Researchers from the University of Texas MD Anderson Cancer Center have found that computed tomography (CT) scans may provide a secondary benefit in the treatment of pancreatic cancer. The scans may also provide information on how well chemotherapy will penetrate a tumor, thus predicting the effectiveness of treatment. This is the first study in humans to address the issue of chemotherapy delivery to pancreatic tumors.
Jason Fleming, M.D., professor in Surgical Oncology and corresponding author, notes, “We found that the distribution of intravenous dye used in CT scans is a surrogate for chemotherapy delivery in the tumor. Our results indicate that combining data from routine CT scans and using a mathematical formula developed by our team can predict response, guide patient treatment and lead to efforts to improve drug delivery.”
It is difficult to work with pancreatic tumors as they contain nonfunctional or disorganized blood vessels. These tumors also contain fibrotic tissue and abnormal molecules that can create barriers for a drug to find its target providing for a poor diagnosis. As a result, approximately 40,000 individuals will die in 2014, according to the American Cancer Society.
Fleming notes, “Chemotherapy is used every day, however we’ve done very little to demonstrate that the drug actually reaches the tumor efficiently. Results from our previous clinical trials at MD Anderson taught us that when chemotherapy kills most of a patient’s tumor, the patient has a better chance of being a long-term survivor.”
Twelve patients with primary pancreatic disease underwent a surgical resection, and during surgery received an infusion of gemcitabine. Gemcitabine is used as a chemotherapy drug which has the ability to be transported into the nucleus by way of an equilibrative nucleoside transporter (hENT1). Earlier studies have demonstrated that this transporter’s expression varies in pancreatic tumors which links the protein to a drug response. After surgery, tumor DNA was analyzed for penetration of gemcitabine.
Patients with dense fibrotic tumors with little hENT1 protein demonstrated to have minimal uptake of gemcitabine. Overly, gemcitabine penetration varied among the 12 patients and those patients that had higher gemcitabine penetration responded better to therapy with improved outcomes.
The researchers then examined past CT scans from another group of 176 patients to compare how the drug reached tumor cells. Twelve of these patients received gemcitabine infusion during surgery, 110 received presurgical gemcitabine-based chemoradiation, and 55 patients received upfront surgery to remove the tumor.
Researchers reviewed the data from this new group and noticed visual differences in tumors as a results of CT contrast being absorbed differently. Researchers wondered whether the intravenous contrast used for CT scans was able to predict the path and absorption of gemcitabine. As it turns out, it does. Fleming and colleagues used models to measure factors that influence drug delivery and discovered that resected tumors demonstrated up to a 6-fold difference in drug incorporation.
Flemming notes, “This work is showing that solid tumors are much more heterogeneous than we thought with respect to drug delivery. Going forward the implication is that molecular information from a biopsy of the tumor can be combined with data from a standard CT scan to place patients into categories that predict their response to therapy.” The results suggest that other solid tumors could be studied in a similar fashion. They are now looking at existing drugs such as losartan (high blood-pressure drug) which may be able to change the makeup of dense tumors. This would allow for better chemotherapy delivery for patients who might have a poor response.
Flemming adds, “The nice thing is that we can measure the effect of these new and repurposed drugs by using intravenous contrast in CT scans as a surrogate, so we don’t have to commit a patient to receive a cancer drug until we know it improves the tumor characteristics. However, more research is needed to match a patient’s tumors with the most appropriate drugs for that tumor.