Immunotherapy in rare tumors: From bench to bedside

Rare tumors, although individually uncommon, collectively account for a considerable proportion of cancer diagnoses and pose unique clinical and biological challenges. Their intrinsic heterogeneity, scarcity of preclinical models, and the lack of large-scale clinical trials have historically limited therapeutic innovation. Immunotherapy has reshaped the treatment landscape for several malignancies, yet its translation to rare cancers remains fragmentary. The immune microenvironment and molecular determinants of response in these entities are still largely unexplored. To fully exploit the potential of immune-based strategies, integrative approaches combining molecular pathology, spatial profiling, and artificial intelligence (AI) are essential to uncover novel therapeutic vulnerabilities and guide precision immunotherapy in rare tumors.The goal of this Research Topic is to create a comprehensive platform that unites basic scientists, computational biologists, pathologists, and clinicians working on immunotherapy for rare tumors. By integrating molecular and spatial data with AI-based predictive tools, this initiative seeks to deepen our understanding of tumor–immune dynamics and accelerate the translation of discoveries into clinical benefit. The convergence of digital pathology, machine learning, and high-throughput molecular profiling offers unprecedented opportunities to define immune phenotypes, stratify patients, and design rational combination therapies. Through this collaborative effort, we aim to foster innovative and cross-disciplinary research that expands the therapeutic frontiers for patients affected by rare cancers, who often lack targeted treatment options and evidence-based clinical guidance.This Research Topic welcomes original research, reviews, mini-reviews, and clinical case reports focused on immunotherapy in rare malignancies. Areas of interest include:• Immune landscape characterization and mechanisms of immune escape in rare tumors;• Translational and preclinical models (organoids, xenografts, co-cultures, spatial transcriptomics);• Identification of predictive and prognostic biomarkers of immunotherapy response;• Clinical evidence, case series, and real-world data on checkpoint inhibitors, adoptive cell therapy, and novel immunomodulators;• Development and application of AI-driven predictive algorithms to integrate omics, digital pathology, and clinical data for therapeutic decision-making. Multidisciplinary and multicenter contributions bridging basic, computational, and clinical research are particularly encouraged. Topic editors: Annabella Di Mauro, Azizul Haque Source: https://www.frontiersin.org/research-topics/76356/immunotherapy-in-rare-tumors-from-bench-to-bedside