Day 2 :
National Yang-Ming University, Taiwan
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.
Nottingham Trent University
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.
Nowicky Pharma”/ Ukrainian Anti-Cancer Institute, Austria
Dr. Wassil Nowicky — Dipl. Ing., Dr. techn., DDDr. h. c., Director of “Nowicky Pharma” and President of the Ukrainian Anti-Cancer Institute (Vienna, Austria). Has finished his study at the Radiotechnical Faculty of the Technical University of Lviv (Ukraine) with the end of 1955 with graduation to “Diplomingeniueur” in 1960 which title was nostrificated in Austria in 1975. Inventor of the anticancer preparation on basis of celandine alkaloids “NSC-631570”. Author of over 300 scientific articles dedicated to cancer research. Dr. Wassil Nowicky is a real member of the New York Academy of Sciences, member of the European Union for applied immunology and of the American Association for scientific progress, honorary doctor of the Janka Kupala University in Hrodno, doctor “honoris causa” of the Open international university on complex medicine in Colombo, honorary member of the Austrian Society of a name of Albert Schweizer. He has received the award for merits of National guild of pharmacists of America. the award of Austrian Society of sanitary, hygiene and public health services and others.
When NSC-631570 has been used in clinic, it was observed that the patients treated with this drug tolerate the concomitant radiotherapy much better. The adverse effects of this aggressive treatment modality were significantly reduced to minimal. This gave reason to study radioprotective properties of NSC‐631570 in the in vitro and in vivo tests.
It was proven the radioprotective effect of NSC‐631570 was far superior compared to such of its raw materials taken separately, both measured by survival of mice irradiated by different doses and by the protection coefficient. For example, at a dose of 5.25 Gy protection coefficient of NSC‐631570 was 95.0 ± 4.6 vs 50.8 ± 4.6 in the control. These observations suggested that the radio protective effect of Ukrain differs significantly from such of its raw materials. The radioprotective effect of NSC‐631570 was also studied and confirmed on in vitro models on the human skin firbroblasts HSF1 and HSF2 as well as lung fibroblasts CCD32‐LU. As evaluation parameters were chosen cytotoxicity, apoptosis induction, cell cycle course, and the expression of TP53 and p21. Additionally, following malignant cell lines were used: MDA‐MB‐231 (human breast tumor), PA‐TU‐8902 (pancreas cancer), CCL‐221 (colorectal cancer), and U‐138MG (glioblastoma). The cytotoxicity of NSC‐631570 was time‐ and dose dependent. The combination of NSC‐631570 plus ionizing radiation (IR) enhanced toxicity in CCL‐221 and U‐138MG cells, but not in MDA‐MB‐231 and PATU‐8902 cells. Most strikingly, a radioprotective effect was found in normal human skin and lung fibroblasts. Flow cytometry analyses supported differential and cell line‐specific cytotoxicity of NSC‐631570. CCL‐221 and U‐138MG cells accumulated in G2 after 24h treatment with NSC‐631570, whereas no alterations were detected in the other tumor cells and normal fibroblasts tested. Differential effects of NSC‐631570 in modulating radiation toxicity of human cancer cell lines and its protective effect in normal human fibroblasts suggest that this agent may be beneficial for clinical radiochemotherapy.