Foundation models have become a powerful tool in single-cell transcriptomics, enabling broad generalization across tasks such as cell type annotation, data integration, and drug response prediction. Yet, most current models are trained predominantly on healthy cells, with a strong bias toward peripheral blood mononuclear cells. This raises an important question: how well do these models generalize to cancer-specific contexts? In this talk, I will explore whether training a foundation model exclusively on malignant cells—across diverse cancer types—can improve performance on tasks relevant to cancer biology and treatment. I will introduce CancerFoundation, a single-cell foundation model trained on malignant cells from over 40 tumor types. The model incorporates strategies for addressing tissue imbalance and technical variation, including domain-invariant training and tailored sampling. Through this work, I aim to address whether disease-specific pretraining can better capture the molecular features of cancer and improve the utility of foundation models in oncology applications such as batch integration and drug response prediction. Citation Format: Alexander Theus, Florian Barkmann, David Wissel, Tobias Scheithauer, Maria Brbic, Valentina Boeva. Cancer-specific foundation models: Friend or foe in healthcare AI? [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr IA02.