Ontology-driven indexing of public datasets for translational bioinformatics

The volume of publicly available genomic scale data is increasing. Genomic datasets in public repositories are annotated with free-text fields describing the pathological state of the studied sample. These annotations are not mapped to concepts in any ontology, making it difficult to integrate these datasets across repositories. We have previously developed methods to map text-annotations of tissue microarrays to concepts in the NCI thesaurus and SNOMED-CT

Integrative bioinformatics and graph-based methods for predicting adverse effects of developmental drugs

Adverse drug effects are complex phenomena that involve the interplay between drug molecules and their protein targets at various levels of biological organisation, from molecular to organismal. Many factors are known to contribute toward the safety profile of a drug, including the chemical properties of the drug molecule itself, the biological properties of drug targets and other proteins that are involved in pharmacodynamics and pharmacokinetics aspects of drug action, and the characteristics of the intended patient population. A multitude of scattered publicly available resources exist that cover these important aspects of drug activity. These include manually curated biological databases, high-throughput experimental results from gene expression and human genetics resources as well as drug labels and registered clinical trial records. This thesis proposes an integrated analysis of these disparate sources of information to help bridge the gap between the molecular and the clinical aspects of drug action. For example, to address the commonly held assumption that narrowly expressed proteins make safer drug targets, an integrative data-driven analysis was conducted to systematically investigate the relationship between the tissue expression profile of drug targets and the organs affected by clinically observed adverse drug reactions. Similarly, human genetics data were used extensively throughout the thesis to compare adverse symptoms induced by drug molecules with the phenotypes associated with the genes encoding their target proteins. One of the main outcomes of this thesis was the generation of a large knowledge graph, which incorporates diverse molecular and phenotypic data in a structured network format. To leverage the integrated information, two graph-based machine learning methods were developed to predict a wide range of adverse drug effects caused by approved and developmental therapies

Relation Prediction over Biomedical Knowledge Bases for Drug Repositioning

Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying other essential relations (e.g., causation, prevention) between biomedical entities is also critical to understand biomedical processes. Hence, it is crucial to develop automated relation prediction systems that can yield plausible biomedical relations to expedite the discovery process. In this dissertation, we demonstrate three approaches to predict treatment relations between biomedical entities for the drug repositioning task using existing biomedical knowledge bases. Our approaches can be broadly labeled as link prediction or knowledge base completion in computer science literature. Specifically, first we investigate the predictive power of graph paths connecting entities in the publicly available biomedical knowledge base, SemMedDB (the entities and relations constitute a large knowledge graph as a whole). To that end, we build logistic regression models utilizing semantic graph pattern features extracted from the SemMedDB to predict treatment and causative relations in Unified Medical Language System (UMLS) Metathesaurus. Second, we study matrix and tensor factorization algorithms for predicting drug repositioning pairs in repoDB, a general purpose gold standard database of approved and failed drug–disease indications. The idea here is to predict repoDB pairs by approximating the given input matrix/tensor structure where the value of a cell represents the existence of a relation coming from SemMedDB and UMLS knowledge bases. The essential goal is to predict the test pairs that have a blank cell in the input matrix/tensor based on the shared biomedical context among existing non-blank cells. Our final approach involves graph convolutional neural networks where entities and relation types are embedded in a vector space involving neighborhood information. Basically, we minimize an objective function to guide our model to concept/relation embeddings such that distance scores for positive relation pairs are lower than those for the negative ones. Overall, our results demonstrate that recent link prediction methods applied to automatically curated, and hence imprecise, knowledge bases can nevertheless result in high accuracy drug candidate prediction with appropriate configuration of both the methods and datasets used

Extent of non-publication in cohorts of studies approved by research ethics committees or included in trial registries

BACKGROUND: The synthesis of published research in systematic reviews is essential when providing evidence to inform clinical and health policy decision-making. However, the validity of systematic reviews is threatened if journal publications represent a biased selection of all studies that have been conducted (dissemination bias). To investigate the extent of dissemination bias we conducted a systematic review that determined the proportion of studies published as peer-reviewed journal articles and investigated factors associated with full publication in cohorts of studies (i) approved by research ethics committees (RECs) or (ii) included in trial registries. METHODS AND FINDINGS: Four bibliographic databases were searched for methodological research projects (MRPs) without limitations for publication year, language or study location. The searches were supplemented by handsearching the references of included MRPs. We estimated the proportion of studies published using prediction intervals (PI) and a random effects meta-analysis. Pooled odds ratios (OR) were used to express associations between study characteristics and journal publication. Seventeen MRPs (23 publications) evaluated cohorts of studies approved by RECs; the proportion of published studies had a PI between 22% and 72% and the weighted pooled proportion when combining estimates would be 46.2% (95% CI 40.2%-52.4%, I2 = 94.4%). Twenty-two MRPs (22 publications) evaluated cohorts of studies included in trial registries; the PI of the proportion published ranged from 13% to 90% and the weighted pooled proportion would be 54.2% (95% CI 42.0%-65.9%, I2 = 98.9%). REC-approved studies with statistically significant results (compared with those without statistically significant results) were more likely to be published (pooled OR 2.8; 95% CI 2.2-3.5). Phase-III trials were also more likely to be published than phase II trials (pooled OR 2.0; 95% CI 1.6-2.5). The probability of publication within two years after study completion ranged from 7% to 30%. CONCLUSIONS: A substantial part of the studies approved by RECs or included in trial registries remains unpublished. Due to the large heterogeneity a prediction of the publication probability for a future study is very uncertain. Non-publication of research is not a random process, e.g., it is associated with the direction of study findings. Our findings suggest that the dissemination of research findings is biased

Oropharyngeal Squamous Cell Carcinoma Treatment in the Era of Immune Checkpoint Inhibitors

From MDPI via Jisc Publications RouterHistory: accepted 2021-06-24, pub-electronic 2021-06-25Publication status: PublishedFunder: The Swedish Cancer Foundation; Grant(s): 20 0704While head and neck squamous cell carcinomas (HNSCC) are marginally decreasing due to the reduction in exposure to the major risk factors, tobacco and alcohol, the incidence of high-risk human papillomavirus (HPV)-positive oropharynx squamous cell carcinomas (OPSCC), especially those in the tonsil and base of tongue subsites, are increasing. Patients with the latter are younger, display a longer overall survival, and show a lower recurrence rate after standard-of-care treatment than those with HPV-negative OPSCC. This may reflect an important role for immune surveillance and control during the natural history of the virally driven tumour development. Immune deviation through acquisition of immune-suppressive factors in the tumour microenvironment (TME) is discussed in relation to treatment response. Understanding how the different immune factors are integrated in the TME battleground offers opportunities for identifying prognostic biomarkers as well as novel therapeutic strategies. OPSCC generally receive surgery or radiotherapy for early-stage tumour treatment, but many patients present with locoregionally advanced disease requiring multimodality therapies which can involve considerable complications. This review focuses on the utilization of newly emerged immune checkpoint inhibitors (PD-1/PD-L1 pathway) for treatment of HNSCC, in particular HPV-positive OPSCC, since they could be less toxic and more efficacious. PD-1/PD-L1 expression in the TME has been extensively investigated as a biomarker of patient response but is yet to provide a really effective means for stratification of treatment. Extensive testing of combinations of therapeutic approaches by types and sequencing will fuel the next evolution of treatment for OPSCC

Prostate cancer - what about oligometastatic disease and stereotactic ablative radiotherapy? - a narrative review

Background and Objective: Oligometastatic prostate cancer (OMPC) encompasses a heterogenous group of clinical entities defined by the timing of the development of metastases. These include de novo oligometastatic, oligorecurrent and oligoprogressive prostate cancer (PrCa). We describe the evidence supporting the use of stereotactic ablative radiotherapy (SABR) to the oligometastases to improve patient outcomes in each of these settings. Methods: Published clinical trials relevant to ‘OMPC’ and ‘SABR’ where used for this narrative review. Key Content and Findings: The driving force behind this narrative review is the constantly evolving field of OMPC with an increasing number of salvage radiotherapy options changing the current treatment paradigm. We now have evidence to support that disease control can be optimised with SABR as shown in several practice changing trials including ‘ORIOLE’, ‘STOMP’ for PrCa and ‘SABR-COMET’ showing a survival advantage with a tumour agnostic salvage approach. We also describe the challenges with data interpretation and cost implications. Challenges include the small sample size for most reported trials, in combination with a lack of cost-efficiency analysis. Conclusions: SABR is a promising treatment approach for OMPC with a proven clinical benefit in some clinical settings and its use will expand in the future

Prognostic and predictive value of clinical and biochemical factors in breast cancer patients with bone metastases receiving "metronomic" zoledronic acid

<p>Abstract</p> <p>Background</p> <p>To assess prognostic and predictive effects of clinical and biochemical factors in our published randomized study of a weekly low dose (metronomic arm) versus a conventional dosage of zoledronic acid (conventional arm) in breast cancer patients with bone metastases.</p> <p>Methods</p> <p>Treatment outcome of 60 patients with bone metastases were used to assess impacts of following potential prognostic factors, estrogen receptor status, lymph node status, 2 year-disease free interval (DFI), numbers of chemotherapy regimens administered, interventions, and serum levels of VEGF, N-telopeptide of type I collagen (NTx), CEA, and CA 15-3.</p> <p>Results</p> <p>In univariate analyses, patients pretreated with 2 or fewer chemotherapy regimens, ER-positive tumors, 3 or fewer lymph nodes, DFI of more than 2 years, serum VEGF of less than 500 pg/mL after 3 months of intervention, serum CEA and CA 15-3 of less than ULN, and baseline serum NTx of less than 18 nM BCE had significantly longer progression free survival (PFS). The multivariate analysis showed that ER positivity (hazard ratio [HR], 0.295; 95% confidence interval [CI], 0.141-0.618; P = 0.001), serum VEGF of less than 500 pg/mL after 3 months of intervention (HR, 2.220; 95% CI, 1.136-4.338; P = 0.020), baseline serum NTx of less than 18 nM BCE (HR, 2.842; 95% CI, 1.458-5.539; P = 0.001), and 2 or fewer chemotherapy regimens received (HR, 7.803; 95% CI, 2.884-21.112; P = 0.000) were associated with a better PFS. When evaluating the predictive effect of the biochemical factors, an interaction between NTx and zoledronic acid intervention was shown (P = 0.005). The HR of weekly low dose versus a conventional dosage of zoledronic acid was estimated to be 2.309 (99% CI, 1.067-5.012) in patients with baseline serum NTx of more than 18 nM BCE, indicating a superiority of weekly low dose of zoledronic acid.</p> <p>Conclusions</p> <p>ER, serum VEGF level after intervention, and numbers of chemotherapy regimens administered are prognostic but not predictive factors in breast cancer patients with bone metastases. Patients with baseline serum NTx of more than 18 nM BCE might benefit more from weekly low-dose of zoledronic acid.</p> <p>Trial registration</p> <p>ClinicalTrials.gov unique identifier: ClinicalTrials.gov: <a href="http://www.clinicaltrials.gov/ct2/show/NCT00524849">NCT00524849</a></p

Extent of non-publication in cohorts of studies approved by research ethics committees or included in trial registries

Background: The synthesis of published research in systematic reviews is essential when providing evidence to inform clinical and health policy decisionmaking. However, the validity of systematic reviews is threatened if journal publications represent a biased selection of all studies that have been conducted (dissemination bias). To investigate the extent of dissemination bias we conducted a systematic review that determined the proportion of studies published as peerreviewed journal articles and investigated factors associated with full publication in cohorts of studies (i) approved by research ethics committees (RECs) or (ii) included in trial registries. Copyright:Methods and Findings: Four bibliographic databases were searched for methodological research projects (MRPs) without limitations for publication year, language or study location. The searches were supplemented by handsearching the references of included MRPs. We estimated the proportion of studies published using prediction intervals (PI) and a random effects meta-analysis. Pooled odds ratios (OR) were used to express associations between study characteristics and journal publication. Seventeen MRPs (23 publications) evaluated cohorts of studies approved by RECs; the proportion of published studies had a PI between 22% and 72% and the weighted pooled proportion when combining estimates would be 46.2% (95% CI 40.2%-52.4%, I2594.4%). Twenty-two MRPs (22 publications) evaluated cohorts of studies included in trial registries; the PI of the proportion published ranged from 13% to 90% and the weighted pooled proportion would be 54.2% (95% CI 42.0%-65.9%, I2598.9%). REC-approved studies with statistically significant results (compared with those without statistically significant results) were more likely to be published (pooled OR 2.8; 95% CI 2.2-3.5). Phase-III trials were also more likely to be published than phase II trials (pooled OR 2.0; 95% CI 1.6- 2.5). The probability of publication within two years after study completion ranged from 7% to 30%.Conclusions: A substantial part of the studies approved by RECs or included in trial registries remains unpublished. Due to the large heterogeneity a prediction of the publication probability for a future study is very uncertain. Non-publication of research is not a random process, e.g., it is associated with the direction of study findings. Our findings suggest that the dissemination of research findings is biased