Metastasis in colorectal cancer is the leading cause of cancer related morbidity and mortality and the liver is the most common site of metastasis. Curative treatment for colorectal liver metastasis can be achieved by surgical resection, but in 70% of the cases, recurrence will occur within two year of resection. Subsequent treatment typically consists of palliative chemotherapy for disease control and palliation of symptoms. The overall objective of this application is to improve clinical outcomes of patients with colorectal cancer liver metastasis by enabling individualized treatment planning for patients based upon the biology of their disease. The hypothesis is that distinct biological phenotypes, such as expression of cell surface membrane protein, can be identified and characterized to guide treatment. In our preliminary data, we utilized a proteomic based approach to identify calreticulin as an abundant cell surface protein in colorectal cancer liver metastasis that can be used as a potential therapeutic target. To further characterize calreticulin in colorectal cancer liver metastasis, in this grant, we proposed to 1) Determine if targeting calreticulin can be used to treat colorectal liver metastasis in vivo and 2) Determine if the presence of calreticulin in colorectal cancer liver metastasis samples by immunohistochemical (IHC) staining can be used as a prognostic marker. The completion of our application will rigorously test our approach of using established computational modeling to identify targetable cell surface protein markers. This will provide both biological insight and bring us a step closer to personalized medicine for our patients with colorectal cancer liver metastasis.
Approximately 150,000 people will be diagnosed with colorectal cancer each year and 30% will present with metastatic disease. The liver is the most common site of metastasis and clinicians are now faced with the challenge of choosing the most appropriate therapy for individual patients given multiple treatment options. We believe that distinct biological phenotypes, such as expression of cell surface membrane proteins, can be used to guide therapy for patients with colorectal cancer liver metastasis. The completion of our application will rigorously test our approach of using established computational modeling approaches to identify new and novel therapeutic targets and validating these targets in a preclinical murine model of colorectal cancer. This will provide both biological insight and bring us a step closer to personalized medicine for our patients with colorectal cancer liver metastasis.
