Institute of Information Science Academia Sinica
Topic: Pathway-based diagnosis and modeling of cancer development and progression
Speaker: Dr. Han-Yu Chuang (Bioinformatics Scientist, Illumina, Inc.)
Date: 2012-01-16 (Mon) 14:00 – 16:00
Location: Auditorium 106 at new IIS Building
Host: Dr. HK Tsai

Abstract:

The advent of whole-genome expression profiling technology has made it possible to identify transcriptional dysregulation that contribute to or result from disease mechanisms and can also serve as biomarkers for disease.  However, expression-alone classification can be challenging in complex diseases due to factors such as genetic heterogeneity across patients or noise in mRNA levels.  Moreover, it remains unclear how these marker genes interrelate within a larger functional network.

In this talk, I will start from an well-discussed approach to integrate gene expression with protein interactions to dissect cancer development and outcome.  The resulted prognostic markers are not individual genes or proteins, but as sets of coherently expressed genes whose products interact within a larger human protein interaction network.  In breast cancer, we show that this integrated strategy predict the risk of metastasis potential more accurately than previous approaches based only on gene expression.  More than being more reproducible and robust, our network markers also give molecular models for how the cancer susceptibility genes might be associated with cancer metastasis.

I will then discuss how we apply this network-based analysis to develop a new system for accurately stratifying patients of chronic lymphocytic leukemia (CLL) at different risk levels of disease progression.  The network markers represent an array of disease pathways whose expression converge over time among patients regardless their initial risk levels, implicating novel understanding for cancer evolution and for the development of treatment strategies.

Besides incorporating protein interaction network into gene expression analyses, we also identify condition-responsive genes within canonical pathways to infer dysfunctional pathway activation.  Contrast to methods based on static pathway knowledge, our dynamic pathway markers lead to better clinical performance for various cancers, including leukemia, prostate cancer, breast cancer and lung cancer.