Andrea Bild, Ph.D.

Andrea Bild, Ph.D., serves as professor in the Division of Molecular Pharmacology within the Department of Medical Oncology & Therapeutics Research. She comes to City of Hope from the University of Utah, where she was an associate professor and director of Genome Sciences.

 

Dr. Bild obtained her B.S. at the University of Florida, her Ph.D. at the University of Colorado, and carried out her postdoctoral training at Duke University.

 

Dr. Bild’s research program focuses on cancer, and uses large-scale translational genomic and pharmacological studies to interrogate and treat tumor heterogeneity and evolution to refractory states. She has led multiple collaborative groups with the goal of characterizing and treating cancer.

 

As a member of the National Cancer Institute’s Cancer Systems Biology Consortium and principal investigator of multi-institutional grants, her team focuses on the development and application of multi-omic tools in the clinic for cancer prevention and treatment. With clinician collaborators, Dr. Bild’s team has initiated and carried out multiple clinical trials that use systems biology and genomic characterization of patient tumors to prevent cancer resistance and progression.

Bild’s research program focuses on cancer and rare diseases, and uses large-scale translational genomic, systems biology and pharmacological studies to interrogate and treat human disease.

 

She has led multiple collaborative groups with the goal of characterizing and treating disease, and has been successful in developing research-based clinical trial studies that positively impact patient care. By integrating broad disciplines centered on translational sciences, she promotes innovative scientific exploration and enables scientists to tether clinically impactful results to the enhancement of patient care. A subset of her key research initiatives, further detailed in her team’s publications, include:

 

Matching Biological Basis of Disease to Treatment Strategies for Translational Research

Bild’s focus on translational research comes from an understanding that it is necessary to bring leading-edge science techniques to the clinic immediately. New strategies are needed to better personalize drug therapies, as are better approaches to combat drug resistance. Systems biology can be the common platform for classifying disease states, biological networks and drug response. By finding the gene expression profiles for each of these features, her team is able to identify those profiles that “match” disease to biology to drug and more effectively treat patients.

 

Disease Prevention and Early Intervention Strategies

Despite the uptick in rates of discovery for diseases, such as rare and infectious diseases, there has been no corresponding uptick in drug discovery to treat them. Without mechanisms in place to rapidly identify compounds likely to be safe and effective for treating these novel diseases, health care systems risk suboptimal patient care. Bild’s team’s published research shows that it is possible to use genomic analysis to link the right drugs to individual patients. Her team currently utilizes novel genomic approaches to provide individualized and accurate assessments for unknown or novel diseases and high-risk populations.

 

Computational Tools for Genomic Studies

A significant component of Bild’s research program involves the development of novel computational and systems biology tools and resources in order to interrogate biologically relevant signaling events in human disease. Specifically, in close collaborations with her statistician and mathematician colleagues, Bild has contributed to the development of algorithms critical for distilling genomic and experimental data to biologically and therapeutically meaningful information.

 

Combating Tumor Subclone Heterogeneity

Breast and ovarian cancers are comprised of heterogeneous populations of tumors cells characterized by mutations that distinguish each cell subpopulation from one another. During treatment, tumor “subclones,” defined as a set of unique cells within a tumor, follow unique evolutionary and resistance trajectories. Using computational tools to organize this complex variation, Bild’s team will develop a new class of systems models that define subclone evolution and acquisition of oncogenic phenotypes during treatment in order to identify key chemoresistant cell states within patient cohorts. These mechanistic models will identify points of vulnerability for drug targeting. Bild’s clinical trials will be aimed at blocking transition of tumors to a resistant state by blocking critical resistant phenotypes.