By Michael A. O'Donnell, MD, FACS, et al.
BERKELEY, CA (UroToday.com) - Treatment algorithms were originally developed to guide disease management based on clinical presentation. Later, global factors based on epidemiological studies of large cohorts allowed for improvements of these algorithms based on characteristics such as pathology, tumor stage and basic laboratory data. More recently, novel characteristics have been identified and incorporated into more sophisticated models to predict response to treatment. The original CUETO study by Fernandez-Gomez and colleagues added to our current understanding of which patients are at high risk of progression and recurrence.1 These authors identified gender, age, grade, tumor status, multiplicity and associated CIS as risk factors for recurrence.
However, such studies are limited by questions regarding their applicability to other populations who are treated with slightly different treatment algorithms, such as the variations in treatment common between Europe and America. In our study, we attempted to show that the risk factors identified by the CUETO group are relevant for different populations undergoing a slightly different treatment algorithm. We were able to do this as we had a large database of over 1100 patients who were treated with BCG and interferon alpha as part of a national multicenter trial. The database, unfortunately, did not contain data on progression to muscle invasive disease so we are unable to address that portion of the CUETO study. The utility of such studies, both the original Fernandez-Gomez paper and our paper, is that they allow patients to be risk-stratified, identifying those who may need additional surveillance given a high risk of recurrence. Further, by showing that the risk factors identified by Fernandez-Gomez and colleagues can apply to a different population undergoing slightly different treatments, the true validity of the underlying risk factors is strengthened.
While much work remains in identifying clinical characteristics which place patients at higher risk of failure, novel approaches are being investigated which use variations in genetic makeup of individual tumors to identify which tumors are more likely to respond to therapy. Examples of this concept are becoming more common in clinical practice and range from her2 neu status dictating treatment in breast cancer to loss of heterozygosity status at 1p and 16q dictating chemotherapy regimes in Wilms’ tumors. In the field of bladder cancer, Dr. Theodorescu is currently attempting to predict a patient’s probability of responding to chemotherapy based on the tumor and its similarity to known cell lines.2
In addition to maximizing a patient’s response to treatment, identifying patients who are unlikely to respond to our current treatment algorithms allows research to target these individuals with novel approaches and to further explore the biology of their specific disease, yielding new insights into the field as a whole.
We commend Dr. Fernandez-Gomez and colleagues for their recent work and look forward to further working toward the day when medicine truly becomes personalized to the patient.
References:
- Fernandez-Gomez J, Madero R, Solsona E et al. Predicting nonmuscle invasive bladder cancer recurrence and progression in patients treated with bacillus calmette-guerin: the CUETO scoring model. J Urol 2009;182:2195-2203.
- Smith SC, Baras AS, Lee JK, Theodorescu D. The COXEN Principle: translating signatures of in vitro chemosensitivity into tools for clinical outcome prediction and drug discovery in cancer. Cancer Res 2010;70:1753-8.
Written by:
Henry M. Rosevear, Andrew J. Lightfoot, Kenneth G. Nepple, and Michael A. O'Donnell as part of Beyond the Abstract on UroToday.com. This initiative offers a method of publishing for the professional urology community. Authors are given an opportunity to expand on the circumstances, limitations etc... of their research by referencing the published abstract.
UroToday.com Bladder Cancer Section