A systematic review of allergen cross-reactivity: translating basic concepts into clinical relevance Allergen cross-reactivity, defined as the recognition of two or more allergen molecules by antibodies or T cells of the same specificity, is a critical factor in diagnosing and managing allergic patients. Molecular allergology, which uses allergen molecules as tools for diagnosis and treatment, complements traditional methods based on skin and in vitro allergen extract testing. It allows for accurate identification of the offending allergen, evaluation of potential severity, and personalized allergen immunotherapy. However, allergen cross-reactivity can interfere with allergen extract testing, complicating diagnosis. Understanding allergen cross-reactivity at the immunologic level and translating it into clinical practice is challenging due to the increasing number of characterized allergenic molecules, lack of dedicated resources, and the need for a personalized approach. Knowledge sharing can improve clinical use, innovative diagnostic tools, and interdisciplinary research. This review provides a comprehensive and unbiased state-of-the-art systematic review on allergen cross-reactivity. It includes basic, translational, and clinical definitions, clinical vignettes, and an overview of online allergen databases. Allergen cross-reactivity is influenced by the allergen, the host's immune response, and the context of their interaction. Sequence homology, which is based on evolutionary biology, is a key factor in assessing cross-reactivity. However, clinically relevant cross-reactivity may occur even for unrelated proteins, and high sequence identity does not always indicate cross-reactivity. Allergen cross-reactivity can be investigated using in vitro assays, such as inhibition immunoassays and functional tests like the basophil activation test. These methods help determine the ability of allergens to induce immune responses and predict clinical relevance. The use of marker allergens, which are specific to a species, genus, or family, helps identify genuine sensitization. Cross-reactive allergens, which share epitopes and are expressed in multiple sources, can complicate diagnosis and treatment. The review highlights the importance of molecular allergology in improving diagnostic accuracy, therapeutic stratification, and patient management. It discusses the role of computational algorithms in determining sequence similarity and the impact of ligand binding and matrix components on allergen cross-reactivity. The review also addresses the clinical relevance of allergen cross-reactivity, including examples such as the cross-reactivity between Hymenoptera venom allergens and salivary proteins, and the cross-reactivity between plant and animal allergens. The review concludes that allergen cross-reactivity is a complex phenomenon that requires a multidisciplinary approach. It emphasizes the need for further research to improve diagnostic tools, enhance patient management, and develop new therapeutic strategies. The review also highlights the potential of artificial intelligence and machine learning in predicting allergen cross-reactivity and improving clinical outcomes.A systematic review of allergen cross-reactivity: translating basic concepts into clinical relevance Allergen cross-reactivity, defined as the recognition of two or more allergen molecules by antibodies or T cells of the same specificity, is a critical factor in diagnosing and managing allergic patients. Molecular allergology, which uses allergen molecules as tools for diagnosis and treatment, complements traditional methods based on skin and in vitro allergen extract testing. It allows for accurate identification of the offending allergen, evaluation of potential severity, and personalized allergen immunotherapy. However, allergen cross-reactivity can interfere with allergen extract testing, complicating diagnosis. Understanding allergen cross-reactivity at the immunologic level and translating it into clinical practice is challenging due to the increasing number of characterized allergenic molecules, lack of dedicated resources, and the need for a personalized approach. Knowledge sharing can improve clinical use, innovative diagnostic tools, and interdisciplinary research. This review provides a comprehensive and unbiased state-of-the-art systematic review on allergen cross-reactivity. It includes basic, translational, and clinical definitions, clinical vignettes, and an overview of online allergen databases. Allergen cross-reactivity is influenced by the allergen, the host's immune response, and the context of their interaction. Sequence homology, which is based on evolutionary biology, is a key factor in assessing cross-reactivity. However, clinically relevant cross-reactivity may occur even for unrelated proteins, and high sequence identity does not always indicate cross-reactivity. Allergen cross-reactivity can be investigated using in vitro assays, such as inhibition immunoassays and functional tests like the basophil activation test. These methods help determine the ability of allergens to induce immune responses and predict clinical relevance. The use of marker allergens, which are specific to a species, genus, or family, helps identify genuine sensitization. Cross-reactive allergens, which share epitopes and are expressed in multiple sources, can complicate diagnosis and treatment. The review highlights the importance of molecular allergology in improving diagnostic accuracy, therapeutic stratification, and patient management. It discusses the role of computational algorithms in determining sequence similarity and the impact of ligand binding and matrix components on allergen cross-reactivity. The review also addresses the clinical relevance of allergen cross-reactivity, including examples such as the cross-reactivity between Hymenoptera venom allergens and salivary proteins, and the cross-reactivity between plant and animal allergens. The review concludes that allergen cross-reactivity is a complex phenomenon that requires a multidisciplinary approach. It emphasizes the need for further research to improve diagnostic tools, enhance patient management, and develop new therapeutic strategies. The review also highlights the potential of artificial intelligence and machine learning in predicting allergen cross-reactivity and improving clinical outcomes.