Team #3: Drug Responses and New Therapies

Predicting Drug Response and Finding New Therapies


Team 3 is working to discover which drugs are most effective for patients with AML and t-AML. They are employing two complementary strategies to achieve this goal. First, pharmacogenomics experts on the team are linking drug sensitivity data with genetic signature data to design individualized treatments to maximize benefit and minimize the likelihood of toxic side effects. Second, systems biology experts on the team are screening chemical libraries to identify new chemicals that inhibit leukemia cell growth, which may lead to new therapies for this disease.

The Project In Detail

Most physicians have had the sobering experience of prescribing medications that, despite good intentions, caused serious side effects. Individualized drug therapy, based on an individual’s unique genetic make-up, is especially desirable in the field of cancer where the toxic side effects of chemotherapy can be life-threatening. Also, prescribing chemotherapy that does not work in certain individuals could result in missing a critical window of time for therapeutic success. For patients with t-AML, there are currently very few effective therapeutic options, all of which have significant side effects. The goals of this project are 1) to identify genetic markers that will predict an individual’s sensitivity to chemotherapeutic drugs (both in treatment-related side effects and clinical response), and 2) to identify new and more effective therapies to treat leukemia by investigating why some therapies work and others do not.

Predicting Drug Response
Team 3 researchers are performing pharmacogenomic studies, which are studies that examine how genetic variations influence an individual’s response to drugs. These genetic variations are known as single nucleotide polymorphisms (SNPs), which are changes found at a single site in DNA. SNPs can be used as a diagnostic tool to predict an individual’s response to therapy. This information will allow clinicians to prevent the toxic side effects of chemotherapy by selecting an appropriate drug or lowering the dose of a current drug. Therefore, based on a patient’s genetic profile, clinicians can optimize or “personalize” drug therapy to ensure that the patient responds well with minimal side effects.

In this project, the team is using a cell-based research approach to find personalized therapies for leukemia. They hypothesize that a set of DNA variations in healthy normal cells and mutations in cancer cells affect an individual’s sensitivity to chemotherapeutic drugs used to treat AML and t-AML, and that these variations/mutations vary by ethnicity. To test this hypothesis, they are examining the toxicity of drugs (including Cytarabine, Daunorubicin, and Etoposide in over 450 cell lines collected from the International HapMap project, which is creating a map of SNPs that occur in the human genome to help researchers identify genetic variations that influence health and disease. Over two million SNPs have been characterized in these cells, which are derived from normal, healthy individuals across the world, including Caucasians, Western Africans, Asians, African Americans from the southern United States, and Mexican Americans. University of Chicago researchers are identifying which SNPs, or genetic markers, can explain the differences in drug response among these cell lines. An important goal of this work is to test the most promising SNPs, or genetic markers, that are associated with drug sensitivity in clinical trials.

Finding New Therapies
Researchers are also searching for new drugs to treat leukemia because effective treatments do not exist for t-AML. Previously, University of Chicago researchers identified a genetic signature that predicts the death of acute lymphoblastic leukemia cells (a type of leukemia that occurs when immature white blood cells are overproduced in the bone marrow) in response to treatment with chemotherapeutic drugs. Based on this success, researchers are searching for a “death” signature in AML cells. The identification of such a signature can be used to identify new drugs for the treatment of AML.

To identify this “death” signature, Team 3 is examining the genetic effect of chemotherapeutic drugs known to cause cell death in AML cells. They are looking for similarities in gene expression between different AML cell lines that die after drug exposure. Subsequently, researchers will identify new drugs to treat leukemia by screening for compounds that produce the same “death” signature. Using robotic technology, the team will screen a library of 80,000 commercially-available compounds in different AML cell lines. After a set of promising compounds have been identified, the team will further test these compounds to determine which ones will be suitable for testing in clinical trials.


Predicting Drug Response

Finding New Therapies

How does this Work Relate to the Work of Other Research Teams?

Members in Team 3 will work closely with Team 1 to identify SNPs associated with t-AML that may predict response to therapy or that can be used to individualize therapy to minimize the risk for t-AML. Team 3 will also work closely with members in Team 5 to integrate and validate new genetic markers that predict drug sensitivity in planned clinical trials. Importantly, Team 6 will use this information to develop a database to guide physicians when making personalized treatment decisions. Team 3 will also validate the effectiveness of newly-discovered compounds in collaboration with Team 4 and test those that are suitable for the treatment of t-AML in clinical trials (Team 5).

Project-Related Activities & Publications

Population-specific genetic variants important in susceptibility to cytarabine arabinoside cytotoxicity
Mapping Genes that Contribute to Daunorubicin-Induced Cytotoxicity
A genome-wide approach to identify genetic variants that contribute to etoposide-induced cytotoxicity