Precision Medicine in Crohn’s Disease
Philippe Pinton
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A plea for predictive multi-omic levels computational models
Note: the text below, in which I am summarizing a recent webinar and course to medical fellows I have performed about precision medicine considering Crohn’s Disease as an example, reflects my personal opinion.
Precision medicine refers to the tailoring of medical treatment to the individual characteristics of each patient (1).
Crohn’s Disease (CD) is a chronic, relapsing and remitting inflammatory bowel disease (IBD) of unclear etiology and of complex physiopathology involving the genome, exposome, microbiome and immunome. It can affect the entire gastrointestinal tract, leading to significant physical morbidity and psychological burden. The incidence of CD continues to rise world widely (2-4). While there have been dramatic therapeutic advancements, seventy percent of the CD patients still require surgery at some point of their journey (5,6).
After a step-up or an accelerated step-up therapeutic approach, evaluating the treatment efficacy with disease activity indices like the Crohn’s Disease Activity Index (CDAI), and a top-down approach, inducing remission with biologics, the now treat-to-target, a pro-active approach, is refining the goal of CD treatment, considering inflammation improvement by Patient-Reported-Outcomes (PRO), imaging and objective biomarkers as fecal calprotectin and C-reactive protein (7). This approach should help physicians to take better-informed decisions to optimize treatment to achieve clinical remission with mucosal healing.
The natural history of CD is markedly characterized by a variable disease course, alternating periods of clinical remission and active disease flares, favoring penetrating and/or stricturing complications. Altering the natural CD course to prevent disease progression and cumulative intestinal damage should be the long-term goal of therapy. Patients with mucosal healing within the first year after CD diagnosis have better quality of life and long-term outcomes (8,9).
Biologic and small-molecule therapeutics have demonstrated some efficacy in altering the natural course of CD but they are not appropriate for every CD phenotype. Therefor, therapy strategies for CD patients should be optimized with a pro-active and personalized approach (7,10,11).
Precision medicine in CD is a must have. Precision medicine is the right therapy to the right patient at the right time on the basis of right individual patient’s characteristics, which may include symptoms, images, biomarkers and probably genetics. Today, each of them still unfortunately requires further exploration and more stringent definition or determination (11).
In CD, there is a disconnection between clinical symptoms and mucosal inflammation, which may lead to either over-treated or under-treated situations. Specific and reliable biomarkers bridging subjective symptoms and objective imaging-evidence may provide the physician with more reliable data about the degree of intestinal mucosal inflammation, yet with limited accuracy. Experimental genetics suggest future prospects for disease-subtype classification and therapeutic intervention, yet with many uncertainties (12).
Such remaining inaccuracies and uncertainties should not let us think that the concept of precision medicine in CD is unrealistic. Even if discussed for years and yet seems no closer to clinical practice today. The concept is favorably on its move and it has been achieved in other fields, in oncology notably (13).
The treat-to-target approach certainly ignites the precision medicine needed to help solve the matter of CD heterogeneity and variable responses to treatment. It has the potential to increase the clinical benefit of treatment for the most common CD phenotypes (7). However, it does not consider a full multi-omic frame and it requires time, time to observe any effect of the treatment on the natural course of the disease, which may mean weeks or months or years. To avoid such loss of time and chance, predictive models are urgently encouraged and needed. So that personalized medicine can provide maximal clinical benefit to each patient.
Predicting the progression of CD under any available treatment strategies at the time of diagnosis using multi-omic clinically important criteria will foster personalized treatment algorithms aiming to improve clinical outcomes over time (14). Ultimately, the goal would be the preferential selection of the specific therapy to ensure the maximal clinical benefit (11,15). Empowering not only practitioners but also patients with this information at diagnosis may aid progress towards personalizing care in CD.
Accurate pre-treatment prediction of response to biologic and small-molecule therapeutics would enable better treatment selection for patients. Currently, there are no clinically available biomarkers that accurately predict treatment response, although a large number of potential candidates have been studied and extensively reviewed recently (11,15). Same comment values for PRO (clinical symptoms), microbiotal signature, genetic profile and imaging (endoscopy, ultrasound, magnetic resonance enterography, …). Each has its own limitations and cannot be considered individually.
The current progresses towards artificial intelligence, precision medicine and likely complex inter-relationships of these multi-omic data in CD should lead us to develop computational models that integrate such complexity to better phenotype patients in precisely targeting an individual patient’s inflammation, allowing matching of the patient to the most appropriate treatment, and later on monitoring and follow-up (16). Eventually achieving cure rather than simply mucosal or histological healing should be the ultimate goal.
Many challenges remain to develop and roll out efficient and reliable computational models such as: the physiopathology of CD, the non-specificity of the clinical symptoms, the biomarker discovery, the definition of the outcomes, the gap between the available and the needed big data qualities, the multi-sourcing of these big data, the differences in infrastructure between the multiple healthcare systems, the complexity of the algorithms to develop, etc.
However, there are grounds for significant confidence and optimism. The development of machine learning and data mining models have been initiated for risk prediction using logistic regression (17,18). More recently, several clinical predictive models have been built (19-22). Still, predictive models that integrate mechanical (physiopathology) and statistical (phenotypes stratification) approaches to guide treatment initiation or decision in CD lack. Only such composite or hybrid models capture causalities between biology and disease (16). Hopefully, it should not be an unsuccessful Grail quest (23).
References
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