Existing pathology foundation models often inherit natural-image backbones that overlook the heterogeneous and irregular organization of pathological regions of interest. CARE introduces a two-stage pretraining strategy that first learns morphological structure from large-scale whole-slide images and then aligns adaptive region representations with molecular signals from RNA and protein profiles. Using only a fraction of the pretraining data common in prior work, CARE delivers strong average performance across 33 downstream benchmarks for classification, molecular prediction, and survival analysis.