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Yang, Peter

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Yang

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Peter

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Yang, Peter
Author's Publications: search.filters.isAuthorOfPublication.2513db5a-0d38-42ff-ae24-5176d160fee8

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    Automated Synthesis and Visualization of a Chemotherapy Treatment Regimen Network
    (2013) Warner, Jeremy; Yang, Peter; Alterovitz, Gil
    Cytotoxic treatments for cancer remain highly toxic, expensive, and variably efficacious. Many chemotherapy regimens are never directly compared in randomized clinical trials (RCTs); as a result, the vast majority of guideline recommendations are ultimately derived from human expert opinion. We introduce an automated network meta-analytic approach to this clinical problem, with nodes representing regimens and edges direct comparison via RCT(s). A chemotherapy regimen network is visualized for the primary treatment of chronic myelogenous leukemia (CML). Node and edge color, size, and opacity are all utilized to provide additional information about the quality and strength of the depicted evidence. Historical versions of the network are also created. With this approach, we were able to compactly compare the results of 17 CML regimens involving RCTs of 9700 patients, representing the accumulation of 45 years of evidence. Our results closely parallel the recommendations issued by a professional guidelines organization, the National Comprehensive Cancer Network (NCCN). This approach offers a novel method for interpreting complex clinical data, with potential implications for future objective guideline development.
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    On the Bayesian Derivation of a Treatment-based Cancer Ontology
    (American Medical Informatics Association, 2014) Gao, Michael; Warner, Jeremy; Yang, Peter; Alterovitz, Gil
    Traditional cancer classifications are primarily based on anatomical locations. As knowledge is heavily compartmentalized in the oncological specialties, discovering new targets for existing drugs (drug inference) can take years. Furthermore, our lack of understanding of the mechanisms underlying drug efficacy sometimes undercuts the effectiveness of genetic approaches to drug inference. This study tackles the twin problems of cancer reclassification and drug inference by constructing a global cancer ontology inductively from treatment regimens. A topological abstraction algorithm was performed on the bipartite graph of drugs and cancers to highlight important edges, and a Bayesian algorithm was then applied to determine a new treatment-based classification of cancer, producing 6 highly significant clusters (p < 0.05), confirmed by Fisher’s exact test and enrichment analyses. Edge probabilities derived from its drug inference routine matched real edge frequencies (R2 ≈ 0.96). Drug inference results were reinforced by the identification of relevant published Phase II and III clinical trials, and the drug inference routine differentiated between high- and low-likelihood targets (p < 0.05). This novel treatment-based ontology has the potential to reorganize cancer research and provide powerful tools for drug inference using global patterns of drug efficacy.