IL7 in combination with radiotherapy stimulates a memory T-cell response to improve outcomes in HNSCC models.

Clinically approved head and neck squamous cell carcinoma (HNSCC) immunotherapies manipulate the immune checkpoint blockade (ICB) axis but have had limited success outside of recurrent/metastatic disease. Interleukin-7 (IL7) has been shown to be essential for effector T-cell survival, activation, and proliferation. Here, we show that IL7 in combination with radiotherapy (RT) is effective in activating CD8 + T-cells for reducing tumor growth. Our studies were conducted using both human papillomavirus related and unrelated orthotopic HNSCC murine models. Immune populations from the tumor, draining lymph nodes, and blood were compared between treatment groups and controls using flow cytometry, proteomics, immunofluorescence staining, and RNA sequencing. Treatment with RT and IL7 (RT + IL7) resulted in significant tumor growth reduction, high CD8 T-cell tumor infiltration, and increased proliferation of T-cell progenitors in the bone marrow. IL7 also expanded a memory-like subpopulation of CD8 T-cells. These results indicate that IL7 in combination with RT can serve as an effective immunotherapy strategy outside of the conventional ICB axis to drive the antitumor activity of CD8 T-cells.

PPARα Agonism Enhances Immune Response to Radiotherapy While Dietary Oleic Acid Results in Counteraction.

PURPOSE: Head and neck cancer (HNC) improvements are stagnant, even with advances in immunotherapy. Our previous clinical trial data show that altered fatty acid (FA) metabolism correlates with outcome. We hypothesized that pharmacologic and dietary modulation of FA catabolism will affect therapeutic efficacy. EXPERIMENTAL DESIGN: We performed in vivo and in vitro experiments using PPARα agonism with fenofibrate (FF) or high oleic acid diets (OAD) with radiotherapy, generating metabolomic, proteomic, stable isotope tracing, extracellular flux analysis, and flow-cytometric data to investigate these alterations. RESULTS: FF improved antitumor efficacy of high dose per fraction radiotherapy in HNC murine models, whereas the OAD reversed this effect. FF-treated mice on the control diet had evidence of increased FA catabolism. Stable isotope tracing showed less glycolytic utilization by ex vivo CD8+ T cells. Improved efficacy correlated with intratumoral alterations in eicosanoid metabolism and downregulated mTOR and CD36. CONCLUSIONS: Metabolic intervention with increased FA catabolism improves the efficacy of HNC therapy and enhances antitumoral immune response.

Single-cell eQTL models reveal dynamic T cell state dependence of disease loci.

Non-coding genetic variants may cause disease by modulating gene expression. However, identifying these expression quantitative trait loci (eQTLs) is complicated by differences in gene regulation across fluid functional cell states within cell types. These states-for example, neurotransmitter-driven programs in astrocytes or perivascular fibroblast differentiation-are obscured in eQTL studies that aggregate cells1,2. Here we modelled eQTLs at single-cell resolution in one complex cell type: memory T cells. Using more than 500,000 unstimulated memory T cells from 259 Peruvian individuals, we show that around one-third of 6,511 cis-eQTLs had effects that were mediated by continuous multimodally defined cell states, such as cytotoxicity and regulatory capacity. In some loci, independent eQTL variants had opposing cell-state relationships. Autoimmune variants were enriched in cell-state-dependent eQTLs, including risk variants for rheumatoid arthritis near ORMDL3 and CTLA4; this indicates that cell-state context is crucial to understanding potential eQTL pathogenicity. Moreover, continuous cell states explained more variation in eQTLs than did conventional discrete categories, such as CD4+ versus CD8+, suggesting that modelling eQTLs and cell states at single-cell resolution can expand insight into gene regulation in functionally heterogeneous cell types.

Multimodally profiling memory T cells from a tuberculosis cohort identifies cell state associations with demographics, environment and disease.

Multimodal T cell profiling can enable more precise characterization of elusive cell states underlying disease. Here, we integrated single-cell RNA and surface protein data from 500,089 memory T cells to define 31 cell states from 259 individuals in a Peruvian tuberculosis (TB) progression cohort. At immune steady state >4 years after infection and disease resolution, we found that, after accounting for significant effects of age, sex, season and genetic ancestry on T cell composition, a polyfunctional type 17 helper T (TH17) cell-like effector state was reduced in abundance and function in individuals who previously progressed from Mycobacterium tuberculosis (M.tb) infection to active TB disease. These cells are capable of responding to M.tb peptides. Deconvoluting this state-uniquely identifiable with multimodal analysis-from public data demonstrated that its depletion may precede and persist beyond active disease. Our study demonstrates the power of integrative multimodal single-cell profiling to define cell states relevant to disease and other traits.

IFN-γ and TNF-α drive a CXCL10+ CCL2+ macrophage phenotype expanded in severe COVID-19 lungs and inflammatory diseases with tissue inflammation.

BACKGROUND: Immunosuppressive and anti-cytokine treatment may have a protective effect for patients with COVID-19. Understanding the immune cell states shared between COVID-19 and other inflammatory diseases with established therapies may help nominate immunomodulatory therapies. METHODS: To identify cellular phenotypes that may be shared across tissues affected by disparate inflammatory diseases, we developed a meta-analysis and integration pipeline that models and removes the effects of technology, tissue of origin, and donor that confound cell-type identification. Using this approach, we integrated > 300,000 single-cell transcriptomic profiles from COVID-19-affected lungs and tissues from healthy subjects and patients with five inflammatory diseases: rheumatoid arthritis (RA), Crohn's disease (CD), ulcerative colitis (UC), systemic lupus erythematosus (SLE), and interstitial lung disease. We tested the association of shared immune states with severe/inflamed status compared to healthy control using mixed-effects modeling. To define environmental factors within these tissues that shape shared macrophage phenotypes, we stimulated human blood-derived macrophages with defined combinations of inflammatory factors, emphasizing in particular antiviral interferons IFN-beta (IFN-ß) and IFN-gamma (IFN-γ), and pro-inflammatory cytokines such as TNF. RESULTS: We built an immune cell reference consisting of > 300,000 single-cell profiles from 125 healthy or disease-affected donors from COVID-19 and five inflammatory diseases. We observed a CXCL10+ CCL2+ inflammatory macrophage state that is shared and strikingly abundant in severe COVID-19 bronchoalveolar lavage samples, inflamed RA synovium, inflamed CD ileum, and UC colon. These cells exhibited a distinct arrangement of pro-inflammatory and interferon response genes, including elevated levels of CXCL10, CXCL9, CCL2, CCL3, GBP1, STAT1, and IL1B. Further, we found this macrophage phenotype is induced upon co-stimulation by IFN-γ and TNF-α. CONCLUSIONS: Our integrative analysis identified immune cell states shared across inflamed tissues affected by inflammatory diseases and COVID-19. Our study supports a key role for IFN-γ together with TNF-α in driving an abundant inflammatory macrophage phenotype in severe COVID-19-affected lungs, as well as inflamed RA synovium, CD ileum, and UC colon, which may be targeted by existing immunomodulatory therapies.

IFN- γ and TNF- α drive a CXCL10 + CCL2 + macrophage phenotype expanded in severe COVID-19 and other diseases with tissue inflammation.

Immunosuppressive and anti-cytokine treatment may have a protective effect for patients with COVID-19. Understanding the immune cell states shared between COVID-19 and other inflammatory diseases with established therapies may help nominate immunomodulatory therapies. Using an integrative strategy, we built a reference by meta-analyzing > 300,000 immune cells from COVID-19 and 5 inflammatory diseases including rheumatoid arthritis (RA), Crohn's disease (CD), ulcerative colitis (UC), lupus, and interstitial lung disease. Our cross-disease analysis revealed that an FCN1 + inflammatory macrophage state is common to COVID-19 bronchoalveolar lavage samples, RA synovium, CD ileum, and UC colon. We also observed that a CXCL10 + CCL2 + inflammatory macrophage state is abundant in severe COVID-19, inflamed CD and RA, and expresses inflammatory genes such as GBP1, STAT1 , and IL1B . We found that the CXCL10 + CCL2 + macrophages are transcriptionally similar to blood-derived macrophages stimulated with TNF- α and IFN- γ ex vivo . Our findings suggest that IFN- γ , alongside TNF- α , might be a key driver of this abundant inflammatory macrophage phenotype in severe COVID-19 and other inflammatory diseases, which may be targeted by existing immunomodulatory therapies.

Multimodal single-cell approaches shed light on T cell heterogeneity.

Single-cell methods have revolutionized the study of T cell biology by enabling the identification and characterization of individual cells. This has led to a deeper understanding of T cell heterogeneity by generating functionally relevant measurements - like gene expression, surface markers, chromatin accessibility, T cell receptor sequences - in individual cells. While these methods are independently valuable, they can be augmented when applied jointly, either on separate cells from the same sample or on the same cells. Multimodal approaches are already being deployed to characterize T cells in diverse disease contexts and demonstrate the value of having multiple insights into a cell's function. But, these data sets pose new statistical challenges for integration and joint analysis.