ICIBM 2016

Han Liang, Ph.D.

Han Liang, Ph.D.

The University of Texas MD Anderson Cancer Center
Saturday, December 10, 2016
1:30 pm - 2:00 pm


Dr. Liang received Ph.D. training from the Quantitative and Computational Biology program at Princeton University, which provides graduate education in the interface of biology, the physical sciences, and computational science. His PhD thesis is about RNA informatics on translation termination and alternative splicing. As a postdoctoral researcher, Dr. Liang completed three years of research on computational and evolutionary genomics at the University of Chicago, where his research was about microRNA regulation and gene duplication. Currently, Dr. Liang's research interests include the analysis of next-generation sequencing data, the integration of cancer genomics data, microRNA regulation, and the evolutionary process of tumor cells.

Functional Proteomics as a Major Approach for Precision Cancer Medicine

In contrast to the recent exploration of next-generation sequencing data, the applications of large-scale proteomic data in cancer research and patient care have been relatively limited. This is despite proteins comprising the basic functional units in various biological processes and being major targets for cancer therapy. Reverse-phase protein arrays (RPPAs) offer a powerful functional proteomic approach to elucidate the molecular basis of human cancer and to evaluate biomarkers and mechanisms underlying sensitivity and resistance to cancer therapy1,2. This rapidly maturing quantitative antibody-based assay can assess a large number of protein markers in many samples in a cost-effective, sensitive and high-throughput manner. Its value has been documented through hundreds of papers. Our team at MD Anderson Cancer Center has been a leader in the implementation of this technology. Our platform currently contains ~300 protein markers, covering all major signaling pathways. Using this platform, we have characterized >10,000 patient samples through The Cancer Genome Atlas (TCGA)3. Integrating TCGA data and RPPA data from other independent patient cohorts, we have developed protein-based prognostic models for stratifying patients into different risk groups4,5. Moreover, we have characterized more than 700 cancer cell lines. Our dataset recapitulates the effects of mutated pathways on protein expression observed in patient samples, and demonstrates that protein markers provide information content for predicting drug sensitivity that is not available from the corresponding mRNAs. To provide a valuable resource for the broader biomedical research community, we developed interactive, user-friendly bioinformatic resources, TCPA6 and MCLP, for analyzing these data in a rich context. More recently, our RPPA platform has been designated as one of two National Cancer Institute (USA) Genome Characterization Centers to characterize the samples from important ongoing NCI initiatives. In this role, it will characterize patient samples from the Exceptional Responders Initiative and the ALCHEMIST precision medicine trials, and other projects such as the MD Anderson Moon Shot program. Together, the RPPA platform represents a very powerful approach for developing and implement novel prognostic and therapeutic strategies for precision cancer medicine