The small set of biological markers usually used for RA diagnosis or prognosis is unable to predict individual responsiveness to TBA [14
]. Therefore, to enable such a prediction, global approaches based on proteomics or transcriptomics have been recently considered [27
]. However, in the context of RA, proteomic analysis is still under development [27
]. Moreover, very few informative transcripts have been identified by gene profiling [16
] and the few studies that used this approach have relied on the differences in transcript levels measured at baseline versus two to three days after treatment onset [17
]. This required exposure of every patient to treatment. Furthermore, the narrow time frame of this procedure may blur some significant but late variations with respect to baseline, which eventually limits transcript informativeness. In contrast, we have now measured transcript levels at baseline as the single predictor of responsiveness. In clinical practice, prediction can then be done without any exposure to treatment, which enables it to be restricted to responders.
Three months of treatment was chosen as the endpoint of our study, as recently recommended by international experts [29
], because the objective of an efficient RA treatment is a rapid response. Should this early evaluation at three months disclose a moderate or absent response, this procedure allows another treatment to be used as early as possible. Also, using the DAS28 evolution at three months for classifying our 33 patients as responders or non-responders turned out to be quite reliable in the long run. Indeed, 22 out of 33 patients could be followed for three more years and their infliximab responsiveness, or lack thereof, did not vary over this period, even when increasing infliximab amount and frequency in non-responders (data not shown).
We aimed to identify a list of transcripts whose combined levels could be related to infliximab/methotrexate responsiveness. In fact, infliximab used alone is known to be efficient only for a short durationbecause the rapid production of anti-infliximab antibodies counteracts the drug's effect, whereas methotrexate advantageously limits this occurrence. The mixture of a cytokine inhibitor (infliximab) and an inhibitor of cell proliferation (methotrexate) is likely to regulate or even co-regulate a complex set of genes; this is a limitation if an understanding of some underlying events in RA is desired.
Gene expression was measured in PBMCs because this is an acknowledged, non-invasive procedure for diagnosis or prognosis of autoimmune diseases [30
]. Specifically, in the context of RA, PBMCs as a surrogate tissue are advantageous as they allow for screening in any subject, whereas synovium is amenable to analysis in only a few patients. However, a drawback of such PBMC analysis is the lack of a clear-cut relationship between PBMCs and the affected synovium, which prevents the resulting data from providing an understanding of the RA-associated events in joints. Also, we analysed the PBMC transcriptome with an arbitrary collection of approximately 10,000 cDNA probes [21
]. Since this restrictive procedure cannot measure every transcript expressed in the PBMCs, it does not intend to provide a genome-wide view of the RA-associated gene dysregulations in this tissue. Yet, this approach is quite acceptable when inferring prognosis from gene profiling is the major task.
Overall, the present study was not designed primarily to increase our understanding of RA physiopathology but is mostly suited to the predictive use of some combined transcript levels. Our data illustrate that a non-invasive transcriptome analysis done in PBMCs with an array of probes devoid of a specific selection towards the disease under study enables the efficient prediction of treatment responsiveness. Whether these conclusions are solid whatever the microarray/qRT-PCR platforms used, depend on a restricted PBMC subpopulation, or, above all, are useful in the context of an actual therapeutic decision, remains to be tested.
test and/or SAM, we identified a short list of 25 to 37 transcripts whose combined expression levels in PBMCs are an efficient discriminator of responders versus non-responders to infliximab/methotrexate. Many of the 25 transcripts identified by t
test were no longer significant when using Bonferroni's correction to adjust statistics for the multiple transcripts analysed, but Bonferroni's correction has been recognized as a drastic one when used in this context, which contrasts with the SAM-associated false discovery rate [31
]. Moreover, the t
test and SAM cross-validated each other for most of the 20 transcripts eventually selected for qRT-PCR as 13 out of 20 (65%) such transcripts were significant with both tests (Table ). Measuring these 20 transcript levels by qRT-PCR indicated that their performance as a predictor of responsiveness was equal to that obtained with 37 transcripts. Ultimately, a given combination of 8 selected transcripts (75% of them being significant by t
test and SAM) as a predictor of responsiveness was as powerful as any higher number of transcripts. This observation that a given combination of very few transcripts can equal or even outperform the predictive strength of a higher number of transcripts has also been reported in another context, namely the response to hepatitis C treatment [32
]. This small size for an informative gene set is most encouraging when the need comes for the development of a reliable, fast and cheap assay for measuring informative transcript levels in a clinical setting.
Consistent with the limitations noted above, our list of 29 transcripts did not disclose any significant series of transcripts whose altered levels could point to the physiopathological importance of a predominating function or pathway. Indeed, these transcripts covered such diverse proteins and functions as: ribosomal components (LAMR1, MRPL22, RPL35, RPS16, RPS28), which may suggest the existence of a TNFα-dependent pathway in the control of translation; cell adhesion and inhibition of cell migration/invasion (LAMR1, MUCDHL, MTCPB1); cytochromes (CYP3A4, CYP4F12) and cytochrome oxidase (COX7A2L); proteasome-mediated proteolysis (FBXO5, PSMB9); various enzymes (AADAT, PFKFB4); intra- or extracellular signalling (AKAP9, CXCL5, PTPN12, RASGRP3, TBL2, THRAP3), including regulators of the ERK pathway (EPS15, SCAM-1); and innate or adaptive immunity (KNG1, MCP, PSMB9, HLA-DPB1). Two transcripts, namely MUSTN1 and HLA-DPB1, are noteworthy; the MUSTN1 transcript codes for a protein involved in bone development and regeneration [33
] and some alleles of the HLA-DPB1
gene have been associated with a relatively high risk of RA occurrence [34
The opposite variations in transcript levels seen in responders compared to non-responders at three months strongly suggest that the informative transcripts retained in our study originated from TNFα-regulated genes. In fact, TNFα-dependent expression of the CXCL5
, and PSMB9
genes, as noted here, has been previously described [35
]. However, only two of our transcripts, namely MCP and PTPN12, are found among lists of genes that are directly regulated by the TNFα/NFκB pathway, whether in RA [41
] or in another context [42
]. Therefore, it is likely that most of our transcripts are indirect TNFα targets. This view fits with the fact that the opposite variations in responders versus non-responders were observed weeks after the start of TBA. The reason why the transcript levels exhibited a limited trend to up-regulation at three months in responders along with a predominating repression in non-responders (Figure ) also fits with indirect TNFα target genes, whose regulation would depend on one or more TNFα-dependent transcriptional repressor(s). The difference in responders versus non-responders could then result from genetic polymorphisms in binding sites for such repressors.
This situation of variations in binding of transcription factors has been previously described in RA [11
]. Notably, the -308G/G genotype of the TNFα
gene promoter is known to be associated with a better response to infliximab compared to the -308A/G or A/A genotype [11
]. Other binding sites for repressor(s) could be located in any gene that belongs to the pathway from TNFα signalling to its indirect target genes whose transcripts were found here. If so, identifying such binding site polymorphisms that could predict the extent of responsiveness to TBA deserves further studies. Beyond this, it might well be worth combining the HLA-DRB1
genotype, itself a predictor of responsiveness to methotrexate/sulphasalazine/hydroxychloroquine in RA [45
], with our measure of informative transcript levels, as this might enhance the predictive power of such indicators.