No abstract

BackgroundIntegrating multiple sources of pharmacovigilance evidence has the potential to advance the science of safety signal detection and evaluation. In this regard, there is a need for more research on how to integrate multiple disparate evidence sources while making the evidence computable from a knowledge representation perspective (i.e., semantic enrichment). Existing frameworks suggest well-promising outcomes for such integration but employ a rather limited number of sources. In particular, none have been specifically designed to support both regulatory and clinical use cases, nor have any been designed to add new resources and use cases through an open architecture. This paper discusses the architecture and functionality of a system called Large-scale Adverse Effects Related to Treatment Evidence Standardization (LAERTES) that aims to address these shortcomings.ResultsLAERTES provides a standardized, open, and scalable architecture for linking evidence sources relevant to the association of drugs with health outcomes of interest (HOIs). Standard terminologies are used to represent different entities. For example, drugs and HOIs are represented in RxNorm and Systematized Nomenclature of Medicine -- Clinical Terms respectively. At the time of this writing, six evidence sources have been loaded into the LAERTES evidence base and are accessible through prototype evidence exploration user interface and a set of Web application programming interface services. This system operates within a larger software stack provided by the Observational Health Data Sciences and Informatics clinical research framework, including the relational Common Data Model for observational patient data created by the Observational Medical Outcomes Partnership. Elements of the Linked Data paradigm facilitate the systematic and scalable integration of relevant evidence sources.ConclusionsThe prototype LAERTES system provides useful functionality while creating opportunities for further research. Future work will involve improving the method for normalizing drug and HOI concepts across the integrated sources, aggregated evidence at different levels of a hierarchy of HOI concepts, and developing more advanced user interface for drug-HOI investigations.

e14557 Background: In immuno-oncology (IO), the correlation between clinical trial endpoints, specifically, objective response rate (ORR), disease control rate (DCR) or progression free survival (PFS), and overall survival (OS) is poorly understood. The effect of IO agents as opposed to chemotherapy, is not on tumor cells, but on immune cells and OS benefit has been observed in absence of PFS benefit. However, decisions on registration trials often rely on PFS from Phase II trials. Methods: We conducted a systematic literature review with PubMed and Embase (Jan. 2005–Nov. 2016), supplemented with oncology conference proceedings (2014–2016). Eligible studies were randomized controlled trials (RCT) that investigated ≥1 immune checkpoint blockers (CBs) targeting programmed death proteins (PD-1/PD-L1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), and reported their relative effect on OS and on ≥1 of the clinical endpoints, DCR, ORR, and PFS. Log-transformed hazard ratios were fitted using weighted regression models to determine the power of relative effects on clinical endpoints to predict OS effects, with correlation coefficients estimated and presented with adjusted R2 (high values indicating better goodness-of-fit). Results: This analysis included 18 RCTs involving 7140, patients. Most studies (10/18) evaluated the efficacy of CBs vs conventional chemotherapy, whereas 8 studies compared the efficacy of ≥1 CB. Among trials that evaluated anti-CTLA-4, the adjusted R2 for the relative efficacy of CBs on DCR, ORR, PFS, and relative efficacy of CBs on OS were 0.160 ( P= 0.156), 0.016 ( P= 0.332), and 0.000 ( P= 0.623) respectively. Among trials that evaluated either anti-PD-1 or anti-PD-L1, the adjusted R2 were 0.038 ( P= 0.401), 0.066 ( P= 0.251), and 0.432 ( P= 0.032), for DCR, ORR and PFS respectively. Among trials that evaluated CBs in melanoma, the adjusted R2 were 0.030 ( P= 0.267), 0.028 ( P= 0.279), and 0.192 ( P= 0.154), for DCR, ORR, and PFS respectively. Conclusions: No clear correlations were observed between relative effects of conventional clinical endpoints and OS for CBs. New surrogate endpoints may be needed to better predict OS benefit for CBs and other forms of cancer immunotherapy.

The unprecedented growth of digital literacy has sparked new mandates for Colleges of Teacher Education, challenging faculty to find innovative ways to incorporate digital literacies into curriculum. This research project paired candidates with elementary grade students for reading intervention using technology. The results provided rich descriptions of specific characteristics of the intervention which proved to be beneficial. The mixed method embedded design allowed researchers to collect both qualitative and quantitative data to provide insights into the research questions. Qualitative data showed that candidates felt that the inclusion of technology with reading intervention caused a higher level of engagement from students. Furthermore, quantitative data showed the intervention increased the reading achievement of students in the five areas of effective reading instruction.

Tumor explant models (human tumor tissue directly transplanted into mice) are believed to conserve original tumor characteristics, including molecular heterogeneity and tumor architecture. Thus, they better represent patient population and can provide more relevant predictive insight into clinical outcome. They potentially offer a clinically relevant model in which to compare drug response to genomic and molecular profiles. So called next generation sequencing (NGS) technologies (Exome sequencing and RNA-seq) are becoming the standard choices for genetic profiling due to the rich information they can provide. The sequence based nature of these technologies offers the potential to differentiate results by species and remove mouse derived stromal ‘contamination’ when assessing tumor cells directly. This also presents a unique opportunity to differentiate and assess molecular interaction between the tumor and the stromal tissue. To this aim, we have developed algorithms to differentiate NGS sequences between human and mouse. Using mixtures of human and mouse derived cell lines we demonstrated that the algorithm achieved high sensitivity (99.8%) and specificity (99.5%). More than 90% of sequence reads mapped to both human and mouse can be effectively differentiated. We applied the approach to differentiate human and mouse sequences for several explant models derived from both exome sequencing and RNA-seq. We demonstrated that the process resulted in more accurate expression quantification, and reduction of false positives in variance calling. In addition, we compared the variance calling between paired exome and RNA-seq and found that the two methods are complimentary to each other where genes were expressed. We also highlighted a number of interesting features and anomalies between RNAseq and exome data. In summary, we demonstrated the feasibility of using exome and RNA-seq to effectively differentiate human and mouse sequences in tumor explants models. We highlight the value of this approach to rationally guide their application in drug discovery. Citation Format: Zhongwu Lai, James Bradford, Qi Zhang, Sara Dempster, Brian Dougherty, Celina D'Cruz, Jonathan Dry. Characterization of tumor explant models using next-generation sequencing. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3235. doi:10.1158/1538-7445.AM2013-3235