Molecular Diagnosis of Allergic Contact Dermatitis

Plain-language summary

Allergic contact dermatitis (ACD) is a common inflammatory skin disease, affecting approximately 15-20% of the general population in industrialized countries and ranking first among occupational diseases in many European countries. ACD typically presents as a severe skin inflammation with redness, edema, oozing and crusting. It is characterized by a delayed type IV hypersensitivity response mediated by allergen-specific T cells in sensitized individuals. Current diagnosis relies on clinical investigations by diagnostic patch testing with suspected allergenic chemicals. The patch test method aims at reproducing the eczematous lesions by applying occlusive patches containing the suspected allergens to the patient's healthy skin. This is a time consuming and costly process. It requires experienced medical staff to read the reaction, and is only performed by a limited number of expert dermato-allergologists across Europe (which limits the accessibility of suspected ACD patients to diagnosis). Finally, if the robustness of the patch-test method is undisputable, it cannot be neglected that patch-test results are sometimes false positive or non-relevant, which leads to non-appropriate disease management. Therefore, there is today an urgent need for the availability of new ex vivo/in vitro tools based on the modern understanding of the immune mechanisms of ACD to enhance the current diagnostic procedure, and open new avenues for a personalized diagnosis of skin ACD. In this context, the team "Epidermal Immunity and Allergy" (CIRI, Inserm U1111) recently characterized the molecular signatures of ACD (using microarrays), based on positive patch-test reactions to reference chemical allergens or non-allergenic irritants. It was shown that there are unique molecular profiles and signaling pathways characterizing each inflammation. Machine learning methods were then developed to identify and validate classification algorithms based on the expression levels of a minimum set of biomarkers (n=12), enabling very good discrimination between allergen-induced and irritant-induced patch-test inflammation (which was confirmed by complementary quantitative RT-PCR analyses). Finally, some patients with weak positive patch-test reactions to allergens show no/low marks of allergy molecular signature, questioning about the reliability/relevance of their patch-tests results. Our results therefore stress the value of molecular profiling of patch-test reactions to improve/reinforce clinical ACD diagnosis, and to help the dermatologist to discriminate true versus false positive patch test reactions. Importantly, those results also open new avenues for the development of a future point care diagnosis. Indeed, it is currently is estimated that only 20% of patients being sent for allergology work-ups suffer from true skin allergy (i.e. patients with positive patch-tests, combined with relevant clinical history and confirmatory use tests). Most of the patients (80%) are in fact suffering from skin irritation. Therefore, the detection of ACD biomarkers in active eczema lesions could provide the dermatologist with major information to improve and accelerate its clinical diagnosis. This could also prevent numerous patients (negative for ACD biomarkers) being sent for unnecessary allergology work-ups. However, to date, it remains to be demonstrated that (i) the same panel of ACD biomarkers is expressed both in acute eczema lesions and positive patch-test reactions, and that (ii) the detection of these biomarkers allows for a sensitive and reliable diagnosis of skin allergy. The main objective of the study will be to make the proof of concept that the expression of allergy biomarkers correlates with patients suffering from true ACD (i.e. patients with high biomarker expression in acute lesions, positive patch-tests and relevant clinical history), versus those developing skin irritation (no/low biomarker expression in acute lesions, negative patch-tests, and lack of clinical history).

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