Abstract B017: From healthy brain to cancer cells: Understanding the origins of glioblastoma through the lens of Darwinian evolution

Abstract Cancer remains one of the most complex and devastating diseases, in part because tumors are not static entities but dynamic ecosystems that evolve in real time. Over the decades, researchers have proposed two major frameworks toexplain tumor development: 1) the clonal evolution model, which emphasizes the stepwise accumulation of mutations insingle cells, and 2) the cancer stem cell model, which posits a hierarchical tumor structure driven by a small population of stem-like cells capable of self-renewal and tumor initiation. In brain tumors, as in other cancer types, the presence of cancer stem cells has been demonstrated; however, their evolutionary role in glioblastoma, the most aggressive primary brain tumor, remains poorly understood. A key unanswered question is how cancer stem cells in the brain evolve intomalignant populations and how evolutionary processes shape this transition. Since Darwin, evolutionary biologists have sought to understand how traits evolve across lineages and have developed several tools to address questions regarding how lineages, species and populations have evolved through time. In recent years there has been an increase in the use of evolutionary statistical methods to study cancer evolution. These approaches remain niche in a few tumor types (e. g. , blood and lung cancer), but the rapid growth of sequencing technologies and large-scale datasets has created new opportunities to address evolutionary questions across cancer types that have been understudied (e. g. , brain tumor). Researchers are now using whole-genome, exome, single-cell, spatial, and multi-omic data to reconstruct tumor histories, test adaptive hypotheses, and in few cases, quantify rates and modes of evolution within tumors and between tumor types. However, there is not yet a consolidated framework for which evolutionary analyses are most appropriate to test cancer evolution, nor accessible guidance on how to implement these approaches in oncological datasets. We synthesize evolutionary statistical methods currently used in brain cancer research and propose additional approaches for future studies. We identify more than 30 evolutionary tools developed to study trait evolution, diversification, and lineage dynamics in biological systems, of which only one has been applied to brain cancer. We argue that adapting these methods offers a powerful opportunity to investigate cancer stem cell dynamics and tumor progression in glioblastoma and other understudied cancers. By bridging evolutionary biology and cancer genomics, this work provides a roadmap for expanding the evolutionary toolkit used to study tumor evolution and for generating testable hypotheses about malignant transformation. Citation Format: Jhan C. Salazar Salazar, Sean W. McHugh, Maria F. Gonzalez-Aponte, Hugo Guerrero-Cazares. From healthy brain to cancer cells: Understanding the origins of glioblastoma through the lens of Darwinian evolution abstract. In: Proceedings of the AACR Special Conference in Cancer Research: Brain Cancer; 2026 Mar 23-25; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86 (6Suppl): Abstract nr B017.