Any cancer cell migrating from a tumor to set up shop elsewhere in the body will face a brutal attack from an immune system programmed to seek and destroy abnormal cells. But two recent studies from Stanford Medicine show that the hearty few that manage to infiltrate nearby lymph nodes carry out a stunning biological coup — convincing the body’s defense system to accept them as part of its own tissues. This savvy rebranding gives tumor cells a free pass to easily metastasize to any site in the body and significantly worsen cancer prognoses.
The studies, conducted in laboratory mice, human cells and human tissue samples from cancer patients, upend the idea that lymph nodes — often the first site of metastasis — are simply passive downstream harbors for circulating cancer cells that have broken loose from nearby tumors… Continue reading.
Computers have revolutionized many fields, so it isn’t surprising that they may be transforming cancer research. Computers are now being used to model the molecular and cellular changes associated with individual tumors, allowing scientists to simulate the tumor’s response to different combinations of chemotherapy drugs.
Modeling big data to improve personalized cancer treatment was the focus of a recent episode of the Sirius radio show “The Future of Everything.” On hand was Sylvia Plevritis, PhD, a professor of biomedical data science and of radiology at Stanford, who discussed her work with Stanford professor and radio show host Russ Altman, MD, PhD… Continue reading.
WASHINGTON, D.C.— The American Institute for Medical and Biological Engineering (AIMBE) has announced the pending induction of Sylvia K. Plevritis, Ph.D., Sylvia Plevritis, PhD Professor, Departments of Radiology and (by courtesy) Management Science and Engineering co-Chief, Integrative Biomedical Imaging Informatics at Stanford (IBIIS) Director, NCI Stanford Center for Cancer Systems Biology (CCSB), Department of Radiology, Stanford University School of Medicine, to its College of Fellows. Dr. Plevritis was nominated, reviewed, and elected by peers and members of the College of Fellows For outstanding contributions to multi-disciplinary cancer research that integrates computation, genomics, medical imaging and population sciences..
At age 47, Melanie Lemons has already had her ovaries removed. With a few clicks of her computer’s mouse, she can see her estimated chance of survival if she has her breasts removed as well.
Women like Lemons — who have a high risk for developing breast and ovarian cancers — used to face their heart-wrenching decisions without much of a road map. Now, with information at her fingertips from a tool developed by School of Medicine researchers, she can wrestle with the tough choices on her own terms.
“I go back to it regularly,” Lemons said, “just to remind myself what the numbers are and to play with them — because I can change all the variables.”
Launched earlier this year, the online, interactive tool allows women with known mutations in the BRCA1 or BRCA2 genes, which put them at high risk for cancer, to see what their chances of survival would be after taking different preventive measures at different ages. The tool, which can be found on the Stanford Cancer Institute website at http://brcatool.stanford.edu/, already receives around 1,000 visits per month. High-risk cancer patients are praising it as an empowering way to help cope with and plan for preventive treatments.
Sylvia Plevritis was excited. It was December 2003, and she had just learned that the National Cancer Institute was offering millions of dollars to researchers in a variety of non-biological fields to study how cancerous tumors behave and grow. She told her boss, Gary Glazer, MD, chair of Stanford’s radiology department, “This is my Christmas present. They are talking to me.”
So Plevritis, who has a PhD in electrical engineering, emailed mathematicians, computer scientists, engineers and biochemists across the campus — anyone she thought would be interested in pursuing the grant. Her message went viral as recipients sent it on to others who might want to be involved.
At the time, Plevritis was deep into her second career: a public-health-focused effort to optimize magnetic resonance imaging technology to diagnose breast cancers. But she wasn’t a laboratory scientist, or a physician.
“At first I wasn’t focused on the molecular biology of the tumor, but on how to deliver data that a health policy-maker would need to recommend new clinical guidelines,” she says. “But as I grew more curious about cancer progression, I’d read papers discussing the effect of one gene on one pathway in a cell, and I’d think, ‘We’re looking at this disease in a very narrow way. We need to think methodically of an underlying network of hundreds or thousands of interactions that drive a cell to divide.’” In other words, a kind of a circuit.
The Center for Cancer Systems Biology held its first annual symposium May 2-3 on campus. CCSB was launched in 2010 with a $12.8 million award from the National Institutes of Health and is one of 12 centers sponsored by the agency.
The CCSB meshes biological and computational research to reconstruct molecular networks in the study of non-solid tumors such as adult myeloid leukemia, follicular lymphoma and T-cell acute lymphoblastic leukemia. The center is also working to establish resources for complex data analysis and an education and outreach component targeted to the Stanford cancer research community and the community at large.
“Many opportunities for breakthrough discoveries in cancer lie at the intersection of multiple disciplines,” said associate professor of radiology Sylvia Plevritis, PhD.
The Stanford University School of Medicine has been awarded $12.8 million over five years by the National Cancer Institute to establish a Center for Cancer Systems Biology. The center is one of 12 recently funded by the NCI to stimulate integrative systems approaches and the application of computational modeling to cancer research.
“Our work views cancer as a complex system,” said associate professor of radiology Sylvia Plevritis, PhD. “Instead of focusing on the function of one gene or protein, we want to identify a molecular network that captures interactions between many genes and proteins. Our approach differs from more traditional scientific methods. Rather than starting with a hypothesis then collecting data to test it, we start by collecting global expression data, analyze the data with computational methods to generate a hypothesis, then collect new data to test the hypothesis.”