Class of 2015 Abstract / Objectives: The aim of this study was to develop a computational process capable of hypothesizing potential chemotherapeutic agents for the treatment of skin cutaneous melanoma given an annotated chemotherapy molecular target database and patient-specific genetic tumor profiles. Methods: Aberrational profiles for a total of 246 melanoma patients indexed by the Cancer Genome Atlas (TCGA) for whom complete somatic mutational, mRNA expression, and protein expression data was available were queried against an annotated targeted therapy database using Visual Basic for Applications and Python in conjunction with Microsoft Excel. Identities of positively and negatively associated therapy-profile matches were collected and ranked. Results: Subjects included in the analysis were predominantly Caucasian (93%), non-Hispanic (95.9%), female (59%), and characterized as having stage III clinical disease (37.4%). The most frequently occurring positive and negative therapy associations were determined to be 17-AAG (tanespimycin; 42.3%) and sorafenib (41.9%), respectively. Mean total therapy hypotheses per patient did not differ significantly with regard to either positive or negative associations (p=0.1951 and 0.4739 by one-way ANOVA, respectively) when stratified by clinical melanoma stage. Conclusions: The developed process does not appear to offer discernably different therapy hypotheses amongst clinical stages of cutaneous melanoma based upon genetic data alone. The therapy-matching algorithm may be useful in quickly retrieving potential therapy hypotheses based upon the genetic characteristics of one or many subjects specified by the user.

Methods: We address these issues by transforming the TCGA data into the Semantic Web standard Resource Description Format (RDF), link it to relevant datasets in the Linked Open Data (LOD) cloud and further propose an efficient data distribution strategy to host the resulting 20.4 billion triples data via several SPARQL endpoints. Having the TCGA data distributed across multiple SPARQL endpoints, we enable biomedical scientists to query and retrieve information from these SPARQL endpoints by proposing a TCGA tailored federated SPARQL query processing engine named TopFed. Results: We compare TopFed with a well established federation engine FedX in terms of source selection and query execution time by using 10 different federated SPARQL queries with varying requirements. Our evaluation results show that TopFed selects on average less than half of the sources (with 100% recall) with query execution time equal to one third to that of FedX. Conclusion: With TopFed, we aim to offer biomedical scientists a single-point-of-access through which distributed TCGA data can be accessed in unison. We believe the proposed system can greatly help researchers in the biomedical domain to carry out their research effectively with TCGA as the amount and diversity of data exceeds the ability of local resources to handle its retrieval and parsing.

Methods: We address these issues by transforming the TCGA data into the Semantic Web standard Resource Description Format (RDF), link it to relevant datasets in the Linked Open Data (LOD) cloud and further propose an efficient data distribution strategy to host the resulting 20.4 billion triples data via several SPARQL endpoints. Having the TCGA data distributed across multiple SPARQL endpoints, we enable biomedical scientists to query and retrieve information from these SPARQL endpoints by proposing a TCGA tailored federated SPARQL query processing engine named TopFed. Results: We compare TopFed with a well established federation engine FedX in terms of source selection and query execution time by using 10 different federated SPARQL queries with varying requirements. Our evaluation results show that TopFed selects on average less than half of the sources (with 100% recall) with query execution time equal to one third to that of FedX. Conclusion: With TopFed, we aim to offer biomedical scientists a single-point-of-access through which distributed TCGA data can be accessed in unison. We believe the proposed system can greatly help researchers in the biomedical domain to carry out their research effectively with TCGA as the amount and diversity of data exceeds the ability of local resources to handle its retrieval and parsing.