Biomarkers for FL Relapse

The American Journal of Clinical Oncology published a very interesting article a couple of weeks ago that described research on biomarkers in Follicular Lymphoma. The article is called "Surveillance Strategies in Follicular Non-Hodgkin’s Lymphoma’s Using Molecular and Genetic Markers Improve Cost-efficiencies Over Routine Imaging Studies."

The idea behind the research is that we have a difficult time identifying patients who may transform or have POD24 (Progression of Disease within 24 months). Right now, about 80% of FL patients have good responses to treatment and then experience long survivals. But about 20% either transform or relapse after treatment within two years, and this 20% tends to have a shorter Overall Survival. 

The tools we now have to identify these folks are not very effective. They include things like FLIPI,the Follicular Lymphoma International Prognostic Index.  The FLIPI was never intended to be used as a tool to guess the likelihood of an individual patient's survival. If you take this little quiz, you'll see that it includes things like age, which is kind of useless -- thousands of people live long long lives with FL no matter what their age at diagnosis. The FLIPI index was developed many years ago, and has not been very effective in identifying that 20% of patients.

Over time, other prognostic models have been developed that look at factors that are a little bit more personal. For examples, the m7-FLIP which took the original FLIPI and added some possible biomarkers. This was an improvement, but still didn't completely identify that 20% as well as it was hoped. Too many of those high-risk FL patients were only identified after they transformed or relapsed within 24 months.

Other newer prognostic tools were developed in the years after that, like POD24-PI, PRIMA23, and ICA13. The authors of the article look at how effective they have been. Like m7-FLIPI, they do a good job, but not a perfect job, being anywhere from 43% to 86% effective.

The researchers looked at all of these models and essentially tried to find the best parts of them. Each prognostic model was developed from research tat looked for biomarkers, and those studies were all looking for (and finding) slightly different things. So looking at all of the data together might find some places that they overlap or that they do things that the others don't do.    

In the end, they came up with a model that they say is more comprehensive than the other models, and may do a better job of identifying high-risk patients as early as diagnosis. At this point, patients need to transform or relapse before they know for sure that they were in the high-risk group.  Identifying them early will allow doctors to possibly treat them more aggressively, and may in the future help to point researchers toward new targets for new treatments. 

The research team identified 10 biomarkers that seemed to only be present in high-risk FL patients. They did this by looking at data from patients who relapsed over a 14 year period. The biomarkers usually involve genetic mutations, which make them easier to identify.  

Of course, research like this is based on past results, so it's hard to know how effective it will be in actual patients at diagnosis. That's how these things often work -- it seems like it will be effective, but then they discover something else that their model hadn't picked up. And if that happens, that's OK -- that's how science works, and any step forward is great. But it does seem like this could be a step forward for a group that needs some help. 

I find it interesting that this study is being framed in financial terms. As the title says, a more effective model might "Improve Cost-efficiencies Over Routine Imaging Studies." In other words, a fairly simple genetic test that identifies a high-risk patient early on will mean fewer blood tests and scans and biopsies, and all the cost that goes with it. 

Lower costs are certainly important, and goodness knows that financial toxicity is a major problem for cancer patients. But all of the other benefits that come with improved prognostics matter even more  -- better Quality of Life, better mental health, and most importantly, improved Overall Survival. 

I'm hoping we see more data from this research in the next year or two as it gets applied to patients who are diagnosed now in the future.  I'll certainly be keeping an eye out.