HRProtoKD: A hierarchical and relational prototype based knowledge distillation framework for few-shot cancer molecular subtyping

Accurate molecular subtyping of cancers is critical for drug discovery and disease treatment but re-mains challenging due to the scarcity of labeled data and the intrinsic heterogeneity of cancer biology. While current methods address this issue via few-shot learning (FSL), they often overlook the hierarchical relation-ships among cancer subtypes and the transfer of relational knowledge between models, both of which are crucial for improving generalization under limited supervision. In this work, we propose HRProtoKD, a novel hierarchical and relational prototype-based knowledge distillation framework designed for few-shot cancer subtype classification. HRProtoKD first employs hierarchical inter-class prototype clustering to capture the underlying class relationships. A prototype-wise contrastive loss is then introduced to enhance intra-class compactness and interclass separability. Furthermore, relational prototype knowledge distillation is applied to transfer structural knowledge from a teacher model to a student model. In addition, we construct three benchmark datasets for few-shot cancer molecular subtyping. Extensive experiments conducted on these datasets demonstrate that HRProtoKD consistently outperforms state-of-the-art meta-learning baselines under both 1-shot and 5-shot learning settings, thereby advancing precision oncology and supporting data-driven approaches for drug discovery and therapeutic development.