Search

INTRODUCTION Trial recruitment is a crucial factor for precision oncology, potentially improving patient outcomes and generating new scientific evidence. To identify suitable, biomarker-based trials for patients' clinicians need to screen multiple clinical trial registries which lack support for modern trial designs and offer only limited options to filter for in- and exclusion criteria. Several registries provide trial information but are limited regarding factors like timeliness, quality of information and capability for semantic, terminology enhanced searching for aspects like specific inclusion criteria. METHODS We specified a Fast Healthcare Interoperable Resources (FHIR) Implementation Guide (IG) to represent clinical trials and their meta data. We embedded it into a community driven approach to maintain clinical trial data, which is fed by openly available data sources and later annotated by platform users. A governance model was developed to manage community contributions and responsibilities. RESULTS We implemented Community Annotated Trial Search (CATS), an interactive platform for clinical trials for the scientific community with an open and interoperable information model. It provides a base to collaboratively annotate clinical trials and serves as a comprehensive information source for community members. Its terminology driven annotations are coined towards precision oncology, but its principles can be transferred to other contexts. CONCLUSION It is possible to use the FHIR standard and an open-source information model represented in our IG to build an open, interoperable clinical trial register. Advanced features like user suggestions and audit trails of individual resource fields could be represented by extending the FHIR standard. CATS is the first implementation of an open-for-collaboration clinical trial registry with modern oncological trial designs and machine-to-machine communication in mind and its methodology could be extended to other medical fields besides precision oncology. Due to its well-defined interfaces, it has the potential to provide automated patient recruitment decision support for precision oncology trials in digital applications. [ABSTRACT FROM AUTHOR]

Abstract Background Genetic variants are likely to contribute to a portion of prostate cancer risk. Full elucidation of the genetic etiology of prostate cancer is difficult because of incomplete penetrance and genetic and phenotypic heterogeneity. Current evidence suggests that genetic linkage to prostate cancer has been found on several chromosomes including the X; however, identification of causative genes has been elusive. Methods Parametric and non-parametric linkage analyses were performed using 26 microsatellite markers in each of 11 groups of multiple-case prostate cancer families from the International Consortium for Prostate Cancer Genetics (ICPCG). Meta-analyses of the resultant family-specific linkage statistics across the entire 1,323 families and in several predefined subsets were then performed. Results Meta-analyses of linkage statistics resulted in a maximum parametric heterogeneity lod score (HLOD) of 1.28, and an allele-sharing lod score (LOD) of 2.0 in favor of linkage to Xq27-q28 at 138 cM. In subset analyses, families with average age at onset less than 65 years exhibited a maximum HLOD of 1.8 (at 138 cM) versus a maximum regional HLOD of only 0.32 in families with average age at onset of 65 years or older. Surprisingly, the subset of families with only 2–3 affected men and some evidence of male-to-male transmission of prostate cancer gave the strongest evidence of linkage to the region (HLOD = 3.24, 134 cM). For this subset, the HLOD was slightly increased (HLOD = 3.47 at 134 cM) when families used in the original published report of linkage to Xq27-28 were excluded. Conclusions Although there was not strong support for linkage to the Xq27-28 region in the complete set of families, the subset of families with earlier age at onset exhibited more evidence of linkage than families with later onset of disease. A subset of families with 2–3 affected individuals and with some evidence of male to male disease transmission showed stronger linkage signals. Our results suggest that the genetic basis for prostate cancer in our families is much more complex than a single susceptibility locus on the X chromosome, and that future explorations of the Xq27-28 region should focus on the subset of families identified here with the strongest evidence of linkage to this region.

Clinical cancer registries are a valuable data source for health services research (HSR). HSR is in need of high quality routine care data for its evaluations. However, the secondary use of routine data - such as documented cancer cases in a disease registry - poses new challenges in terms of data quality, IT-management, documentation processes and data privacy. In the clinical cancer registry Heilbronn-Franken, real-world data from the Giessen Tumor Documentation System (GTDS) was utilized for analyses of patients' disease processes and guideline adherence in follow-up care. A process was developed to map disease state definitions to fields of the GTDS database and extract patients' disease progress information. Thus, the disease process of sub-cohorts could be compared to each other, e.g., comparison of disease free survival of HER2 (human epidermal growth factor receptor 2) -positive and -negative women who were treated with Trastuzumab, a targeted therapy applied in breast cancer. In principle, such comparisons are feasible and of great value for HSR as they depict a routine care setting of a diverse patient cohort. Yet, local documentation practice, missing flow of information from external health care providers or small sub-cohorts impede the analyses of clinical cancer registries data bases and usage for HSR. [ABSTRACT FROM AUTHOR]

Survival time prediction at the time of diagnosis is of great importance to make decisions about treatment and long-term follow-up care. However, predicting the outcome of cancer on the basis of clinical information is a challenging task. We now examined the ability of ten different data mining algorithms (Perceptron, Rule Induction, Support Vector Machine, Linear Regression, Naïve Bayes, Decision Tree, k-nearest Neighbor, Logistic Regression, Neural Network, Random Forest) to predict the dichotomous attribute "5-yearsurvival" based on seven attributes (sex, UICC-stage, etc.) which are available at the time of diagnosis. For this study we made use of the nationwide German research data set on colon cancer provided by the Robert Koch Institute. To assess the results a comparison between data mining algorithms and physicians' opinions was performed. Therefore, physicians guessed the survival time by leveraging the same seven attributes. The average accuracy of the physicians' opinion was 59%, the average accuracy of the machine learning algorithms was 67.7%. [ABSTRACT FROM AUTHOR]

Clinical cancer registries are a valuable data source for health services research (HSR). HSR is in need of high quality routine care data for its evaluations. However, the secondary use of routine data - such as documented cancer cases in a disease registry - poses new challenges in terms of data quality, IT-management, documentation processes and data privacy. In the clinical cancer registry Heilbronn-Franken, real-world data from the Giessen Tumor Documentation System (GTDS) was utilized for analyses of patients' disease processes and guideline adherence in follow-up care. A process was developed to map disease state definitions to fields of the GTDS database and extract patients' disease progress information. Thus, the disease process of sub-cohorts could be compared to each other, e.g., comparison of disease free survival of HER2 (human epidermal growth factor receptor 2)-positive and -negative women who were treated with Trastuzumab, a targeted therapy applied in breast cancer. In principle, such comparisons are feasible and of great value for HSR as they depict a routine care setting of a diverse patient cohort. Yet, local documentation practice, missing flow of information from external health care providers or small sub-cohorts impede the analyses of clinical cancer registries data bases and usage for HSR.