Explore the vast range of titles available.

Background Systemic lupus erythematosus (SLE) is a complex multi-system disease, characterized by both autoimmune and autoinflammatory clinical and laboratory features. The role of type I interferon (IFN) in SLE has been demonstrated from the 2000s, by gene expression analyses showing significant over-expression of genes related to type I IFN signalling pathway (IFN signature). However, several studies questioned the role of measuring the intensity of IFN signature (IFN score) to chase SLE activity. We would assess if the IFN signature can help the clinical and therapeutic stratification of patients with pediatric SLE. Methods We measured the IFN score in peripheral whole blood from a series of subjects with childhood-onset SLE and correlated the results with clinical and laboratory parameters. Results Thirty-one subjects were included in the study, among which the 87% displayed a positive IFN score. The only significant relation was found for high IFN score in subjects with normocomplementemia. No correlation was observed between IFN score and SLEDAI-2K, BILAG-2004 and SLICC. Patients with high IFN score and normal complement levels also presented lower anti-dsDNA antibodies. Conclusions The integration between IFN signature analysis and complement levels may easily distinguish two groups of subjects, in which the autoimmune or autoinflammatory component of the disease seems to be prevalent.

Precision medicine is part of five focus areas of the Challenges in IBD research document, which also includes preclinical human IBD mechanisms, environmental triggers, novel technologies, and pragmatic clinical research. The Challenges in IBD Research document provides a comprehensive overview of current gaps in inflammatory bowel diseases (IBD) research and delivers actionable approaches to address them. It is the result of a multidisciplinary input from scientists, clinicians, patients, and funders, and represents a valuable resource for patient centric research prioritization. In particular, the precision medicine section is focused on highlighting the main gap areas that must be addressed to get closer to treatments tailored to the biological and clinical characteristics of each patient, which is the aim of precision medicine. The main gaps were identified in: 1) understanding and predicting the natural history of IBD: disease susceptibility, activity, and behavior; 2) predicting disease course and treatment response; and 3) optimizing current and developing new molecular technologies. Suggested approaches to bridge these gaps include prospective longitudinal cohort studies to identify and validate precision biomarkers for prognostication of disease course, and prediction and monitoring of treatment response. To achieve this, harmonization across studies is key as well as development of standardized methods and infrastructure. The implementation of state-of-the-art molecular technologies, systems biology and machine learning approaches for multi-omics and clinical data integration and analysis will be also fundamental. Finally, randomized biomarker-stratified trials will be critical to evaluate the clinical utility of validated signatures and biomarkers in improving patient outcomes and cost-effective care.

Heterogeneity in the septic response has hindered efforts to understand pathophysiology and develop targeted therapies. Source of infection, with different causative organisms and temporal changes, might influence this heterogeneity. To investigate individual and temporal variations in the transcriptomic response to sepsis due to fecal peritonitis, and to compare these with the same parameters in community-acquired pneumonia. We performed genome-wide gene expression profiling in peripheral blood leukocytes of adult patients admitted to intensive care with sepsis due to fecal peritonitis (n = 117) or community-acquired pneumonia (n = 126), and of control subjects without sepsis (n = 10). A substantial portion of the transcribed genome (18%) was differentially expressed compared with that of control subjects, independent of source of infection, with eukaryotic initiation factor 2 signaling being the most enriched canonical pathway. We identified two sepsis response signature (SRS) subgroups in fecal peritonitis associated with early mortality (P = 0.01; hazard ratio, 4.78). We defined gene sets predictive of SRS group, and serial sampling demonstrated that subgroup membership is dynamic during intensive care unit admission. We found that SRS is the major predictor of transcriptomic variation; a small number of genes (n = 263) were differentially regulated according to the source of infection, enriched for IFN signaling and antigen presentation. We define temporal changes in gene expression from disease onset involving phagosome formation as well as natural killer cell and IL-3 signaling. The majority of the sepsis transcriptomic response is independent of the source of infection and includes signatures reflecting immune response state and prognosis. A modest number of genes show evidence of specificity. Our findings highlight opportunities for patient stratification and precision medicine in sepsis.

This provides examples of current and future applications of deep learning in pharmacogenomics, including: identification of novel regulatory variants located in noncoding domains of the genome and their function as applied to pharmacoepigenomics; patient stratification from medical records; and the mechanistic prediction of drug response, targets and their interactions. Deep learning encapsulates a family of machine learning algorithms that has transformed many important subfields of artificial intelligence over the last decade, and has demonstrated breakthrough performance improvements on a wide range of tasks in biomedicine. We anticipate that in the future, deep learning will be widely used to predict personalized drug response and optimize medication selection and dosing, using knowledge extracted from large and complex molecular, epidemiological, clinical and demographic datasets.

Gliomas are the most common primary brain tumors in adults. They arise in the glial tissue and primarily occur in the brain. Low‐grade tumors of World Health Organization (WHO) grade II tend to progress to high‐grade gliomas of WHO grade III and, eventually, glioblastoma of WHO grade IV, which is the most common and deadly glioma, with a median survival of 12–15 months after final diagnosis. Knowledge of the molecular biology and genetics of glioblastoma has increased significantly in the past few years, giving rise to classification methods that can help in management and stratification of glioblastoma patients. However, glioblastoma remains an incurable disease. Glioblastoma cells have acquired genetic and metabolic adaptations in order to sustain tumor growth and progression, including changes in energetic metabolism, invasive capacity, migration, and angiogenesis, that make it very difficult to find suitable therapeutic targets and to develop effective drugs. The current standard of care for glioblastoma patients is surgery followed by radiotherapy plus concomitant and adjuvant chemotherapy with temozolomide. Although progress in glioblastoma therapies in recent years has been more limited than in other tumors, numerous drugs and targets are being proposed and many clinical trials are underway to develop effective subtype‐specific treatments. Glioblastoma occurs in the brain and is the most common and deadly glioma, with a median survival of 12 to 15 months after final diagnosis. It remains as an incurable disease. Different aspects of the molecular biology and clinical management of glioblastoma, with a special focus on new therapeutic strategies under development, are summarized.

Interindividual variability in drug responses and disease susceptibility is common in the clinic. Currently, personalized medicine is highly valued, the idea being to prescribe the right medicine to the right patient. Metabolomics has been increasingly applied in evaluating the therapeutic outcomes of clinical drugs by correlating the baseline metabolic profiles of patients with their responses, i.e., pharmacometabonomics, as well as prediction of disease susceptibility among population in advance, i.e., patient stratification. The accelerated advance in metabolomics technology pinpoints the huge potential of its application in personalized medicine. In current review, we discussed the novel applications of metabolomics with typical examples in evaluating drug therapy and patient stratification, and underlined the potential of metabolomics in personalized medicine in the future.

Background The aim of this study was to investigate the prognostic value of the PAM50 intrinsic subtypes and risk of recurrence (ROR) score in patients with early breast cancer and long-term follow-up. A special focus was placed on hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2−) pN0 patients not treated with chemotherapy. Methods Patients with early breast cancer ( n  = 653) enrolled in the observational Oslo1 study (1995–1998) were followed for distant recurrence and breast cancer death. Clinicopathological parameters were collected from hospital records. The primary tumors were analyzed using the Prosigna® PAM50 assay to determine the prognostic value of the intrinsic subtypes and ROR score in comparison with pathological characteristics. The primary endpoints were distant disease-free survival (DDFS) and breast cancer-specific survival (BCSS). Results Of 653 tumors, 52.2% were classified as luminal A, 26.5% as luminal B, 10.6% as HER2-enriched, and 10.7% as basal-like. Among the HR+/HER2− patients ( n  = 476), 37.8% were categorized as low risk by ROR score, 22.7% as intermediate risk, and 39.5% as high risk. Median follow-up durations for BCSS and DDFS were 16.6 and 7.1 years, respectively. Multivariate analysis showed that intrinsic subtypes (all patients) and ROR risk classification (HR+/HER2− patients) yielded strong prognostic information. Among the HR+/HER2− pN0 patients with no adjuvant treatment ( n  = 231), 53.7% of patients had a low ROR, and their prognosis at 15 years was excellent (15-year BCSS 96.3%). Patients with intermediate risk had reduced survival compared with those with low risk ( p  = 0.005). In contrast, no difference in survival between the low- and intermediate-risk groups was seen for HR+/HER2− pN0 patients who received tamoxifen only. Ki-67 protein, grade, and ROR score were analyzed in the unselected, untreated pT1pN0 HR+/HER2− population ( n  = 171). In multivariate analysis, ROR score outperformed both Ki-67 and grade. Furthermore, 55% of patients who according to the PREDICT tool ( http://www.predict.nhs.uk/ ) would be considered chemotherapy candidates were ROR low risk (33%) or luminal A ROR intermediate risk (22%). Conclusions The PAM50 intrinsic subtype classification and ROR score improve classification of patients with breast cancer into prognostic groups, allowing for a more precise identification of future recurrence risk and providing an improved basis for adjuvant treatment decisions. Node-negative patients with low ROR scores had an excellent outcome at 15 years even in the absence of adjuvant therapy.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that eventually leads to dementia and death of the patient. Despite the enormous amounts of resources and efforts for AD drug development during the last three decades, no effective treatments have been developed that can slow or halt the progression of the disease. Currently available drugs for treating AD can only improve clinical symptoms temporarily with moderate efficacies. In recent years, the scientific community has realized these challenges and reconsidered the future directions of AD drug development. The most significant recent changes in AD drug development strategy include shifting from amyloid-based targets to other targets, such as tau, and efforts toward better designs for clinical trials. However, most AD drug development is still focused on a single mechanism or target, which is the conventional strategy for drug development. Although multifactorial mechanisms and, on this basis, multi-target strategies have been proposed in recent years, this approach has not been widely recognized and accepted by the mainstream of AD drug development. Here, we emphasize the multifactorial mechanisms of AD and discuss the urgent need for a paradigm shift in AD drug development from a single target to multiple targets, either with the multi-target–directed ligands approach or the combination therapy approach. We hope this article will increase the recognition of the multifactorial nature of AD and promote this paradigm shift. We believe that such a shift will facilitate successful development of effective AD therapies.

The purpose of our study was to carry out a meta-analysis of current literature to determine whether total mastectomy and breast-conserving surgery induce different outcomes in quality of life, based on the breast-cancer-specific module of the European Organizaation for Research and Treatment of Cancer core questionnaire (EORTC QLQ-BR23) used postoperatively. A systematic literature search of PubMed and EMBASE was conducted. Observational clinical studies that compared the quality of life in different surgery groups and presented empirical findings were selected. Six studies met the inclusion criteria. Breast-conserving surgery has statistically significant better outcomes than mastectomy in three of the eight outcomes measured in the EORTC QLQ-BR23, namely body image (standard mean difference, SMD = 1.742, 95% CI 0.579–2.905, p = 0.003), future perspective (SMD = 0.606, 95% CI 0.075–1.138, p = 0.025) and systemic therapy side effects (SMD = −0.641, 95% CI 0.101–1.181, p = 0.020). Our study highlighted that breast-conserving surgery was preferred over mastectomy because breast-conserving surgery leads to better outcomes in body image, future perspectives and less systemic side effects.

Recent research in the field of osteoarthritis (OA) has focused on understanding the underlying molecular and clinical phenotypes of the disease. This narrative review article focuses on recent advances in our understanding of the phenotypes of OA and proposes that the disease represents a diversity of clinical phenotypes that are underpinned by a number of molecular mechanisms, which may be shared by several phenotypes and targeted more specifically for therapeutic purposes. The clinical phenotypes of OA supposedly have different underlying etiologies and pathogenic pathways and they progress at different rates. Large OA population cohorts consist of a majority of patients whose disease progresses slowly and a minority of individuals whose disease may progress faster. The ability to identify the people with relatively rapidly progressing OA can transform clinical trials and enhance their efficiency. The identification, characterization, and classification of molecular phenotypes of rapidly progressing OA, which represent patients who may benefit most from intervention, could potentially serve as the basis for precision medicine for this disabling condition. Imaging and biochemical markers (biomarkers) are important diagnostic and research tools that can assist with this challenge.