Prognostic Value of Spatial and Topological Features of Tumor Microenvironment in Classic Hodgkin Lymphoma

Classic Hodgkin lymphoma (CHL) constitutes a B-cell malignant lymphoid neoplasm derived from the germinal center. Despite current treatment protocols based on chemotherapy, radiotherapy, anti-cluster of differentiation (CD) 30 antibody-drug conjugates, immunotherapy, and hematopoietic stem cell transplantation (HSCT), between 10% and 20% of CHL patients fail to achieve a complete response. The reasons underlying this lack of treatment sensitivity remain unclear. Traditionally, clinical and analytical variables have constituted the cornerstone of CHL prognostic model development. However, in recent years, the distribution and spatial relationships of cancer and immune cells within the CHL tumor microenvironment (TME) have emerged as novel potential candidates for risk stratification and treatment personalization. Underpinning this field of research, advances in digital image analysis (DIA) and computational pathology (CP) tools have been fundamental, as these methods enable objective quantification of TME elements and the definition of their topological arrangement. Novel CHL prognostic models integrating data across DNA sequencing in peripheral blood (liquid biopsy), single-cell RNA sequencing (scRNAseq), spatial transcriptomics, positron emission tomography/computed tomography (PET/CT) imaging, and topological features of TME could inform better clinical decision-making in the near future. In this work, we review the current state of CP and DIA studies in CHL, emphasizing the transition from traditional histopathological characterization to computational biology, highlighting the prognostic value of TME components, and proposing an updated framework for CHL tumor evolution and cellular dynamics as ecological systems. This study aims to review the contributions of DIA and CP in clinical and translational research on CHL. The results of this study may contribute to the identification of new prognostic biomarkers and their use in both the design of risk stratification models and clinical trials for CHL.