CA19-9 as a Potential Target for Radiolabeled Antibody-Based Positron Emission Tomography of Pancreas Cancer.

Introduction. Sensitive and specific imaging of pancreas cancer are necessary for accurate diagnosis, staging, and treatment. The vast majority of pancreas cancers express the carbohydrate tumor antigen CA19-9. The goal of this study was to determine the potential to target CA19-9 with a radiolabeled anti-CA19-9 antibody for imaging pancreas cancer. Methods. CA19-9 was quantified using flow cytometry on human pancreas cancer cell lines. An intact murine anti-CA19-9 monoclonal antibody was labeled with a positron emitting radionuclide (Iodine-124) and injected into mice harboring antigen positive and negative xenografts. MicroPET/CT were performed at successive time intervals (72 hours, 96 hours, 120 hours) after injection. Radioactivity was measured in blood and tumor to provide objective confirmation of the images. Results. Antigen expression by flow cytometry revealed approximately 1.3 × 10(6) CA19-9 antigens for the positive cell line and no expression in the negative cell line. Pancreas xenograft imaging with Iodine-124-labeled anti-CA19-9 mAb demonstrated an average tumor to blood ratio of 5 and positive to negative tumor ratio of 20. Conclusion. We show in vivo targeting of our antigen positive xenograft with a radiolabeled anti-CA19-9 antibody. These data demonstrate the potential to achieve anti-CA19-9 antibody based positron emission tomography of pancreas cancer

From Budgets To Video News Releases: Television News Components In Agricultural Communications Programs At Land·Grant Universities

The 133-item questionnaire was designed to learn, first, whether a given agricultural communications department had a television news component (TNC): and for those that did the resource commitment to each of them, the types and natures of the projects produced, how audiences were defined, and answers relating to production, distribution, marketing, equipment and demographics

An Open-Source Data Manager for Network Models

Developing simulation and optimisation models for resource networks like water or energy systems increasingly involves integrating multiple data sources and software. Connecting multiple models and managing data accessed by different groups of analysts is a software challenge. Many resource systems are represented in computer models as networks of nodes and links, driven by a range of objectives and rules. We present a data storage platform, written in Python, which exploits the commonality of network representations to store data for multiple model types within a single deployment. This open-source platform provides a common source of data to multiple models using consistent data formats, reducing likelihood of error compared to file based data management. When deployed as a web service, it allows data to be shared securely among authorised users over the internet, facilitating collaboration. A case study describes the hosting of a water utility planning model, with an accompanying worked example

Targeting CEA in Pancreas Cancer Xenografts with a Mutated scFv-Fc Antibody Fragment

BackgroundSensitive antibody-based tumor targeting has the potential not only to image metastatic and micrometastatic disease, but also to be the basis of targeted therapy. The vast majority of pancreas cancers express carcinoembryonic antigen (CEA). Thus, we sought to evaluate the potential of CEA as a pancreatic cancer target utilizing a rapidly clearing engineered anti-CEA scFv-Fc antibody fragment with a mutation in the Fc region [anti-CEA scFv-Fc H310A].MethodsImmunohistochemistry (IHC) with the antibody fragment was used to confirm expression of CEA on human pancreas cancer specimens. In vivo tumor targeting was evaluated by tail vein injection of I124-labeled anti-CEA scFv-Fc(H310A) into mice harboring CEA-positive and -negative xenografts. MicroPET/CT imaging was performed at successive time intervals. Radioactivity in blood and tumor was measured after the last time point. Additionally, unlabeled anti-CEA scFv-Fc(H310A) was injected into CEA-positive tumor bearing mice and ex vivo IHC was performed to identify the presence of the antibody to define the microscopic intratumoral pattern of targeting.ResultsModerate to strong staining by IHC was noted on 84% of our human pancreatic cancer specimens and was comparable to staining of our xenografts. Pancreas xenograft imaging with the radiolabeled anti-CEA scFv-Fc(H310A) antibody demonstrated average tumor/blood ratios of 4.0. Immunolocalization demonstrated peripheral antibody fragment penetration of one to five cell diameters (0.75 to 1.5 μm).ConclusionsWe characterized a preclinical xenograft model with respect to CEA expression that was comparable to human cases. We demonstrated that the anti-CEA scFv-Fc(H310A) antibody exhibited antigen-specific tumor targeting and shows promise as an imaging and potentially therapeutic agent

Propensity scores using missingness pattern information: a practical guide.

Electronic health records are a valuable data source for investigating health-related questions, and propensity score analysis has become an increasingly popular approach to address confounding bias in such investigations. However, because electronic health records are typically routinely recorded as part of standard clinical care, there are often missing values, particularly for potential confounders. In our motivating study-using electronic health records to investigate the effect of renin-angiotensin system blockers on the risk of acute kidney injury-two key confounders, ethnicity and chronic kidney disease stage, have 59% and 53% missing data, respectively. The missingness pattern approach (MPA), a variant of the missing indicator approach, has been proposed as a method for handling partially observed confounders in propensity score analysis. In the MPA, propensity scores are estimated separately for each missingness pattern present in the data. Although the assumptions underlying the validity of the MPA are stated in the literature, it can be difficult in practice to assess their plausibility. In this article, we explore the MPA's underlying assumptions by using causal diagrams to assess their plausibility in a range of simple scenarios, drawing general conclusions about situations in which they are likely to be violated. We present a framework providing practical guidance for assessing whether the MPA's assumptions are plausible in a particular setting and thus deciding when the MPA is appropriate. We apply our framework to our motivating study, showing that the MPA's underlying assumptions appear reasonable, and we demonstrate the application of MPA to this study.Economic and Social Research Council [Grant Number ES/J5000/21/1]; Medical Research Council [Project Grant MR/M013278/1]; Health Data Research UK [Grant Number EPNCZO90], which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcom

Propensity scores using missingness pattern information: a practical guide.

Electronic health records are a valuable data source for investigating health-related questions, and propensity score analysis has become an increasingly popular approach to address confounding bias in such investigations. However, because electronic health records are typically routinely recorded as part of standard clinical care, there are often missing values, particularly for potential confounders. In our motivating study-using electronic health records to investigate the effect of renin-angiotensin system blockers on the risk of acute kidney injury-two key confounders, ethnicity and chronic kidney disease stage, have 59% and 53% missing data, respectively. The missingness pattern approach (MPA), a variant of the missing indicator approach, has been proposed as a method for handling partially observed confounders in propensity score analysis. In the MPA, propensity scores are estimated separately for each missingness pattern present in the data. Although the assumptions underlying the validity of the MPA are stated in the literature, it can be difficult in practice to assess their plausibility. In this article, we explore the MPA's underlying assumptions by using causal diagrams to assess their plausibility in a range of simple scenarios, drawing general conclusions about situations in which they are likely to be violated. We present a framework providing practical guidance for assessing whether the MPA's assumptions are plausible in a particular setting and thus deciding when the MPA is appropriate. We apply our framework to our motivating study, showing that the MPA's underlying assumptions appear reasonable, and we demonstrate the application of MPA to this study.Economic and Social Research Council [Grant Number ES/J5000/21/1]; Medical Research Council [Project Grant MR/M013278/1]; Health Data Research UK [Grant Number EPNCZO90], which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcom

Integrating genome-wide polygenic risk scores and non-genetic risk to predict colorectal cancer diagnosis using UK Biobank data: population based cohort study

Objective: To evaluate the benefit of combining polygenic risk scores with the QCancer-10 (colorectal cancer) prediction model for non-genetic risk to identify people at highest risk of colorectal cancer. Design: Population based cohort study. Setting: Data from the UK Biobank study, collected between March 2006 and July 2010. Participants: 434 587 individuals with complete data for genetics and QCancer-10 predictions were included in the QCancer-10 plus polygenic risk score modelling and validation cohorts. Main outcome measures: Prediction of colorectal cancer diagnosis by genetic, non-genetic, and combined risk models. Using data from UK Biobank, six different polygenic risk scores for colorectal cancer were developed using LDpred2 polygenic risk score software, clumping, and thresholding approaches, and a model based on genome-wide significant polymorphisms. The top performing genome-wide polygenic risk score and the score containing genome-wide significant polymorphisms were combined with QCancer-10 and performance was compared with QCancer-10 alone. Case-control (logistic regression) and time-to-event (Cox proportional hazards) analyses were used to evaluate risk model performance in men and women. Results: Polygenic risk scores derived using the LDpred2 program performed best, with an odds ratio per standard deviation of 1.584 (95% confidence interval 1.536 to 1.633), and top age and sex adjusted C statistic of 0.733 (95% confidence interval 0.710 to 0.753) in logistic regression models in the validation cohort. Integrated QCancer-10 plus polygenic risk score models out-performed QCancer-10 alone. In men, the integrated LDpred2 model produced a C statistic of 0.730 (0.720 to 0.741) and explained variation of 28.2% (26.3 to 30.1), compared with 0.693 (0.682 to 0.704) and 21.0% (18.9 to 23.1) for QCancer-10 alone. In women, the C statistic for the integrated LDpred2 model was 0.687 (0.673 to 0.702) and explained variation was 21.0% (18.7 to 23.7), compared with 0.645 (0.631 to 0.659) and 12.4% (10.3 to 14.6) for QCancer-10 alone. In the top 20% of individuals at highest absolute risk, the sensitivity and specificity of the integrated LDpred2 models for predicting colorectal cancer diagnosis was 47.8% and 80.3% respectively in men, and 42.7% and 80.1% respectively in women, with increases in absolute risk in the top 5% of risk in men of 3.47-fold and in women of 2.77-fold compared with the median. Illustrative decision curve analysis indicated a small incremental improvement in net benefit with QCancer-10 plus polygenic risk score models compared with QCancer-10 alone. Conclusions: Integrating polygenic risk scores with QCancer-10 modestly improves risk prediction over use of QCancer-10 alone. Given that QCancer-10 data can be obtained relatively easily from health records, use of polygenic risk score in risk stratified population screening for colorectal cancer currently has no clear justification. The added benefit, cost effectiveness, and acceptability of polygenic risk scores should be carefully evaluated in a real life screening setting before implementation in the general population

Integrating genome-wide polygenic risk scores and non-genetic risk to predict colorectal cancer diagnosis: a cohort study in UK Biobank

OBJECTIVE: To evaluate the benefit of combining polygenic risk scores with the QCancer-10 (colorectal cancer) prediction model for non-genetic risk to identify people at highest risk of colorectal cancer. DESIGN: Population based cohort study. SETTING: Data from the UK Biobank study, collected between March 2006 and July 2010. PARTICIPANTS: 434 587 individuals with complete data for genetics and QCancer-10 predictions were included in the QCancer-10 plus polygenic risk score modelling and validation cohorts. MAIN OUTCOME MEASURES: Prediction of colorectal cancer diagnosis by genetic, non-genetic, and combined risk models. Using data from UK Biobank, six different polygenic risk scores for colorectal cancer were developed using LDpred2 polygenic risk score software, clumping, and thresholding approaches, and a model based on genome-wide significant polymorphisms. The top performing genome-wide polygenic risk score and the score containing genome-wide significant polymorphisms were combined with QCancer-10 and performance was compared with QCancer-10 alone. Case-control (logistic regression) and time-to-event (Cox proportional hazards) analyses were used to evaluate risk model performance in men and women. RESULTS: Polygenic risk scores derived using the LDpred2 program performed best, with an odds ratio per standard deviation of 1.584 (95% confidence interval 1.536 to 1.633), and top age and sex adjusted C statistic of 0.733 (95% confidence interval 0.710 to 0.753) in logistic regression models in the validation cohort. Integrated QCancer-10 plus polygenic risk score models out-performed QCancer-10 alone. In men, the integrated LDpred2 model produced a C statistic of 0.730 (0.720 to 0.741) and explained variation of 28.2% (26.3 to 30.1), compared with 0.693 (0.682 to 0.704) and 21.0% (18.9 to 23.1) for QCancer-10 alone. In women, the C statistic for the integrated LDpred2 model was 0.687 (0.673 to 0.702) and explained variation was 21.0% (18.7 to 23.7), compared with 0.645 (0.631 to 0.659) and 12.4% (10.3 to 14.6) for QCancer-10 alone. In the top 20% of individuals at highest absolute risk, the sensitivity and specificity of the integrated LDpred2 models for predicting colorectal cancer diagnosis was 47.8% and 80.3% respectively in men, and 42.7% and 80.1% respectively in women, with increases in absolute risk in the top 5% of risk in men of 3.47-fold and in women of 2.77-fold compared with the median. Illustrative decision curve analysis indicated a small incremental improvement in net benefit with QCancer-10 plus polygenic risk score models compared with QCancer-10 alone. CONCLUSIONS: Integrating polygenic risk scores with QCancer-10 modestly improves risk prediction over use of QCancer-10 alone. Given that QCancer-10 data can be obtained relatively easily from health records, use of polygenic risk score in risk stratified population screening for colorectal cancer currently has no clear justification. The added benefit, cost effectiveness, and acceptability of polygenic risk scores should be carefully evaluated in a real life screening setting before implementation in the general population

An online evidence-based dictionary of common adverse events of antidepressants: a new tool to empower patients and clinicians in their shared decision-making process

Background: Adverse events (AEs) are commonly reported in clinical studies using the Medical Dictionary for Regulatory Activities (MedDRA), an international standard for drug safety monitoring. However, the technical language of MedDRA makes it challenging for patients and clinicians to share understanding and therefore to make shared decisions about medical interventions. In this project, people with lived experience of depression and antidepressant treatment worked with clinicians and researchers to co-design an online dictionary of AEs associated with antidepressants, taking into account its ease of use and applicability to real-world settings. Methods: Through a pre-defined literature search, we identified MedDRA-coded AEs from randomised controlled trials of antidepressants used in the treatment of depression. In collaboration with the McPin Foundation, four co-design workshops with a lived experience advisory panel (LEAP) and one independent focus group (FG) were conducted to produce user-friendly translations of AE terms. Guiding principles for translation were co-designed with McPin/LEAP members and defined before the finalisation of Clinical Codes (CCs, or non-technical terms to represent specific AE concepts). FG results were thematically analysed using the Framework Method. Results: Starting from 522 trials identified by the search, 736 MedDRA-coded AE terms were translated into 187 CCs, which balanced key factors identified as important to the LEAP and FG (namely, breadth, specificity, generalisability, patient-understandability and acceptability). Work with the LEAP showed that a user-friendly language of AEs should aim to mitigate stigma, acknowledge the multiple levels of comprehension in ‘lay’ language and balance the need for semantic accuracy with user-friendliness. Guided by these principles, an online dictionary of AEs was co-designed and made freely available (https://thesymptomglossary.com). The digital tool was perceived by the LEAP and FG as a resource which could feasibly improve antidepressant treatment by facilitating the accurate, meaningful expression of preferences about potential harms through a shared decision-making process. Conclusions: This dictionary was developed in English around AEs from antidepressants in depression but it can be adapted to different languages and cultural contexts, and can also become a model for other interventions and disorders (i.e., antipsychotics in schizophrenia). Co-designed digital resources may improve the patient experience by helping to deliver personalised information on potential benefits and harms in an evidence-based, preference-sensitive way