Abstract LB-304: Computational analysis of patient-to-patient signaling heterogeneity elucidates patient-specific clinical outcomes

Discovery of personalized, which is also called as patient-specific, signaling mechanism is helpful for better understanding of cancer progress and optimal design of treatment strategy. However, this would require algorithms that are not restricted by grouped samples and that account for personalized signaling pathways of individuals. Here, we introduce such algorithms in an open-source software program that we called as Personalized Signaling Pathway Prediction (PSPP). In the PSPP, we developed a new network-based approach, PathCrawl, to address the personalized signaling pathway from a patient9s multi-dimensional genomic data, such as RNA gene expression, DNA methylation, DNA mutation, etc. The PathCrawl takes advantages of the Evolutionary Multiple-objective Optimization model to iteratively optimize molecules and signaling interactions in the candidate signaling network of a cancer, based on multiple-dimensional variations derived from the multi-dimensional genomic data of this patient. The PSPP employs a randomized process to evaluate the statistical significance of the identified signaling molecules and interactions. We applied the PSPP to study two glioblastoma multiforme (GBM) patients from TCGA, with identical physical conditions (male, white, and 65 years old), but different vital and tumor statuses. One patient (TCGA-19-2623) is alive and tumor-free after radiotherapy and one cycle chemotherapy with Temodar, whereas another patient (TCGA-06-2567) is dead with tumor after the same treatments. In order to involve the known GBM genes and associated signaling information into the candidate signaling network, we considered our previous published cancer signaling elements, cancer signaling bridges (CSBs) in this study. This network comprises 41 GBM proteins in the Online Mendelian Inheritance in Man (OMIM) database, 721 CSB proteins, 4,924 upstream signaling proteins, 3,723 protein complexes, and 22,056 signaling interactions. The genomic data from primary tumor include mRNA gene expression (AffymetrixU133A), DNA methylation (IlluminaMethylation27), and DNA copy number (AgilentCGH244A). The identified patient-specific signaling pathways are useful to explain why a patient is survived but another not. We found that the dead patient highly activated a calcium- and zinc-binding protein, S100AB (P The advantage of the PSPP is that the novel computational method is able to study the signaling mechanism from patient to patient. It will be also helpful to discover the signaling mechanisms associated with other clinical outcomes, such as drug resistance, tumor recurrence, and tumor phenotype. Citation Format: Guangxu Jin, Liang Liu, Ruisheng Wang, Keqin Liu, Kyle Mastsuo, Kun Wei, Xiaobo Zhou. Computational analysis of patient-to-patient signaling heterogeneity elucidates patient-specific clinical outcomes. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-304. doi:10.1158/1538-7445.AM2015-LB-304