Friday, April 2, 2010

Why Do Genomic Cancer Diagnostics Cost So Much?

After reading the introduction to this PLoS ONE article, I started to wonder why there are there several published microarray expression profiles for cancer progression yet relatively few microarray-based diagnostics used in a clinical setting. Although this PLoS ONE paper focuses on analysis of ovarian cancer (and mentions the lack of a clinical microarray diagnostic for ovarian cancer), the paper also cites the current use of a breast cancer diagnostic called MammaPrint.

After reading the wikipedia entry on MammaPrint, I was surprised to learn that it took 5 years for the diagnostic to reach the market following the initial publication showing that the expression profiles for a set of 70 genes could successfully predict the cancer progression. This information is important because more aggressive treatments early in cancer progression may be able to help cancer patients who would otherwise have a high mortality rate (as predicted by their gene expression profile). I was also surprised to learn the high price of both MammaPrint and its competitor Oncotype DX. Although I do not think I can provide a complete answer to why these prices are so high, I would like to take a moment to first demonstrate that the price of these tests far exceeds the cost to conduct the test and then discuss how I think these costs can be offset by decreasing the amount of time and effort that it takes to bring a medical diagnostic tool to the market.

Based upon their wikipedia entries, the MammaPrint diagnostic costs $4,200 and the Oncotype DX test costs $3,978. To give you an idea about how much it actually costs to carry out this test, it costs $350 for a full service microarray analysis (including labor and data analysis) of an Agilent Whole Human Genome Microarray for on-campus customers at the UT-Southwestern Micoarray facility. This is comparable to the cost of most of the microarray facilites that I have worked with, and Agilent produces high quality microarrays. Now, most laboratory kits have a warning that they are “intended for research purposes only,” and this is probably true for the human Agilent array. However, I think this warning is mostly to avoid litigation and not due to a severe lack of technical accuracy, and I expect the actual cost for a clinical microarray test to be in the hundreds (not thousands) of dollars. I’m sure that this high cost is the product of a combination of factors, such as research costs, legal costs, patent law, and the US healthcare system. However, I’m going to focus on ways to potentially cut research costs because that is the area that I know most about.

Now, I want to make clear that the initial publication of a potential diagnostic test is not sufficient to prove the widespread effectiveness of that test. For example, the microarray test for ovarian cancer in the PLoS ONE article had substantially better predictive power on the training dataset than when applied to a new dataset. Therefore, I want to make clear that I do think follow-up studies were necessary to prove the effectiveness of MammaPrint.  However, I still don’t think it should have taken 5 years to test the effectiveness of this diagnostic and I think effectiveness can be determined without as much government regulation.

Before MammaPrint could be put into widespread use, it had to gain FDA approval. This required multiple verification studies, and this is the crucial event that defines the 5 year gap between initial publication and availability on the free market. First off, I don’t think FDA approval should be necessary for diagnostics. I do think physicians need some way to quickly access the effectiveness of a medical diagnostic and/or therapeutic, but I think there are more better ways to determine the effectiveness of a given treatment. For example, a relatively recently posted TED talk by Jamie Heywood discusses how his start-up Patients Like Me, developed by three MIT engineers, can diagnose medical treatments more quickly and effectively than clinical trails. This website analyses a database of information provided by patients, and therapeutic effectiveness can be assessed immediately based upon currently available data. In the very least, I think this company could be an excellent model for a more formal system using data from physicians that does not carry all the restrictions of a clinical trail. These changes should decrease the cost of medical care because companies claim that these price markups are necessary to recoup the costs of research and development, and a more streamlined process for accessing the effectiveness of treatments will decrease research costs.

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