Some Difficulties Detecting an OS Benefit
- Crossover: For ethical reasons, when an investigational therapy shows a significant improvement or promise over the control arm in a study, patients can crossover and receive the study drug. This option is good for patients but, since the investigational drug will be given to patients in both arms of the study, it hinders the ability to detect OS related to a specific treatment.
- Post-Study Therapies: Measuring OS can be confounded by one or more lines of therapy after disease progression. Post-study therapies produce contamination effects that can impede the confirmation of OS benefits for a single drug or regimen.9
- Post-Progression Survival (PPS) Length: Detecting an OS benefit is especially difficult for cancers with long periods of PPS. The probability of finding a statistically significant difference in OS decreases as the median survival of post-progression increases.10
- Study Size and Duration: The use of OS requires long-term follow-up, and because of a lower event rate, it also requires a larger number of patients than PFS.9 Without advanced knowledge to enrich a population of trial participants of likely responders, statistical rules dictate that the sample size of trial participants necessary to determine the survival benefit for new drugs be increased in order to show significant differences among treatments.4
These investigational challenges come amid a number of other countervailing realities. One of those is the growing expectation among professional cancer societies to seek greater gains from clinical trials than what has been achieved in the past.7
The other, despite our recent advances, remains our limited understanding of tumor genetics. Regarding the first point, with greater expectations of results, comes an even greater scrutiny on the cost of cancer drugs. While reducing health care costs is a shared and noble goal, if not carefully applied, it could come at the expense of treatment access for patients. One reason why is related to the second point—our naiveté of cancer itself.
Consider the recent discovery from researchers at The Cancer Genome Atlas Research Network. The scientists there have just completed the largest and most diverse tumor genetic analysis ever conducted. What they found is that cancers are more likely to be genetically similar based on the type of cell in which the cancer originated, not on the type of tissue.
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This discovery alters traditional ideas of how cancers are diagnosed and treated, and can have a profound effect on the future landscape of drug development.11This information should serve as a gentle reminder of how much we have yet to learn, and of the potential for missteps in the application of a broad-based, one-size-fits-all approach to evaluating the effectiveness of cancer therapies, investigational or otherwise.
In recent years, therapies targeting the molecular driver of tumors have been developed for a number of cancers. Their effectiveness is directly related to the genetic variation found among individual patients.
Given the evolving nature of our understanding of cancer biology, the eager pursuit to define clinical meaningfulness by assigning cost-effectiveness thresholds to specific treatments and types of cancer, should not come at the expense of the role that incremental improvements provide to the future of health care innovation; not to mention the significance those improvements may have on a subset of patients.
Additionally, real-world experience (capturing data on the actual experience of patients outside of a controlled clinical trial) has and will continue to lead to new insights that can dramatically change the definition of value for any given treatment.
Detecting OS benefits in metastatic disease is one of several issues facing investigators today. In some sense, it exists because of our successes. As the standards of care evolve over time, expectations of clinical endpoints will shift as well. As we learn more about cancer, the efficiency of our investigations into newer treatments and novel combinations will likely increase.
The speed at which this occurs will depend on our ability to optimize research knowledge and real-world experience. Investigating new cancer drugs is an enormous challenge. Decisions based on limited and evolving knowledge that have the effect of reducing access to those new drugs, will likely slow the progress down.
|Disclaimer: Stephen A. D’Amato is a science policy analyst for Pfizer and a practicing pharmacist in New York. All views and opinions expressed in this article are those of the author alone and do not necessarily reflect the views of Pfizer.|
- American Society of Clinical Oncology. CancerProgress.Net. Major milestones against cancer. http://www.cancerprogress.net/timeline/major-milestones-against-cancer. Accessed August 25, 2014.
- Maroun JA. The significance of progression-free survival as an endpoint in evaluating the therapeutic value of antineoplastic agents. Curr Oncol. 2011:18(suppl 2):S3-S4.
- Experts in Chronic Myeloid Leukemia. The price of drugs for chronic myeloid leukemia (CML) is a reflection of the unsustainable prices of cancer drugs: from the perspective of a large group of CML experts. Blood. 2013;121(22):4439-4442.
- Sargent DJ, Hayes DF. Assessing the measure of a new drug: is survival the only thing that matters? J Clin Oncol. 2008;26(12):1922-1923.
- Booth CM, Eisenhauer EA. Progression-free survival: meaningful or simply measurable? J Clin Oncol. 2012;30(10):1030-1033.
- Sidhu R, Rong A, Dahlberg S. Evaluation of progression-free survival as a surrogate endpoint for survival in chemotherapy and targeted agent metastatic colorectal cancer trials. Clin Cancer Res. 2013;19(5):969-976.
- Ellis LM, Bernstein DS, Voest EE, et al. American Society of Clinical Oncology perspective: raising the bar for clinical trials by defining clinically meaningful outcomes. J Clin Oncol. 2014;32(12):1277-1280.
- Mayfield E. National Cancer Institute Cancer Bulletin. Progression-free Survival: Patient Benefit or Lower Standard? http://www.cancer.gov/ncicancerbulletin/archive/2008/051308/page7. Published May 13, 2008. Accessed September 25, 2014.
- Hotte SJ, Bjarnason GA, Heng DY, et al. Progression-free survival as a clinical trial endpoint in advanced renal cell carcinoma. Curr Oncol. 2011;18(suppl 2):S11-S19.
- Broglio KR, Perry DA. Detecting an overall survival benefit that Is derived from progression-free survival. J Natl Cancer Inst. 2009;101(23):1642-1649.
- University of North Carolina Health Care. Largest cancer genetic analysis reveals new way of classifying cancer. http://www.biocompare.com/Life-Science-News/166655-Largest-Cancer-Genetic-Analysis-Reveals-New-Way-Of-Classifying-Cancer/. Published August 8, 2014. Accessed August 16, 2014.