Final Activity Report Summary - DONMFCCRP (Development of new models for cancer chemotherapy response prediction)
Up to date clinical tests for predicting cancer chemotherapy response are not available and individual markers have shown little predictive value. Today, the only used factor in clinical decisions is staging, which reflects tumour size and spread. The need for an individual-tailored cancer therapy is great, since it could significantly increase the patient survival.
In our first study we applied gene expression signatures derived from chemotherapy-resistant and -sensitive cell lines to effectively predict clinical survival after doxorubicin monotherapy. Our approach demonstrated the significance of in vitro experiments in the development of new strategies for cancer chemotherapy response prediction. In another study we focused on the resistance at the in vivo concentrations, and constructed predictive gene expression signatures for 11 anticancer agents.
We have set up a new biobank focused on cancer chemotherapy resistance. We collected more than 250 lung- brain- ovarian- and colon cancer specimens in six different locations in Budapest, Hungary. We have constructed a low-density microarray to predict clinical response against two different anticancer agents simultaneously.
Identifying gene expression signatures associated with resistance is only the first step in an effective therapy against cancer. To decipher the regulatory networks responsible for a common change in a set of co-regulated transcripts, we have conducted an in silico comparative promoter analysis. Using bioinformatics approaches we have set up putative regulatory networks, which provide insight into the mechanisms of resistance.
In summary, we have developed technologies which can be used for predicting cancer chemotherapy response. Moreover, we have identified genes and transcription factors as new therapy targets for future anticancer agents.
In our first study we applied gene expression signatures derived from chemotherapy-resistant and -sensitive cell lines to effectively predict clinical survival after doxorubicin monotherapy. Our approach demonstrated the significance of in vitro experiments in the development of new strategies for cancer chemotherapy response prediction. In another study we focused on the resistance at the in vivo concentrations, and constructed predictive gene expression signatures for 11 anticancer agents.
We have set up a new biobank focused on cancer chemotherapy resistance. We collected more than 250 lung- brain- ovarian- and colon cancer specimens in six different locations in Budapest, Hungary. We have constructed a low-density microarray to predict clinical response against two different anticancer agents simultaneously.
Identifying gene expression signatures associated with resistance is only the first step in an effective therapy against cancer. To decipher the regulatory networks responsible for a common change in a set of co-regulated transcripts, we have conducted an in silico comparative promoter analysis. Using bioinformatics approaches we have set up putative regulatory networks, which provide insight into the mechanisms of resistance.
In summary, we have developed technologies which can be used for predicting cancer chemotherapy response. Moreover, we have identified genes and transcription factors as new therapy targets for future anticancer agents.