Day 2 :
Nottingham Trent University
Time : 9:00-9:50
Prof Graham Ball is Professor of Bioinformatics at Nottingham Trent University and CSO of CompanDX UK and CompanDX China Ltd. He is Associate Director the John Van Geest Cancer Research Centre and biostatistics lead on three clinical projects. He has been involved in the development and validation of bioinformatics algorithms using Artificial Neural Networks for the last 18 years. He has 115 journal papers and 5 patents in this area. After a PhD (UN funded) and a Post Doc modelling environmental interactions with ANNs at NTU, in 2000 he shifted the focus of his analysis to proteomic and genomic data searching for proteins and genes associated with cancer. His current research interests are directed at the classification and characterisation of biological systems including diagnostic and classification modelling of microbial pathogens, cancer clinical pathology, allergic responses and viral diseases through the use of ANNs and other machine learning techniques. He is involved in the molecular characterisation of Breast Cancer with Prof Ian Ellis’ team at Nottingham University Hospitals Trust.
Cancer is a complex disease with a myriad of forms and prognoses occurring within each type. For example, in breast cancer using genomic profiling in excess of 80 sub types have been identified. The ability to characterise the disease for each patient may offer the potential to assess the molecular sub-type of the disease and thus accurately determine the patients’ prognostic outcome. Methodologies such as mass spectrometry-based proteomics, RNASeq and gene expression arrays offer the potential for characterisation of disease derived samples using a huge number of proteins or genes. This depth of information while providing a comprehensive overview of a disease state also proves problematic in its complexity. One has to search through potentially hundreds of thousands of pieces of information for consistent features that address a clinical question in the population.
The human mind is very good at finding patterns in a system but is not able to conduct the task repetitively for large numbers of parameters. Conversely computers are very good at searching for features in such a data space, but previously defined statistical methods are not able to cope with the high complexity. Here we present the application of Artificial Neural Networks (ANNs, a form of artificial intelligence having the characteristics of both human pattern recognition and computer automated searching) to finding genomic solutions to questions in cancer. Here we present the use of a range of statistical and artificial intelligence-based machine learning techniques to develop prognostic models for breast cancer.
Here we present results of use of ANN algorithms for biomarker discovery, whereby we have undertaken a parallel analysis of multiple molecular databases for breast cancer and have identified markers that drive proliferation and thus predict response to anthracycline.
National Yang-Ming University, Taiwan
Keynote: MicroRNA-203 diminishes the stemness of human colon cancer cells mainly by suppressing GATA6 expression
Time : 9:50-10:40
Yeu Su has completed his PhD from University of Wisconsin-Madison. He is a Professor of the Institute of Biopharmaceutical Sciences of National Yang-Ming University, a premier research University in Taiwan. He has published more than 55 papers in reputed journals and has been serving as an Editorial Board Member of several repute journals. His research interests are colorectal carcinogenesis and new drug discovery.
The interaction between hyaluronan (HA) and CD44, an important cancer stem cell marker, is known to stimulate a variety of tumor cell-specific functions including their stemness. MicroRNA-203 (miR-203) can be down regulated by such an interaction in human colorectal cancer (CRC) cells which results in the increase of their stemness; however, its underlying mechanism has yet been defined. Here, we show that miR-203 overexpression and sequestration in HCT-116 and HT-29 human CRC cells reduces and enhances their stemness, respectively. We subsequently find that GATA6 is a direct target of miR-203 and upregulated expression of this transcription factor not only restores the self-renewal abilities of the miR-203-overexpressing HCT-116 and HT-29 cells but also promotes the stemness properties of their parental counterparts. More importantly, we show that silencing the expression of either LRH-1 or Hes-1 is sufficient to diminish the stemness-promoting effects of GATA6. Together, our findings delineate the mechanism underlying the stemness-inhibitory effects of miR-203 in human CRC cells and suggest this miR-203 as a potential therapeutic agent for colorectal cancer.