Systematic review of time to subsequent therapy as a candidate surrogate endpoint in advanced solid tumors.

Aim: Time to subsequent therapy (TST) is an end point that may complement progression-free survival (PFS) and overall survival (OS) in determining the treatment effect of anticancer drugs and may be a potential surrogate for PFS and OS. We systematically reviewed the correlation between TST and both PFS and OS in published phase 2/3 studies in advanced solid tumors. Materials & methods: Trial-level correlational analyses were performed for TST versus PFS (by investigator and/or central review) and TST versus OS. Results: Of 21 included studies, nine (43%) used 'time to first subsequent therapy or death' (TFST) as the TST end point; 11 (57%) used different definitions ('other TST end points'). There was a strong correlation between TFST and PFS by investigator (medians: R2 = 0.88; hazard ratio [HR]: R2 = 0.91) and TFST versus PFS by central review (medians: R2 = 0.86; HRs: R2 = 0.84). For TFST versus OS there was medium/poor correlation for medians (R2 = 0.64) and HRs (R2 = 0.02). Conclusion: TFST strongly correlates with PFS, but not with OS.

In a recent study, researchers investigated how we can measure the effectiveness of cancer drugs. They focused on a specific measure called 'time to next therapy', which is the duration between two treatments patients receive. By analyzing the relationship between time to next therapy and disease progression, they discovered a strong correlation. This suggests that in the future, time to next therapy could potentially help to measure how well a cancer treatment works. However, when it comes to predicting patient survival, the relationship was not as strong. This implies that time to next therapy is not a reliable indicator of patient survival. To fully understand whether time to next therapy can effectively measure the effectiveness of anticancer drugs, further research is necessary.

Hemostasis Proteins in Invasive Meningococcal and Nonmeningococcal Infections: A Prospective Multicenter Study.

OBJECTIVES: We aimed to describe the variation of hemostasis proteins in children with bacterial infections due to different pathogens ( Neisseria meningitidis, Streptococcus pneumoniae, Staphylococcus aureus , and group A streptococcus [GAS]) and to study hemostasis proteins in relation to mortality. DESIGN: Preplanned analysis in prospective cohort study. SETTING: Hospitals in five European countries (Austria, The Netherlands, Spain, Switzerland, and the United Kingdom). PATIENTS: Admitted children (2012-2016) with community-acquired infections due to meningococci ( n = 83), pneumococci ( n = 64), S. aureus (n = 50), and GAS ( n = 44) with available serum samples collected less than 48 hours after admission. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Fibronectin, plasminogen activator inhibitor type 1 (PAI-1), thrombomodulin, and a disintegrin and metalloproteinase with a thrombospondin type 1 motif, member 13 (ADAMTS-13) were measured in serum in 2019-2020. Additionally, von Willebrand factor, protein C, protein S, and factor IX were measured in citrate plasma available from a subset of patients. Outcome measures included in-hospital mortality and disease severity (need for ventilation/inotropes, Pediatric Index of Mortality score).Of 241 children, 21 (8.7%) died and 177 (73.5%) were admitted to PICU. Mortality rate was similar for the pathogen groups. Levels of fibronectin and thrombomodulin differed for the different pathogens ( p < 0.05). Fibronectin levels were lower in GAS infections than in S. pneumoniae and S. aureus infections but did not differ from meningococcal infections. Thrombomodulin levels in meningococcal infections were higher than in S. aureus and pneumococcal infections. Overall, the area under the curve for mortality was 0.81 (95% CI, 0.70-0.92) for thrombomodulin and 0.78 (95% CI, 0.69-0.88) for ADAMTS-13. The association of each hemostasis protein did not vary across pathogens for any of the outcome measures. CONCLUSIONS: Hemostatic disturbances in childhood bacterial infections are not limited to meningococcal sepsis but occur with a comparable severity across nonmeningococcal infections. High thrombomodulin and high ADAMTS-13 had good discriminative ability for mortality. Our results emphasize the importance of hemostatic disturbances in meningococcal and nonmeningococcal pediatric bacterial infections.

"T2-high" in severe asthma related to blood eosinophil, exhaled nitric oxide and serum periostin.

Type-2 (T2) immune responses in airway epithelial cells (AECs) classifies mild-moderate asthma into a T2-high phenotype. We examined whether currently available clinical biomarkers can predict AEC-defined T2-high phenotype within the U-BIOPRED cohort.The transcriptomic profile of AECs obtained from brushings of 103 patients with asthma and 44 healthy controls was obtained and gene set variation analysis used to determine the relative expression score of T2 asthma using a signature from interleukin (IL)-13-exposed AECs.37% of asthmatics (45% nonsmoking severe asthma, n=49; 33% of smoking or ex-smoking severe asthma, n=18; and 28% mild-moderate asthma, n=36) were T2-high using AEC gene expression. They were more symptomatic with higher exhaled nitric oxide fraction (F eNO) and blood and sputum eosinophils, but not serum IgE or periostin. Sputum eosinophilia correlated best with the T2-high signature. F eNO (≥30 ppb) and blood eosinophils (≥300 cells·µL-1) gave a moderate prediction of T2-high asthma. Sputum IL-4, IL-5 and IL-13 protein levels did not correlate with gene expression.T2-high severe asthma can be predicted to some extent from raised levels of F eNO, blood and sputum eosinophil counts, but serum IgE or serum periostin were poor predictors. Better bedside biomarkers are needed to detect T2-high.

Evolutionary dynamics of local pandemic H1N1/2009 influenza virus lineages revealed by whole-genome analysis.

Virus gene sequencing and phylogenetics can be used to study the epidemiological dynamics of rapidly evolving viruses. With complete genome data, it becomes possible to identify and trace individual transmission chains of viruses such as influenza virus during the course of an epidemic. Here we sequenced 153 pandemic influenza H1N1/09 virus genomes from United Kingdom isolates from the first (127 isolates) and second (26 isolates) waves of the 2009 pandemic and used their sequences, dates of isolation, and geographical locations to infer the genetic epidemiology of the epidemic in the United Kingdom. We demonstrate that the epidemic in the United Kingdom was composed of many cocirculating lineages, among which at least 13 were exclusively or predominantly United Kingdom clusters. The estimated divergence times of two of the clusters predate the detection of pandemic H1N1/09 virus in the United Kingdom, suggesting that the pandemic H1N1/09 virus was already circulating in the United Kingdom before the first clinical case. Crucially, three clusters contain isolates from the second wave of infections in the United Kingdom, two of which represent chains of transmission that appear to have persisted within the United Kingdom between the first and second waves. This demonstrates that whole-genome analysis can track in fine detail the behavior of individual influenza virus lineages during the course of a single epidemic or pandemic.

Best practices in the real-world data life cycle.

With increasing digitization of healthcare, real-world data (RWD) are available in greater quantity and scope than ever before. Since the 2016 United States 21st Century Cures Act, innovations in the RWD life cycle have taken tremendous strides forward, largely driven by demand for regulatory-grade real-world evidence from the biopharmaceutical sector. However, use cases for RWD continue to grow in number, moving beyond drug development, to population health and direct clinical applications pertinent to payors, providers, and health systems. Effective RWD utilization requires disparate data sources to be turned into high-quality datasets. To harness the potential of RWD for emerging use cases, providers and organizations must accelerate life cycle improvements that support this process. We build on examples obtained from the academic literature and author experience of data curation practices across a diverse range of sectors to describe a standardized RWD life cycle containing key steps in production of useful data for analysis and insights. We delineate best practices that will add value to current data pipelines. Seven themes are highlighted that ensure sustainability and scalability for RWD life cycles: data standards adherence, tailored quality assurance, data entry incentivization, deploying natural language processing, data platform solutions, RWD governance, and ensuring equity and representation in data.

Osteoarticular Infections in Pediatric Hospitals in Europe: A Prospective Cohort Study From the EUCLIDS Consortium.

Background: Pediatric osteoarticular infections (POAIs) are serious diseases requiring early diagnosis and treatment. Methods: In this prospective multicenter cohort study, children with POAIs were selected from the European Union Childhood Life-threatening Infectious Diseases Study (EUCLIDS) database to analyze their demographic, clinical, and microbiological data. Results: A cohort of 380 patients with POAIs, 203 with osteomyelitis (OM), 158 with septic arthritis (SA), and 19 with both OM and SA, was analyzed. Thirty-five patients were admitted to the Pediatric Intensive Care Unit; out of these, six suffered from shock, one needed an amputation of the right foot and of four left toes, and two had skin transplantation. According to the Pediatric Overall Performance Score, 36 (10.5%) showed a mild overall disability, 3 (0.8%) a moderate, and 1 (0.2%) a severe overall disability at discharge. A causative organism was detected in 65% (247/380) of patients. Staphylococcus aureus (S. aureus) was identified in 57.1% (141/247) of microbiological confirmed cases, including 1 (0.7%) methicillin-resistant S. aureus (MRSA) and 6 (4.2%) Panton-Valentine leukocidin (PVL)-producing S. aureus, followed by Group A Streptococcus (18.2%) and Kingella kingae (8.9%). K. kingae and PVL production in S. aureus were less frequently reported than expected from the literature. Conclusion: POAIs are associated with a substantial morbidity in European children, with S. aureus being the major detected pathogen. In one-third of patients, no causative organism is identified. Our observations show an urgent need for the development of a vaccine against S. aureus and for the development of new microbiologic diagnostic guidelines for POAIs in European pediatric hospitals.

The simplicity of metazoan cell lineages.

Developmental processes are thought to be highly complex, but there have been few attempts to measure and compare such complexity across different groups of organisms. Here we introduce a measure of biological complexity based on the similarity between developmental and computer programs. We define the algorithmic complexity of a cell lineage as the length of the shortest description of the lineage based on its constituent sublineages. We then use this measure to estimate the complexity of the embryonic lineages of four metazoan species from two different phyla. We find that these cell lineages are significantly simpler than would be expected by chance. Furthermore, evolutionary simulations show that the complexity of the embryonic lineages surveyed is near that of the simplest lineages evolvable, assuming strong developmental constraints on the spatial positions of cells and stabilizing selection on cell number. We propose that selection for decreased complexity has played a major role in moulding metazoan cell lineages.

Precision reimbursement for precision medicine: the need for patient-level decisions between payers, providers and pharmaceutical companies.

Healthcare costs have been dramatically rising in developed economies worldwide. A key driver of cost increases has been high-cost drugs. The current model of reimbursement is not configured for drugs with uncertain outcomes. Future reimbursement will require better allocation of available healthcare system funds. Technological advancements have opened the door to a new type of outcomes-based reimbursement, enabling value exchange between payers and pharmaceutical companies, which we term precision reimbursement. Precision reimbursement extends beyond value-based contracts, with decisions at individual rather than aggregate level. For precision reimbursement to be adopted, there are data, computation and infrastructure requirements. All stakeholders benefit in moving to precision reimbursement for optimal resource allocation, risk sharing and, ultimately, improved outcomes. There are implementation challenges including cost, change management, information governance and development of surrogate markers. The overarching trend in medicine is toward personalised interventions, with precision reimbursement as the logical consequence.

Introducing PIONEER: a project to harness big data in prostate cancer research.

Prostate Cancer Diagnosis and Treatment Enhancement Through the Power of Big Data in Europe (PIONEER) is a European network of excellence for big data in prostate cancer, consisting of 32 private and public stakeholders from 9 countries across Europe. Launched by the Innovative Medicines Initiative 2 and part of the Big Data for Better Outcomes Programme (BD4BO), the overarching goal of PIONEER is to provide high-quality evidence on prostate cancer management by unlocking the potential of big data. The project has identified critical evidence gaps in prostate cancer care, via a detailed prioritization exercise including all key stakeholders. By standardizing and integrating existing high-quality and multidisciplinary data sources from patients with prostate cancer across different stages of the disease, the resulting big data will be assembled into a single innovative data platform for research. Based on a unique set of methodologies, PIONEER aims to advance the field of prostate cancer care with a particular focus on improving prostate-cancer-related outcomes, health system efficiency by streamlining patient management, and the quality of health and social care delivered to all men with prostate cancer and their families worldwide.

Author Correction: Introducing PIONEER: a project to harness big data in prostate cancer research.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Correction: The tree balance signature of mass extinction is erased by continued evolution in clades of constrained size with trait-dependent speciation.

[This corrects the article DOI: 10.1371/journal.pone.0179553.].

Mycobacterium tuberculosis Exploits a Molecular Off Switch of the Immune System for Intracellular Survival.

Mycobacterium tuberculosis (M. tuberculosis) survives and multiplies inside human macrophages by subversion of immune mechanisms. Although these immune evasion strategies are well characterised functionally, the underlying molecular mechanisms are poorly understood. Here we show that during infection of human whole blood with M. tuberculosis, host gene transcriptional suppression, rather than activation, is the predominant response. Spatial, temporal and functional characterisation of repressed genes revealed their involvement in pathogen sensing and phagocytosis, degradation within the phagolysosome and antigen processing and presentation. To identify mechanisms underlying suppression of multiple immune genes we undertook epigenetic analyses. We identified significantly differentially expressed microRNAs with known targets in suppressed genes. In addition, after searching regions upstream of the start of transcription of suppressed genes for common sequence motifs, we discovered novel enriched composite sequence patterns, which corresponded to Alu repeat elements, transposable elements known to have wide ranging influences on gene expression. Our findings suggest that to survive within infected cells, mycobacteria exploit a complex immune "molecular off switch" controlled by both microRNAs and Alu regulatory elements.

Opportunities and obstacles for deep learning in biology and medicine.

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

The tree balance signature of mass extinction is erased by continued evolution in clades of constrained size with trait-dependent speciation.

The kind and duration of phylogenetic topological "signatures" left in the wake of macroevolutionary events remain poorly understood. To this end, we examined a broad range of simulated phylogenies generated using trait-biased, heritable speciation probabilities and mass extinction that could be either random or selective on trait value, but also using background extinction and diversity-dependence to constrain clade sizes. In keeping with prior results, random mass extinction increased imbalance of clades that recovered to pre-extinction size, but was a relatively weak effect. Mass extinction that was selective on trait values tended to produce clades of similar or greater balance compared to random extinction or controls. Allowing evolution to continue past the point of clade-size recovery resulted in erosion and eventual erasure of this signal, with all treatments converging on similar values of imbalance, except for very intense extinction regimes targeted at taxa with high speciation rates. Return to a more balanced state with extended post-extinction evolution was also associated with loss of the previous phylogenetic root in most treatments. These results further demonstrate that while a mass extinction event can produce a recognizable phylogenetic signal, its effects become increasingly obscured the further an evolving clade gets from that event, with any sharp imbalance due to unrelated evolutionary factors.

The fmo genes of Caenorhabditis elegans and C. briggsae: characterisation, gene expression and comparative genomic analysis.

The flavin-containing monooxygenase (FMO) gene family is conserved and ancient with representatives present in almost all phyla so far examined. The genes encode FAD-, NADP- and O(2)-dependent enzymes that catalyse oxygenation of soft-nucleophilic heteroatom centres in a range of substrates. Although usually classified as xenobiotic-metabolising enzymes, examples of FMOs exist that have evolved to metabolise specific endogenous substrates as part of a discrete physiological process. The genome of Caenorhabditis elegans contains five predicted genes encoding putative homologs of mammalian FMOs, K08C7.2, K08C7.5, Y39A1A.19, F53F4.5 and H24K24.5, which we have named fmo and numbered fmo-1 to fmo-5, respectively. As a first step towards determining their functional role(s), we have experimentally characterised these C. elegans fmo genes including analysing reporter gene expression patterns and RNAi phenotypes. Two major gene expression patterns were observed, either intestinal or hypodermal, but no gross RNAi phenotypes were found possibly due to functional redundancy. The internal structures of fmo-2, fmo-3 and fmo-4 have been compared with orthologs identified in the related nematode C. briggsae. For each orthologous pair, a global comparison of the paired upstream intergenic regions was performed and a number of conserved noncoding sequences, which may represent potential cis-regulatory elements, identified. Phylogenetic analysis reveals that several of the fmo homologs are the result of gene duplication along the lineage leading to the nematodes.

Phylogenetically nested comparisons for testing correlates of species richness: a simulation study of continuous variables.

Explaining the uneven distribution of species among lineages is one of the oldest questions in evolution. Proposed correlations between biological traits and species diversity are routinely tested by making comparisons between phylogenetic sister clades. Several recent studies have used nested sister-clade comparisons to test hypotheses linking continuously varying traits, such as body size, with diversity. Evaluating the findings of these studies is complicated because they differ in the index of species richness difference used, the way in which trait differences were treated, and the statistical tests employed. In this paper, we use simulations to compare the performance of four species richness indices, two choices about the branch lengths used to estimate trait values for internal nodes and two statistical tests under a range of models of clade growth and character evolution. All four indices returned appropriate Type I error rates when the assumptions of the method were met and when branch lengths were set proportional to time. Only two of the indices were robust to the different evolutionary models and to different choices of branch lengths and statistical tests. These robust indices had comparable power under one nonnull scenario. Regression through the origin was consistently more powerful than the t-test, and the choice of branch lengths exerts a strong effect on both the validity and power. In the light of our simulations, we re-evaluate the findings of those who have previously used nested comparisons in the context of species richness. We provide a set of simple guidelines to maximize the performance of phylogenetically nested comparisons in tests of putative correlates of species richness.

A comparison of the effects of random and selective mass extinctions on erosion of evolutionary history in communities of digital organisms.

The effect of mass extinctions on phylogenetic diversity and branching history of clades remains poorly understood in paleobiology. We examined the phylogenies of communities of digital organisms undergoing open-ended evolution as we subjected them to instantaneous "pulse" extinctions, choosing survivors at random, and to prolonged "press" extinctions involving a period of low resource availability. We measured age of the phylogenetic root and tree stemminess, and evaluated how branching history of the phylogenetic trees was affected by the extinction treatments. We found that strong random (pulse) and strong selective extinction (press) both left clear long-term signatures in root age distribution and tree stemminess, and eroded deep branching history to a greater degree than did weak extinction and control treatments. The widely-used Pybus-Harvey gamma statistic showed a clear short-term response to extinction and recovery, but differences between treatments diminished over time and did not show a long-term signature. The characteristics of post-extinction phylogenies were often affected as much by the recovery interval as by the extinction episode itself.

Evolutionary pathways of the pandemic influenza A (H1N1) 2009 in the UK.

The emergence of the influenza (H1N1) 2009 virus provided a unique opportunity to study the evolution of a pandemic virus following its introduction into the human population. Virological and clinical surveillance in the UK were comprehensive during the first and second waves of the pandemic in 2009, with extensive laboratory confirmation of infection allowing a detailed sampling of representative circulating viruses. We sequenced the complete coding region of the haemagglutinin (HA) segment of 685 H1N1 pandemic viruses selected without bias during two waves of pandemic in the UK (April-December 2009). Phylogenetic analysis showed that although temporal accumulation of amino acid changes was observed in the HA sequences, the overall diversity was less than that typically seen for seasonal influenza A H1N1 or H3N2. There was co-circulation of multiple variants as characterised by signature amino acid changes in the HA. A specific substitution (S203T) became predominant both in UK and global isolates. No antigenic drift occurred during 2009 as viruses with greater than four-fold reduction in their haemagglutination inhibition (HI) titre ("low reactors") were detected in a low proportion (3%) and occurred sporadically. Although some limited antigenic divergence in viruses with four-fold reduction in HI titre might be related to the presence of 203T, additional studies are needed to test this hypothesis.

Phylogenetic biodiversity assessment based on systematic nomenclature.

Biodiversity assessment demands objective measures, because ultimately conservation decisions must prioritize the use of limited resources for preserving taxa. The most general framework for the objective assessment of conservation worth are those that assess evolutionary distinctiveness, e.g. Genetic (Crozier 1992) and Phylogenetic Diversity (Faith 1992), and Evolutionary History (Nee & May 1997). These measures all attempt to assess the conservation worth of any scheme based on how much of the encompassing phylogeny of organisms is preserved. However, their general applicability is limited by the small proportion of taxa that have been reliably placed in a phylogeny. Given that phylogenizaton of many interesting taxa or important is unlikely to occur soon, we present a framework for using taxonomy as a reasonable surrogate for phylogeny. Combining this framework with exhaustive searches for combinations of sites containing maximal diversity, we provide a proof-of-concept for assessing conservation schemes for systematized but un-phylogenised taxa spread over a series of sites. This is illustrated with data from four studies, on North Queensland flightless insects (Yeates et al. 2002), ants from a Florida Transect (Lubertazzi & Tschinkel 2003), New England bog ants (Gotelli & Ellison 2002) and a simulated distribution of the known New Zealand Lepidosauria (Daugherty et al. 1994). The results support this approach, indicating that species, genus and site numbers predict evolutionary history, to a degree depending on the size of the data set.