Cultured blood T-cell responses predict anti-TNF therapy response in patients with ulcerative colitis

Precision medicine in inflammatory bowel disease

Inflammatory bowel disease (IBD) is an incurable disease characterized by remission-relapse cycles throughout its course. Both Crohn's disease (CD) and ulcerative colitis (UC), the two main forms of IBD, exhibit tendency to develop complications and substantial heterogeneity in terms of frequency and severity of relapse, thus posing great challenges to the clinical management for IBD. Current treatment strategies are effective in different ways in induction and maintenance therapies for IBD. Recent advances in studies of genetics, pharmacogenetics, proteomics and microbiome provide a strong driving force for identifying molecular markers of prognosis and treatment response, which should help clinicians manage IBD patients more effectively, and then, improve clinical outcomes and reduce treatment costs of patients. In this review, we summarize and discuss precision medicine in IBD, focusing on predictive markers of disease course and treatment response, and monitoring indices during therapeutic drug monitoring.

Precision medicine and drug optimization in adult inflammatory bowel disease patients

Inflammatory bowel diseases (IBD) encompass two main entities including ulcerative colitis and Crohn's disease. Although having a common global pathophysiological mechanism, IBD patients are characterized by a significant interindividual heterogeneity and may differ by their disease type, disease locations, disease behaviours, disease manifestations, disease course as well as treatment needs. Indeed, although the therapeutic armamentarium for these diseases has expanded rapidly in recent years, a proportion of patients remains with a suboptimal response to medical treatment due to primary non-response, secondary loss of response or intolerance to currently available drugs. Identifying, prior to treatment initiation, which patients are likely to respond to a specific drug would improve the disease management, avoid unnecessary side effects and reduce the healthcare expenses. Precision medicine classifies individuals into subpopulations according to clinical and molecular characteristics with the objective to tailor preventative and therapeutic interventions to the characteristics of each patient. Interventions would thus be performed only on those who will benefit, sparing side effects and expense for those who will not. This review aims to summarize clinical factors, biomarkers (genetic, transcriptomic, proteomic, metabolic, radiomic or from the microbiota) and tools that could predict disease progression to guide towards a step-up or top-down strategy. Predictive factors of response or non-response to treatment will then be reviewed, followed by a discussion about the optimal dose of drug required for patients. The time at which these treatments should be administered (or rather can be stopped in case of a deep remission or in the aftermath of a surgery) will also be addressed. IBD remain biologically complex, with multifactorial etiopathology, clinical heterogeneity as well as temporal and therapeutic variabilities, which makes precision medicine especially challenging in this area. Although applied for many years in oncology, it remains an unmet medical need in IBD.

Personalizing Treatment in IBD: Hype or Reality in 2020? Can We Predict Response to Anti-TNF?

The advent of anti-TNF agents as the first approved targeted therapy in the treatment of inflammatory bowel disease (IBD) patients has made a major impact on our existing therapeutic algorithms. They have not only been approved for induction and maintenance treatment in IBD patients, but have also enabled us to define and achieve novel therapeutic outcomes, such as combination of clinical symptom control and endoscopic remission, as well as mucosal healing. Nevertheless, approximately one third of treated patients do not respond to initiated anti-TNF therapy and these treatments are associated with sometimes severe systemic side-effects. There is therefore the currently unmet clinical need do establish predictive markers of response to identify the subgroup of IBD patients, that have a heightened probability of response. There have so far been approaches from different fields of IBD research, to descry markers that would empower us to apply TNF-inhibitors in a more rational manner. These markers encompass findings from disease-related and clinical factors, pharmacokinetics, biochemical markers, blood and stool derived parameters, pharmacogenomics, microbial species, metabolic compounds, and mucosal factors. Furthermore, changes in the intestinal immune cell composition in response to therapeutic pressure of anti-TNF treatment have recently been implicated in the process of molecular resistance to these drugs. Insights into factors that determine resistance to anti-TNF therapy give reasonable hope, that a more targeted approach can then be utilized in these non-responders. Here, IL-23 could be identified as one of the key factors determining resistance to TNF-inhibitors. Growing insights into the molecular mechanism of action of TNF-inhibitors might also enable us to derive critical molecular markers that not only mediate the clinical effects of anti-TNF therapy, but which level of expression might also correlate with its therapeutic efficacy. In this narrative review, we present an overview of currently identified possible predictive markers for successful anti-TNF therapy and discuss identified molecular pathways that drive resistance to these substances. We will also point out the necessity and difficulty of developing and validating a diagnostic marker concerning clinically relevant outcome parameters, before they can finally enter daily clinical practice and enable a more personalized therapeutic approach.

Predictors of Primary Response to Biologic Treatment [Anti-TNF, Vedolizumab, and Ustekinumab] in Patients With Inflammatory Bowel Disease: From Basic Science to Clinical Practice

BACKGROUND

Inflammatory bowel diseases [IBD]-ulcerative colitis and Crohn's disease-are commonly treated with biologic drugs. However, only approximately two-thirds of patients have an initial response to these therapies. Personalised medicine has the potential to optimise efficacy, decrease the risk of adverse drug events, and reduce costs by establishing the most suitable therapy for a selected patient.

AIM

The present study reviews the potential predictors of short-term primary response to biologic treatment, including not only anti-tumour necrosis factor [TNF] agents [such as infliximab, adalimumab, certolizumab, and golimumab] but also vedolizumab and ustekinumab.

METHODS

We performed a systematic bibliographical search to identify studies investigating predictive factors of response to biologic therapy.

RESULTS

For anti-TNF agents, most of the evaluated factors have not demonstrated usefulness, and many others are still controversial. Thus, only a few factors may have a potential role in the prediction of the response, including disease behaviour/phenotype, disease severity, C-reactive protein, albumin, cytokine expression in serum, previous anti-TNF therapy, some proteomic markers, and some colorectal mucosa markers. For vedolizumab, the availability of useful predictive markers seems to be even lower, with only some factors showing a limited value, such as the expression of α4β7 integrin in blood, the faecal microbiota, some proteomic markers, and some colorectal mucosa markers. Finally, in the case of ustekinumab, no predictive factor has been reported yet to be helpful in clinical practice.

CONCLUSION

In summary, currently no single marker fulfils all criteria for being an appropriate prognostic indicator of response to any biologic treatment in IBD.

Multiple Cytokine Profiling: A New Model to Predict Response to Tumor Necrosis Factor Antagonists in Ulcerative Colitis Patients

BACKGROUND AND AIMS

Ulcerative colitis (UC) is a form of inflammatory bowel disease, and antibodies against tumor necrosis factor (anti-TNF) are used for treatment. Many patients are refractory or lose response to anti-TNF, and predicting response would be an extremely valuable clinical tool. Unlike most biomarkers, cytokines directly mediate inflammation, and their measurement may predict the likelihood of response or no response.

METHODS

Serum samples were obtained from 49 UC patients before infliximab infusions, and levels of 17 cytokines were measured using a multiplex assay. The Fisher linear discriminant analysis (FLDA) was applied to the cytokine values to predict which patients would respond to infliximab. "Response" was defined as clinical remission after the third infusion, and "no response" was defined as lack of remission after the third infusion.

RESULTS

The Fisher linear discriminant analysis model identified a subset of seven predictor cytokines: TNF-α, IL-12, IL-8, IL-2, IL-5, IL1-β, and IFN-γ. The obtained canonical coefficients enabled to calculate discriminant scores as linear combinations of the cytokines; model classified thepatients as responders and nonresponders with a sensitivity of 84.2% and a specificity of 93.3%. Overall, the yield of the FLDA model was 89.8% of the total 49 patients.

CONCLUSIONS

An unbiased, statistically derived, predictive model based on measurement of serum cytokines before therapy may predict a positive or negative outcome from the administration of anti-TNF to UC patients. Because accurately measuring cytokines is simple and inexpensive, the model may be a valuable new tool to complement other laboratory parameters used in the management of IBD patients.

Systematic review: predictive biomarkers of therapeutic response in inflammatory bowel disease-personalised medicine in its infancy

BACKGROUND

Inflammatory bowel disease (IBD) is characterised by substantial heterogeneity in treatment response. With an expanding number of therapeutic agents, identifying optimal treatment at the patient level remains a major challenge.

AIM

To systematically review the available literature on predictive biomarkers of therapeutic response in IBD.

METHODS

An electronic literature search was performed on 30 January 2018 using MEDLINE, EMBASE and the Cochrane Library. Retrospective, prospective, uncontrolled and controlled studies reporting on biomarkers predicting therapeutic response in paediatric and adult IBD populations were eligible for inclusion. The methodological quality of the included studies was assessed using the QUIPS tool. Due to anticipated heterogeneity and limited data, a qualitative, rather than quantitative, assessment was planned.

RESULTS

Of the 10 638 citations identified, 92 articles met the inclusion criteria. Several potential DNA, mRNA and protein markers were evaluated as predictive biomarkers. Most studies focused on predicting response to anti-TNF agents. Substantial between-study heterogeneity was identified with respect to both the biomarkers studied and the definition of response. None of the included studies received a low risk of bias rating for all six domains. Currently, none of the biomarkers is sufficiently predictive for clinical use.

CONCLUSIONS

The search for predictive biomarkers is still in its infancy and current evidence is limited. Future research efforts should take into account the high patient heterogeneity within prospective trials with objective response assessment. Predictive models will most likely comprise a combination of several molecular markers from integrated omics-levels and clinical characteristics.

Reproducing the human mucosal environment ex vivo: inflammatory bowel disease as a paradigm

PURPOSE OF REVIEW

The medical management of inflammatory bowel disease (IBD) remains problematic with a pressing need for innovation in drug development as well as delivery of personalized therapies. Both the disease's inherent pathophysiologic complexity and heterogeneity in its etiology conspire in making it difficult to accurately model for either the purposes of basic research or drug development. Multiple attempts at creating meaningful experimental models have fallen short of adequately recapitulating the disease and most do not capture any aspect of the cause or the effects of patient heterogeneity that underlays most of the difficulties faced by physicians and their patients. In vivo animal models, tissue culture systems, and more recent synthetic biology approaches are all too simplistically reductionist for the task. However, ex vivo culture platforms utilizing patient biopsies offer a system that more closely mimics end-stage disease processes that can be studied in detail and subjected to experimental manipulations.

RECENT FINDINGS

Recent studies describe further optimization of mucosal explant cultures in order to increase tissue viability and maintain a polarized epithelial layer. Current applications of the platform include studies of the interplay between the epithelial, immune and stromal compartment of the intestinal tissue, investigation of host-microbial interactions, preclinical evaluation of candidate drugs and uncovering mechanisms of action of established or emerging treatments for IBD.

SUMMARY

Patient explant-based assays offer an advanced biological system in IBD that recapitulates disease complexity and reflects the heterogeneity of the patient population. In its current stage of development, the system can be utilized for drug testing prior to the costlier and time-consuming evaluation by clinical trials. Further refinement of the technology and establishment of assay readouts that correlate with therapeutic outcomes will yield a powerful tool for personalized medicine approaches in which individual patient responses to available treatments are assessed a priori, thus reducing the need for trial and error within the clinical setting.

How to predict response to anti-tumour necrosis factor agents in inflammatory bowel disease

Anti-tumor necrosis factor (TNF) agents have changed the therapeutic approach to inflammatory bowel disease (IBD). However, a considerable proportion of patients either do not primarily respond or lose response to treatment. Despite the long-standing experience in the use of these drugs, still there is the need of identifying the possible predictors of efficacy. Areas covered: We critically review the current knowledge on predictors of response to anti-TNF therapy - both those available in clinical practice and those still under investigation. Multiple factors are involved in treatment success, including disease phenotype and severity, adherence to medications, and pharmacogenomic, pharmacokinetic, and immunologic factors. Literature search was conducted in PubMed using keywords 'inflammatory bowel disease,' 'Crohn's disease,' and 'ulcerative colitis,' matched with 'antitumor necrosis factor,' 'biologic therapy,' 'clinical response,' 'predictors,' and 'efficacy,' Relevant articles were selected for review. Expert commentary: While the role of several factors in clinical practice is clearly established, other investigational markers have been proposed, mostly in small studies, yet for many of them little external validation exists. Therapeutic drug monitoring is emerging as a pivotal strategy to guide decisions in clinical practice. In the near future, novel markers could improve our ability to direct treatment and personalize therapy.

Anti-TNF Therapy Response in Patients with Ulcerative Colitis Is Associated with Colonic Antimicrobial Peptide Expression and Microbiota Composition

BACKGROUND AND AIMS

Anti-tumour necrosis factor [TNF] therapy is used in patients with ulcerative colitis [UC], but not all patients respond to treatment. Antimicrobial peptides [AMPs] and the gut microbiota are essential for gut homeostasis and may be important for treatment outcome. The aim of this study was to determine AMP and microbiota profiles in patients with UC before anti-TNF therapy start and correlate these data to treatment outcome.

METHODS

Serum and biopsies were obtained from UC patients naïve to biological therapy [n = 56] before anti-TNF therapy start [baseline]. Fecal samples were taken at baseline and Weeks 2 and 6. Quantitative proteomic analysis was performed in mucosal biopsies. Expression of AMPs and cytokines was determined in biopsies and serum. Microbiota analysis of fecal samples was performed using GA-map™ Dysbiosis Test and real-time quantitative polymerase chain reaction [rtPCR]. Treatment response was evaluated 12-14 weeks after baseline.

RESULTS

At baseline, proteomic analysis of biopsies showed that treatment responders and non-responders had differential expression of AMPs. Eleven AMP and AMP-related genes were analysed by rtPCR in mucosal biopsies and could together discriminate responders from non-responders at baseline. The most important nominators for response were increased expression of defensin 5 and eosinophilic cationic protein. Microbiota analysis revealed lower dysbiosis indexes and higher abundance of Faecalibacterium prausnitzii in responders compared with non-responders at baseline. Also, abundance of F. prausnitzii increased during induction therapy in responders.

CONCLUSIONS

Anti-TNF therapy responders and non-responders display distinctly separate patterns of mucosal AMP expression and gut microbiota before treatment start. This indicates that intestinal antimicrobial/microbial composition can influence treatment outcome.