Reply

To the Editor: I read with interest the article by Sanayama et al on the biomarkers they identified using genome-wide DNA microarray analysis of gene expression in peripheral blood mononuclear cells from rheumatoid arthritis (RA) patients treated with tocilizumab (TCZ) for the first time (1). Sanayama and colleagues studied a training cohort, a validation cohort, and a cohort of healthy volunteers to compare those who responded to TCZ to those who did not. I have several concerns about the methodology and conclusions of this study. The authors claim that they have identified candidate biomarkers that may be used to predict response to TCZ. I find it difficult to reach this conclusion given that TCZ-treated patients were not compared to control treatment arms/ patients. How do we know these candidate biomarkers do not work exactly the same way in patients treated with tumor necrosis factor inhibitors, other biologic agents, or diseasemodifying antirheumatic drugs only, let alone untreated patients? We do not even know if this is specific to RA patients, since a disease control cohort was not studied. Unfortunately, inattention to the need for appropriate control groups is nothing new to the basic sciences (2). Further, the authors included a healthy control group with subjects who were, on average, a decade younger than subjects in the treatment cohorts, even though one of the main criteria for choosing a control group is that characteristics of the control subjects should match, as much as possible, characteristics of the subjects in the treatment groups, at least with regard to demographic characteristics. Is this what is considered “good” matching of the control groups? Finally, the above issues become even more troublesome when the very small number of patients studied is considered, although the authors note this as a limitation in their study. Sanayama et al also state that they did not have enough patients to exclude Type I or II errors or perform multivariate analyses. This raises questions as to how they decided on the number of patients to include in their study. How would they determine if there were enough patients to show a real difference, or if the difference they found was not a statistical fluke? Was there no hypothesis development involved in the design? When none of the fundamental statistical testing is performed, how do they conclude that their results are scientifically valid and, more importantly, clinically useful? Recognizing and listing limitations is a good start, but it should be noted that these are not simply limitations; they are incompatible with good science. In short, a very small number of patients was studied (which did not allow for adequate statistical analysis), control groups of patients with other diseases or receiving other treatments were not used, and the healthy control group was not adequately matched. The search for biomarkers seems to be a never-ending quest. However, we need to be comfortable with the idea that there may not be any biomarker that will be useful in individual patients, where the real clinical applicability of any biomarker would be tested as physicians treat patients one at a time, not as groups. Emerging data suggest that clinically useful biomarkers are unlikely to be found for our common diseases (3–5). Therefore, we need vigorous, scientifically sound methodology for any investigation looking into these issues to shield us from premature findings and to prevent further waste of research resources. This study falls short of that goal. Dr. Yazici has received consulting fees, research support, and/or honoraria from Bristol-Myers Squibb, Celgene, Genentech, and UCB (less than $10,000 each), as well as research grants from Bristol-Myers Squibb, Celgene, and Genentech.