Identifying functionally important cell states and structure within heterogeneous tumors remains a significant biological and computational challenge. Current clustering- or trajectory-based models are ill-equipped to address the notion that cancer cells reside along a phenotypic continuum. We present Archetypal Analysis network (AAnet), a neural network that learns archetypal states within a phenotypic continuum in single-cell data. Unlike traditional archetypal analysis, AAnet learns archetypes (AT) in a simplex-shaped neural network latent space. Using preclinical and clinical models of breast cancer, AAnet resolves distinct cell states and processes, including cell proliferation, hypoxia, metabolism, and immune interactions. Primary tumor ATs are recapitulated in matched liver, lung, and lymph node metastases. Spatial transcriptomics reveals archetypal organization within the tumor and intra-archetypal mirroring between cancer and adjacent stromal cells. AAnet identifies GLUT3 within the hypoxic AT that proves critical for tumor growth and metastasis. AAnet is a powerful tool, capturing complex, functional cell states from multimodal data. SIGNIFICANCE: Defining critical cell states among cells that reside along a phenotypic continuum is a current biological and computational challenge. In this study, we present AAnet, a neural network that learns archetypal cell states of cancer cells. AAnet defines discrete spatially localized ATs that resolve intratumoral heterogeneity.