Dr. Christina Curtis Uses Systems Biology to Improve the Diagnosis, Prevention, and Treatment of Cancer

Dr. Christina Curtis, a Professor of Medicine, Genetics and Biomedical Data Science at Stanford University, uses integrated computational and experimental approaches to investigate tumor evolution, identify new therapeutic targets and biomarkers in cancers, and for mapping the biology of malignancy across scales. 

She initially became interested in cancer genetics at 15 years old when she lost someone in her family due to malignant disease. This led her to work in a laboratory focusing on genetics as an undergraduate, which was around the time that the human genome was sequenced.

Dr. Curtis described this pivotal point in her career journey: “There was a big question [in the field]. What are we going to do with this genomics data once we can generate more of it at scale?” So, she decided to pursue a Ph.D. in computational biology to learn how to analyze and harness the power of biological data.

In addition to computational sciences, Dr. Curtis also studied molecular biology, population genetics, immunology, and aging throughout her training. She said, “I think there's a lot to be learned by working in different fields, and I still draw from my experiences today. I think there's a lot of potential for innovation by bringing together different approaches.” 

Now, her laboratory examines cancer as a complex systems level process that involves multiple organ systems. Dr. Curtis also examines cancer as a continuum of disease stages related to dynamic evolution. Using new technologies, her team has been able to build predictive models that reveal genomic drivers of aggressive cancers and identify tumors that are more likely to spread (i.e., metastasize) in the body.

She explained, “Once you can measure something, you can model it. And then you can potentially make predictions about the future, and that's clearly a big unmet need in the cancer field.” From her perspective, translating this information can ultimately benefit patients by improving diagnosis, prognosis, and clinical treatment decisions. 

Additionally, while describing her cancer systems biology research, she said, “There's a huge opportunity to do more with the data we have.” Dr. Curtis refers to her lab as big data parasites as they spend a lot of time working to consume and analyze public data for cancer research. She thinks this is more efficient and allows them to retrieve data elements with information from experts across multiple labs to feed into and refine their cancer modeling approaches. 

Dr. Curtis is also using systems-level strategies to advance the understanding of precancers and early stages of malignancy to inform the development of cancer prevention approaches. 

As she was wrestling with cancer data showing that some tumors are born to be bad, her mom was diagnosed with stage four lung cancer. Then, a month later, her dad was diagnosed with stage two colon cancer. Sadly, her mom’s advanced disease rapidly progressed, and she died three months later. However, her dad is now a cancer survivor and continues to be a role model in her life. 

Reflecting on this personal experience, Dr. Curtis said, “There’s this huge gap in what we can do in the metastatic stage versus the earlier stage of cancer. This really forced me to stop and realize that the more we can do early in cancer, the greater impact we can have on the population.” 

She also thinks there’s urgency in moving exciting cancer systems biology discoveries forward to clinical translation so that they benefit patients sooner. For example, findings from her laboratory are being used in a clinical trial to stratify patients with breast cancer for treatments. Dr. Curtis said, “I'm excited that we're going to continue to push on translating this cancer research, and I think that's the next frontier.”