Mathematical modeling to fight breast cancer
Virginia Tech researchers have developed a model that may eventually help explain and overcome drug resistance in breast cancer treatment.
*These questions and answers were jointly developed by a group of Education and Outreach leaders at meeting sponsored by the National Institutes of Health (NIH) in May 2013.
Accurately predicting which patients will respond to specific cancer therapies is a major goal for systems biology. New advances in genetic sequencing now make it possible to list all disrupted genes in each patient's tumor. We also know how some of these disrupted genes determine tumor cell response to therapy. Certain genetic mutations are being used to guide physicians towards therapies that are more likely to be effective.
Systems biology researchers are taking tens of thousands of measurements of gene activity in each tumor and using mathematical modeling to piece together all of these data into maps of interconnected genes. Better maps of how cancer cells are wired will make it possible to predict how each patient will respond to specific therapy with greater precision.
Despite exciting advances in predicting how groups of patients are likely to benefit from certain therapies on average, we are not yet able to accurately predict how each individual patient will respond to treatment. Each individual cancer is unique in its genetic makeup and researchers are investigating methods of analyzing each tumor.
We are looking forward to the day when, thanks to systems biology, doctors can analyze each tumor, and with results from a virtual cell and large datasets, can quickly develop the optimal treatment for a cancer.
Unfortunately, some cancers that initially respond well to therapy and shrink find ways to evade treatment and grow back. There may be many reasons that such cancers become resistant to therapy. Systems biology researchers are using many different types of information to computationally predict drug resistance before it is observed. If successful predictions can be made, resistance could be delayed or even blocked.
Teams of system biology researchers, including clinicians, mathematicians, biologists and engineers, are working together to tackle this important challenge on multiple fronts. Using systematic measurements, mathematical models and computer-based algorithms, they are exploring how cancer drugs change the way that genes are interconnected within cancer cells and how certain genes enable drug escape.
Researchers are also mapping interactions between the many different cells that compose each tumor. Different cells within a tumor can have unique genetics and functions, and understanding how these cells interact with each other and with normal cells in the body should enable better predictions about which tumors are likely to come back.
While some anti-cancer therapies are effective at killing cancer cells, many also interfere with normal cells in the body, causing unwanted side effects. The ability to predict effectiveness of a drug before it is administered in a patient could prevent harmful side effects and even mortality associated with drug therapy.
Since tumors can become resistant to individual cancer drugs, scientists think that the most effective cancer therapies of the future will actually be combinations of drugs that work together to prevent tumor growth. However, the process of testing all possible combinations of drugs in clinical trials is very time-consuming and expensive.
To overcome this challenge, systems biology researchers are systematically testing large numbers of drug combinations in the laboratory by assessing cancer cell behavior following such treatments. They construct mathematical models to pinpoint the most effective drug combinations with minimal side effects, identifying specific regimens that warrant further clinical testing. Additionally, they are exploring the dynamics of how side effects to current treatments arise and attempting to predict the side effects of future treatments before they even enter a clinical trial setting. Solving these drug combination puzzles will impact clinical practice for years to come.
While many effective anti-cancer therapies exist, only a small fraction of cancer patients currently benefit from the latest and most effective therapies. For some cancer types, treatment regimens have remained stagnant for decades and few effective therapies exist. There is a huge need for new cancer treatments, but the current drug discovery process is inefficient and expensive.
Systems biology researchers are trying to speed up this process by systematically testing new drugs across many types of cancers. Then, by using mathematical modeling and computational predictions, they rapidly solve complex puzzles to hone in on drugs likely to work in specific patient cohorts. This process is accelerated further by engaging mathematicians and engineers across the world in massive online problem-solving contests.
Sometimes, promising new candidate drugs are identified but require further development to test their effectiveness. Such development can be particularly challenging when scientists do not know exactly how the candidate drug works. Systems biology researchers take thousands of measurements after treating cancer cells with candidate drugs and use computational modeling to predict which pathways the drugs may use to kill the cancer cell. The innovative tools used in systems biology can help characterize new anti-cancer qualities of available drugs that are used to treat other diseases or that were abandoned by pharmaceutical companies but are safe for humans.