Olivier Elemento | bioRxiv Science

bioRxiv2016

The Precision Medicine Knowledge Base: an online application for collaborative editing, maintenance and sharing of structured clinical-grade cancer mutations interpretations

ObjectiveThis paper describes the Precision Medicine Knowledge Base (PMKB; https://pmkb.weill.cornell.edu), an interactive online application for collaborative editing, maintenance and sharing of structured clinical-grade cancer mutations interpretations.\n\nMaterials and MethodsPMKB was built using the Ruby on Rails Web application framework. Leveraging existing standards such as Human Genome Variation Society (HGVS) variant description format, we implemented a data model that links variants to tumor-specific and tissue-specific interpretations. Key features of PMKB include support for all major variant types, standardized authentication, distinct user roles including high-level approvers, detailed activity history. A REpresentational State Transfer (REST) application-programming interface (API) was implemented to query the PMKB programmatically.\n\nResultsAt the time of writing, PMKB contains 457 variant descriptions with 281 clinical-grade interpretations. The EGFR, BRAF, KRAS, and KIT genes are associated with the largest numbers of interpretable variants. The PMKBs interpretations have been used in over 1,500 AmpliSeq tests and 750 whole exome sequencing tests. The interpretations are accessed either directly via the Web interface or programmatically via the existing API.\n\nDiscussionAn accurate and up-to-date knowledge base of genomic alterations of clinical significance is critical to the success of precision medicine programs. The open-access, programmatically accessible PMKB represents an important attempt at creating such a resource in the field of oncology.\n\nConclusionThe PMKB was designed to help collect and maintain clinical-grade mutation interpretations and facilitates reporting for clinical cancer genomic testing. The PMKB was also designed to enable the creation of clinical cancer genomics automated reporting pipelines via an API.

Genomics↗

bioRxiv2016

A virtual B cell lymphoma model to predict effective combination therapy

AbstractThe complexity of cancer signaling networks limits the efficacy of most single agent treatments and brings about challenges in identifying effective combinatorial therapies. Using chronic active B cell receptor (BCR) signaling in diffuse large B cell lymphoma (DLBCL) as a model system, we established a computational framework to optimize combinatorial therapy in silico. We constructed a detailed kinetic model of the BCR signaling network, which captured the known complex crosstalk between the NF{kappa}B, ERK and AKT pathways; and multiple feedback loops. Combining this signaling model with a data-derived tumor growth model we predicted viability responses of many single drug and drug combinations that are in agreement with experimental data. Under this framework, we exhaustively predicted and ranked the efficacy and synergism of all possible combinatorial inhibitions of eleven currently targetable kinases in the BCR signaling network. Our work established a detailed kinetic model of the core BCR signaling network and provides the means to explore the large space of possible drug combinations.\n\nAuthor SummaryUsing chronic active B cell receptor (BCR) signaling in diffuse large B cell lymphoma(DLBCL) as a model system, we developed a kinetic-modeling based computational framework for predicting effective combination therapy in silico. By integrative modeling of signal transduction, drug kinetics and tumor growth, we were able to directly predict drug-induced cell viability response at various dosages, which were in agreement with published cell line experimental data. We implemented computational screening methods that identified potent and synergistic combinations in silico and validated our independent predictions in vitro.

Systems Biology↗