29 research outputs found

    Integrative Analysis of Novel Metabolic Subtypes in Pancreatic Cancer Fosters New Prognostic Biomarkers

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    Background: Most of the patients with Pancreatic Ductal Adenocarcinoma (PDA) are not eligible for a curative surgical resection. For this reason there is an urgent need for personalized therapies. PDA is the result of complex interactions between tumor molecular profile and metabolites produced by its microenvironment. Despite recent studies identified PDA molecular subtypes, its metabolic classification is still lacking.Methods: We applied an integrative analysis on transcriptomic and genomic data of glycolytic genes in PDA. Data were collected from public datasets and molecular glycolytic subtypes were defined using hierarchical clustering. The grade of purity of the cancer samples was assessed estimating the different amount of stromal and immunological infiltrate among the identified PDA subtypes. Analyses of metabolomic data from a subset of PDA cell lines allowed us to identify the different metabolites produced by the metabolic subtypes. Sera of a cohort of 31 PDA patients were analyzed using Q-TOF mass spectrometer to measure the amount of metabolic circulating proteins present before and after chemotherapy.Results: Our integrative analysis of glycolytic genes identified two glycolytic and two non-glycolytic metabolic PDA subtypes. Glycolytic patients develop disease earlier, have poor prognosis, low immune-infiltrated tumors, and are characterized by a gain in chr12p13 genomic region. This gain results in the over-expression of GAPDH, TPI1, and FOXM1. PDA cell lines with the gain of chr12p13 are characterized by an higher lipid uptake and sensitivity to drug targeting the fatty acid metabolism. Our sera proteomic analysis confirms that TPI1 serum levels increase in poor prognosis gemcitabine-treated patients.Conclusions: We identify four metabolic PDA subtypes with different prognosis outcomes which may have pivotal role in setting personalized treatments. Moreover, our data suggest TPI1 as putative prognostic PDA biomarker

    Immune-Complexome Analysis Identifies Immunoglobulin-Bound Biomarkers That Predict the Response to Chemotherapy of Pancreatic Cancer Patients

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    Pancreatic Ductal Adenocarcinoma (PDA) is an aggressive malignancy with a very poor outcome. Although chemotherapy (CT) treatment has poor efficacy, it can enhance tumor immunogenicity. Tumor-Associated Antigens (TAA) are self-proteins that are overexpressed in tumors that may induce antibody production and can be PDA theranostic targets. However, the prognostic value of TAA-antibody association as Circulating Immune Complexes (CIC) has not yet been elucidated, mainly due to the lack of techniques that lead to their identification. In this study, we show a novel method to separate IgG, IgM, and IgA CIC from sera to use them as prognostic biomarkers of CT response. The PDA Immune-Complexome (IC) was identified using a LTQ-Orbitrap mass spectrometer followed by computational analysis. The analysis of the IC of 37 PDA patients before and after CT revealed differential associated antigens (DAA) for each immunoglobulin class. Our method identified different PDA-specific CIC in patients that were associated with poor prognosis patients. Finally, CIC levels were significantly modified by CT suggesting that they can be used as effective prognostic biomarkers to follow CT response in PDA patients

    Impact of the COVID-19 pandemic on contact tracing of patients with pulmonary tuberculosis

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    Background: The COVID-19 pandemic could have negative effects on tuberculosis (TB) control. The objective was to assess the impact of the pandemic in contact tracing, TB and latent tuberculosis infection (LTBI) in contacts of patients with pulmonary TB in Catalonia (Spain). Methods: Contact tracing was carried out in cases of pulmonary TB detected during 14 months in the pre-pandemic period (1 January 2019 to 28 February 2020) and 14 months in the pandemic period (1 March 2020 to 30 April 2021). Contacts received the tuberculin skin test and/or interferon gamma release assay and it was determined whether they had TB or LTBI. Variables associated with TB or LTBI in contacts (study period and sociodemographic variables) were analyzed using adjusted odds ratio (aOR) and the 95% confidence intervals (95% CI). Results: The pre-pandemic and pandemic periods showed, respectively: 503 and 255 pulmonary TB reported cases (reduction of 50.7%); and 4676 and 1687 contacts studied (reduction of 36.1%). In these periods, the proportion of TB cases among the contacts was 1.9% (84/4307) and 2.2% (30/1381) (P = 0.608); and the proportion of LTBI was 25.3% (1090/4307) and 29.2% (403/1381) (P < 0.001). The pandemic period was associated to higher LTBI proportion (aOR = 1.3; 95% CI 1.1–1.5), taking into account the effect on LTBI of the other variables studied as sex, age, household contact and migrant status. Conclusions: COVID-19 is affecting TB control due to less exhaustive TB and LTBI case detection. An increase in LTBI was observed during the pandemic period. Efforts should be made to improve detection of TB and LTBI among contacts of TB cases.This study was supported by the Ministry of Science and Innovation, Institute of Health Carlos III (Project PI18/01751) and Fondo Europeo de Desarrollo Regional (FEDER-Una manera de hacer Europa)