Merck Serono

About: Merck Serono is a company organization based out in Rome, Italy. It is known for research contribution in the topics: Population & Cancer. The organization has 1925 authors who have published 1952 publications receiving 63517 citations. The organization is also known as: EMD Serono.

Guidelines for the use and interpretation of assays for monitoring autophagy

Abstract: In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field.

Applications of machine learning in drug discovery and development.

Abstract: Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all stages of drug discovery. Examples include target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials. Applications have ranged in context and methodology, with some approaches yielding accurate predictions and insights. The challenges of applying ML lie primarily with the lack of interpretability and repeatability of ML-generated results, which may limit their application. In all areas, systematic and comprehensive high-dimensional data still need to be generated. With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential to speed up the process and reduce failure rates in drug discovery and development. Machine learning has been applied to numerous stages in the drug discovery pipeline. Here, Vamathevan and colleagues discuss the most useful techniques and how machine learning can promote data-driven decision making in drug discovery and development. They highlight major hurdles in the field, such as the required data characteristics for applying machine learning, which will need to be solved as machine learning matures.

RORγt drives production of the cytokine GM-CSF in helper T cells, which is essential for the effector phase of autoimmune neuroinflammation

Abstract: Although the role of the T(H)1 and T(H)17 subsets of helper T cells as disease mediators in autoimmune neuroinflammation remains a subject of some debate, none of their signature cytokines are essential for disease development. Here we report that interleukin 23 (IL-23) and the transcription factor RORγt drove expression of the cytokine GM-CSF in helper T cells, whereas IL-12, interferon-γ (IFN-γ) and IL-27 acted as negative regulators. Autoreactive helper T cells specifically lacking GM-CSF failed to initiate neuroinflammation despite expression of IL-17A or IFN-γ, whereas GM-CSF secretion by Ifng(-/-)Il17a(-/-) helper T cells was sufficient to induce experimental autoimmune encephalomyelitis (EAE). During the disease effector phase, GM-CSF sustained neuroinflammation via myeloid cells that infiltrated the central nervous system. Thus, in contrast to all other known helper T cell-derived cytokines, GM-CSF serves a nonredundant function in the initiation of autoimmune inflammation regardless of helper T cell polarization.

Avelumab in patients with chemotherapy-refractory metastatic Merkel cell carcinoma: a multicentre, single-group, open-label, phase 2 trial

Abstract: Summary Background Merkel cell carcinoma is a rare, aggressive skin cancer with poor prognosis in patients with advanced disease. Current standard care uses various cytotoxic chemotherapy regimens, but responses are seldom durable. Tumour oncogenesis is linked to Merkel cell polyomavirus integration and ultraviolet-radiation-induced mutations, providing rationale for treatment with immunotherapy antibodies that target the PD-L1/PD-1 pathway. We assessed treatment with avelumab, an anti-PD-L1 monoclonal antibody, in patients with stage IV Merkel cell carcinoma that had progressed after cytotoxic chemotherapy. Methods In this multicentre, international, prospective, single-group, open-label, phase 2 trial, patients with stage IV chemotherapy-refractory, histologically confirmed Merkel cell carcinoma (aged ≥18 years) were enrolled from 35 cancer treatment centres and academic hospitals in North America, Europe, Australia, and Asia. Key eligibility criteria were an ECOG performance status of 0 or 1, measurable disease by Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1, adequate haematological, hepatic, and renal function, and immune-competent status (patients with HIV, immunosuppression, haematological malignancies, and previous organ transplantation were excluded). Patient selection was not based on PD-L1 expression or Merkel cell polyomavirus status. Collection of biopsy material or use of archival tissue for these assessments was mandatory. Avelumab was given intravenously at a dose of 10 mg/kg every 2 weeks. The primary endpoint was confirmed objective response (complete response or partial response) assessed according to RECIST version 1.1 by an independent review committee. Safety and clinical activity were assessed in all patients who received at least one dose of study drug (the modified intention-to-treat population). This trial is registered with ClinicalTrials.gov as NCT02155647. Findings Between July 25, 2014, and Sept 3, 2015, 88 patients were enrolled and received at least one dose of avelumab. Patients were followed up for a median of 10·4 months (IQR 8·6–13·1). The proportion of patients who achieved an objective response was 28 (31·8% [95·9% CI 21·9–43·1]) of 88 patients, including eight complete responses and 20 partial responses. Responses were ongoing in 23 (82%) of 28 patients at the time of analysis. Five grade 3 treatment-related adverse events occurred in four (5%) patients: lymphopenia in two patients, blood creatine phosphokinase increase in one patient, aminotransferase increase in one patient, and blood cholesterol increase in one patient; there were no treatment-related grade 4 adverse events or treatment-related deaths. Serious treatment-related adverse events were reported in five patients (6%): enterocolitis, infusion-related reaction, aminotransferases increased, chondrocalcinosis, synovitis, and interstitial nephritis (n=1 each). Interpretation Avelumab was associated with durable responses, most of which are still ongoing, and was well tolerated; hence, avelumab represents a new therapeutic option for advanced Merkel cell carcinoma. Funding Merck KGaA, Darmstadt, Germany.

A stroma-related gene signature predicts resistance to neoadjuvant chemotherapy in breast cancer

Abstract: To better understand the relationship between tumor-host interactions and the efficacy of chemotherapy, we have developed an analytical approach to quantify several biological processes observed in gene expression data sets. We tested the approach on tumor biopsies from individuals with estrogen receptor-negative breast cancer treated with chemotherapy. We report that increased stromal gene expression predicts resistance to preoperative chemotherapy with 5-fluorouracil, epirubicin and cyclophosphamide (FEC) in subjects in the EORTC 10994/BIG 00-01 trial. The predictive value of the stromal signature was successfully validated in two independent cohorts of subjects who received chemotherapy but not in an untreated control group, indicating that the signature is predictive rather than prognostic. The genes in the signature are expressed in reactive stroma, according to reanalysis of data from microdissected breast tumor samples. These findings identify a previously undescribed resistance mechanism to FEC treatment and suggest that antistromal agents may offer new ways to overcome resistance to chemotherapy.