85 research outputs found

    Immune signatures predict development of autoimmune toxicity in patients with cancer treated with immune checkpoint inhibitors

    Full text link
    Background: Immune checkpoint inhibitors (ICIs) are among the most promising treatment options for melanoma and non-small cell lung cancer (NSCLC). While ICIs can induce effective anti-tumor responses, they may also drive serious immune-related adverse events (irAEs). Identifying biomarkers to predict which patients will suffer from irAEs would enable more accurate clinical risk-benefit analysis for ICI treatment and may also shed light on common or distinct mechanisms underpinning treatment success and irAEs. Methods: In this prospective multi-center study, we combined a multi-omics approach including unbiased single-cell profiling of over 300 peripheral blood mononuclear cell (PBMC) samples and high-throughput proteomics analysis of over 500 serum samples to characterize the systemic immune compartment of patients with melanoma or NSCLC before and during treatment with ICIs. Findings: When we combined the parameters obtained from the multi-omics profiling of patient blood and serum, we identified potential predictive biomarkers for ICI-induced irAEs. Specifically, an early increase in CXCL9/CXCL10/CXCL11 and interferon-γ (IFN-γ) 1 to 2 weeks after the start of therapy are likely indicators of heightened risk of developing irAEs. In addition, an early expansion of Ki-67+ regulatory T cells (Tregs) and Ki-67+ CD8+ T cells is also likely to be associated with increased risk of irAEs. Conclusions: We suggest that the combination of these cellular and proteomic biomarkers may help to predict which patients are likely to benefit most from ICI therapy and those requiring intensive monitoring for irAEs. Funding: This work was primarily funded by the European Research Council, the Swiss National Science Foundation, the Swiss Cancer League, and the Forschungsförderung of the Kantonsspital St. Gallen

    Immune signatures predict development of autoimmune toxicity in patients with cancer treated with immune checkpoint inhibitors.

    Get PDF
    BACKGROUND Immune checkpoint inhibitors (ICIs) are among the most promising treatment options for melanoma and non-small cell lung cancer (NSCLC). While ICIs can induce effective anti-tumor responses, they may also drive serious immune-related adverse events (irAEs). Identifying biomarkers to predict which patients will suffer from irAEs would enable more accurate clinical risk-benefit analysis for ICI treatment and may also shed light on common or distinct mechanisms underpinning treatment success and irAEs. METHODS In this prospective multi-center study, we combined a multi-omics approach including unbiased single-cell profiling of over 300 peripheral blood mononuclear cell (PBMC) samples and high-throughput proteomics analysis of over 500 serum samples to characterize the systemic immune compartment of patients with melanoma or NSCLC before and during treatment with ICIs. FINDINGS When we combined the parameters obtained from the multi-omics profiling of patient blood and serum, we identified potential predictive biomarkers for ICI-induced irAEs. Specifically, an early increase in CXCL9/CXCL10/CXCL11 and interferon-γ (IFN-γ) 1 to 2 weeks after the start of therapy are likely indicators of heightened risk of developing irAEs. In addition, an early expansion of Ki-67+ regulatory T cells (Tregs) and Ki-67+ CD8+ T cells is also likely to be associated with increased risk of irAEs. CONCLUSIONS We suggest that the combination of these cellular and proteomic biomarkers may help to predict which patients are likely to benefit most from ICI therapy and those requiring intensive monitoring for irAEs. FUNDING This work was primarily funded by the European Research Council, the Swiss National Science Foundation, the Swiss Cancer League, and the Forschungsförderung of the Kantonsspital St. Gallen

    Origin and pathways of the mineral dust transport to two Spanish EARLINET sites: Effect on the observed columnar and range-resolved dust optical properties

    Get PDF
    In this paper, is presented a method for estimation of the effect of the transport process to aerosol optical properties. Aerosol optical data retrieved by lidars and sun-photometer measurements, are applied to Saharan dust events observed simultaneously at the two EARLINET/AERONET sites of Barcelona and Granada during the periods of June–September of 2012 and 2013. For this purpose, elastic lidar profiles and sun-photometer columnar retrievals are analyzed together with satellite observations and dust forecast models. Granada presents more than twice Saharan dust outbreaks compared to Barcelona. The scenarios favoring the Saharan dust outbreaks are identified in both places. The mineral dust originating in the Sahara region and arriving at both stations is usually transport wither over the Atlas Mountains or through an Atlantic pathway. Analyses of dust events affecting both stations reveal how differences in the transport process lead to differences in the aerosol optical properties measured at each station. Mean dust related Ångström exponent is 1.8 times higher in Barcelona than in Granada. This difference is a result of the additional contribution of anthropogenic aerosol, mainly in the aerosol fine mode, during the transport of the mineral dust plume over the Iberian Peninsula.Andalusia Regional Government through the project P12-RNM-2409Spanish Ministry of Economy and Competitiveness through the project CGL2013-45410-

    BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

    Full text link
    Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License
    • …
    corecore