Xiaowei Wang, Ph.D.
Project Overview:
In 2009, it is estimated about 11,000 women in the U.S. will be diagnosed with cervical cancer, and about 4,000 of those women will die from this deadly disease (statistics from the American Cancer Society). This is a significant public health problem because cervical cancer is the third-leading cause of cancer death in women ages 15-34 and the fifth-leading cause of cancer death in women ages 35-54. Consequently, cervical cancer is a leading cause of life-years lost (26.0 years).
Most cervical cancer patients receive standard radiotherapy and chemotherapy; however, clinical outcomes vary significantly and are hard to predict. Thus, a method for more accurately predicting treatment outcomes would help oncologists and patients decide when (and when not) to be aggressive. Unfortunately, despite intense studies in the past decades on cervical cancer, researchers are still trying to establish a robust method to reliably predict the disease outcome. Traditional clinicopathologic features, such as tumor grade and stage, have limited prognostic value. Thus, new prediction markers/variables need to be developed and explored to achieve the needed improved prognostic performance.
Here, we present a research plan to identify cervical cancer patients with poor outcome after standard therapy. The early identification of more malignant cervical tumors will make it possible to apply more effective treatment plan to these high-risk patients. As a result, the recurrence rate is expected to decrease, leading to longer survival time. The reliable prediction of patient outcome would make it possible to provide individualized cervical cancer therapy to improve overall patient survival.
Final Report:
Estimates are that in 2009, about 11,000 women in the U.S. will be diagnosed with cervical cancer, and about 4,000 of them will die from this deadly disease. This is a significant public health problem and is also a leading cause of life years lost. Most cervical cancer patients receive standard radiotherapy and chemotherapy; however, clinical outcomes vary significantly and are difficult to predict. Thus, a method for more accurately predicting treatment outcomes would help oncologists and patients decide when (and when not) to be aggressive. However, traditional clinicopathologic features, such as tumor grade and stage have limited prognostic value.
