报告题目1：Biology based Proteogenomic Translator for Cancer Marker Discovery towards Precision Medicine---CPTAC-PGDAC At Mount Sinai
报 告 人：Pei Wang，Mount Sinai School of Medicine
联 系 人：李婧， firstname.lastname@example.org
In this talk, I will provide an overview of the research activities in our Proteogenomic Data analysis center (PGDAC) of CPTAC (Clinical Proteomics Tumor Analysis Consortium). The goal of our PGDAC is to improve understanding of the proteogenomic complexity of tumors. Towards this goal, we apply network based system learning to reveal causative molecular regulatory relationships contributing to varieties of phenotypes in cancer using CPTAC proteomic/genomic data. We first use a mixed effects model to (1) fix the batch effects in data from multi-plex proteomics experiments; and (2) handle the large amount of missing data from abundance-dependent missing mechanisms in proteomic data. We then utilize a multivariate penalized regression framework to construct the global regulatory networks to elucidate how protein or pathway activities are shaped by genomic alterations in tumor cells. We also construct protein co-expression networks based on global-, phosphor-, glyco- and other PTM-proteomics data. In addition, we model tumor and normal tissues jointly, so that tumor specific interactions and network modules will be inferred with better accuracy. These network modules and protein sets are then tested for their associations with disease phenotypes. And we then utilize network based tools to identify driver players in selected proteins signature sets. These driver proteins could play important roles in shaping the overall function of regulatory system, and thus serve as good candidates for cancer biomarkers and drug targets.
A Penalized Multivariate Linear Mixed Effects Model for Integrative Proteo-genomics Analysi Based on iTRAQ Data
报 告 人：Dr. Weiping Ma，Mount Sinai School of Medicine
联 系 人：李婧， email@example.com
Recent development in high throughput proteomics and genomics profiling makes it possible to study regulations of genetic factors on protein activities in a systematic manner. In this paper, we propose a new statistical method – ProMAP -- a penalized multivariate linear mixed effects model for integrative proteo-genomic analysis. The motivation problem is to systematically characterize the regulatory relationships between proteins and DNA copy number alterations in breast tumor samples based on iTRAQ (isobaric tag for relative and absolute quantitation) data and SNP array data from CPTAC-TCGA studies. Because of the dynamic nature of iTRAQ technique and mass spectrometry instruments, data from iTRAQ experiments usually have severe batch effects, high percentages of missing and non-ignorable missing-data patterns. Thus, we utilize a linear mixed effects model to account for the batch structure and explicitly incorporate the batch-level abundance-dependent-missing-data mechanism of iTRAQ data in ProMAP. In addition, we employ a multivariate regression framework to characterize the multiple-to-multiple regulatory relationships between DNA copy number alterations and proteins. Moreover, we utilize proper statistical regularization to facilitate the detection of master genetic regulators, which affect the activities of many proteins and often play important roles in genetic regulatory networks. The performance of ProMAP is illustrated through extensive simulation studies. In the end, we apply ProMap to the CPTAC-TCGA breast cancer data sets, and identify novel regulatory relationships between DNA copy number alterations and protein expression profiles in breast cancer tumors.