Predicting Tumor Patterns Using Ubiquitin Pathway Genes

October 23, 2018

Professor Han Liang speaks with Cancer Network about the ubiquitin pathway and its role in cancer research.

Han Liang, PhD, is a professor and deputy department chair at the department of bioinformatics and computational biology and a professor in the department of systems biology at the University of Texas MD Anderson Cancer Center, Houston. Liang’s research focuses on the integrated analysis of cancer genomics and development of bioinformatic tools. Cancer Network sat downwith Liang totalk about his team’s identification of candidate ubiquitin-pathway gene drivers of tumorigenesis and tumor progression across 33 cancer types, and the clinical implications of their findings.

 

Cancer Network: What functions does protein ubiquitination play in healthy cells?

Dr. Liang: Protein ubiquitination functions in a variety of biological processes including protein degradation via the proteasome, protein subcellular localization alteration, protein activation, and protein interactions.

Cancer Network: What makes the ubiquitin pathway a promising target for cancer research?

Dr. Liang: There are two main reasons. First, there has been extensive evidence relating malfunction of the ubiquitin pathway with tumor initiation and progression. Second, unlike standard targeted therapy which targets a protein’s biological activity, targeting the ubiquitin pathway regulates the protein levels and can be used for undruggable targets such as MYC and b-catenin.

Cancer Network: How did your team go about identifying candidate tumor-driving ubiquitin-related genes?

Dr. Liang: Using the genomic and proteomic data from The Cancer Genome Atlas, we performed comprehensive molecular characterization of a manually curated list of 928 ubiquitin-related genes and 95 deubiquitinase genes across 9,125 patients from 33 cancer types. We used a variety of bioinformatics algorithms to identify candidate tumor-driving ubiquitin-related genes, including [The Broad Institute’s] MutSigCV for mutation drivers (those genes accumulating more mutations than expected) and GISTIC2 for copy number alteration drivers (those genes showing unusually high frequency of amplifications or deletions in tumors).

Cancer Network: What did you find?

Dr. Liang: There are three key findings in our study. First, candidate ubiquitin pathway drivers which had significant somatic mutation and copy number alterations were systematically cataloged. Second, genes in the ubiquitin pathway tend to be overexpressed in a range of cancer types compared to their matched normal tissues. Finally, our multi-platform integrative analysis of ubiquitin pathway genes revealed a group of patients who consistently correlated with worse prognosis across cancer types. And these patients showed differential ubiquitin pathway expression underlying the perturbation of many fundamental signaling pathways, notably cell cycle and DNA damage repair.

Cancer Network: How might these findings inform the development of new cancer treatments?

Dr. Liang: Current progress on developing drugs that target ubiquitin system has been limited, partly due to the lack of systemic characterization of driver mutations, copy number alteration patterns, and dysregulated expression profiles in the ubiquitin pathway across cancer types. This study hopefully lays [the groundwork] for exploring ubiquitin pathway genes that are cancer molecular drivers and of clinical relevance as drug candidates and prognostic markers.