Although MTX has been used for many years to treat juvenile arthritis, there are still no reliable biomarkers with which to predict good or poor response to MTX, and the mechanisms of MTX and its interactions with inflammatory pathways are still unclear. Several recent studies have suggested that genes which are differentially expressed in a pathological process are also more likely to harbour disease-associated genetic variants for a specific disease. This has been demonstrated in prostate cancer and type I diabetes: such SNPs have been labeled ‘functionally interpolating SNPs or FitSNPs”[47
]. In addition there are clearly genetic influences upon gene expression levels, and so called ‘very important pharmacogenes (VIPs)’ have been previously shown to have expression patterns that are associated with genetic variation[49
]. Other gene expression profiling studies, in cancer, have identified transcripts whose early expression correlates with good or poor response to MTX[50
]. In this study we have generated transcriptional profiling data from children given MTX for treatment of JIA, in order to identify pathways which are altered at a transcriptional level after MTX therapy, and then tested whether variation in highly differentially expressed genes makes a contribution to the genetic component of drug response. To our knowledge this is the first pharmacogenomic study in which this ‘dual’ approach has been applied.
Using analysis of genes differentially expressed in peripheral blood cells after MTX treatment in a group of 11 children with JIA, we first identified genes which had a fold change of 1.7 and reached significance of p<0.05. From the differentially expressed probes (n=1222) after MTX treatment, we used the IPA database to identify canonical pathways that were implicated by the differentially expressed genes in the dataset. 28 pathways reached a significance level of p<0.05. Many of these pathways were related to immune cell functioning, signalling and cytokines, while others indicated involvement of the one-carbon folate, purine and pyrimidine synthesis pathways, as expected. The top pathway, PI3K/AKT signalling, is known to be of functional importance in regulatory T cells, and the negative regulator of IL-2 signalling PTEN (increased 2.6 fold post MTX) is critical to maintaining tolerance and maintains the anergic phenotype of regulatory cells[31
]. Several other immunosuppressive pathways including TGFß signalling and the PPAR (Peroxisome proliferator-activated receptors) pathway were identified, although at a lower significance level. In addition, genes known to be involved in MTX metabolism including thymidylate synthase (TYMS) and Serine hydroxymethyltransferase 1 (SHMT1) were found to have altered gene expression after 6 months of MTX therapy. TYMS is involved in generation of dihydrofolate and is inhibited by MTX polyglutamates, while SHMT1 is a pivotal enzyme in the folate pathway that synthesizes 5, 10-methylene tetrahydrofolate [26
]. These genes are being investigated further.
One possible caveat relating to our strategy, would be that genes down regulated after successful MTX treatment may reflect the gene signature of active inflammation in JIA, which would be predicted to fall in expression after treatment. To address this issue, direct comparison of our data with a study of gene expression in JIA was performed. This study by Griffin et al used gene expression profiling of PBMC to identify distinct gene signatures in active JIA[23
]. Comparison of the top 75 genes up-regulated in these signatures with our initial 1222 probe sets, showed overlap of only 6 genes (DUSP4, CREM, C9orf3, TcnRNA MAD1L1, and ACPP
); of these, 4 were upregulated in the Griffin study (active JIA), and higher pre-treatment in our study. However the majority of genes in our differentially expressed list were not in the signature of ‘active JIA’ suggesting that our data do not simply represent a ‘downregulation of disease’ signature.
One previous study in adult RA used gene expression profiling from whole blood before and after MTX or anti-TNF treatment, but considered only genes that are involved in the NFκB pathway and did not identify genes specifically altered by MTX[53
]. In the cancer field, where MTX doses administered are higher than those used to treat arthritis, differential gene expression in leukaemic cells has been used to screen for genes whose expression may predict a poor response to MTX[50
]. That study did not attempt to relate genetic variation to the expression findings. A caveat of our study is that gene expression data generated from peripheral blood, may not represent the inflamed tissue (synovium). However we adopted this approach since one longterm goal is to generate predictive biomarkers that will be clinically amenable to simple testing in a blood test. A further caveat is that from pediatric blood samples it is challenging to generate cell-specific gene expression profiling data, to indicate which cell types within the PBMC contribute most to changes demonstrated: further functional studies on sorted cells will be required to elucidate this.
To ask if the ‘top ranking genes’ of our differentially expressed gene dataset contained SNPs that could inform us about the genetic component of drug response to MTX in JIA, we selected a small number of genes from those which were differentially expressed for genotyping analysis in a large cohort of children recruited to CHARMS. We took a strategy of screening the whole gene using a tagging SNP approach, this ensured that we had >95% coverage for the genes investigated, which is a more thorough approach than some existing studies which tend to only look at only a few SNPs in candidate genes.
To identify effects, we chose to compare extreme ends of the response spectrum, comparing non-responders to those who achieved or exceeded an ACR-Ped70 response. In the SLC16A7 gene, which was highly up-regulated after administration of MTX, we found three SNPs significantly associated with response to MTX treatment. However the haplotype analysis suggested that rs2711655 lies on the same ‘risk’ haplotype as rs3763980 and that it is the latter SNP, which is driving the association. We also found evidence for a ‘protective’ haplotype and the minor allele of rs10877333 is driving this association. Interestingly the SNP rs3763980 is a non-synonymous coding SNP, resulting in a substitution at amino acid 445 threonine to serine. This is in a predicted cytoplasmic domain, with implications for phosphorylation and signaling and is therefore a potential functional SNP.
We also validated the association of the SNP rs3763980 with risk of non-response to MTX in an independent cohort of JIA patients collected in the US. In this cohort a different definition for response was used, based upon joint count alone. Again the ends of the spectrum were compared, using ‘best responders’ versus non-responders. Interestingly, when the CHARMS clinical dataset was recoded using joint count alone, there was still a trend towards association of SNP rs3763980 with non-response, although this no longer reached statistical significance (p_trend = 0.08 OR 1.63 95%CI 0.94-2.84). Meta-analysis of the cohorts strengthened the evidence for association of the SNP, rs3763980, with a combined p-value of 0.002. However, further validation of these results in large cohorts will be necessary.
Interestingly, the data also show that the SNP also influences expression of the gene. Thus the minor allele of rs3763980 is associated with increased expression of SLC16A7 in transformed B cells from healthy donors. In arthritis patients this allele was associated with a poor response to MTX. Although it is difficult to extrapolate directly from control B cell data, to peripheral blood cells from patients with arthritis, this result does strongly suggest an association between SLC genotype at the rs3763980 SNP, and gene expression level. Further functional studies will be required to elucidate how altered protein levels of SLC16A7 influence response to MTX.
The SLC16A7 (MCT2) protein is a transporter of lactate and pyruvate, whose expression is enhanced by activation of the PI3K/AKT pathway, the most significant pathway identified in our pathway analysis (). There is evidence that SLC16A7 is co-regulated with PPARalpha, (also upregulated by MTX), which inhibits production of many inflammatory cytokines[54
]. At present the functional relationship of SLC16A7 expression and MTX mechanisms, or the coding change (T455S) caused by the SNP associated with non-response remain unclear, but warrant further investigation.
In conclusion, we believe this to be the first study in which gene expression profiling, in cells from a readily available source (peripheral blood) have been used to select novel gene candidates for analysis of genetic components to response to MTX, in childhood arthritis. We have identified pathways which are altered by MTX, many of which are central regulating pathways for the immune system. In addition for a small number of genes within these pathways we have gone one step further to determine whether genetic variation plays a role in determining differences in response. In one of these genes, SLC16A7, we have identified a SNP that is associated with risk of non-response to MTX, with validation in an independent cohort. Our findings will require validation in larger studies and other cohorts of patients treated with MTX for arthritis, or other inflammatory conditions. Further investigation and functional studies of pathways and genes identified here should contribute valuable insights into efficacy of MTX in inflammatory arthritis. This approach to identifying pathways and genes that may be involved in the efficacy and response to a commonly used drug in childhood arthritis may ultimately allow us to develop methods to predict response and thereby select individual treatment for patients with greater accuracy.