patients based on the intratumoral abundance of B cells identified that the B-cell rich patient group had increased expression of CXCL13 and greater abundance of PD1+ CD8 T cells. The presence of B cells and PD1+ CD8 T cells correlated positively with the presence of intratumoral tertiary lymphoid structures (TLS). We then assessed the predictive and prognostic utility of these cell types and TLS within publicly available stage 3 and 4 lung adenocarcinoma (LUAD) RNA-Seq datasets. As previously described by others, pre-treatment expression of intratumoral 12-chemokine TLS gene signature is associated with progression free survival (PFS) in patients who receive treatment with immune checkpoint inhibitors (ICI). Notably and unexpectedly pre-treatment percentages of intratumoral B cells are associated with PFS in patients who receive surgery, chemotherapy, or radiation. Further studies to confirm these findings would allow for more effective patient selection for both ICI and non-ICI treatments.

Multiregional single-cell proteogenomic analysis of ccRCC reveals cytokine drivers of intratumor spatial heterogeneity.

Intratumor heterogeneity (ITH) represents a major challenge for anticancer therapies. An integrated, multidimensional, multiregional approach dissecting ITH of the clear cell renal cell carcinoma (ccRCC) tumor microenvironment (TME) is employed at the single-cell level with mass cytometry (CyTOF), multiplex immunofluorescence (MxIF), and single-nucleus RNA sequencing (snRNA-seq) and at the bulk level with whole-exome sequencing (WES), RNA-seq, and methylation profiling. Multiregional analyses reveal unexpected conservation of immune composition within each individual patient, with profound differences among patients, presenting patient-specific tumor immune microenvironment signatures despite underlying genetic heterogeneity from clonal evolution. Spatial proteogenomic TME analysis using MxIF identifies 14 distinct cellular neighborhoods and, conversely, demonstrated architectural heterogeneity among different tumor regions. Tumor-expressed cytokines are identified as key determinants of the TME and correlate with clinical outcome. Overall, this work signifies that spatial ITH occurs in ccRCC, which may drive clinical heterogeneity and warrants further interrogation to improve patient outcomes.

Precise reconstruction of the TME using bulk RNA-seq and a machine learning algorithm trained on artificial transcriptomes.

Cellular deconvolution algorithms virtually reconstruct tissue composition by analyzing the gene expression of complex tissues. We present the decision tree machine learning algorithm, Kassandra, trained on a broad collection of >9,400 tissue and blood sorted cell RNA profiles incorporated into millions of artificial transcriptomes to accurately reconstruct the tumor microenvironment (TME). Bioinformatics correction for technical and biological variability, aberrant cancer cell expression inclusion, and accurate quantification and normalization of transcript expression increased Kassandra stability and robustness. Performance was validated on 4,000 H&E slides and 1,000 tissues by comparison with cytometric, immunohistochemical, or single-cell RNA-seq measurements. Kassandra accurately deconvolved TME elements, showing the role of these populations in tumor pathogenesis and other biological processes. Digital TME reconstruction revealed that the presence of PD-1-positive CD8+ T cells strongly correlated with immunotherapy response and increased the predictive potential of established biomarkers, indicating that Kassandra could potentially be utilized in future clinical applications.

Follicular Lymphoma Microenvironment Characteristics Associated with Tumor Cell Mutations and MHC Class II Expression.

Follicular lymphoma (FL) is a B-cell malignancy with a complex tumor microenvironment that is rich in nonmalignant immune cells. We applied single-cell RNA sequencing to characterize the diverse tumor and immune cell populations of FL and identified major phenotypic subsets of FL T cells, including a cytotoxic CD4 T-cell population. We characterized four major FL subtypes with differential representation or relative depletion of distinct T-cell subsets. By integrating exome sequencing, we observed that somatic mutations are associated with, but not definitive for, reduced MHC expression on FL cells. In turn, expression of MHCII genes by FL cells was associated with significant differences in the proportions and targetable immunophenotypic characteristics of T cells. This provides a classification framework of the FL microenvironment in association with FL genotypes and MHC expression, and informs different potential immunotherapeutic strategies based upon tumor cell MHCII expression. SIGNIFICANCE: We have characterized the FL-infiltrating T cells, identified cytotoxic CD4 T cells as an important component that is associated with tumor cell-intrinsic characteristics, and identified sets of targetable immune checkpoints on T cells that differed from FLs with normal versus low MHC expression. See related commentary by Melnick, p. 374. This article is highlighted in the In This Issue feature, p. 369.

Diagnostic Utility of RNA-Seq for Evaluation of PD-L1 Expression in Clear Cell Renal Cell Carcinoma.

BACKGROUND: Although there are immune checkpoint inhibitors (ICIs) available for the treatment of renal cell carcinoma (RCC), the utility of PD-L1 detection by immunohistochemistry (IHC) as a predictive biomarker in clear cell RCC (ccRCC) remains controversial. Nevertheless, alternative methods for PD-L1 detection, such as RNA sequencing (RNA-Seq), may be clinically useful in ccRCC; therefore, we sought to determine the ability of RNA-Seq to accurately and sensitively detect PD-L1 expression across different ccRCC clinical samples in comparison with IHC. PATIENTS AND METHODS: Patients with ccRCC (n=127) who received treatment from Washington University in St. Louis between 2018 and 2020 were identified. Tumors from these patients were analyzed using RNA-Seq and IHC. RESULTS: PD-L1 detection by RNA-Seq strongly correlated with IHC (P < .001), which was further validated using two independent datasets. Furthermore, RNA-Seq analysis identified an immune-enriched (higher PD-L1 positivity) and an immune-desert (lower PD-L1 positivity) microenvironment of ccRCC, which also correlated with IHC (P < .00001). CONCLUSION: The results demonstrate the ability of RNA-Seq to detect PD-L1 in various ccRCC clinical samples compared to IHC. Ultimately, these findings suggest that PD-L1 detection by RNA-Seq can be further developed to determine the clinical utility of this methodology in ccRCC.

Lack of intrafollicular memory CD4 + T cells is predictive of early clinical failure in newly diagnosed follicular lymphoma.

Despite a characteristic indolent course, a substantial subset of follicular lymphoma (FL) patients has an early relapse with a poor outcome. Cells in the microenvironment may be a key contributor to treatment failure. We used a discovery and validation study design to identify microenvironmental determinants of early failure and then integrated these results into the FLIPI. In total, 496 newly diagnosed FL grade 1-3 A patients who were prospectively enrolled into the MER cohort from 2002 to 2012 were evaluated. Tissue microarrays were stained for CD4, CD8, FOXP3, CD32b, CD14, CD68, CD70, SIRP-α, TIM3, PD-1, and PD-L1. Early failure was defined as failing to achieve event-free survival at 24 months (EFS24) in immunochemotherapy-treated patients and EFS12 in all others. CyTOF and CODEX analysis were performed to characterize intratumoral immunophenotypes. Lack of intrafollicular CD4 expression was the only predictor of early failure that replicated with a pooled OR 2.37 (95%CI 1.48-3.79). We next developed a bio-clinical risk model (BioFLIPI), where lack of CD4 intrafollicular expression moved patients up one FLIPI risk group, adding a new fourth high-risk group. Compared with BioFLIPI score of 1, patients with a score of 2 (OR 2.17; 95% CI 1.08-4.69), 3 (OR 3.53; 95% CI 1.78-7.54), and 4 (OR 8.92; 95% CI 4.00-21.1) had increasing risk of early failure. The favorable intrafollicular CD4 T cells were identified as activated central memory T cells, whose prognostic value was independent from genetic features. In conclusion, lack of intrafollicular CD4 expression predicts early failure in FL and combined with FLIPI improves identification of high-risk patients; however, independent validation is warranted.

Conserved pan-cancer microenvironment subtypes predict response to immunotherapy.

The clinical use of molecular targeted therapy is rapidly evolving but has primarily focused on genomic alterations. Transcriptomic analysis offers an opportunity to dissect the complexity of tumors, including the tumor microenvironment (TME), a crucial mediator of cancer progression and therapeutic outcome. TME classification by transcriptomic analysis of >10,000 cancer patients identifies four distinct TME subtypes conserved across 20 different cancers. The TME subtypes correlate with patient response to immunotherapy in multiple cancers, with patients possessing immune-favorable TME subtypes benefiting the most from immunotherapy. Thus, the TME subtypes act as a generalized immunotherapy biomarker across many cancer types due to the inclusion of malignant and microenvironment components. A visual tool integrating transcriptomic and genomic data provides a global tumor portrait, describing the tumor framework, mutational load, immune composition, anti-tumor immunity, and immunosuppressive escape mechanisms. Integrative analyses plus visualization may aid in biomarker discovery and the personalization of therapeutic regimens.

Single-cell Spatial Proteomic Revelations on the Multiparametric MRI Heterogeneity of Clinically Significant Prostate Cancer.

PURPOSE: Multiparametric MRI (mpMRI) has become an indispensable radiographic tool in diagnosing prostate cancer. However, mpMRI fails to visualize approximately 15% of clinically significant prostate cancer (csPCa). The molecular, cellular, and spatial underpinnings of such radiographic heterogeneity in csPCa are unclear. EXPERIMENTAL DESIGN: We examined tumor tissues from clinically matched patients with mpMRI-invisible and mpMRI-visible csPCa who underwent radical prostatectomy. Multiplex immunofluorescence single-cell spatial imaging and gene expression profiling were performed. Artificial intelligence-based analytic algorithms were developed to examine the tumor ecosystem and integrate with corresponding transcriptomics. RESULTS: More complex and compact epithelial tumor architectures were found in mpMRI-visible than in mpMRI-invisible prostate cancer tumors. In contrast, similar stromal patterns were detected between mpMRI-invisible prostate cancer and normal prostate tissues. Furthermore, quantification of immune cell composition and tumor-immune interactions demonstrated a lack of immune cell infiltration in the malignant but not in the adjacent nonmalignant tissue compartments, irrespective of mpMRI visibility. No significant difference in immune profiles was detected between mpMRI-visible and mpMRI-invisible prostate cancer within our patient cohort, whereas expression profiling identified a 24-gene stromal signature enriched in mpMRI-invisible prostate cancer. Prostate cancer with strong stromal signature exhibited a favorable survival outcome within The Cancer Genome Atlas prostate cancer cohort. Notably, five recurrences in the 8 mpMRI-visible patients with csPCa and no recurrence in the 8 clinically matched patients with mpMRI-invisible csPCa occurred during the 5-year follow-up post-prostatectomy. CONCLUSIONS: Our study identified distinct molecular, cellular, and structural characteristics associated with mpMRI-visible csPCa, whereas mpMRI-invisible tumors were similar to normal prostate tissue, likely contributing to mpMRI invisibility.

Clinical and Biological Subtypes of B-cell Lymphoma Revealed by Microenvironmental Signatures.

Diffuse large B-cell lymphoma (DLBCL) is a biologically and clinically heterogeneous disease. Transcriptomic and genetic characterization of DLBCL has increased the understanding of its intrinsic pathogenesis and provided potential therapeutic targets. However, the role of the microenvironment in DLBCL biology remains less understood. Here, we performed a transcriptomic analysis of the microenvironment of 4,655 DLBCLs from multiple independent cohorts and described four major lymphoma microenvironment categories that associate with distinct biological aberrations and clinical behavior. We also found evidence of genetic and epigenetic mechanisms deployed by cancer cells to evade microenvironmental constraints of lymphoma growth, supporting the rationale for implementing DNA hypomethylating agents in selected patients with DLBCL. In addition, our work uncovered new therapeutic vulnerabilities in the biochemical composition of the extracellular matrix that were exploited to decrease DLBCL proliferation in preclinical models. This novel classification provides a road map for the biological characterization and therapeutic exploitation of the DLBCL microenvironment. SIGNIFICANCE: In a translational relevant transcriptomic-based classification, we characterized the microenvironment as a critical component of the B-cell lymphoma biology and associated it with the DLBCL clinical behavior establishing a novel opportunity for targeting therapies.This article is highlighted in the In This Issue feature, p. 1307.

Elevated pre-activation basal level of nuclear NF-κB in native macrophages accelerates LPS-induced translocation of cytosolic NF-κB into the cell nucleus.

Signaling via Toll-like receptor 4 (TLR4) in macrophages constitutes an essential part of the innate immune response to bacterial infections. Detailed and quantified descriptions of TLR4 signal transduction would help to understand and exploit the first-line response of innate immune defense. To date, most mathematical modelling studies were performed on transformed cell lines. However, properties of primary macrophages differ significantly. We therefore studied TLR4-dependent activation of NF-κB transcription factor in bone marrow-derived and peritoneal primary macrophages. We demonstrate that the kinetics of NF-κB phosphorylation and nuclear translocation induced by a wide range of bacterial lipopolysaccharide (LPS) concentrations in primary macrophages is much faster than previously reported for macrophage cell lines. We used a comprehensive combination of experiments and mathematical modeling to understand the mechanisms of this rapid response. We found that elevated basal NF-κB in the nuclei of primary macrophages is a mechanism increasing native macrophage sensitivity and response speed to the infection. Such pre-activated state of macrophages accelerates the NF-κB translocation kinetics in response to low agonist concentrations. These findings enabled us to refine and construct a new model combining both NF-κB phosphorylation and translocation processes and predict the existence of a negative feedback loop inactivating phosphorylated NF-κB.

The differences in immunoadjuvant mechanisms of TLR3 and TLR4 agonists on the level of antigen-presenting cells during immunization with recombinant adenovirus vector.

BACKGROUND: Agonists of TLR3 and TLR4 are effective immunoadjuvants for different types of vaccines. The mechanisms of their immunostimulatory action differ significantly; these differences are particularly critical for immunization with non-replicating adenovirus vectors (rAds) based vaccines. Unlike traditional vaccines, rAd based vaccines are not designed to capture vaccine antigens from the external environment by antigen presenting cells (APCs), but rather they are targeted to the de novo synthesis of vaccine antigens in APCs transfected with rAd. To date, there is no clear understanding about approaches to improve the efficacy of rAd vaccinations with immunoadjuvants. In this study, we investigated the immunoadjuvant effect of TLR3 and TLR4 agonists on the level of activation of APCs during vaccination with rAds. RESULTS: We demonstrated that TLR3 and TLR4 agonists confer different effects on the molecular processes in APCs that determine the efficacy of antigen delivery and activation of antigen-specific CD4+ and CD8+ T cells. APCs activated with agonists of TLR4 were characterized by up-regulated production of target antigen mRNA and protein encoded in rAd, as well as enhanced expression of the co-activation receptors CD80, CD86 and CD40, and pro-inflammatory cytokines TNF-α, IL6 and IL12. These effects of TLR4 agonists have provided a significant increase in the number of antigen-specific CD4+ and CD8+ T cells. TLR3 agonist, on the contrary, inhibited transcription and synthesis of rAd-encoded antigens, but improved expression of CD40 and IFN-ß in APCs. The cumulative effect of TLR3 agonist have resulted in only a slight improvement in the activation of antigen-specific T cells. Also, we demonstrated that IFN-ß and TNF-α, secreted by APCs in response to TLR3 and TLR4 agonists, respectively, have an opposite effect on the transcription of the targeted gene encoded in rAd. Specifically, IFN-ß inhibited, and TNF-α stimulated the expression of target vaccine antigens in APCs. CONCLUSIONS: Our data demonstrate that agonists of TLR4 but not TLR3 merit further study as adjuvants for development of vaccines based on recombinant adenoviral vectors.

Anticancer Mechanisms in Two Murine Bone Marrow-Derived Dendritic Cell Subsets Activated with TLR4 Agonists.

Dendritic cells (DCs) are well-known for their functions in orchestrating the innate and adaptive arms of immune defense. However, under certain conditions, DCs can exert tumoricidal activity. We have elucidated the mechanism of tumor suppression by TLR4-activated bone marrow-derived DCs (BMDCs) isolated from BALB/c mice. We identified that two distinct subsets of BMDCs (CD11b+CD11c+I-A/Eint and CD11b+CD11c+I-A/Ehigh) have different cytotoxic mechanisms of action. The cytotoxicity of the former subset is mediated through NO and reactive oxygen species and type I IFN (IFN-ß), whereas the latter subset acts only through IFN-ß. TLR4 agonists, LPS or pharmaceutical-grade ImmunoMax, activate CD11c+ BMDCs, which, in turn, directly kill 4T1 mouse breast cancer cells or inhibit their proliferation in an MHC-independent manner. These data define two populations of BMDCs with different mechanisms of direct cytotoxicity, as well as suggest that the I-A/Eint subset could be less susceptible to counteracting mechanisms in the tumor microenvironment and support investigation of similar subsets in human DCs.

Vaccination potential of B and T epitope-enriched NP and M2 against Influenza A viruses from different clades and hosts.

To avoid outbreaks of influenza virus epidemics and pandemics among human populations, modern medicine requires the development of new universal vaccines that are able to provide protection from a wide range of influenza A virus strains. In the course of development of a universal vaccine, it is necessary to consider that immunity must be generated even against viruses from different hosts because new human epidemic virus strains have their origins in viruses of birds and other animals. We have enriched conserved viral proteins-nucleoprotein (NP) and matrix protein 2 (M2)-by B and T-cell epitopes not only human origin but also swine and avian origin. For this purpose, we analyzed M2 and NP sequences with respect to changes in the sequences of known T and B-cell epitopes and chose conserved and evolutionarily significant epitopes. Eventually, we found consensus sequences of M2 and NP that have the maximum quantity of epitopes that are 100% coincident with them. Consensus epitope-enriched amino acid sequences of M2 and NP proteins were included in a recombinant adenoviral vector. Immunization with Ad5-tet-M2NP induced strong CD8 and CD4 T cells responses, specific to each of the encoded antigens, i.e. M2 and NP. Eight months after immunization with Ad5-tet-M2NP, high numbers of M2- and NP-responding "effector memory" CD44posCD62neg T cells were found in the mouse spleens, which revealed a long-term T cell immune memory conferred by the immunization. In all, the challenge experiments showed an extraordinarily wide-ranging efficacy of protection by the Ad5-tet-M2NP vaccine, covering 5 different heterosubtypes of influenza A virus (2 human, 2 avian and 1 swine).