IntroductionThe aim of this study was to investigate patient reported outcomes (PROs) of functional ability and health related quality of life (HRQoL) in patients with early (rheumatoid) arthritis during one year of remission steered treatment.MethodsIn this study, 610 patients with early rheumatoid arthritis (RA) or undifferentiated arthritis (UA) were treated with methotrexate (MTX) and tapered high dose of prednisone. Patients in early remission (Disease Activity Score (DAS) <1.6 after 4 months) tapered prednisone to zero and when in persistent remission, also tapered MTX. Patients not in early remission were randomized to either MTX + hydroxychloroquine + sulphasalazine + prednisone (arm 1) or to MTX + adalimumab (arm 2). Every 4 months, patients filled out the Health Assessment Questionnaire (HAQ) and the McMaster Toronto Arthritis Patient Preference Questionnaire (MACTAR), the Short Form 36 (SF-36) and visual analogue scales (VAS). Change scores were compared between treatment groups. The association with achieving remission was analyzed using linear mixed models.ResultsDuring year 1, patients who achieved early remission had the most improvement in PROs with scores comparable to the general population. Patients in the randomization arms showed less improvement. Scores were comparable between the arms. There was a significant association between achieving remission and scores of HAQ, MACTAR and physical HRQoL.ConclusionsIn early arthritis, PROs of functional ability and HRQoL after one year of remission steered treatment reach normal values in patients who achieved early remission. In patients not in early remission, who were randomized to two strategy arms, PROs improved less, with similar scores in both treatment arms.Trial registrationsISRCTN11916566 and EudraCT2006-006186-16

Reversal of cancer gene expression is predictive of therapeutic potential and can be used to find new indications for existing drugs (drug repositioning). Gene expression reversal potential is currently calculated, in almost all studies, by pre-aggregating all tumour samples into a single group signature or a limited number of molecular subtype signatures. Here, we investigate whether drug repositioning based on individual tumour sample gene expression signatures outperforms the use of tumour group and subtype signatures. The tumour signatures were created using 534 tumour samples and 72 matched normal samples from 530 clear cell renal cell carcinoma (ccRCC) patients. More than 20,000 drug signatures were extracted from the CMAP and LINCS databases. We show that negative enrichment of individual tumour samples correlated (Spearman’s rho = 0.15) much better with the amount of differentially expressed genes in drug signatures than with the tumour group signature (Rho = 0.08) and the 4 tumour subtype signatures (Rho 0.036-0.11). Targeted drugs used against ccRCC, such as sirolimus and temsirolimus, which could not be identified with the pre-aggregated tumour signatures could be recovered using individual sample analysis. Thus, drug repositioning can be personalized by taking into account the gene expression profile of the individual’s tumour sample.

To find new potentially therapeutic drugs against clear cell Renal Cell Carcinoma (ccRCC), within drugs currently prescribed for other diseases (drug repositioning), we previously searched for drugs which are expected to bring the gene expression of 500 + ccRCC samples from The Cancer Genome Atlas closer to that of healthy kidney tissue samples. An inherent limitation of this bulk RNA-seq data is that tumour samples consist of a varying mixture of cancerous and non-cancerous cells, which influences differential gene expression analyses. Here, we investigate whether the drug repositioning candidates are expected to target the genes dysregulated in ccRCC cells by studying the association with tumour purity. When all ccRCC samples are analysed together, the drug repositioning potential of identified drugs start decreasing above 80% estimated tumour purity. Because ccRCC is a highly vascular tumour, attributed to frequent loss of VHL function and subsequent activation of Hypoxia-Inducible Factor (HIF), we stratified the samples by observed activation of the HIF-pathway. After stratification, the association between estimated tumour purity and drug repositioning potential disappears for HIF-activated samples. This result suggests that the identified drug repositioning candidates specifically target the genes expressed by HIF-activated ccRCC tumour cells, instead of genes expressed by other cell types part of the tumour micro-environment.

Background: Batch effects were not accounted for in most of the studies of computational drug repositioning based on gene expression signatures. It is unknown how batch effect removal methods impact the results of signature-based drug repositioning. Herein, we conducted differential analyses on the Connectivity Map (CMAP) database using several batch effect correction methods to evaluate the influence of batch effect correction methods on computational drug repositioning using microarray data and compare several batch effect correction methods. Results: Differences in average signature size were observed with different methods applied. The gene signatures identified by the Latent Effect Adjustment after Primary Projection (LEAPP) method and the methods fitted with Linear Models for Microarray Data (limma) software demonstrated little agreement. The external validity of the gene signatures was evaluated by connectivity mapping between the CMAP database and the Library of Integrated Network-based Cellular Signatures (LINCS) database. The results of connectivity mapping indicate that the genes identified were not reliable for drugs with total sample size (drug + control samples) smaller than 40, irrespective of the batch effect correction method applied. With total sample size larger than 40, the methods correcting for batch effects produced significantly better results than the method with no batch effect correction. In a simulation study, the power was generally low for simulated data with sample size smaller than 40. We observed best performance when using the limma method correcting for two principal components. Conclusion: Batch effect correction methods strongly impact differential gene expression analysis when the sample size is large enough to contain sufficient information and thus the downstream drug repositioning. We recommend including two or three principal components as covariates in fitting models with limma when sample size is sufficient (larger than 40 drug and controls combined).

The survival of patients with clear cell metastatic renal cell carcinoma (cc-mRCC) has improved substantially since the introduction of tyrosine kinase inhibitors (TKI). With the fact that TKIs interact with immune responses, we investigated whether polymorphisms of genes involved in immune checkpoints are related to the clinical outcome of cc-mRCC patients treated with sunitinib as first TKI. Twenty-seven single-nucleotide polymorphisms (SNP) in (PD-L1), (PD-1), and were tested for a possible association with progression-free survival (PFS) and overall survival (OS) in a discovery cohort of 550 sunitinib-treated cc-mRCC patients. SNPs with a significant association ( < 0.05) were tested in an independent validation cohort of 138 sunitinib-treated cc-mRCC patients. Finally, data of the discovery and validation cohort were pooled for meta-analysis. rs231775 and rs7866740 showed significant associations with OS in the discovery cohort after correction for age, gender, and Heng prognostic risk group [HR, 0.84; 95% confidence interval (CI), 0.72-0.98; = 0.028, and HR, 0.73; 95% CI, 0.54-0.99; = 0.047, respectively]. In the validation cohort, the associations of both SNPs with OS did not meet the significance threshold of < 0.05. After meta-analysis, rs231775 showed a significant association with OS (HR, 0.83; 95% CI, 0.72-0.95; = 0.008). Patients with the GG genotype had longer OS (35.1 months) compared with patients with an AG (30.3 months) or AA genotype (24.3 months). No significant associations with PFS were found. The G-allele of rs231775 in the gene is associated with an improved OS in sunitinib-treated cc-mRCC patients and could potentially be used as a prognostic biomarker..