The classification of cancer has evolved over millennia, and centuries of work has laid the groundwork for modern cancer classification, which continues to evolve with advances in our understanding of cancer biology in tandem with improvement in the technologies, tools and frameworks used to characterize them. This thesis builds on the historical legacy of cancer classification by integrating single-cell transcriptomic approaches to explore the molecular complexity and intratumoral heterogeneity (ITH) of cancer. By defining and analyzing diverse transcriptomic states, known as metaprograms, in three aggressive cancer types—glioblastoma (GB), triple-negative breast cancer (TNBC), and diffuse midline glioma (DMG)—this work offers a more refined and precise lens through which to understand tumor progression and develop personalized therapeutic strategies. Using high-resolution single-cell RNA sequencing (scRNA-seq), spatially-resolved transcriptomics (SRT), and patient-derived organoid models, we identify distinct metaprograms that shape tumor progression, resistance, and patient outcomes.

Starting with DMG, we use spatial transcriptomics to map tumor-specific phenotypes, uncovering a novel neural stem cell-like population that interacts with the tumor microenvironment. This phenotype, defined by key progenitor markers, demonstrates plasticity, likely contributing to DMG’s resistance to therapy. By studying nonmalignant cells in the DMG microenvironment, we propose that specific cell types support tumor growth and evolution, highlighting potential therapeutic interventions. We then apply scRNA-seq to GB, revealing the presence of multiple metaprograms, including those linked to stem-like properties, invasion, and immune evasion. These metaprograms provide insights into how GB cells adapt and evolve in response to their microenvironment, uncovering potential therapeutic targets for this highly resistant cancer. In TNBC, we develop a comprehensive TNBC-Map by integrating single cell-datasets from patient biopsies, identifying nine core malignant metaprograms. These metaprograms encompass biological processes such as immune modulation, epithelial-to-mesenchymal transition (EMT), and vasculogenic mimicry. By correlating these metaprograms with patient survival, we identify distinct patterns of molecular activity that could guide the development of more personalized and effective treatments for TNBC.

Across these studies, we assess the power of metaprogram analysis to dissect cancer heterogeneity, offering a deeper understanding of the functional states driving tumor progression. This knowledge enables the identification of patient-specific molecular signatures, paving the way for precision medicine approaches. This thesis lays the groundwork for metaprogram-based cancer diagnostics and provides a foundation for the future integration of multi-omic precision medicine strategies that target specific cancer cell states, ultimately improving patient outcomes.