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Christina Curtis, PhD, MSc

Assistant Professor, Medicine and Genetics
Stanford University, School of Medicine
Stanford, California

Current Research

  • Seeking to understand the causes of drug resistance in breast cancers.
  • Studies are ongoing to validate a potential biomarker of resistance.
  • These studies will help define new therapeutic targets and better treatments.

Throughout the course of cancer therapy, some cancer cells change their characteristics, making them resistant to the drug. Because these cells don’t die, they can form a new tumor in the future, which will also likely be resistant to therapies. Dr. Curtis uses a variety of technologies to understand what happens at the molecular level to make tumor cells resistant to therapy. 

Full Research Summary

Tumors are composed of a mixture of cells that are genetically unique and have different properties. This intra-tumor complexity poses numerous clinical challenges, including drug resistance. Drug resistance represents a major cause of breast cancer mortality and the underlying mechanisms of this phenomenon remain poorly understood.

Dr. Curtis and her team are working to characterize the intrinsic molecular features that cause some breast tumor cells to be resistant to chemotherapeutic agents and/or targeted therapies. She is hoping to better understand how populations of cancer cells evolve with exposure to therapy, leading to the outgrowth of resistant cells that leads to tumor progression or breast cancer recurrence.

Towards these goals, Dr. Curtis is combining advanced genomic techniques, functional assays, and computational methods to analyze existing breast cancer datasets as well as longitudinal breast tumor samples taken before, during, and after the course of therapy. Using this framework, they have demonstrated that tumors progress, or “evolve”, according to two main paths.

Over the past year, Dr. Curtis’ team developed a computational model of breast tumor progression using patient-derived genomic data. They have demonstrated that breast tumors can harbor many pre-existing genetic alterations that cause them to be resistant to therapy. They are using this new quantitative approach to forecast disease progression.

These efforts will enable more precise treatment decisions, thereby sparing patients ineffective therapy, and inform the development of patient-tailored treatment strategies.

Biography

Dr. Curtis is an Assistant Professor of Medicine and Genetics in the School of Medicine at Stanford University where she leads the Cancer Systems Biology Group and serves as Co-Director of the Molecular Tumor Board at the Stanford Cancer Institute. She received her doctorate in Molecular and Computational Biology in 2007 and completed a postdoctoral fellowship in Computational Biology at the University of Cambridge in 2010. Dr. Curtis was the recipient of several young investigator awards, including the 2012 V Foundation for Cancer, V Scholar Award and the 2012 STOP Cancer Research Career Development Award.

Dr. Curtis’s laboratory pursues innovative experimental approaches and data-driven modeling to address outstanding questions in cancer systems biology. In particular, her research seeks to delineate mechanisms of tumor progression and therapeutic resistance. For example, she and her team have developed an experimental and computational framework to interrogate tumor evolutionary dynamics and the timeline of neoplastic progression. They are also developing approaches to model therapeutic resistance. By coupling this approach with high-resolution genomic profiling of patient samples, this research will enable a paradigm shift in patient stratification and will ultimately inform optimal treatment strategies.

Another aspect of her research has focused on the integration of diverse genomic data types to elucidate inter-individual variation and mechanisms of tumorigenesis. For example, she leads a seminal study that redefined the molecular map of breast cancer through a detailed characterization of the genomic and transcriptomic landscape of 2,000 breast cancers. Using integrative genomics and statistical approaches, this work identified novel subtypes of breast cancer with distinct clinical outcomes and subtype-specific driver genes. Ongoing efforts in this area will guide the development of novel targeted therapeutics and improved prognostic signatures.

BCRF Investigator Since

2011

Donor Recognition

The Ulta Beauty Award

Area(s) of Focus