RESUMEN
Innate lymphoid cells (ILCs) play a key role in tissue-mediated immunity and can be controlled by coreceptor signaling. Here, we define a subset of ILCs that are Tbet+NK1.1- and are present within the tumor microenvironment (TME). We show programmed death-1 receptor (PD-1) expression on ILCs within TME is found in Tbet+NK1.1- ILCs. PD-1 significantly controlled the proliferation and function of Tbet+NK1.1- ILCs in multiple murine and human tumors. We found tumor-derived lactate enhanced PD-1 expression on Tbet+NK1.1- ILCs within the TME, which resulted in dampened the mammalian target of rapamycin (mTOR) signaling along with increased fatty acid uptake. In line with these metabolic changes, PD-1-deficient Tbet+NK1.1- ILCs expressed significantly increased IFNγ and granzyme B and K. Furthermore, PD-1-deficient Tbet+NK1.1- ILCs contributed toward diminished tumor growth in an experimental murine model of melanoma. These data demonstrate that PD-1 can regulate antitumor responses of Tbet+NK1.1- ILCs within the TME.
Asunto(s)
Linfocitos , Neoplasias , Ratones , Animales , Humanos , Inmunidad Innata , Receptor de Muerte Celular Programada 1/genética , Receptor de Muerte Celular Programada 1/metabolismo , Microambiente Tumoral , Neoplasias/metabolismo , Apoptosis , Mamíferos/metabolismoRESUMEN
BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a debilitating, progressive disease with a median survival time of 3-5 years. Diagnosis remains challenging and disease progression varies greatly, suggesting the possibility of distinct subphenotypes. METHODS AND RESULTS: We analysed publicly available peripheral blood mononuclear cell expression datasets for 219 IPF, 411 asthma, 362 tuberculosis, 151 healthy, 92 HIV and 83 other disease samples, totalling 1318 patients. We integrated the datasets and split them into train (n=871) and test (n=477) cohorts to investigate the utility of a machine learning model (support vector machine) for predicting IPF. A panel of 44 genes predicted IPF in a background of healthy, tuberculosis, HIV and asthma with an area under the curve of 0.9464, corresponding to a sensitivity of 0.865 and a specificity of 0.89. We then applied topological data analysis to investigate the possibility of subphenotypes within IPF. We identified five molecular subphenotypes of IPF, one of which corresponded to a phenotype enriched for death/transplant. The subphenotypes were molecularly characterised using bioinformatic and pathway analysis tools identifying distinct subphenotype features including one which suggests an extrapulmonary or systemic fibrotic disease. CONCLUSIONS: Integration of multiple datasets, from the same tissue, enabled the development of a model to accurately predict IPF using a panel of 44 genes. Furthermore, topological data analysis identified distinct subphenotypes of patients with IPF which were defined by differences in molecular pathobiology and clinical characteristics.
Asunto(s)
Asma , Infecciones por VIH , Fibrosis Pulmonar Idiopática , Humanos , Leucocitos Mononucleares , Fibrosis Pulmonar Idiopática/genética , Fibrosis Pulmonar Idiopática/diagnóstico , FenotipoRESUMEN
OBJECTIVES: During 2020, the UK's Department of Health and Social Care (DHSC) established the Moonshot programme to fund various diagnostic approaches for the detection of SARS-CoV-2, the pathogen behind the COVID-19 pandemic. Mass spectrometry was one of the technologies proposed to increase testing capacity. METHODS: Moonshot funded a multi-phase development programme, bringing together experts from academia, industry and the NHS to develop a state-of-the-art targeted protein assay utilising enrichment and liquid chromatography tandem mass spectrometry (LC-MS/MS) to capture and detect low levels of tryptic peptides derived from SARS-CoV-2 virus. The assay relies on detection of target peptides, ADETQALPQRK (ADE) and AYNVTQAFGR (AYN), derived from the nucleocapsid protein of SARS-CoV-2, measurement of which allowed the specific, sensitive, and robust detection of the virus from nasopharyngeal (NP) swabs. The diagnostic sensitivity and specificity of LC-MS/MS was compared with reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) via a prospective study. RESULTS: Analysis of NP swabs (n=361) with a median RT-qPCR quantification cycle (Cq) of 27 (range 16.7-39.1) demonstrated diagnostic sensitivity of 92.4% (87.4-95.5), specificity of 97.4% (94.0-98.9) and near total concordance with RT-qPCR (Cohen's Kappa 0.90). Excluding Cq>32 samples, sensitivity was 97.9% (94.1-99.3), specificity 97.4% (94.0-98.9) and Cohen's Kappa 0.95. CONCLUSIONS: This unique collaboration between academia, industry and the NHS enabled development, translation, and validation of a SARS-CoV-2 method in NP swabs to be achieved in 5 months. This pilot provides a model and pipeline for future accelerated development and implementation of LC-MS/MS protein/peptide assays into the routine clinical laboratory.
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COVID-19 , SARS-CoV-2 , Humanos , Pandemias , COVID-19/diagnóstico , Prueba de COVID-19 , Espectrometría de Masas en Tándem/métodos , Cromatografía Liquida , Estudios Prospectivos , Técnicas de Laboratorio Clínico/métodos , Sensibilidad y Especificidad , PéptidosRESUMEN
Background: The COVID-19 pandemic has created pressure on healthcare systems worldwide. Tools that can stratify individuals according to prognosis could allow for more efficient allocation of healthcare resources and thus improved patient outcomes. It is currently unclear if blood gene expression signatures derived from patients at the point of admission to hospital could provide useful prognostic information. Methods: Gene expression of whole blood obtained at the point of admission from a cohort of 78 patients hospitalised with COVID-19 during the first wave was measured by high resolution RNA sequencing. Gene signatures predictive of admission to Intensive Care Unit were identified and tested using machine learning and topological data analysis, TopMD. Results: The best gene expression signature predictive of ICU admission was defined using topological data analysis with an accuracy: 0.72 and ROC AUC: 0.76. The gene signature was primarily based on differentially activated pathways controlling epidermal growth factor receptor (EGFR) presentation, Peroxisome proliferator-activated receptor alpha (PPAR-α) signalling and Transforming growth factor beta (TGF-ß) signalling. Conclusions: Gene expression signatures from blood taken at the point of admission to hospital predicted ICU admission of treatment naïve patients with COVID-19.
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COVID-19 , COVID-19/genética , Receptores ErbB , Expresión Génica , Humanos , Unidades de Cuidados Intensivos , PPAR alfa , Pandemias , Factor de Crecimiento Transformador betaRESUMEN
Melanoma cell adhesion molecule (MCAM, CD146, MUC18) is a heavily glycosylated transmembrane protein and a marker of melanoma metastasis. It is expressed in advanced primary melanoma and metastasis but rarely in benign naevi or normal melanocytes. More and more evidence has shown that activation of the MCAM on cell surface plays a vital role in melanoma progression and metastasis. However, the natural MCAM binding ligand that initiates MCAM activation in melanoma so far remains elusive. This study revealed that galectin-3, a galactoside-binding protein that is commonly overexpressed in many cancers including melanoma, is naturally associated with MCAM on the surface of both skin and uveal melanoma cells. Binding of galectin-3 to MCAM, via O-linked glycans on the MCAM, induces MCAM dimerization and clustering on cell surface and subsequent activation of downstream AKT signalling. This leads to the increases of a number of important steps in melanoma progression of cell proliferation, adhesion, migration, and invasion. Thus, galectin-3 is a natural binding ligand of MCAM in melanoma, and their interaction activates MCAM and promotes MCAM-mediated melanoma progression. Targeting the galectin-3-MCAM interaction may potentially be a useful therapeutic strategy for melanoma treatment.
Asunto(s)
Galectina 3 , Melanoma , Humanos , Antígeno CD146/metabolismo , Galectina 3/genética , Ligandos , Proteínas Proto-Oncogénicas c-akt , Melanoma/patología , GalactósidosRESUMEN
BACKGROUND: Tumor infiltrating lymphocytes play a key role in antitumor responses; however, while several memory T-cell subtypes have been reported in inflammatory and neoplastic conditions, the proportional representation of the different subsets of memory T cells and their functional significance in cancer is unclear. Keratinocyte skin cancer is one of the most common cancers globally, with cutaneous squamous cell cancer (cSCC) among the most frequent malignancies capable of metastasis. METHODS: Memory T-cell subsets were delineated in human cSCCs and, for comparison, in non-lesional skin and blood using flow cytometry. Immunohistochemistry was conducted to quantify CD103+ cells in primary human cSCCs which had metastasized (P-M) and primary cSCCs which had not metastasized (P-NM). TIMER2.0 (timer.cistrome.org) was used to analyze TCGA cancer survival data based on ITGAE expression. Immunofluorescence microscopy was performed to determine frequencies of CD8+CD103+ cells in P-M and P-NM cSCCs. RESULTS: Despite intertumoral heterogeneity, most cSCC T cells were CCR7-/CD45RA- effector/resident memory (TRM) lymphocytes, with naive, CD45RA+/CCR7- effector memory re-expressing CD45RA, CCR7+/L-selectin+ central memory and CCR7+/L-selectin- migratory memory lymphocytes accounting for smaller T-cell subsets. The cSCC CD8+ T-cell population contained a higher proportion of CD69+/CD103+ TRMs than that in non-lesional skin and blood. These cSCC CD69+/CD103+ TRMs exhibited increased IL-10 production, and higher CD39, CTLA-4 and PD-1 expression compared with CD103- TRMs in the tumor. CD103+ cells were more frequent in P-M than P-NM cSCCs. Analysis of TCGA data demonstrated that high expression of ITGAE (encoding CD103) was associated with reduced survival in primary cutaneous melanoma, breast carcinoma, renal cell carcinoma, kidney chromophobe cancer, adrenocortical carcinoma and lower grade glioma. Immunofluorescence microscopy showed that the majority of CD103 was present on CD8+ T cells and that CD8+CD103+ cells were significantly more frequent in P-M than P-NM cSCCs. CONCLUSION: These results highlight CD8+CD103+ TRMs as an important functional T-cell subset associated with poorer clinical outcome in this cancer.
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Antígenos CD/genética , Antígenos CD/metabolismo , Linfocitos T CD8-positivos/inmunología , Carcinoma de Células Escamosas/inmunología , Cadenas alfa de Integrinas/genética , Cadenas alfa de Integrinas/metabolismo , Linfocitos Infiltrantes de Tumor/inmunología , Neoplasias Cutáneas/inmunología , Carcinoma de Células Escamosas/genética , Estudios de Casos y Controles , Perfilación de la Expresión Génica , Regulación Neoplásica de la Expresión Génica , Humanos , Memoria Inmunológica , Fenotipo , Pronóstico , Neoplasias Cutáneas/genética , Análisis de Supervivencia , Regulación hacia ArribaRESUMEN
Formalin-fixed paraffin embedded (FFPE) clinical tissues represent an abundant and unique resource for translational proteomic studies. In the US, melanoma is the 5th and 6th most common cancer in men and women, respectively, affecting over 230,000 people annually and metastasising in 5-15% of cases. Median survival time for distant metastatic melanoma is 6-9 months with a 5-year-survival of < 15%. In this study, 24 primary FFPE tumours which have metastasised (P-M) and 24 primary FFPE tumours which did not metastasise (P-NM) were subjected to proteomic profiling. In total, 2750 proteins were identified, of which 16 were significantly differentially expressed. Analysis of TCGA data demonstrated that expression of the genes encoding for 6 of these 16 proteins had a significant effect on survival in cutaneous melanoma. Pathway analysis of the proteomics data revealed mechanisms likely involved in the process of melanoma metastasis, including cytoskeleton rearrangement, extracellular changes and immune system alterations. A machine learning prediction model scoring an AUC of 0.922, based on these 16 differentially expressed proteins was able to accurately classify samples into P-M and P-NM. This study has identified potential biomarkers and key processes relating to melanoma metastasis using archived clinical samples, providing a basis for future studies in larger cohorts.