Team #6: Atlas of Therapies
Developing an Atlas of Therapies
Team 6 researchers are usingto interpret the data generated from Team 1, Team 2, Team 3 and Team 4. Their data analysis will identify how deregulated cellular processes cause , identify drugs that would inhibit newly-found therapeutic targets, and predict the response to single drugs or combinations of drugs, thereby informing clinical trials developed by Team 5.
Biologists and clinicians face complex decisions and numerous choices in designing laboratory and clinical trials to test new therapeutic agents for AML. In response, University of Chicago researchers are developing an atlas that models the biological effects of therapies used to treat patients based on theirprofiles. Researchers in Team 6 are analyzing large amounts of functional and genomic data produced by Teams 1-4 along with publicly-available genomic datasets. Through computational modeling, they are specifically developing 1) models of mechanistic signatures that predict response to therapy; and 2) a “Simulator” that predicts the biological response to combinations of therapeutic agents. These developments will enable researchers to identify how dysregulated biological processes cause leukemia, and as a result, design better experiments and clinical trials to test new, targeted agents.
Developing Models of Mechanistic Signatures that Predict Response to Therapy
The identification of mechanistic signatures would allow clinicians to predict a patient’s response to therapy and, therefore, select an appropriate drug regimen. Mechanistic signatures are profiles of biological systems that are affected by therapeutic drugs. However, the identification of such signatures is an enormous undertaking because it requires the analysis of countless biological samples from patients who experience both desirable and undesirable responses to treatment as well as an analysis of multiple networks that influence the response to therapy. Team 6 is overcoming these barriers by developing new algorithms to perform an integrated analysis of genetic information discovered by Teams 1-4 as well as over 1,000 -wide measurements of AML patients that have been amassed in publicly-available databases. Using bioinformatical approaches, researchers are identifying key genetic profiles and cellular characteristics that are associated with AML patients who are resistant to . This work will generate the information needed for Team 6 to model the regulatory networks that influence a patient’s response to AML therapy and, therefore, allow them to predict a patient’s response to both current and new drugs.
Developing a “Simulator” for Laboratory and Clinical Studies
Team 6 is also designing a web application, called OncoGPS, that will enable researchers to simulate the effect of new combinations of drugs on biological pathways that are dysregulated in t-AML patients. The development of this application will be based on the extensive work and expertise of the team on both and gene networks, and represent the culmination of work across all of the research teams. This new tool will provide researchers the unprecedented opportunity to examine the effect of current and newly-discovered therapeutic agents and combinations of agents. At present, these types of studies cannot be performed in the laboratory because of the prohibitive costs involved and the limited availability of leukemia cells from patients. A long term goal of this project is to develop a complementary application, called the OncoGPS Navigator, which would help patients navigate through therapeutic choices and understand the adverse events and benefits considered by their clinicians in making personalized treatment decisions. This tool would help patients make more informed decisions regarding their treatment and reduce their anxiety in making such decisions.
Team 6 is performing computational modeling of data generated by Teams 1-4. This work involves the analysis of genetic profiles associated with t-AML at diagnosis and following therapy both in the laboratory and in clinical trials (Team 5). The information obtained in these studies will be integrated to determine the cellular processes that influence leukemia and their response to therapy. Researchers will be able to make predictions related to drug response, and based on validation of these predictions in the laboratory (Teams 3 and 4), develop more effective clinical trials (Team 5).