Recent WHO-IARC data reports that cancer is the major cause of morbidity and mortality, with approximately 14 million new cases and 8 million cancer-related deaths in 2012 (GLOBOCAN-2014). Traditional chemotherapeutic agents remain inefficient for the treatment of cancer with poor prognosis. Therefore there is a need for targeted therapies. Our group has been working on the molecular biology of liver cancer with a specific aim of discovery of novel candidate molecules for the treatment of this deadly Disease. Our focus is liver cancer, which is the 5th most common and 3rd lethal cancer and associated with poor overall survival. Major molecular and cellular biology approaches
UniGOPred is an automated protein function prediction tool and GO prediction database of UniProtKB for all three categories of GO. It is composed of two components:
We have constructed a detailed map of AKT-signaling pathway by compiling the data in around 498 publications. Currently, the pathway has 254 proteins and 478 connections with inhibition, activation or just binding edge properties in Cytoscape. Sequences and available PDB structures have been mapped to the proteins in AKT signaling pathway and ready to be used in the next step. PI3K/AKT/MTOR cell survival signaling is frequently altered in cancer cells through genetic or epigenetic modifications. Therefore this pathway is considered as a good candidate for targeted drug discovery. Our wet lab studies mainly focuses on the screening of the small molecules on AKT-signaling pathway and in vitro validation of resulting target set of proteins and in vivo xenograft validation of anti cancer activities Laboratory work
Computerized microscopy-based image processing is a key technology for determining the information relevant for diagnosis and prognosis of various diseases including cancer. Tool based on the determination of tumor heterogeneity ratio in histopathological images may enhance cancer diagnosis prognosis and treatment approaches. By using an automated microscopy and image processing system, high differential diagnostic accuracy can be achieved, while human factors such as subjectiveness and workload can be reduced. The required high–throughput acquisition of microscopic images from pathological tissue samples can be done e.g. by automating conventional microscopes (which is usually error-prone and costly) or by the use of commercially available slide scanners (which are usually closed systems and rather expensive).
In collaboration with Dr. Enis Cetin, Dr. Aysegül Uner, Cigdem Gunduz Demir and Dr. Christian Münzenmayer goal is to develop new methods and systems to improve diagnostic and prognostic techniques in cancer treatment by computer-assisted microscopy.