In a recent paper in the journal Blood, Dr. Bart Barlogie and his team at the University of Arkansas in Little Rock claim they are “Curing myeloma at last: defining criteria and providing the evidence.” This is a bold statement. I would argue that while the paper provides a statistical/computer model, achieving and documenting true cure demands a follow-up of individual patients and cannot be predicted by a computer model.
But let’s examine the approach taken here more closely to see why. In order to comment intelligently on this paper, one must search through the details of the methods used to define cure and provide the “evidence.” The term “cure fraction” is used in the paper. It is derived from a combination of relative survival estimates (myeloma patient survival versus a patient without myeloma) and a complex computer model derived from a statistical approach used in 1982 for breast cancer. Is this a valid statistical approach to derive a “cure fraction”? The authors of this statistical paper emphasize the constraints of the approach. Two risk groups are required for the statistics to work. Dr. Barlogie divides his patients into “high” and “low” risk so that this approach can be applied, but as readers are probably aware, the Mayo Clinic mSMART approach divides patients into three risk groups. Which is correct or better? We don’t know.
After careful review, it appears that the “cure fraction” from the Little Rock “model” is considered to be: patients with “high risk” disease by gene expression profiling (GEP) who remain in complete remission (CR) for > 5 years and “low risk” patients by GEP who stay in CR for > 10 years. Are such patients cured? Clearly they are doing well, but the risk of relapse is not zero. The label of “cure” is still just a statistical prediction, which is treatment and model dependent. The outcome for each individual patient is determined by individual staging and prognostic factors, as well as non-myeloma related co-morbidities. Although calculating a “cure fraction” using the proposed model does allow comparisons between the different “total therapy” regimens used in Little Rock, it does not affirm that individual patients are actually cured.
What is more helpful is to offer criteria that can predict a likely very good outcome at the start of therapy and/or early in the disease course. This is the strategy of the IMF’s Black Swan Research Initiative. Using MRD assessment as a primary tool, it is possible to predict likely long remissions with complete remissions, especially with CR or stringent CR for patients with initial stable response. Without claiming that patients are necessarily “cured” (meaning that there will never be a relapse even after 10 years), one can predict chronic disease control, which is most helpful to individual patients. Thus, for example, in a young patient with good risk features–(ISS Stage I), normal F.I.S.H. testing (no t[4;14]; 17p-; 1q+ ), and normal LDH at baseline who achieves stringent CR and flow MRD-negative status–a very good outcome can be predicted.
The total therapy regimens clearly produce excellent outcomes for some patients. But in 2014, most patients and investigators are looking to novel therapy/transplant regimens that can be successful without the automatic double transplant approach plus other elements of the total therapy program. The pioneering value of total therapy has triggered the search for other ways forward that can occur within the Black Swan Research Initiative, which allows use of MRD assessment to achieve the best outcomes.
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