SEARCH · Search bioRxiv

Results for “clinical trials”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources

bioRxiv2017

MatchMiner: An open source computational platform for real-time matching of cancer patients to precision medicine clinical trials using genomic and clinical criteria

BackgroundMolecular profiling of cancers is now routine at many cancer centers, and the number of precision cancer medicine clinical trials, which are informed by profiling, is steadily rising. Additionally, these trials are becoming increasingly complex, often having multiple arms and many genomic eligibility criteria. Currently, it is a challenging for physicians to match patients to relevant clinical trials using the patients genomic profile, which can lead to missed opportunities. Automated matching against uniformly structured and encoded genomic eligibility criteria is essential to keep pace with the complex landscape of precision medicine clinical trials.\n\nResultsTo meet these needs, we built and deployed an automated clinical trial matching platform called MatchMiner at the Dana-Farber Cancer Institute (DFCI). The platform has been integrated with Profile, DFCIs enterprise genomic profiling project, which contains tumor profile data for >20,000 patients, and has been made available to physicians across the Institute. As no current standard exists for encoding clinical trial eligibility criteria, a new language called Clinical Trial Markup Language (CTML) was developed, and over 178 genomically-driven clinical trials were encoded using this language. The platform is open source and freely available for adoption by other institutions.\n\nConclusionMatchMiner is the first open platform developed to enable computational matching of patient-specific genomic profiles to precision cancer medicine clinical trials. Creating MatchMiner required developing clinical trial eligibility standards to support genome-driven matching and developing intuitive interfaces to support practical use-cases. Given the complexity of tumor profiling and the rapidly changing multi-site nature of genome-driven clinical trials, open source software is the most efficient, scalable, and economical option for matching cancer patients to clinical trials.

bioinformatics↗

bioRxiv2017

Statistical power of clinical trials has increased whilst effect size remained stable: an empirical analysis of 137 032 clinical trials between 1975-2017

BackgroundBiomedical studies with low statistical power are a major concern in the scientific community and are one of the underlying reasons for the reproducibility crisis in science. If randomized clinical trials, which are considered the backbone of evidence-based medicine, also suffer from low power, this could affect medical practice.\n\nMethodsWe analysed the statistical power in 137 032 clinical trials between 1975 and 2017 extracted from meta-analyses from the Cochrane database of systematic reviews. We determined study power to detect standardized effect sizes according to Cohen, and in meta-analysis with p-value below 0.05 we based power on the meta-analysed effect size. Average power, effect size and temporal patterns were examined.\n\nResultsThe number of trials with power [≥]80% was low but increased over time: from 9% in 1975-1979 to 15% in 2010-2014. This increase was mainly due to increasing sample sizes, whilst effect sizes remained stable with a median Cohens h of 0.21 (IQR 0.12-0.36) and a median Cohens d of 0.31 (0.19-0.51). The proportion of trials with power of at least 80% to detect a standardized effect size of 0.2 (small), 0.5 (moderate) and 0.8 (large) was 7%, 48% and 81%, respectively.\n\nConclusionsThis study demonstrates that sufficient power in clinical trials is still problematic, although the situation is slowly improving. Our data encourages further efforts to increase statistical power in clinical trials to guarantee rigorous and reproducible evidence-based medicine.

epidemiology↗

bioRxiv2018

Genetic variability and potential effects on clinical trial outcomes: perspectives in Parkinson’s disease

BackgroundImproper randomization in clinical trials can result in the failure of the trial to meet its primary end-point. The last [~]10 years have revealed that common and rare genetic variants are an important disease factor and sometimes account for a substantial portion of disease risk variance. However, the burden of common genetic risk variants is not often considered in the randomization of clinical trials and can therefore lead to additional unwanted variance between trial arms. We simulated clinical trials to estimate false negative and false positive rates and investigated differences in single variants and mean genetic risk scores (GRS) between trial arms to investigate the potential effect of genetic variance on clinical trial outcomes at different sample sizes.\n\nMethodsSingle variant and genetic risk score analyses were conducted in a clinical trial simulation environment using data from 5851 Parkinsons Disease patients as well as two simulated virtual cohorts based on public data. The virtual cohorts included a GBA variant cohort and a two variant interaction cohort. Data was resampled at different sizes (n = 200-5000 for the Parkinsons Disease cohort) and (n = 50-800 and n = 50-2000 for virtual cohorts) for 1000 iterations and randomly assigned to the two arms of a trial. False negative and false positive rates were estimated using simulated clinical trials, and percent difference in genetic risk score and allele frequency was calculated to quantify disparity between arms.\n\nFindingsSignificant genetic differences between the two arms of a trial are found at all sample sizes. Approximately 90% of the iterations had at least one statistically significant difference in individual risk SNPs between each trial arm. Approximately 10% of iterations had a statistically significant difference between trial arms in polygenic risk score mean or variance. For significant iterations at sample size 200, the average percent difference for mean GRS between trial arms was 130.87%, decreasing to 29.87% as sample size reached 5000. In the GBA only simulations we see an average 18.86% difference in GRS scores between trial arms at n = 50, decreasing to 3.09% as sample size reaches 2000. Balancing patients by genotype reduced mean percent difference in GRS between arms to 36.71% for the main cohort and 2.00% for the GBA cohort at n = 200. When adding a drug effect to the simulations, we found that unbalanced genetics with an effect on the chosen measurable clinical outcome can result in high false negative rates among trials, especially at small sample sizes. At a sample size of n = 50 and a targeted drug effect of -0.5 points in UPDRS per year, we discovered 33.9% of trials resulted in false negatives.\n\nInterpretationsOur data support the hypothesis that within genetically unmatched clinical trials, particularly those below 1000 participants, heterogeneity could confound true therapeutic effects as expected. This is particularly important in the changing environment of drug approvals. Clinical trials should undergo pre-trial genetic adjustment or, at the minimum, post-trial adjustment and analysis for failed trials. Clinical trial arms should be balanced on genetic risk variants, as well as cumulative variant distributions represented by GRS, in order to ensure the maximum reduction in trial arm disparities. The reduction in variance after balancing allows smaller sample sizes to be utilized without risking the large disparities between trial arms witnessed in typical randomized trials. As the cost of genotyping will likely be far less than greatly increasing sample size, genetically balancing trial arms can lead to more cost-effective clinical trials as well as better outcomes.

genomics↗

bioRxiv2018

The design, analysis and application of mouse clinical trials in oncology drug development

Mouse clinical trials (MCTs) are becoming widely used in pre-clinical oncology drug development. In this study, we provide some general guidelines on the design, analysis and application of MCTs. We first established empirical quantitative relationships between mouse number and measurement accuracy for both categorical and continuous efficacy endpoints, and showed that more mice are needed to achieve given accuracy for syngeneic models than for PDXs and CDXs. There is considerable disagreement between categorical methods on calling drug responses as objective response, indicating limitations of such approaches. We then introduced linear mixed models, or LMMs, to describe MCTs as clustered longitudinal studies, which explicitly model growth and drug response heterogeneities across mouse models and among mice within a mouse model. Several case studies were used to demonstrate the advantages of LMMs in discovering biomarkers and exploring a drugs mechanism of action. We also introduced the additive frailty models to perform survival analysis on MCTs, which more accurately estimate hazard ratios by modeling the clustered population structures in MCTs. We performed computational simulations for LMMs and frailty models to generate statistical power curves, and showed that statistical power is close for designs with similar total number of mice at given drug efficacy. Finally, we explained how MCTs can explain discrepant results in clinical trials, hence, MCTs are more than preclinical versions of clinical trials but possess their unique values. Results in the report will make MCTs a better tool for oncology drug development.

cancer biology↗

bioRxiv2015

Why clinical trials are terminated

BackgroundEvidence-based clinical practice relies on unbiased reporting of negative results. Meta-analysis of drug safety and efficacy across many clinical trials is difficult given the unconstrained nature of reasons that are provided to ClinicalTrials.gov to explain clinical trial terminations.\n\nMethods and FindingsWe scanned all trials in ClinicalTrials.gov marked with the \"terminated\" status (N=3122), meaning the trial had been stopped before the scheduled end date. Under the current reporting framework, any number of reasons may be given for termination, and these need not conform to a controlled vocabulary. Here we develop a controlled vocabulary for trial termination, and map each terminated trial to as many as three vocabulary terms. Mapping to this \"ontology of termination\" allows further analysis and conclusions. First, we identify the subset of terminated trials that ended citing safety concerns (6.2%) or failure to establish efficacy (10.8%), and were further able to stratify these rates across trials of different phases. Second, we examine termination reasons where a stricter data model could have preserved more evidentiary value, either because the data model was misused (7.6%) or because the given reason left unclear whether the decision to terminate was based on analysis of the data (74.9%, with 20.4% mentioning a decision-maker that may have had access to the data). Third, we show that imposing a controlled vocabulary of reasons for termination would avoid ambiguity and improve the evidentiary value of clinical trials.\n\nConclusionsWe encourage wider use of an \"ontology of termination\" and propose four questions that should be posed on trial termination. These simple steps would promote transparency and enable ready access to negative trial results for meta-analysis.

Scientific Communication and Education↗

bioRxiv2018

Performance of an IAVI-African Network of Clinical Research Laboratories in Standardized ELISpot and Peripheral Blood Mononuclear Cell Processing in Support of HIV Vaccine Clinical Trials

Immunological assays performed in different laboratories participating in multi-centre clinical trials must be standardized in order to generate comparable and reliable data. This entails standardized procedures for sample collection, processing, freezing and storage. The International AIDS Vaccine Initiative (IAVI) partnered with local institutions to establish Good Clinical Laboratory Practice (GCLP)-accredited laboratories to support clinical trials in Africa, Europe and Asia. Here we report on the performance of seven laboratories based in Africa and Europe in the interferon-gamma enzyme-linked immunospot (IFN-{gamma} ELISpot) assay and peripheral blood mononuclear cell (PBMC) processing over four years. Characterized frozen PBMC samples from 48 volunteer blood packs processed at a central laboratory were sent to participating laboratories. For each stimulus, there were 1751 assays performed over four years. 98% of these ELISpot data were within acceptable ranges with low responses to mock stimuli. There were no significant differences in ELISpot responses at five laboratories actively conducting immunological analyses in support of IAVI sponsored clinical trials or HIV research. In a separate study, 1,297 PBMC samples isolated from healthy HIV-1 negative participants in clinical trials of two prophylactic HIV vaccine candidates were analysed for PBMC yield from fresh blood and cell recovery and viability following freezing and thawing. 94 % and 96 % of samples had fresh PBMC viabilities and cell yields within the pre-defined acceptance criteria while for frozen PBMC, 99 % and 96 % of samples had acceptable viabilities and cell recoveries respectively, along with acceptable ELISpot responses in 95%. These findings demonstrate the competency of laboratories across different continents to generate comparable and reliable data in support of clinical trials.\n\nImportanceThere is a need for the establishment of an African network of laboratories to support large clinical trials across the continent to support and further the development of vaccine candidates against emerging infectious diseases such as Ebola, Zika and dengue viruses and the continued HIV-1 pandemic. This is particularly true in sub-Saharan Africa where the HIV-1 pandemic is most severe. In this report we have demonstrated by using standardized SOPs, training, equipment and reagents that GCLP-accredited clinical trial laboratories based in Africa and Europe can process clinical trial samples and maintain cell integrity and functionality demonstrated by IFN-{gamma} ELISpot testing, producing comparable and reliable data.

immunology↗

bioRxiv2018

Age of onset in genetic prion disease and the design of preventive clinical trials

Regulatory agencies worldwide have adopted programs to facilitate drug development for diseases where the traditional approach of a randomized trial with a clinical endpoint is expected to be prohibitively lengthy or difficult. Here we provide quantitative evidence that this criterion is met for the prevention of genetic prion disease. We assemble age of onset or death data from N=1,094 individuals with high penetrance mutations in the prion protein gene (PRNP), generate survival and hazard curves, and estimate statistical power for clinical trials. We show that, due to dramatic and unexplained variability in age of onset, randomized preventive trials would require hundreds or thousands of at-risk individuals in order to be statistically powered for an endpoint of clinical onset, posing prohibitive cost and delay and likely exceeding the number of individuals available for such trials. Instead, the characterization of biomarkers suitable to serve as surrogate endpoints will be essential for the prevention of genetic prion disease. Biomarker-based trials may require post-marketing studies to confirm clinical benefit. Parameters such as longer trial duration, increased enrollment, and the use of historical controls in a post-marketing study could provide opportunities for subsequent determination of clinical benefit.

neuroscience↗

bioRxiv2016

Optimizing Communication of Emergency Response Adaptive Randomization Clinical Trials to Potential Participants

Introduction: Acute clinical stroke trials are challenging to communicate to patients and families considering participation. Response adaptive randomization (RAR) is a technique that alters the proportion of trial subjects receiving active treatment, based on the outcomes of previous subjects. We aimed to determine how well interactive videos would improve understanding of a simulated acute stroke trial scenario that incorporated a design with RAR. Methods: We performed a cross-sectional study of emergency department patients who were without stroke, altered mental status, or critical illness. Subjects viewed a hypothetical stroke and clinical trial scenario. They were randomized into one of four groups with either an RAR or fixed randomization clinical trial design and with either a standard consent video, or an interactive video. Results: We enrolled 720 participants. In the RAR group with interactive video, 128 out of 149 (85.9%) of the subjects were able to correctly identify the allocation method, compared to the 172 out of 285 (61.6%) in the RAR group with the uninterrupted video for an absolute increase of 25.6% (95% CI 17,33%). The RAR group with interactive video had a higher odds of correct identification of allocation method (O.R. 2.767, 95% CI [1.011,7.570] while controlling for age, sex, ethnicity, education, self-reported understanding of protocol, stroke awareness and agreement to participate in trial. Conclusions: The interactive video increased participant understanding of an RAR design in a simulated stroke scenario. Future research should focus on whether acute trial recruitment can be enhanced using similar techniques.

clinical trials↗

bioRxiv2018

FDAAA TrialsTracker: A live informatics tool to monitor compliance with FDA requirements to report clinical trial results

IntroductionNon-publication of clinical trials results is an ongoing issue. In 2016 the US government updated the results reporting requirements to ClinicalTrials.gov for trials covered under the FDA Amendments Act 2007. We set out to develop and deliver an online tool which publicly monitors compliance with these reporting requirements, facilitates open public audit, and promotes accountability. MethodsWe conducted a review of the relevant legislation to extract the requirements on reporting results. Specific areas of the statutes were operationalized in code based on the results of our policy review, publicly available data from ClinicalTrials.gov, and communications with ClinicalTrials.gov staff. We developed methods to identify trials required to report results, using publicly available registry data; to incorporate additional relevant information such as key dates and trial sponsors; and to determine when each trial became due. This data was then used to construct a live tracking website. ResultsThere were a number of administrative and technical hurdles to successful operationalization of our tracker. Decisions and assumptions related to overcoming these issues are detailed along with clarifications directly from ClinicalTrials.gov. The FDAAA TrialsTracker was successfully launched in February 2018 and provides users with an overview of results reporting compliance. DiscussionClinical trials continue to go unreported despite numerous guidelines, commitments, and legal frameworks intended to address this issue. In the absence of formal sanctions from the FDA and others, we argue tools such as ours - providing live data on trial reporting - can improve accountability and performance. In addition, our service helps sponsors identify their own individual trials that have not yet reported results: we therefore offer positive practical support for sponsors who wish to ensure that all their completed trials have reported.

clinical trials↗

bioRxiv2018

Analysis of HIV-1 latent reservoir and rebound viruses in a clinical trial of anti-HIV-1 antibody 3BNC117

A clinical trial was performed to evaluate 3BNC117, a potent anti_HIV_1 antibody, in infected individuals during suppressive antiretroviral therapy (ART) and subsequent analytical treatment interruption (ATI). The circulating reservoir was evaluated by quantitative and qualitative outgrowth assay (Q2VOA) at entry and after 6 months, prior to ATI. Although there were no significant quantitative changes in the size of the reservoir, the composition of circulating reservoir clones varied over the 6_month period before treatment interruption in a manner that did not correlate with antibody sensitivity. The neutralization profile obtained from the reservoir by Q2VOA was predictive of time to rebound after ATI, and thus of antibody efficacy. Although 3BNC117 binding site amino acid variants found in rebound viruses pre_existed in the latent reservoir, only 3 of 217 rebound viruses were identical to 868 latent viruses. Instead many of the rebound viruses appeared to be recombinants, even in individuals with resistant reservoir viruses. By incorporating the possibility of recombination, 63% of the rebound viruses could have derived from the observed latent reservoir. In conclusion, viruses emerging during ATI in individuals treated with 3BNC117 are not the dominant species found in the circulating reservoir, but instead appear to represent recombinants.\n\nSummaryIn the setting of a clinical trial evaluating the anti_HIV_1 antibody 3BNC117, Cohen et al. demonstrate that rebound viruses that emerge following interruption of antiretroviral therapy are distinct from circulating latent viruses. However, rebound viruses often appear to be recombinants between isolated latent viruses.

clinical trials↗

bioRxiv2018

Application of mechanistic methods to clinical trials in multiple sclerosis: the simvastatin case

The analysis of clinical trials is limited to pre-specified outcomes, thereby precluding a mechanistic understanding of the treatment response. Multivariate mechanistic models can elucidate the causal chain of events by simultaneous analysis of multimodal data that link intermediate variables to outcomes of interest. A double-blind, randomised, controlled, phase 2 clinical trial in secondary progressive multiple sclerosis (MS-STAT, NCT00647348) demonstrated that simvastatin (80mg/day) over two years reduced the brain atrophy rate and was associated with beneficial effects on cognitive and disability outcomes. Therefore, this trial offers an opportunity to apply mechanistic models to investigate the hypothesised pathways that link simvastatin to clinical outcome measures, either directly or indirectly via changes in serum total cholesterol levels and to determine which is the more likely.

neuroscience↗

bioRxiv2016

Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes

Despite many recent advances in systems biology and a marked increase in the availability of high-throughput biological data, the productivity of research and development in the pharmaceutical industry is on the decline. This is primarily due to clinical trial failure rates reaching up to 95% in oncology and other disease areas. We have developed a comprehensive analytical and computational pipeline utilizing deep learning techniques and novel systems biology analytical tools to predict the outcomes of phase I/II clinical trials. The pipeline predicts the side effects of a drug using deep neural networks and estimates drug-induced pathway activation. It then uses the predicted side effect probabilities and pathway activation scores as an input to train a classifier which predicts clinical trial outcomes. This classifier was trained on 577 transcriptomic datasets and has achieved a cross-validated accuracy of 0.83. When compared to a direct gene-based classifier, our multi-stage approach dramatically improves the accuracy of the predictions. The classifier was applied to a set of compounds currently present in the pipelines of several major pharmaceutical companies to highlight potential risks in their portfolios and estimate the fraction of clinical trials that were likely to fail in phase I and II.

bioinformatics↗

bioRxiv2018

Commitments by the biopharmaceutical industry to clinical trials transparency: the evolving environment

BackgroundSponsors of clinical trials have ethical obligations to register protocols, to report study results and to comply with applicable legal requirements.\n\nObjectiveTo evaluate public commitments to trial disclosure and rates of disclosure by members and non-members of the European Federation of Pharmaceutical Industries and Associations (EFPIA) and/or the Pharmaceutical Research and Manufacturers of America (PhRMA).\n\nStudy selectionWebsites of the top 50 biopharmaceutical companies by 2015 sales were searched for statements relating to trial data disclosure. Disclosure of trial results completed by biopharmaceutical industry and non-industry sponsors of at least 30 trials (2006-2015) was assessed using TrialsTracker.\n\nFindingsAmong the top 50 companies, 30 were EFPIA/PhRMA members and 20 were non-members, of which 26 and none, respectively, had a statement on their website committing to the disclosure of trials data. Of 29 377 trials in TrialsTracker, 9511 were industry-sponsored (69 companies) and 19 866 were non-industry-sponsored (254 institutions). The overall mean disclosure rate was 55%, with higher rates for industry (74%) than for non-industry sponsors (46%). Of the 30 companies within the top 50 with data in TrialsTracker, the mean disclosure rate was 76% (77% for EFPIA/PhRMA members [n = 25] versus 67% for non-members [n = 5]).\n\nConclusionsMost of the top 50 biopharmaceutical companies have publicly committed to the disclosure of trial data. Industry sponsors have responded to the ethical and legal demands of trial disclosure to a greater extent than non-industry sponsors, and now disclose three quarters of their trials.

scientific communication and education↗

bioRxiv2018

Design principles for TB vaccines’ clinical trials based on spreading dynamics

Tuberculosis (TB) is one of the most complex diseases from the perspective of mathematical epidemiology. Individuals recently infected with the bacillus Mycobacterium tuberculosis can either develop TB directly in a matter of several weeks, or enter into an asymptomatic latent TB infection state (LTBI) that only occasionally derives into active disease, sometimes even decades after the infection event. The possible interruptions that a vaccine might provoke on these two mechanisms are indistinguishable in phase II clinical trials. In this work, we present a new methodology that allows differentiating vaccines that slow down the progression to disease from vaccines that prevent it. By introducing a stochastic framework for simulating synthetic clinical trials based on transmission models, we show how the method proposed here contributes both to reduce uncertainty in vaccine characterization and impact forecasts as well as to assist the design of clinical trials, improving their probabilities of success.

epidemiology↗

bioRxiv2018

Planning a future randomized clinical trial based on a network of relevant past trials

Background: The important role of network meta-analysis of randomized clinical trials in health technology assessment and guideline development is increasingly recognized. This approach has the potential to obtain conclusive results earlier than with new standalone trials or conventional, pairwise meta-analyses.\n\nMethods: Network meta-analyses can also be used to plan future trials. We introduce a four-steps framework to plan a new trial that aims to identify the optimal new design that will update the existing evidence to best serve timely clinical and public health decision making. The new trial designed within this framework does not need to include all competing interventions and comparisons of interest and can contribute direct and indirect evidence to the updated network meta-analysis. We present the method by virtually planning a new trial to compare biologics in rheumatoid arthritis and a new trial to compare two drugs for relapsing-remitting multiple sclerosis.\n\nResults: A trial design based on updating the evidence from a network meta-analysis of relevant previous trials may require a considerably smaller sample size to reach the same conclusion compared with a trial designed and analyzed in isolation. Challenges in the approach include the complexity of the methodology and the need for a coherent network meta-analysis of previous trials with little heterogeneity.\n\nConclusions: When used judiciously, conditional trial design could significantly reduce waste in clinical research.

epidemiology↗

bioRxiv2017

Exact graph-based analysis of scientific articles on clinical trials

This article describes Amorpha, a software package based on new concept of exact graph-based linguistic analysis. Analytical capabilities of Amorpha are demonstrated using analysis of scientific abstracts on clinical trials from PubMed. Current trends in therapy of breast cancer and psoriatic arthritis were analyzed using 400 abstracts on breast cancer and 131 abstracts on psoriatic arthritis. The spectrum of diseases that currently treated with paclitaxel was extracted from 400 most recent abstracts on paclitaxel.\n\nIn addition to text representation, analytical results are presented as graph images showing essential concepts of a text. Amorpha is not designed specifically to analyze clinical trials and will be also useful for analysis of biological scientific articles and regulatory documents. Amorpha does not require any preliminary knowledge base, ensures full coverage of target text and 100% accuracy of obtained results.

bioinformatics↗

bioRxiv2017

Identification of drug eQTL interactions from repeat transcriptional and environmental measurements in a lupus clinical trial

BackgroundCytokines are critical to human disease and are attractive therapeutic targets given their widespread influence on gene regulation and transcription. Defining the downstream regulatory mechanisms influenced by cytokines is central to defining drug and disease mechanisms. One promising strategy is to use interactions between expression quantitative trait loci (eQTLs) and cytokine levels to define target genes and mechanisms.\n\nResultsIn a clinical trial for anti-IL-6 in patients with systemic lupus erythematosus we measured interferon (IFN) status, anti-IL-6 drug exposure and genome-wide gene expression at three time points (379 samples from 157 individuals). First, we show that repeat transcriptomic measurements increases the number of cis eQTLs identified compared to using a single time point by 64%. Then, after identifying 4,818 cis-eQTLs, we observed a statistically significant enrichment of in vivo eQTL interactions with IFN status (p<0.001 by permutation) and anti-IL-6 drug exposure (p<0.001). We observed 210 and 72 interactions for IFN and anti-IL-6 respectively (FDR<20%). Anti-IL-6 interactions have not yet been described while 99 of the IFN interactions are novel. Finally, we found transcription factor binding motifs interrupted by eQTL interaction SNPs, pointing to key regulatory mediators of these environmental stimuli and therefore potential therapeutic targets for autoimmune diseases. In particular, genes with IFN interactions are enriched for ISRE binding site motifs, while those with anti-IL-6 interactions are enriched for IRF4 motifs.\n\nConclusionThis study highlights the potential to exploit clinical trial data to discover in vivo eQTL interactions with therapeutically relevant environmental variables.

genomics↗

bioRxiv2016

A functional genomic meta-analysis of clinical trials in systemic sclerosis: towards precision medicine and combination therapy

Systemic sclerosis (SSc) is an orphan, systemic autoimmune disease with no FDA-approved treatments. Its heterogeneity and rarity often result in underpowered clinical trials making the analysis and interpretation of associated molecular data challenging. We performed a meta-analysis of gene expression data from skin biopsies of SSc patients treated with five therapies: mycophenolate mofetil (MMF), rituximab, abatacept, nilotinib, and fresolimumab. A common clinical improvement criterion of -20% OR -5 modified Rodnan Skin Score was applied to each study. We developed a machine learning approach that captured features beyond differential expression that was better at identifying targets of therapies than the differential expression alone. Regardless of treatment mechanism, abrogation of inflammatory pathways accompanied clinical improvement in multiple studies suggesting that high expression of immune-related genes indicates active and targetable disease. Our framework allowed us to compare different trials and ask if patients who failed one therapy would likely improve on a different therapy, based on changes in gene expression. Genes with high expression at baseline in fresolimumab non-improvers were downregulated in MMF improvers, suggesting that immunomodulatory or combination therapy may have benefitted these patients. This approach can be broadly applied to increase tissue-specificity and sensitivity of differential expression results.

genomics↗