In this issue of Rheumatology, for the first time a precision medicine trial in PsA is published . PsA is a disease with a wide array of clinical manifestations and a growing set of treatment options [2, 3]. In clinical practice, it is often required to try various biologics before the desired response is attained. The hypothesis driving the current study by Miyagawa et al.  is that immunologic heterogeneity within the overall group of patients classified as having PsA could also be reflected in a differential response to the currently available set of biologics. To test their hypothesis, the authors examined the efficacy of steering the choice of biologic on a per-patient immunologic basis in the strategic arm as compared with a standard-of-care approach, in which a TNF inhibitor was the first-line biologic of choice. The authors used flow cytometry to characterize the cell surface markers on circulating Th cells and used these data to reclassify each individual patient into one of four new categories of patients in the strategic arm. These categories were based on the relative frequencies of Th1 and Th17 cells expressing activation markers and proposed to represent the dominant population(s) driving the disease of that individual patient. Subsequently each patient in the strategic arm was treated with the biologic expected to have the greatest impact on that specific population of T cells. The study approach of choosing the biologic on an individual patient basis fits the concept of precision medicine, and in doing so the authors have addressed recent calls for novel trial design [4, 5]. The main finding of the study was that patients being treated in the strategic arm had an improvement in the arthritis domain compared with patients being treated with the standard of care. Slight caution is warranted as the strategic arm and standard-of-care arm were different in some key factors, including background MTX usage. Although these preliminary results are promising, the study design in itself is a proof of concept that strategic trials are possible in PsA. The novel trial design deserves specific examination from which we can draw lessons for future adaptations in strategic trials. First, reclassifying patients based on their response to therapy is typically done post hoc to determine whether there is a biomarker profile strongly associated with favourable response to the drug in the trial . The current study uses quartiles based on data from healthy controls to determine the flow cytometry cut-off values for reclassifying PsA patients, and it is uncertain if the cut-off values employed are optimally matched to favourable response of the different biologics. Of course, the actual flow cytometry strategy used would need to show good reproducibility, reliability and other performance characteristics to qualify as a true biomarker applied to broader clinical practice. Future strategic trials may yield better efficacy in the strategic arm by including flow cytometry data from the other immune cells involved in the disease, as well as incorporating extra layers of immunologic data (e.g. transcriptomics) into such a classification tree. Another insight we gain from the study is that reclassifying patients based on biomarker profiles can result in skewed treatment allocation: most patients in the strategic arm received secukinumab because they were reclassified as Th17 dominant. Strictly speaking, it remains unknown if secukinumab has the same efficacy as the other biologics in the study, since there have been no head-to-head randomized controlled trials focusing on the arthritis domain. Indirect evidence does suggest that inhibiting IL-17A and TNF are relatively comparable on a group level . However, future investigations using strategic trials may want to include drugs that do have clear superiority and/or inferiority based on group efficacy data, and this needs to be taken into account when designing the trial. It is also important to realize that patients sharing a specific biomarker profile—those being reclassified within the same category—may also represent a subgroup of patients with recalcitrant or active disease. As an example, in SLE the presence of a high IFN gene signature is deemed a potential biomarker for selecting individuals that could respond favourably to IFN-targeting therapy, but at the same time the presence of this signature is also associated with higher disease activity and greater chances of flares . Fortunately, a lot can be learned from ongoing oncological research when it comes to strategic trial design [9, 10]. One potential trial design improvement would be to first reclassify all of the patients in the trial. Within each new category of patients the randomization procedure would then be performed to either the strategic arm or the standard-of-care arm. The latter would ideally follow a predefined protocol. Alternatively, if the drugs being evaluated are known to have comparable efficacy based on group data, within each new category of patients the strategic drug could be compared with a randomly assigned drug. The major rate-limiting step to achieving precision medicine in PsA is to identify cost-effective, accurate predictors of therapy response within the continuously expanding line-up of treatment options. Optimizing strategic trial design is essential to accomplish this balancing act and should be a priority for the future research agenda. Future strategic trials would benefit by incorporating well-established predictors of treatment response and overcome a number of confounders by reclassifying patients before randomizing to either personalized care or standard of care. Funding: No specific funding was received from any bodies in the public, commercial or not-for-profit sectors to carry out the work described in this article. Disclosure statement: The author has declared no conflicts of interest. References 1 Miyagawa I , Nakayamada S , Nakano K et al. . Precision medicine using different biological DMARDs based on characteristic phenotypes of peripheral T helper cells in psoriatic arthritis . Rheumatology 2018 ; doi:10.1093/rheumatology/key069. 2 Baker KF , Isaacs JD. Novel therapies for immune-mediated inflammatory diseases: what can we learn from their use in rheumatoid arthritis, spondyloarthritis, systemic lupus erythematosus, psoriasis, Crohn’s disease and ulcerative colitis? Ann Rheum Dis 2018 ; 77 : 175 – 87 . Google Scholar CrossRef Search ADS PubMed 3 Ritchlin CT , Colbert RA , Gladman DD. Psoriatic arthritis . N Engl J Med 2017 ; 376 : 957 – 70 . Google Scholar CrossRef Search ADS PubMed 4 Ogdie A , Coates L. The changing face of clinical trials in psoriatic arthritis . Curr Rheumatol Rep 2017 ; 19 : 21 . Google Scholar CrossRef Search ADS PubMed 5 Poddubnyy D , Sieper J. What is the best treatment target in axial spondyloarthritis: tumour necrosis factor α, interleukin 17, or both? Rheumatology 2017 . https://doi.org/10.1093/rheumatology/kex361. 6 Teitsma XM , Jacobs JWG , Mokry M et al. . Identification of differential co-expressed gene networks in early rheumatoid arthritis achieving sustained drug-free remission after treatment with a tocilizumab-based or methotrexate-based strategy . Arthritis Res Ther 2017 ; 19 : 170 . Google Scholar CrossRef Search ADS PubMed 7 Mease PJ , van der Heijde D , Ritchlin CT et al. . Ixekizumab, an interleukin-17A specific monoclonal antibody, for the treatment of biologic-naive patients with active psoriatic arthritis: results from the 24-week randomised, double-blind, placebo-controlled and active (adalimumab)-controlled period of the phase III trial SPIRIT-P1. Ann Rheum Dis 2017 ; 76 : 79 – 87 . 8 Rose T , Grützkau A , Klotsche J et al. . Are interferon-related biomarkers advantageous for monitoring disease activity in systemic lupus erythematosus? A longitudinal benchmark study . Rheumatology 2017 ; 56 : 1618 – 26 . Google Scholar CrossRef Search ADS PubMed 9 Hainsworth JD , Meric-Bernstam F , Swanton C et al. . Targeted therapy for advanced solid tumors on the basis of molecular profiles: results from MyPathway, an open-label, phase IIa Multiple Basket Study . J Clin Oncol 2018 ; 36 : 536 – 42 . Google Scholar CrossRef Search ADS PubMed 10 Simon R. Critical review of umbrella, basket, and platform designs for oncology clinical trials . Clin Pharmacol Ther 2017 ; 102 : 934 – 41 . Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: email@example.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)
Rheumatology – Oxford University Press
Published: Jun 2, 2018
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